TF2 0 Long Distance

================ by Jawad Haider

• Long Distance

Long Distance

# Install TensorFlow
# !pip install -q tensorflow-gpu==2.0.0-beta1

try:
  %tensorflow_version 2.x  # Colab only.
except Exception:
  pass

import tensorflow as tf
print(tf.__version__)

`%tensorflow_version` only switches the major version: 1.x or 2.x.
You set: `2.x  # Colab only.`. This will be interpreted as: `2.x`.


TensorFlow 2.x selected.
2.2.0-rc2

# More imports
from tensorflow.keras.layers import Input, SimpleRNN, GRU, LSTM, Dense, Flatten, GlobalMaxPool1D
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import SGD, Adam

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

### build the dataset
# This is a nonlinear AND long-distance dataset
# (Actually, we will test long-distance vs. short-distance patterns)

# Start with a small T and increase it later
T = 10
D = 1
X = []
Y = []

def get_label(x, i1, i2, i3):
  # x = sequence
  if x[i1] < 0 and x[i2] < 0 and x[i3] < 0:
    return 1
  if x[i1] < 0 and x[i2] > 0 and x[i3] > 0:
    return 1
  if x[i1] > 0 and x[i2] < 0 and x[i3] > 0:
    return 1
  if x[i1] > 0 and x[i2] > 0 and x[i3] < 0:
    return 1
  return 0

for t in range(5000):
  x = np.random.randn(T)
  X.append(x)
  y = get_label(x, -1, -2, -3) # short distance
#   y = get_label(x, 0, 1, 2) # long distance
  Y.append(y)

X = np.array(X)
Y = np.array(Y)
N = len(X)

# Try a linear model first - note: it is classification now!
i = Input(shape=(T,))
x = Dense(1, activation='sigmoid')(i)
model = Model(i, x)
model.compile(
  loss='binary_crossentropy',
  optimizer=Adam(lr=0.01),
  metrics=['accuracy'],
)

# train the network
r = model.fit(
  X, Y,
  epochs=100,
  validation_split=0.5,
)

Epoch 1/100
79/79 [==============================] - 0s 5ms/step - loss: 0.7831 - accuracy: 0.5064 - val_loss: 0.7084 - val_accuracy: 0.4972
Epoch 2/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6966 - accuracy: 0.5164 - val_loss: 0.6992 - val_accuracy: 0.4860
Epoch 3/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6929 - accuracy: 0.5176 - val_loss: 0.7003 - val_accuracy: 0.4976
Epoch 4/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6929 - accuracy: 0.5172 - val_loss: 0.6995 - val_accuracy: 0.4948
Epoch 5/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6930 - accuracy: 0.5176 - val_loss: 0.6989 - val_accuracy: 0.4968
Epoch 6/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6919 - accuracy: 0.5204 - val_loss: 0.7013 - val_accuracy: 0.4920
Epoch 7/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6939 - accuracy: 0.5172 - val_loss: 0.7019 - val_accuracy: 0.5088
Epoch 8/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6951 - accuracy: 0.5204 - val_loss: 0.7011 - val_accuracy: 0.4964
Epoch 9/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6927 - accuracy: 0.5124 - val_loss: 0.7002 - val_accuracy: 0.4916
Epoch 10/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6933 - accuracy: 0.5136 - val_loss: 0.7031 - val_accuracy: 0.4936
Epoch 11/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6936 - accuracy: 0.5132 - val_loss: 0.7009 - val_accuracy: 0.4944
Epoch 12/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6927 - accuracy: 0.5256 - val_loss: 0.7014 - val_accuracy: 0.4968
Epoch 13/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6927 - accuracy: 0.5232 - val_loss: 0.6995 - val_accuracy: 0.4952
Epoch 14/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5268 - val_loss: 0.7004 - val_accuracy: 0.4848
Epoch 15/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6934 - accuracy: 0.5276 - val_loss: 0.6979 - val_accuracy: 0.5020
Epoch 16/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6946 - accuracy: 0.5176 - val_loss: 0.6960 - val_accuracy: 0.5000
Epoch 17/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6938 - accuracy: 0.5184 - val_loss: 0.6990 - val_accuracy: 0.4872
Epoch 18/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6933 - accuracy: 0.5188 - val_loss: 0.6988 - val_accuracy: 0.4976
Epoch 19/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5132 - val_loss: 0.7003 - val_accuracy: 0.5016
Epoch 20/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6930 - accuracy: 0.5252 - val_loss: 0.7000 - val_accuracy: 0.4964
Epoch 21/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6923 - accuracy: 0.5216 - val_loss: 0.7006 - val_accuracy: 0.4952
Epoch 22/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6931 - accuracy: 0.5116 - val_loss: 0.7036 - val_accuracy: 0.4908
Epoch 23/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6933 - accuracy: 0.5136 - val_loss: 0.7015 - val_accuracy: 0.4984
Epoch 24/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6922 - accuracy: 0.5348 - val_loss: 0.7017 - val_accuracy: 0.4928
Epoch 25/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6931 - accuracy: 0.5080 - val_loss: 0.7023 - val_accuracy: 0.4968
Epoch 26/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6933 - accuracy: 0.5120 - val_loss: 0.7033 - val_accuracy: 0.4876
Epoch 27/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6920 - accuracy: 0.5308 - val_loss: 0.6994 - val_accuracy: 0.4924
Epoch 28/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6933 - accuracy: 0.5260 - val_loss: 0.7000 - val_accuracy: 0.4896
Epoch 29/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6921 - accuracy: 0.5240 - val_loss: 0.7005 - val_accuracy: 0.4980
Epoch 30/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6927 - accuracy: 0.5204 - val_loss: 0.6998 - val_accuracy: 0.4880
Epoch 31/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6930 - accuracy: 0.5216 - val_loss: 0.7035 - val_accuracy: 0.4940
Epoch 32/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6933 - accuracy: 0.5168 - val_loss: 0.7004 - val_accuracy: 0.5016
Epoch 33/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5228 - val_loss: 0.6990 - val_accuracy: 0.4928
Epoch 34/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6934 - accuracy: 0.5128 - val_loss: 0.6978 - val_accuracy: 0.4964
Epoch 35/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6930 - accuracy: 0.5136 - val_loss: 0.6975 - val_accuracy: 0.4908
Epoch 36/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6931 - accuracy: 0.5208 - val_loss: 0.6988 - val_accuracy: 0.4892
Epoch 37/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6923 - accuracy: 0.5240 - val_loss: 0.7005 - val_accuracy: 0.5012
Epoch 38/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6949 - accuracy: 0.5268 - val_loss: 0.7008 - val_accuracy: 0.4780
Epoch 39/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6938 - accuracy: 0.5104 - val_loss: 0.6999 - val_accuracy: 0.4956
Epoch 40/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5160 - val_loss: 0.7001 - val_accuracy: 0.4920
Epoch 41/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6930 - accuracy: 0.5220 - val_loss: 0.7002 - val_accuracy: 0.4952
Epoch 42/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6928 - accuracy: 0.5180 - val_loss: 0.6994 - val_accuracy: 0.5044
Epoch 43/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6938 - accuracy: 0.5176 - val_loss: 0.6996 - val_accuracy: 0.4912
Epoch 44/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5172 - val_loss: 0.7004 - val_accuracy: 0.4884
Epoch 45/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6938 - accuracy: 0.5232 - val_loss: 0.6996 - val_accuracy: 0.4980
Epoch 46/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6937 - accuracy: 0.5224 - val_loss: 0.7002 - val_accuracy: 0.4916
Epoch 47/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6931 - accuracy: 0.5192 - val_loss: 0.6982 - val_accuracy: 0.4948
Epoch 48/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6934 - accuracy: 0.5124 - val_loss: 0.7022 - val_accuracy: 0.4932
Epoch 49/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6934 - accuracy: 0.5172 - val_loss: 0.6997 - val_accuracy: 0.5000
Epoch 50/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6933 - accuracy: 0.5204 - val_loss: 0.6997 - val_accuracy: 0.4904
Epoch 51/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6921 - accuracy: 0.5316 - val_loss: 0.6997 - val_accuracy: 0.4896
Epoch 52/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6931 - accuracy: 0.5196 - val_loss: 0.6992 - val_accuracy: 0.5024
Epoch 53/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6928 - accuracy: 0.5188 - val_loss: 0.6993 - val_accuracy: 0.4888
Epoch 54/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6929 - accuracy: 0.5160 - val_loss: 0.7029 - val_accuracy: 0.4912
Epoch 55/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6934 - accuracy: 0.5208 - val_loss: 0.7008 - val_accuracy: 0.4940
Epoch 56/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6917 - accuracy: 0.5292 - val_loss: 0.7020 - val_accuracy: 0.4988
Epoch 57/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6941 - accuracy: 0.5220 - val_loss: 0.7004 - val_accuracy: 0.4916
Epoch 58/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6919 - accuracy: 0.5192 - val_loss: 0.6998 - val_accuracy: 0.4932
Epoch 59/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6927 - accuracy: 0.5184 - val_loss: 0.6988 - val_accuracy: 0.4956
Epoch 60/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6928 - accuracy: 0.5176 - val_loss: 0.6993 - val_accuracy: 0.4988
Epoch 61/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6923 - accuracy: 0.5308 - val_loss: 0.6986 - val_accuracy: 0.4944
Epoch 62/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6936 - accuracy: 0.5248 - val_loss: 0.6982 - val_accuracy: 0.4892
Epoch 63/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6935 - accuracy: 0.5100 - val_loss: 0.7012 - val_accuracy: 0.4868
Epoch 64/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6931 - accuracy: 0.5208 - val_loss: 0.7002 - val_accuracy: 0.4976
Epoch 65/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6933 - accuracy: 0.5244 - val_loss: 0.6988 - val_accuracy: 0.4948
Epoch 66/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6940 - accuracy: 0.5108 - val_loss: 0.6994 - val_accuracy: 0.4960
Epoch 67/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6930 - accuracy: 0.5136 - val_loss: 0.7010 - val_accuracy: 0.4820
Epoch 68/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6934 - accuracy: 0.5252 - val_loss: 0.7020 - val_accuracy: 0.4888
Epoch 69/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6925 - accuracy: 0.5240 - val_loss: 0.6987 - val_accuracy: 0.4892
Epoch 70/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6927 - accuracy: 0.5232 - val_loss: 0.6988 - val_accuracy: 0.4980
Epoch 71/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6936 - accuracy: 0.5124 - val_loss: 0.7007 - val_accuracy: 0.4984
Epoch 72/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6943 - accuracy: 0.5252 - val_loss: 0.7007 - val_accuracy: 0.4888
Epoch 73/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6923 - accuracy: 0.5180 - val_loss: 0.6979 - val_accuracy: 0.4968
Epoch 74/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6923 - accuracy: 0.5124 - val_loss: 0.6989 - val_accuracy: 0.4928
Epoch 75/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6942 - accuracy: 0.5176 - val_loss: 0.6995 - val_accuracy: 0.4864
Epoch 76/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6925 - accuracy: 0.5344 - val_loss: 0.7003 - val_accuracy: 0.4940
Epoch 77/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6928 - accuracy: 0.5212 - val_loss: 0.6983 - val_accuracy: 0.4912
Epoch 78/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6933 - accuracy: 0.5256 - val_loss: 0.7017 - val_accuracy: 0.4972
Epoch 79/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6935 - accuracy: 0.5220 - val_loss: 0.7010 - val_accuracy: 0.4900
Epoch 80/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5256 - val_loss: 0.7012 - val_accuracy: 0.4932
Epoch 81/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6926 - accuracy: 0.5332 - val_loss: 0.7000 - val_accuracy: 0.4980
Epoch 82/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6941 - accuracy: 0.5216 - val_loss: 0.6996 - val_accuracy: 0.5052
Epoch 83/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6939 - accuracy: 0.5156 - val_loss: 0.7002 - val_accuracy: 0.5020
Epoch 84/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6920 - accuracy: 0.5164 - val_loss: 0.6986 - val_accuracy: 0.4940
Epoch 85/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6941 - accuracy: 0.5152 - val_loss: 0.7002 - val_accuracy: 0.5008
Epoch 86/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6926 - accuracy: 0.5124 - val_loss: 0.6978 - val_accuracy: 0.4996
Epoch 87/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6930 - accuracy: 0.5268 - val_loss: 0.7010 - val_accuracy: 0.4960
Epoch 88/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6925 - accuracy: 0.5096 - val_loss: 0.7032 - val_accuracy: 0.4924
Epoch 89/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6938 - accuracy: 0.5140 - val_loss: 0.6984 - val_accuracy: 0.4972
Epoch 90/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6930 - accuracy: 0.5220 - val_loss: 0.7006 - val_accuracy: 0.4844
Epoch 91/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6931 - accuracy: 0.5252 - val_loss: 0.7019 - val_accuracy: 0.4912
Epoch 92/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6921 - accuracy: 0.5192 - val_loss: 0.6996 - val_accuracy: 0.4928
Epoch 93/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6941 - accuracy: 0.5052 - val_loss: 0.6996 - val_accuracy: 0.4900
Epoch 94/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6923 - accuracy: 0.5180 - val_loss: 0.7006 - val_accuracy: 0.4960
Epoch 95/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6928 - accuracy: 0.5216 - val_loss: 0.6973 - val_accuracy: 0.4960
Epoch 96/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6940 - accuracy: 0.5172 - val_loss: 0.7002 - val_accuracy: 0.4872
Epoch 97/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6913 - accuracy: 0.5200 - val_loss: 0.7001 - val_accuracy: 0.4948
Epoch 98/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6921 - accuracy: 0.5272 - val_loss: 0.7023 - val_accuracy: 0.4972
Epoch 99/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6930 - accuracy: 0.5164 - val_loss: 0.7003 - val_accuracy: 0.4908
Epoch 100/100
79/79 [==============================] - 0s 4ms/step - loss: 0.6919 - accuracy: 0.5124 - val_loss: 0.6988 - val_accuracy: 0.5004

# Plot the loss
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()

<matplotlib.legend.Legend at 0x7fec38271438>

# Plot the accuracy too - should be around 50%
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.legend()

<matplotlib.legend.Legend at 0x7fec382777b8>

# Now try a simple RNN
inputs = np.expand_dims(X, -1)

# make the RNN
i = Input(shape=(T, D))

# method 1
# x = LSTM(5)(i)
x = SimpleRNN(5)(i)
# x = GRU(5)(i)

# method 2
# x = LSTM(5, return_sequences=True)(i)
# x = GlobalMaxPool1D()(x)

x = Dense(1, activation='sigmoid')(x)
model = Model(i, x)
model.compile(
  loss='binary_crossentropy',
  # optimizer='rmsprop',
#   optimizer='adam',
  optimizer=Adam(lr=0.01),
  # optimizer=SGD(lr=0.1, momentum=0.9),
  metrics=['accuracy'],
)

# train the RNN
r = model.fit(
  inputs, Y,
  epochs=200,
  validation_split=0.5,
)

Epoch 1/200
79/79 [==============================] - 1s 12ms/step - loss: 0.6980 - accuracy: 0.5232 - val_loss: 0.6856 - val_accuracy: 0.5432
Epoch 2/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6726 - accuracy: 0.5752 - val_loss: 0.6726 - val_accuracy: 0.5756
Epoch 3/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6472 - accuracy: 0.6276 - val_loss: 0.6279 - val_accuracy: 0.6808
Epoch 4/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5874 - accuracy: 0.7200 - val_loss: 0.5298 - val_accuracy: 0.7960
Epoch 5/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4572 - accuracy: 0.8532 - val_loss: 0.3922 - val_accuracy: 0.8800
Epoch 6/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3494 - accuracy: 0.9056 - val_loss: 0.3111 - val_accuracy: 0.9036
Epoch 7/200
79/79 [==============================] - 1s 10ms/step - loss: 0.2770 - accuracy: 0.9252 - val_loss: 0.2596 - val_accuracy: 0.9248
Epoch 8/200
79/79 [==============================] - 1s 11ms/step - loss: 0.2400 - accuracy: 0.9316 - val_loss: 0.2448 - val_accuracy: 0.9172
Epoch 9/200
79/79 [==============================] - 1s 10ms/step - loss: 0.2085 - accuracy: 0.9356 - val_loss: 0.2131 - val_accuracy: 0.9280
Epoch 10/200
79/79 [==============================] - 1s 11ms/step - loss: 0.1890 - accuracy: 0.9444 - val_loss: 0.1918 - val_accuracy: 0.9376
Epoch 11/200
79/79 [==============================] - 1s 10ms/step - loss: 0.1724 - accuracy: 0.9464 - val_loss: 0.1774 - val_accuracy: 0.9452
Epoch 12/200
79/79 [==============================] - 1s 11ms/step - loss: 0.1559 - accuracy: 0.9528 - val_loss: 0.1733 - val_accuracy: 0.9432
Epoch 13/200
79/79 [==============================] - 1s 10ms/step - loss: 0.1501 - accuracy: 0.9556 - val_loss: 0.1589 - val_accuracy: 0.9476
Epoch 14/200
79/79 [==============================] - 1s 10ms/step - loss: 0.1448 - accuracy: 0.9572 - val_loss: 0.1582 - val_accuracy: 0.9480
Epoch 15/200
79/79 [==============================] - 1s 11ms/step - loss: 0.1415 - accuracy: 0.9544 - val_loss: 0.1503 - val_accuracy: 0.9520
Epoch 16/200
79/79 [==============================] - 1s 11ms/step - loss: 0.1274 - accuracy: 0.9612 - val_loss: 0.1420 - val_accuracy: 0.9484
Epoch 17/200
79/79 [==============================] - 1s 10ms/step - loss: 0.1220 - accuracy: 0.9620 - val_loss: 0.1324 - val_accuracy: 0.9552
Epoch 18/200
79/79 [==============================] - 1s 11ms/step - loss: 0.1146 - accuracy: 0.9632 - val_loss: 0.1278 - val_accuracy: 0.9612
Epoch 19/200
79/79 [==============================] - 1s 10ms/step - loss: 0.1102 - accuracy: 0.9708 - val_loss: 0.1237 - val_accuracy: 0.9644
Epoch 20/200
79/79 [==============================] - 1s 10ms/step - loss: 0.1083 - accuracy: 0.9620 - val_loss: 0.1209 - val_accuracy: 0.9552
Epoch 21/200
79/79 [==============================] - 1s 11ms/step - loss: 0.1070 - accuracy: 0.9664 - val_loss: 0.1181 - val_accuracy: 0.9608
Epoch 22/200
79/79 [==============================] - 1s 11ms/step - loss: 0.1024 - accuracy: 0.9688 - val_loss: 0.1194 - val_accuracy: 0.9644
Epoch 23/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0990 - accuracy: 0.9716 - val_loss: 0.1166 - val_accuracy: 0.9628
Epoch 24/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0969 - accuracy: 0.9696 - val_loss: 0.1108 - val_accuracy: 0.9584
Epoch 25/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0918 - accuracy: 0.9700 - val_loss: 0.1077 - val_accuracy: 0.9652
Epoch 26/200
79/79 [==============================] - 1s 12ms/step - loss: 0.0926 - accuracy: 0.9732 - val_loss: 0.1160 - val_accuracy: 0.9560
Epoch 27/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0943 - accuracy: 0.9712 - val_loss: 0.1010 - val_accuracy: 0.9676
Epoch 28/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0866 - accuracy: 0.9736 - val_loss: 0.1106 - val_accuracy: 0.9672
Epoch 29/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0881 - accuracy: 0.9768 - val_loss: 0.1083 - val_accuracy: 0.9664
Epoch 30/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0850 - accuracy: 0.9728 - val_loss: 0.1055 - val_accuracy: 0.9656
Epoch 31/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0824 - accuracy: 0.9736 - val_loss: 0.1030 - val_accuracy: 0.9648
Epoch 32/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0801 - accuracy: 0.9772 - val_loss: 0.1006 - val_accuracy: 0.9660
Epoch 33/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0801 - accuracy: 0.9740 - val_loss: 0.0912 - val_accuracy: 0.9712
Epoch 34/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0767 - accuracy: 0.9768 - val_loss: 0.0899 - val_accuracy: 0.9688
Epoch 35/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0783 - accuracy: 0.9780 - val_loss: 0.0996 - val_accuracy: 0.9656
Epoch 36/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0740 - accuracy: 0.9776 - val_loss: 0.0913 - val_accuracy: 0.9704
Epoch 37/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0731 - accuracy: 0.9796 - val_loss: 0.0913 - val_accuracy: 0.9712
Epoch 38/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0728 - accuracy: 0.9776 - val_loss: 0.0913 - val_accuracy: 0.9684
Epoch 39/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0749 - accuracy: 0.9776 - val_loss: 0.0900 - val_accuracy: 0.9724
Epoch 40/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0743 - accuracy: 0.9780 - val_loss: 0.0903 - val_accuracy: 0.9704
Epoch 41/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0738 - accuracy: 0.9756 - val_loss: 0.0965 - val_accuracy: 0.9696
Epoch 42/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0735 - accuracy: 0.9780 - val_loss: 0.0941 - val_accuracy: 0.9696
Epoch 43/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0684 - accuracy: 0.9768 - val_loss: 0.0841 - val_accuracy: 0.9696
Epoch 44/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0729 - accuracy: 0.9752 - val_loss: 0.0926 - val_accuracy: 0.9696
Epoch 45/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0690 - accuracy: 0.9776 - val_loss: 0.0843 - val_accuracy: 0.9772
Epoch 46/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0671 - accuracy: 0.9800 - val_loss: 0.0897 - val_accuracy: 0.9712
Epoch 47/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0622 - accuracy: 0.9836 - val_loss: 0.0944 - val_accuracy: 0.9680
Epoch 48/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0703 - accuracy: 0.9792 - val_loss: 0.0870 - val_accuracy: 0.9724
Epoch 49/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0728 - accuracy: 0.9796 - val_loss: 0.0820 - val_accuracy: 0.9792
Epoch 50/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0679 - accuracy: 0.9800 - val_loss: 0.0891 - val_accuracy: 0.9752
Epoch 51/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0661 - accuracy: 0.9788 - val_loss: 0.0897 - val_accuracy: 0.9708
Epoch 52/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0649 - accuracy: 0.9792 - val_loss: 0.0850 - val_accuracy: 0.9732
Epoch 53/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0655 - accuracy: 0.9832 - val_loss: 0.0773 - val_accuracy: 0.9740
Epoch 54/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0626 - accuracy: 0.9808 - val_loss: 0.0780 - val_accuracy: 0.9732
Epoch 55/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0593 - accuracy: 0.9832 - val_loss: 0.0804 - val_accuracy: 0.9756
Epoch 56/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0672 - accuracy: 0.9764 - val_loss: 0.0800 - val_accuracy: 0.9728
Epoch 57/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0639 - accuracy: 0.9816 - val_loss: 0.0769 - val_accuracy: 0.9748
Epoch 58/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0602 - accuracy: 0.9844 - val_loss: 0.0737 - val_accuracy: 0.9728
Epoch 59/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0614 - accuracy: 0.9816 - val_loss: 0.0773 - val_accuracy: 0.9768
Epoch 60/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0595 - accuracy: 0.9836 - val_loss: 0.0908 - val_accuracy: 0.9756
Epoch 61/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0625 - accuracy: 0.9832 - val_loss: 0.0692 - val_accuracy: 0.9796
Epoch 62/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0569 - accuracy: 0.9840 - val_loss: 0.0730 - val_accuracy: 0.9768
Epoch 63/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0552 - accuracy: 0.9832 - val_loss: 0.0710 - val_accuracy: 0.9772
Epoch 64/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0561 - accuracy: 0.9836 - val_loss: 0.0686 - val_accuracy: 0.9784
Epoch 65/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0547 - accuracy: 0.9848 - val_loss: 0.0705 - val_accuracy: 0.9800
Epoch 66/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0524 - accuracy: 0.9852 - val_loss: 0.0934 - val_accuracy: 0.9736
Epoch 67/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0548 - accuracy: 0.9840 - val_loss: 0.0762 - val_accuracy: 0.9796
Epoch 68/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0528 - accuracy: 0.9836 - val_loss: 0.0666 - val_accuracy: 0.9816
Epoch 69/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0510 - accuracy: 0.9876 - val_loss: 0.0894 - val_accuracy: 0.9756
Epoch 70/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0501 - accuracy: 0.9856 - val_loss: 0.0691 - val_accuracy: 0.9796
Epoch 71/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0511 - accuracy: 0.9868 - val_loss: 0.0680 - val_accuracy: 0.9792
Epoch 72/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0536 - accuracy: 0.9836 - val_loss: 0.0819 - val_accuracy: 0.9768
Epoch 73/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0553 - accuracy: 0.9816 - val_loss: 0.0653 - val_accuracy: 0.9800
Epoch 74/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0554 - accuracy: 0.9852 - val_loss: 0.0711 - val_accuracy: 0.9812
Epoch 75/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0483 - accuracy: 0.9880 - val_loss: 0.0823 - val_accuracy: 0.9732
Epoch 76/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0512 - accuracy: 0.9852 - val_loss: 0.0901 - val_accuracy: 0.9808
Epoch 77/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0539 - accuracy: 0.9844 - val_loss: 0.0752 - val_accuracy: 0.9792
Epoch 78/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0491 - accuracy: 0.9872 - val_loss: 0.0671 - val_accuracy: 0.9816
Epoch 79/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0471 - accuracy: 0.9876 - val_loss: 0.0702 - val_accuracy: 0.9820
Epoch 80/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0547 - accuracy: 0.9828 - val_loss: 0.0739 - val_accuracy: 0.9812
Epoch 81/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0561 - accuracy: 0.9816 - val_loss: 0.0658 - val_accuracy: 0.9808
Epoch 82/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0646 - accuracy: 0.9856 - val_loss: 0.0728 - val_accuracy: 0.9788
Epoch 83/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0576 - accuracy: 0.9824 - val_loss: 0.0738 - val_accuracy: 0.9772
Epoch 84/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0573 - accuracy: 0.9824 - val_loss: 0.0668 - val_accuracy: 0.9824
Epoch 85/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0428 - accuracy: 0.9896 - val_loss: 0.0827 - val_accuracy: 0.9804
Epoch 86/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0480 - accuracy: 0.9888 - val_loss: 0.0679 - val_accuracy: 0.9804
Epoch 87/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0436 - accuracy: 0.9888 - val_loss: 0.0677 - val_accuracy: 0.9848
Epoch 88/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0465 - accuracy: 0.9884 - val_loss: 0.0693 - val_accuracy: 0.9804
Epoch 89/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0417 - accuracy: 0.9896 - val_loss: 0.0731 - val_accuracy: 0.9784
Epoch 90/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0465 - accuracy: 0.9868 - val_loss: 0.0693 - val_accuracy: 0.9840
Epoch 91/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0423 - accuracy: 0.9904 - val_loss: 0.0667 - val_accuracy: 0.9840
Epoch 92/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0436 - accuracy: 0.9892 - val_loss: 0.0683 - val_accuracy: 0.9824
Epoch 93/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0470 - accuracy: 0.9864 - val_loss: 0.0644 - val_accuracy: 0.9824
Epoch 94/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0460 - accuracy: 0.9868 - val_loss: 0.0711 - val_accuracy: 0.9788
Epoch 95/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0398 - accuracy: 0.9896 - val_loss: 0.0688 - val_accuracy: 0.9784
Epoch 96/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0410 - accuracy: 0.9868 - val_loss: 0.0733 - val_accuracy: 0.9836
Epoch 97/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0455 - accuracy: 0.9860 - val_loss: 0.0647 - val_accuracy: 0.9848
Epoch 98/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0390 - accuracy: 0.9888 - val_loss: 0.0671 - val_accuracy: 0.9804
Epoch 99/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0387 - accuracy: 0.9884 - val_loss: 0.0686 - val_accuracy: 0.9812
Epoch 100/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0396 - accuracy: 0.9888 - val_loss: 0.0648 - val_accuracy: 0.9800
Epoch 101/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0384 - accuracy: 0.9908 - val_loss: 0.0626 - val_accuracy: 0.9832
Epoch 102/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0415 - accuracy: 0.9884 - val_loss: 0.0720 - val_accuracy: 0.9792
Epoch 103/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0401 - accuracy: 0.9852 - val_loss: 0.0676 - val_accuracy: 0.9820
Epoch 104/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0396 - accuracy: 0.9900 - val_loss: 0.0642 - val_accuracy: 0.9832
Epoch 105/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0422 - accuracy: 0.9872 - val_loss: 0.0724 - val_accuracy: 0.9856
Epoch 106/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0429 - accuracy: 0.9884 - val_loss: 0.0810 - val_accuracy: 0.9828
Epoch 107/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0385 - accuracy: 0.9888 - val_loss: 0.0699 - val_accuracy: 0.9812
Epoch 108/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0494 - accuracy: 0.9868 - val_loss: 0.0689 - val_accuracy: 0.9832
Epoch 109/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0383 - accuracy: 0.9872 - val_loss: 0.0628 - val_accuracy: 0.9820
Epoch 110/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0373 - accuracy: 0.9896 - val_loss: 0.0707 - val_accuracy: 0.9768
Epoch 111/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0348 - accuracy: 0.9892 - val_loss: 0.0613 - val_accuracy: 0.9852
Epoch 112/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0375 - accuracy: 0.9872 - val_loss: 0.0828 - val_accuracy: 0.9820
Epoch 113/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0419 - accuracy: 0.9904 - val_loss: 0.0732 - val_accuracy: 0.9828
Epoch 114/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0386 - accuracy: 0.9868 - val_loss: 0.0676 - val_accuracy: 0.9816
Epoch 115/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0357 - accuracy: 0.9904 - val_loss: 0.0647 - val_accuracy: 0.9828
Epoch 116/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0346 - accuracy: 0.9912 - val_loss: 0.0648 - val_accuracy: 0.9816
Epoch 117/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0339 - accuracy: 0.9892 - val_loss: 0.0679 - val_accuracy: 0.9816
Epoch 118/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0320 - accuracy: 0.9920 - val_loss: 0.0682 - val_accuracy: 0.9828
Epoch 119/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0329 - accuracy: 0.9916 - val_loss: 0.0638 - val_accuracy: 0.9840
Epoch 120/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0324 - accuracy: 0.9904 - val_loss: 0.0648 - val_accuracy: 0.9844
Epoch 121/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0312 - accuracy: 0.9924 - val_loss: 0.0639 - val_accuracy: 0.9824
Epoch 122/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0328 - accuracy: 0.9884 - val_loss: 0.0642 - val_accuracy: 0.9836
Epoch 123/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0380 - accuracy: 0.9888 - val_loss: 0.0727 - val_accuracy: 0.9840
Epoch 124/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0513 - accuracy: 0.9864 - val_loss: 0.0668 - val_accuracy: 0.9844
Epoch 125/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0475 - accuracy: 0.9884 - val_loss: 0.0730 - val_accuracy: 0.9808
Epoch 126/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0350 - accuracy: 0.9884 - val_loss: 0.0622 - val_accuracy: 0.9848
Epoch 127/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0326 - accuracy: 0.9896 - val_loss: 0.0797 - val_accuracy: 0.9800
Epoch 128/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0371 - accuracy: 0.9908 - val_loss: 0.0654 - val_accuracy: 0.9812
Epoch 129/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0304 - accuracy: 0.9916 - val_loss: 0.0640 - val_accuracy: 0.9844
Epoch 130/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0319 - accuracy: 0.9908 - val_loss: 0.0760 - val_accuracy: 0.9820
Epoch 131/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0325 - accuracy: 0.9904 - val_loss: 0.0625 - val_accuracy: 0.9840
Epoch 132/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0307 - accuracy: 0.9920 - val_loss: 0.0597 - val_accuracy: 0.9848
Epoch 133/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0355 - accuracy: 0.9904 - val_loss: 0.0628 - val_accuracy: 0.9796
Epoch 134/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0347 - accuracy: 0.9884 - val_loss: 0.0627 - val_accuracy: 0.9848
Epoch 135/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0478 - accuracy: 0.9848 - val_loss: 0.0708 - val_accuracy: 0.9832
Epoch 136/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0347 - accuracy: 0.9880 - val_loss: 0.0623 - val_accuracy: 0.9844
Epoch 137/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0284 - accuracy: 0.9928 - val_loss: 0.0598 - val_accuracy: 0.9852
Epoch 138/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0464 - accuracy: 0.9920 - val_loss: 0.0620 - val_accuracy: 0.9824
Epoch 139/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0402 - accuracy: 0.9852 - val_loss: 0.0611 - val_accuracy: 0.9852
Epoch 140/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0291 - accuracy: 0.9912 - val_loss: 0.0594 - val_accuracy: 0.9836
Epoch 141/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0353 - accuracy: 0.9892 - val_loss: 0.0697 - val_accuracy: 0.9828
Epoch 142/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0354 - accuracy: 0.9912 - val_loss: 0.0577 - val_accuracy: 0.9844
Epoch 143/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0330 - accuracy: 0.9908 - val_loss: 0.0756 - val_accuracy: 0.9800
Epoch 144/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0322 - accuracy: 0.9936 - val_loss: 0.0530 - val_accuracy: 0.9848
Epoch 145/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0274 - accuracy: 0.9924 - val_loss: 0.0588 - val_accuracy: 0.9872
Epoch 146/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0265 - accuracy: 0.9924 - val_loss: 0.0548 - val_accuracy: 0.9848
Epoch 147/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0268 - accuracy: 0.9924 - val_loss: 0.0554 - val_accuracy: 0.9840
Epoch 148/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0255 - accuracy: 0.9936 - val_loss: 0.0527 - val_accuracy: 0.9868
Epoch 149/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0324 - accuracy: 0.9920 - val_loss: 0.0594 - val_accuracy: 0.9856
Epoch 150/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0282 - accuracy: 0.9916 - val_loss: 0.0602 - val_accuracy: 0.9844
Epoch 151/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0265 - accuracy: 0.9924 - val_loss: 0.0558 - val_accuracy: 0.9864
Epoch 152/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0275 - accuracy: 0.9916 - val_loss: 0.0588 - val_accuracy: 0.9860
Epoch 153/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0275 - accuracy: 0.9928 - val_loss: 0.0579 - val_accuracy: 0.9864
Epoch 154/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0264 - accuracy: 0.9904 - val_loss: 0.0617 - val_accuracy: 0.9860
Epoch 155/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0290 - accuracy: 0.9904 - val_loss: 0.0514 - val_accuracy: 0.9864
Epoch 156/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0293 - accuracy: 0.9920 - val_loss: 0.0588 - val_accuracy: 0.9840
Epoch 157/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0265 - accuracy: 0.9940 - val_loss: 0.0579 - val_accuracy: 0.9880
Epoch 158/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0252 - accuracy: 0.9928 - val_loss: 0.0725 - val_accuracy: 0.9812
Epoch 159/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0300 - accuracy: 0.9888 - val_loss: 0.0600 - val_accuracy: 0.9844
Epoch 160/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0244 - accuracy: 0.9920 - val_loss: 0.0549 - val_accuracy: 0.9840
Epoch 161/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0264 - accuracy: 0.9928 - val_loss: 0.0641 - val_accuracy: 0.9840
Epoch 162/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0262 - accuracy: 0.9912 - val_loss: 0.0681 - val_accuracy: 0.9784
Epoch 163/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0247 - accuracy: 0.9920 - val_loss: 0.0571 - val_accuracy: 0.9852
Epoch 164/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0226 - accuracy: 0.9944 - val_loss: 0.0585 - val_accuracy: 0.9836
Epoch 165/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0294 - accuracy: 0.9904 - val_loss: 0.0614 - val_accuracy: 0.9836
Epoch 166/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0302 - accuracy: 0.9904 - val_loss: 0.0516 - val_accuracy: 0.9868
Epoch 167/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0314 - accuracy: 0.9916 - val_loss: 0.0550 - val_accuracy: 0.9824
Epoch 168/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0274 - accuracy: 0.9896 - val_loss: 0.0558 - val_accuracy: 0.9828
Epoch 169/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0237 - accuracy: 0.9940 - val_loss: 0.0630 - val_accuracy: 0.9836
Epoch 170/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0252 - accuracy: 0.9912 - val_loss: 0.0737 - val_accuracy: 0.9816
Epoch 171/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0337 - accuracy: 0.9896 - val_loss: 0.0523 - val_accuracy: 0.9872
Epoch 172/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0277 - accuracy: 0.9920 - val_loss: 0.0562 - val_accuracy: 0.9844
Epoch 173/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0292 - accuracy: 0.9924 - val_loss: 0.0613 - val_accuracy: 0.9852
Epoch 174/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0265 - accuracy: 0.9932 - val_loss: 0.0604 - val_accuracy: 0.9864
Epoch 175/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0280 - accuracy: 0.9940 - val_loss: 0.0560 - val_accuracy: 0.9856
Epoch 176/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0383 - accuracy: 0.9912 - val_loss: 0.0514 - val_accuracy: 0.9852
Epoch 177/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0347 - accuracy: 0.9908 - val_loss: 0.0503 - val_accuracy: 0.9864
Epoch 178/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0213 - accuracy: 0.9948 - val_loss: 0.0522 - val_accuracy: 0.9844
Epoch 179/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0241 - accuracy: 0.9932 - val_loss: 0.0493 - val_accuracy: 0.9856
Epoch 180/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0233 - accuracy: 0.9932 - val_loss: 0.0493 - val_accuracy: 0.9876
Epoch 181/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0222 - accuracy: 0.9948 - val_loss: 0.0476 - val_accuracy: 0.9860
Epoch 182/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0272 - accuracy: 0.9904 - val_loss: 0.0577 - val_accuracy: 0.9868
Epoch 183/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0304 - accuracy: 0.9912 - val_loss: 0.0785 - val_accuracy: 0.9840
Epoch 184/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0376 - accuracy: 0.9900 - val_loss: 0.0596 - val_accuracy: 0.9848
Epoch 185/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0244 - accuracy: 0.9944 - val_loss: 0.0520 - val_accuracy: 0.9872
Epoch 186/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0217 - accuracy: 0.9932 - val_loss: 0.0525 - val_accuracy: 0.9840
Epoch 187/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0189 - accuracy: 0.9952 - val_loss: 0.0493 - val_accuracy: 0.9844
Epoch 188/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0193 - accuracy: 0.9948 - val_loss: 0.0554 - val_accuracy: 0.9852
Epoch 189/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0211 - accuracy: 0.9944 - val_loss: 0.0525 - val_accuracy: 0.9860
Epoch 190/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0201 - accuracy: 0.9948 - val_loss: 0.0558 - val_accuracy: 0.9856
Epoch 191/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0227 - accuracy: 0.9932 - val_loss: 0.0549 - val_accuracy: 0.9888
Epoch 192/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0191 - accuracy: 0.9948 - val_loss: 0.0506 - val_accuracy: 0.9840
Epoch 193/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0215 - accuracy: 0.9932 - val_loss: 0.0536 - val_accuracy: 0.9864
Epoch 194/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0200 - accuracy: 0.9936 - val_loss: 0.0518 - val_accuracy: 0.9888
Epoch 195/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0183 - accuracy: 0.9944 - val_loss: 0.0470 - val_accuracy: 0.9876
Epoch 196/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0248 - accuracy: 0.9936 - val_loss: 0.0536 - val_accuracy: 0.9868
Epoch 197/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0272 - accuracy: 0.9912 - val_loss: 0.0642 - val_accuracy: 0.9836
Epoch 198/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0264 - accuracy: 0.9912 - val_loss: 0.0448 - val_accuracy: 0.9872
Epoch 199/200
79/79 [==============================] - 1s 11ms/step - loss: 0.0568 - accuracy: 0.9896 - val_loss: 0.0793 - val_accuracy: 0.9816
Epoch 200/200
79/79 [==============================] - 1s 10ms/step - loss: 0.0396 - accuracy: 0.9904 - val_loss: 0.0707 - val_accuracy: 0.9852

# Plot the loss
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()

<matplotlib.legend.Legend at 0x7fec1f781828>

# Plot the accuracy too
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.legend()

<matplotlib.legend.Legend at 0x7fec380f10b8>

# Now change to the long distance problem

# Start with a small T and increase it later
T = 10
D = 1
X = []
Y = []

for t in range(5000):
  x = np.random.randn(T)
  X.append(x)
  y = get_label(x, 0, 1, 2) # long distance
  Y.append(y)

X = np.array(X)
Y = np.array(Y)
N = len(X)

# Now test our Simple RNN again
inputs = np.expand_dims(X, -1)

# make the RNN
i = Input(shape=(T, D))

# method 1
x = SimpleRNN(5)(i)

x = Dense(1, activation='sigmoid')(x)
model = Model(i, x)
model.compile(
  loss='binary_crossentropy',
  optimizer=Adam(lr=0.01),
  metrics=['accuracy'],
)

# train the RNN
r = model.fit(
  inputs, Y,
  epochs=200,
  validation_split=0.5,
)

Epoch 1/200
79/79 [==============================] - 1s 12ms/step - loss: 0.7009 - accuracy: 0.4996 - val_loss: 0.6948 - val_accuracy: 0.5088
Epoch 2/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6944 - accuracy: 0.5128 - val_loss: 0.6951 - val_accuracy: 0.5076
Epoch 3/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6951 - accuracy: 0.5112 - val_loss: 0.6940 - val_accuracy: 0.5160
Epoch 4/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6933 - accuracy: 0.5112 - val_loss: 0.6935 - val_accuracy: 0.5024
Epoch 5/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6946 - accuracy: 0.5092 - val_loss: 0.6932 - val_accuracy: 0.5140
Epoch 6/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6923 - accuracy: 0.5236 - val_loss: 0.6952 - val_accuracy: 0.5072
Epoch 7/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6922 - accuracy: 0.5248 - val_loss: 0.6935 - val_accuracy: 0.5088
Epoch 8/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6919 - accuracy: 0.5276 - val_loss: 0.6930 - val_accuracy: 0.5184
Epoch 9/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6936 - accuracy: 0.5220 - val_loss: 0.6934 - val_accuracy: 0.5180
Epoch 10/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6914 - accuracy: 0.5356 - val_loss: 0.6923 - val_accuracy: 0.5204
Epoch 11/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6909 - accuracy: 0.5292 - val_loss: 0.6931 - val_accuracy: 0.5232
Epoch 12/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6912 - accuracy: 0.5288 - val_loss: 0.6967 - val_accuracy: 0.5188
Epoch 13/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6921 - accuracy: 0.5184 - val_loss: 0.6934 - val_accuracy: 0.5124
Epoch 14/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6907 - accuracy: 0.5260 - val_loss: 0.6942 - val_accuracy: 0.5188
Epoch 15/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6907 - accuracy: 0.5304 - val_loss: 0.6951 - val_accuracy: 0.5188
Epoch 16/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6905 - accuracy: 0.5292 - val_loss: 0.6921 - val_accuracy: 0.5196
Epoch 17/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6906 - accuracy: 0.5416 - val_loss: 0.6937 - val_accuracy: 0.5296
Epoch 18/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6895 - accuracy: 0.5184 - val_loss: 0.6940 - val_accuracy: 0.5164
Epoch 19/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6903 - accuracy: 0.5304 - val_loss: 0.6949 - val_accuracy: 0.5120
Epoch 20/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6907 - accuracy: 0.5412 - val_loss: 0.6931 - val_accuracy: 0.5184
Epoch 21/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6893 - accuracy: 0.5444 - val_loss: 0.6942 - val_accuracy: 0.5076
Epoch 22/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6913 - accuracy: 0.5328 - val_loss: 0.6936 - val_accuracy: 0.5148
Epoch 23/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6902 - accuracy: 0.5364 - val_loss: 0.6941 - val_accuracy: 0.5260
Epoch 24/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6883 - accuracy: 0.5464 - val_loss: 0.6933 - val_accuracy: 0.5284
Epoch 25/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6887 - accuracy: 0.5392 - val_loss: 0.6921 - val_accuracy: 0.5248
Epoch 26/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6888 - accuracy: 0.5512 - val_loss: 0.6979 - val_accuracy: 0.5088
Epoch 27/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6897 - accuracy: 0.5436 - val_loss: 0.6926 - val_accuracy: 0.5312
Epoch 28/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6899 - accuracy: 0.5512 - val_loss: 0.6925 - val_accuracy: 0.5236
Epoch 29/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6886 - accuracy: 0.5516 - val_loss: 0.6927 - val_accuracy: 0.5208
Epoch 30/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6912 - accuracy: 0.5448 - val_loss: 0.6915 - val_accuracy: 0.5276
Epoch 31/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6860 - accuracy: 0.5572 - val_loss: 0.6932 - val_accuracy: 0.5264
Epoch 32/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6869 - accuracy: 0.5564 - val_loss: 0.6926 - val_accuracy: 0.5232
Epoch 33/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6858 - accuracy: 0.5604 - val_loss: 0.6936 - val_accuracy: 0.5200
Epoch 34/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6862 - accuracy: 0.5580 - val_loss: 0.6934 - val_accuracy: 0.5284
Epoch 35/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6842 - accuracy: 0.5652 - val_loss: 0.6920 - val_accuracy: 0.5384
Epoch 36/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6839 - accuracy: 0.5624 - val_loss: 0.6884 - val_accuracy: 0.5468
Epoch 37/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6796 - accuracy: 0.5712 - val_loss: 0.6856 - val_accuracy: 0.5488
Epoch 38/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6724 - accuracy: 0.5836 - val_loss: 0.6723 - val_accuracy: 0.5784
Epoch 39/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6612 - accuracy: 0.5936 - val_loss: 0.6746 - val_accuracy: 0.5872
Epoch 40/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6565 - accuracy: 0.5996 - val_loss: 0.6659 - val_accuracy: 0.6104
Epoch 41/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6572 - accuracy: 0.6176 - val_loss: 0.6759 - val_accuracy: 0.5988
Epoch 42/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6521 - accuracy: 0.6092 - val_loss: 0.6534 - val_accuracy: 0.6112
Epoch 43/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6309 - accuracy: 0.6384 - val_loss: 0.6267 - val_accuracy: 0.6444
Epoch 44/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6448 - accuracy: 0.6256 - val_loss: 0.6421 - val_accuracy: 0.6244
Epoch 45/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6301 - accuracy: 0.6380 - val_loss: 0.6289 - val_accuracy: 0.6352
Epoch 46/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6370 - accuracy: 0.6360 - val_loss: 0.7009 - val_accuracy: 0.5672
Epoch 47/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6710 - accuracy: 0.5856 - val_loss: 0.6421 - val_accuracy: 0.6224
Epoch 48/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6243 - accuracy: 0.6436 - val_loss: 0.6336 - val_accuracy: 0.6400
Epoch 49/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6238 - accuracy: 0.6516 - val_loss: 0.6313 - val_accuracy: 0.6420
Epoch 50/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6192 - accuracy: 0.6472 - val_loss: 0.6221 - val_accuracy: 0.6492
Epoch 51/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6009 - accuracy: 0.6724 - val_loss: 0.6352 - val_accuracy: 0.6508
Epoch 52/200
79/79 [==============================] - 1s 12ms/step - loss: 0.6152 - accuracy: 0.6468 - val_loss: 0.6018 - val_accuracy: 0.6596
Epoch 53/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5915 - accuracy: 0.6816 - val_loss: 0.5911 - val_accuracy: 0.6592
Epoch 54/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5794 - accuracy: 0.6776 - val_loss: 0.5868 - val_accuracy: 0.6772
Epoch 55/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6113 - accuracy: 0.6520 - val_loss: 0.5924 - val_accuracy: 0.6788
Epoch 56/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5776 - accuracy: 0.6816 - val_loss: 0.6155 - val_accuracy: 0.6468
Epoch 57/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6426 - accuracy: 0.6272 - val_loss: 0.6447 - val_accuracy: 0.6164
Epoch 58/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6070 - accuracy: 0.6544 - val_loss: 0.6043 - val_accuracy: 0.6600
Epoch 59/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5754 - accuracy: 0.6776 - val_loss: 0.5564 - val_accuracy: 0.6996
Epoch 60/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5524 - accuracy: 0.6972 - val_loss: 0.5752 - val_accuracy: 0.6976
Epoch 61/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5632 - accuracy: 0.6916 - val_loss: 0.6242 - val_accuracy: 0.6272
Epoch 62/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6013 - accuracy: 0.6508 - val_loss: 0.5646 - val_accuracy: 0.6940
Epoch 63/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5497 - accuracy: 0.7024 - val_loss: 0.5493 - val_accuracy: 0.6940
Epoch 64/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5718 - accuracy: 0.6868 - val_loss: 0.5542 - val_accuracy: 0.7016
Epoch 65/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5447 - accuracy: 0.7212 - val_loss: 0.5553 - val_accuracy: 0.7104
Epoch 66/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5345 - accuracy: 0.7232 - val_loss: 0.5673 - val_accuracy: 0.7128
Epoch 67/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5677 - accuracy: 0.7200 - val_loss: 0.5253 - val_accuracy: 0.7688
Epoch 68/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5185 - accuracy: 0.7496 - val_loss: 0.5156 - val_accuracy: 0.7620
Epoch 69/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5330 - accuracy: 0.7440 - val_loss: 0.5130 - val_accuracy: 0.7476
Epoch 70/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5412 - accuracy: 0.7452 - val_loss: 0.5197 - val_accuracy: 0.7636
Epoch 71/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4882 - accuracy: 0.7788 - val_loss: 0.5193 - val_accuracy: 0.7600
Epoch 72/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5023 - accuracy: 0.7716 - val_loss: 0.5054 - val_accuracy: 0.7704
Epoch 73/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5104 - accuracy: 0.7764 - val_loss: 0.5132 - val_accuracy: 0.7764
Epoch 74/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4758 - accuracy: 0.8048 - val_loss: 0.5033 - val_accuracy: 0.7928
Epoch 75/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4713 - accuracy: 0.8124 - val_loss: 0.4803 - val_accuracy: 0.8100
Epoch 76/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4762 - accuracy: 0.7996 - val_loss: 0.4966 - val_accuracy: 0.8000
Epoch 77/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4560 - accuracy: 0.8172 - val_loss: 0.4961 - val_accuracy: 0.7932
Epoch 78/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4521 - accuracy: 0.8200 - val_loss: 0.4663 - val_accuracy: 0.8080
Epoch 79/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4624 - accuracy: 0.8080 - val_loss: 0.5134 - val_accuracy: 0.7788
Epoch 80/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5291 - accuracy: 0.7596 - val_loss: 0.5268 - val_accuracy: 0.7668
Epoch 81/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4886 - accuracy: 0.7952 - val_loss: 0.5036 - val_accuracy: 0.7968
Epoch 82/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4483 - accuracy: 0.8192 - val_loss: 0.4857 - val_accuracy: 0.7820
Epoch 83/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4724 - accuracy: 0.7868 - val_loss: 0.5214 - val_accuracy: 0.7624
Epoch 84/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4629 - accuracy: 0.7980 - val_loss: 0.4601 - val_accuracy: 0.8044
Epoch 85/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4590 - accuracy: 0.8020 - val_loss: 0.4799 - val_accuracy: 0.7892
Epoch 86/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4843 - accuracy: 0.7808 - val_loss: 0.5494 - val_accuracy: 0.7452
Epoch 87/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5111 - accuracy: 0.7768 - val_loss: 0.4562 - val_accuracy: 0.8008
Epoch 88/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4392 - accuracy: 0.8204 - val_loss: 0.4450 - val_accuracy: 0.8128
Epoch 89/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4546 - accuracy: 0.8204 - val_loss: 0.4002 - val_accuracy: 0.8484
Epoch 90/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4200 - accuracy: 0.8312 - val_loss: 0.4415 - val_accuracy: 0.8304
Epoch 91/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4022 - accuracy: 0.8484 - val_loss: 0.4678 - val_accuracy: 0.8000
Epoch 92/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4207 - accuracy: 0.8272 - val_loss: 0.4074 - val_accuracy: 0.8504
Epoch 93/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3875 - accuracy: 0.8520 - val_loss: 0.4857 - val_accuracy: 0.8000
Epoch 94/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4068 - accuracy: 0.8356 - val_loss: 0.4879 - val_accuracy: 0.7900
Epoch 95/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4873 - accuracy: 0.8028 - val_loss: 0.4495 - val_accuracy: 0.8416
Epoch 96/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3773 - accuracy: 0.8732 - val_loss: 0.3833 - val_accuracy: 0.8672
Epoch 97/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3640 - accuracy: 0.8776 - val_loss: 0.4010 - val_accuracy: 0.8684
Epoch 98/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3723 - accuracy: 0.8716 - val_loss: 0.3782 - val_accuracy: 0.8744
Epoch 99/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4678 - accuracy: 0.8152 - val_loss: 0.6316 - val_accuracy: 0.7104
Epoch 100/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5038 - accuracy: 0.7836 - val_loss: 0.4439 - val_accuracy: 0.8280
Epoch 101/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4657 - accuracy: 0.8216 - val_loss: 0.4984 - val_accuracy: 0.7936
Epoch 102/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5420 - accuracy: 0.7528 - val_loss: 0.6300 - val_accuracy: 0.6660
Epoch 103/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5793 - accuracy: 0.7136 - val_loss: 0.5262 - val_accuracy: 0.7696
Epoch 104/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5042 - accuracy: 0.7864 - val_loss: 0.4314 - val_accuracy: 0.8424
Epoch 105/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4277 - accuracy: 0.8372 - val_loss: 0.4179 - val_accuracy: 0.8460
Epoch 106/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4170 - accuracy: 0.8296 - val_loss: 0.4903 - val_accuracy: 0.7940
Epoch 107/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3549 - accuracy: 0.8724 - val_loss: 0.3610 - val_accuracy: 0.8752
Epoch 108/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3170 - accuracy: 0.8988 - val_loss: 0.3182 - val_accuracy: 0.9036
Epoch 109/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3382 - accuracy: 0.8820 - val_loss: 0.3694 - val_accuracy: 0.8732
Epoch 110/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4927 - accuracy: 0.7988 - val_loss: 0.5154 - val_accuracy: 0.7928
Epoch 111/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4499 - accuracy: 0.8240 - val_loss: 0.4852 - val_accuracy: 0.7972
Epoch 112/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5167 - accuracy: 0.7772 - val_loss: 0.5273 - val_accuracy: 0.7652
Epoch 113/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5258 - accuracy: 0.7620 - val_loss: 0.4776 - val_accuracy: 0.8088
Epoch 114/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4321 - accuracy: 0.8384 - val_loss: 0.4269 - val_accuracy: 0.8508
Epoch 115/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3960 - accuracy: 0.8632 - val_loss: 0.3897 - val_accuracy: 0.8652
Epoch 116/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3598 - accuracy: 0.8764 - val_loss: 0.3446 - val_accuracy: 0.8836
Epoch 117/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3355 - accuracy: 0.8896 - val_loss: 0.3143 - val_accuracy: 0.9088
Epoch 118/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3117 - accuracy: 0.8960 - val_loss: 0.3016 - val_accuracy: 0.9068
Epoch 119/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4481 - accuracy: 0.8416 - val_loss: 0.4037 - val_accuracy: 0.8580
Epoch 120/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3510 - accuracy: 0.8880 - val_loss: 0.3300 - val_accuracy: 0.8932
Epoch 121/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3791 - accuracy: 0.8748 - val_loss: 0.4971 - val_accuracy: 0.8112
Epoch 122/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4033 - accuracy: 0.8528 - val_loss: 0.3500 - val_accuracy: 0.8864
Epoch 123/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3238 - accuracy: 0.8996 - val_loss: 0.3461 - val_accuracy: 0.8872
Epoch 124/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4098 - accuracy: 0.8520 - val_loss: 0.6394 - val_accuracy: 0.7480
Epoch 125/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5331 - accuracy: 0.7832 - val_loss: 0.4910 - val_accuracy: 0.8080
Epoch 126/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4373 - accuracy: 0.8432 - val_loss: 0.4168 - val_accuracy: 0.8580
Epoch 127/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3445 - accuracy: 0.8852 - val_loss: 0.3493 - val_accuracy: 0.8856
Epoch 128/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3966 - accuracy: 0.8628 - val_loss: 0.3932 - val_accuracy: 0.8676
Epoch 129/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3495 - accuracy: 0.8852 - val_loss: 0.3292 - val_accuracy: 0.8964
Epoch 130/200
79/79 [==============================] - 1s 11ms/step - loss: 0.3341 - accuracy: 0.8888 - val_loss: 0.3204 - val_accuracy: 0.9028
Epoch 131/200
79/79 [==============================] - 1s 11ms/step - loss: 0.3434 - accuracy: 0.8908 - val_loss: 0.4491 - val_accuracy: 0.8332
Epoch 132/200
79/79 [==============================] - 1s 11ms/step - loss: 0.5237 - accuracy: 0.7880 - val_loss: 0.4900 - val_accuracy: 0.7976
Epoch 133/200
79/79 [==============================] - 1s 11ms/step - loss: 0.4198 - accuracy: 0.8484 - val_loss: 0.4675 - val_accuracy: 0.8184
Epoch 134/200
79/79 [==============================] - 1s 10ms/step - loss: 0.3387 - accuracy: 0.8908 - val_loss: 0.3651 - val_accuracy: 0.8804
Epoch 135/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6448 - accuracy: 0.6680 - val_loss: 0.5885 - val_accuracy: 0.6936
Epoch 136/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5542 - accuracy: 0.7360 - val_loss: 0.5517 - val_accuracy: 0.7392
Epoch 137/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5331 - accuracy: 0.7492 - val_loss: 0.5222 - val_accuracy: 0.7568
Epoch 138/200
79/79 [==============================] - 1s 10ms/step - loss: 0.5067 - accuracy: 0.7716 - val_loss: 0.4807 - val_accuracy: 0.7924
Epoch 139/200
79/79 [==============================] - 1s 10ms/step - loss: 0.4294 - accuracy: 0.8052 - val_loss: 0.4049 - val_accuracy: 0.8116
Epoch 140/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6623 - accuracy: 0.6196 - val_loss: 0.6804 - val_accuracy: 0.5624
Epoch 141/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6885 - accuracy: 0.5320 - val_loss: 0.6820 - val_accuracy: 0.5436
Epoch 142/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6836 - accuracy: 0.5268 - val_loss: 0.6772 - val_accuracy: 0.5556
Epoch 143/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6853 - accuracy: 0.5372 - val_loss: 0.6855 - val_accuracy: 0.5480
Epoch 144/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6882 - accuracy: 0.5328 - val_loss: 0.6866 - val_accuracy: 0.5392
Epoch 145/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6861 - accuracy: 0.5448 - val_loss: 0.6867 - val_accuracy: 0.5400
Epoch 146/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6825 - accuracy: 0.5608 - val_loss: 0.6919 - val_accuracy: 0.5484
Epoch 147/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6876 - accuracy: 0.5624 - val_loss: 0.6843 - val_accuracy: 0.5644
Epoch 148/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6837 - accuracy: 0.5612 - val_loss: 0.6810 - val_accuracy: 0.5648
Epoch 149/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6843 - accuracy: 0.5596 - val_loss: 0.6818 - val_accuracy: 0.5644
Epoch 150/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6839 - accuracy: 0.5616 - val_loss: 0.6844 - val_accuracy: 0.5536
Epoch 151/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6880 - accuracy: 0.5312 - val_loss: 0.6752 - val_accuracy: 0.5876
Epoch 152/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6816 - accuracy: 0.5660 - val_loss: 0.6818 - val_accuracy: 0.5632
Epoch 153/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6932 - accuracy: 0.5348 - val_loss: 0.6877 - val_accuracy: 0.5476
Epoch 154/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6868 - accuracy: 0.5360 - val_loss: 0.6820 - val_accuracy: 0.5600
Epoch 155/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6835 - accuracy: 0.5600 - val_loss: 0.6846 - val_accuracy: 0.5500
Epoch 156/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6807 - accuracy: 0.5616 - val_loss: 0.6772 - val_accuracy: 0.5640
Epoch 157/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6892 - accuracy: 0.5400 - val_loss: 0.6904 - val_accuracy: 0.5100
Epoch 158/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6936 - accuracy: 0.5148 - val_loss: 0.6913 - val_accuracy: 0.5220
Epoch 159/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6904 - accuracy: 0.5180 - val_loss: 0.6910 - val_accuracy: 0.5248
Epoch 160/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6913 - accuracy: 0.5260 - val_loss: 0.6924 - val_accuracy: 0.4888
Epoch 161/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6927 - accuracy: 0.5124 - val_loss: 0.6907 - val_accuracy: 0.5220
Epoch 162/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6943 - accuracy: 0.5032 - val_loss: 0.6958 - val_accuracy: 0.5120
Epoch 163/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6947 - accuracy: 0.4876 - val_loss: 0.6931 - val_accuracy: 0.4924
Epoch 164/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6940 - accuracy: 0.4972 - val_loss: 0.6920 - val_accuracy: 0.5240
Epoch 165/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6936 - accuracy: 0.5060 - val_loss: 0.6931 - val_accuracy: 0.5024
Epoch 166/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6947 - accuracy: 0.4984 - val_loss: 0.6931 - val_accuracy: 0.5168
Epoch 167/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6928 - accuracy: 0.5196 - val_loss: 0.6936 - val_accuracy: 0.5188
Epoch 168/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6927 - accuracy: 0.5156 - val_loss: 0.6935 - val_accuracy: 0.5124
Epoch 169/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6936 - accuracy: 0.5144 - val_loss: 0.6939 - val_accuracy: 0.5252
Epoch 170/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6935 - accuracy: 0.5164 - val_loss: 0.6926 - val_accuracy: 0.5288
Epoch 171/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6965 - accuracy: 0.5052 - val_loss: 0.6938 - val_accuracy: 0.5284
Epoch 172/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6928 - accuracy: 0.5264 - val_loss: 0.6968 - val_accuracy: 0.4776
Epoch 173/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6948 - accuracy: 0.5060 - val_loss: 0.6954 - val_accuracy: 0.4988
Epoch 174/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6927 - accuracy: 0.5196 - val_loss: 0.6937 - val_accuracy: 0.5264
Epoch 175/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6922 - accuracy: 0.5316 - val_loss: 0.6917 - val_accuracy: 0.5284
Epoch 176/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6921 - accuracy: 0.5304 - val_loss: 0.6923 - val_accuracy: 0.5292
Epoch 177/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6908 - accuracy: 0.5292 - val_loss: 0.6923 - val_accuracy: 0.5248
Epoch 178/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6937 - accuracy: 0.5164 - val_loss: 0.6915 - val_accuracy: 0.5216
Epoch 179/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6929 - accuracy: 0.5084 - val_loss: 0.6931 - val_accuracy: 0.5072
Epoch 180/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6948 - accuracy: 0.4988 - val_loss: 0.6914 - val_accuracy: 0.5260
Epoch 181/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6944 - accuracy: 0.5032 - val_loss: 0.6938 - val_accuracy: 0.5148
Epoch 182/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6930 - accuracy: 0.5136 - val_loss: 0.6941 - val_accuracy: 0.5080
Epoch 183/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6935 - accuracy: 0.5152 - val_loss: 0.6947 - val_accuracy: 0.5024
Epoch 184/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6933 - accuracy: 0.4952 - val_loss: 0.6958 - val_accuracy: 0.4984
Epoch 185/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6940 - accuracy: 0.5092 - val_loss: 0.6992 - val_accuracy: 0.5260
Epoch 186/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6922 - accuracy: 0.5144 - val_loss: 0.6927 - val_accuracy: 0.5252
Epoch 187/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6922 - accuracy: 0.5200 - val_loss: 0.6918 - val_accuracy: 0.5284
Epoch 188/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6922 - accuracy: 0.5236 - val_loss: 0.6922 - val_accuracy: 0.5244
Epoch 189/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6904 - accuracy: 0.5392 - val_loss: 0.6853 - val_accuracy: 0.5540
Epoch 190/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6791 - accuracy: 0.5796 - val_loss: 0.6795 - val_accuracy: 0.5704
Epoch 191/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6787 - accuracy: 0.5696 - val_loss: 0.6742 - val_accuracy: 0.5720
Epoch 192/200
79/79 [==============================] - 1s 11ms/step - loss: 0.6815 - accuracy: 0.5708 - val_loss: 0.6789 - val_accuracy: 0.5636
Epoch 193/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6819 - accuracy: 0.5672 - val_loss: 0.6796 - val_accuracy: 0.5644
Epoch 194/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6843 - accuracy: 0.5496 - val_loss: 0.6893 - val_accuracy: 0.5508
Epoch 195/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6948 - accuracy: 0.5256 - val_loss: 0.6948 - val_accuracy: 0.4884
Epoch 196/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6938 - accuracy: 0.5128 - val_loss: 0.6924 - val_accuracy: 0.5188
Epoch 197/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6939 - accuracy: 0.4996 - val_loss: 0.6933 - val_accuracy: 0.5076
Epoch 198/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6915 - accuracy: 0.5244 - val_loss: 0.6972 - val_accuracy: 0.5020
Epoch 199/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6951 - accuracy: 0.5020 - val_loss: 0.6947 - val_accuracy: 0.4760
Epoch 200/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6943 - accuracy: 0.5044 - val_loss: 0.6935 - val_accuracy: 0.5160

# Plot the loss
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()

<matplotlib.legend.Legend at 0x7fec1f652c88>

# Plot the accuracy too
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.legend()

<matplotlib.legend.Legend at 0x7fec1fae4470>

# Now test our LSTM
inputs = np.expand_dims(X, -1)

# make the RNN
i = Input(shape=(T, D))

# method 1
x = LSTM(5)(i)

x = Dense(1, activation='sigmoid')(x)
model = Model(i, x)
model.compile(
  loss='binary_crossentropy',
  optimizer=Adam(lr=0.01),
  metrics=['accuracy'],
)

# train the RNN
r = model.fit(
  inputs, Y,
  epochs=200,
  validation_split=0.5,
)

Epoch 1/200
79/79 [==============================] - 1s 9ms/step - loss: 0.6951 - accuracy: 0.4972 - val_loss: 0.6948 - val_accuracy: 0.5068
Epoch 2/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6930 - accuracy: 0.5140 - val_loss: 0.6940 - val_accuracy: 0.4960
Epoch 3/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6930 - accuracy: 0.5072 - val_loss: 0.6948 - val_accuracy: 0.5036
Epoch 4/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6929 - accuracy: 0.5048 - val_loss: 0.6952 - val_accuracy: 0.5044
Epoch 5/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6930 - accuracy: 0.5120 - val_loss: 0.6940 - val_accuracy: 0.4968
Epoch 6/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6924 - accuracy: 0.5128 - val_loss: 0.6943 - val_accuracy: 0.4984
Epoch 7/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6928 - accuracy: 0.5100 - val_loss: 0.6947 - val_accuracy: 0.5008
Epoch 8/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6928 - accuracy: 0.5092 - val_loss: 0.6947 - val_accuracy: 0.4980
Epoch 9/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6915 - accuracy: 0.5064 - val_loss: 0.6953 - val_accuracy: 0.5012
Epoch 10/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6920 - accuracy: 0.5180 - val_loss: 0.6933 - val_accuracy: 0.5072
Epoch 11/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6891 - accuracy: 0.5340 - val_loss: 0.6916 - val_accuracy: 0.5252
Epoch 12/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6919 - accuracy: 0.5268 - val_loss: 0.6932 - val_accuracy: 0.5208
Epoch 13/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6907 - accuracy: 0.5136 - val_loss: 0.6832 - val_accuracy: 0.5584
Epoch 14/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6768 - accuracy: 0.5896 - val_loss: 0.6899 - val_accuracy: 0.5616
Epoch 15/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6580 - accuracy: 0.6140 - val_loss: 0.6567 - val_accuracy: 0.6160
Epoch 16/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6938 - accuracy: 0.5608 - val_loss: 0.6936 - val_accuracy: 0.5384
Epoch 17/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6870 - accuracy: 0.5492 - val_loss: 0.6713 - val_accuracy: 0.5948
Epoch 18/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6445 - accuracy: 0.6264 - val_loss: 0.6664 - val_accuracy: 0.5708
Epoch 19/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6376 - accuracy: 0.5992 - val_loss: 0.6431 - val_accuracy: 0.6356
Epoch 20/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6416 - accuracy: 0.6456 - val_loss: 0.6316 - val_accuracy: 0.6592
Epoch 21/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6230 - accuracy: 0.6392 - val_loss: 0.6345 - val_accuracy: 0.6368
Epoch 22/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6305 - accuracy: 0.6528 - val_loss: 0.6336 - val_accuracy: 0.6588
Epoch 23/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6154 - accuracy: 0.6664 - val_loss: 0.6297 - val_accuracy: 0.6420
Epoch 24/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6160 - accuracy: 0.6576 - val_loss: 0.6263 - val_accuracy: 0.6508
Epoch 25/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6650 - accuracy: 0.6028 - val_loss: 0.6672 - val_accuracy: 0.5772
Epoch 26/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6428 - accuracy: 0.6164 - val_loss: 0.6448 - val_accuracy: 0.6072
Epoch 27/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6322 - accuracy: 0.6248 - val_loss: 0.6388 - val_accuracy: 0.6304
Epoch 28/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6189 - accuracy: 0.6580 - val_loss: 0.6317 - val_accuracy: 0.6568
Epoch 29/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6113 - accuracy: 0.6692 - val_loss: 0.6560 - val_accuracy: 0.5864
Epoch 30/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6224 - accuracy: 0.6264 - val_loss: 0.6117 - val_accuracy: 0.6744
Epoch 31/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6167 - accuracy: 0.6540 - val_loss: 0.6340 - val_accuracy: 0.6412
Epoch 32/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6174 - accuracy: 0.6456 - val_loss: 0.6164 - val_accuracy: 0.6548
Epoch 33/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6041 - accuracy: 0.6556 - val_loss: 0.5988 - val_accuracy: 0.6636
Epoch 34/200
79/79 [==============================] - 0s 6ms/step - loss: 0.5678 - accuracy: 0.7044 - val_loss: 0.5724 - val_accuracy: 0.7308
Epoch 35/200
79/79 [==============================] - 0s 6ms/step - loss: 0.5348 - accuracy: 0.7356 - val_loss: 0.5414 - val_accuracy: 0.7604
Epoch 36/200
79/79 [==============================] - 0s 6ms/step - loss: 0.5063 - accuracy: 0.7720 - val_loss: 0.5096 - val_accuracy: 0.7684
Epoch 37/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4962 - accuracy: 0.7668 - val_loss: 0.4869 - val_accuracy: 0.7852
Epoch 38/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4727 - accuracy: 0.7940 - val_loss: 0.5253 - val_accuracy: 0.7580
Epoch 39/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4697 - accuracy: 0.7928 - val_loss: 0.4571 - val_accuracy: 0.8084
Epoch 40/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4708 - accuracy: 0.7836 - val_loss: 0.4460 - val_accuracy: 0.8120
Epoch 41/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4428 - accuracy: 0.8052 - val_loss: 0.4407 - val_accuracy: 0.8048
Epoch 42/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4210 - accuracy: 0.8120 - val_loss: 0.4297 - val_accuracy: 0.8144
Epoch 43/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4132 - accuracy: 0.8280 - val_loss: 0.4336 - val_accuracy: 0.8144
Epoch 44/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4101 - accuracy: 0.8260 - val_loss: 0.4483 - val_accuracy: 0.8172
Epoch 45/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4504 - accuracy: 0.8136 - val_loss: 0.4487 - val_accuracy: 0.8144
Epoch 46/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4941 - accuracy: 0.7756 - val_loss: 0.5412 - val_accuracy: 0.7388
Epoch 47/200
79/79 [==============================] - 0s 6ms/step - loss: 0.5151 - accuracy: 0.7604 - val_loss: 0.4995 - val_accuracy: 0.7672
Epoch 48/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4766 - accuracy: 0.7840 - val_loss: 0.4901 - val_accuracy: 0.7740
Epoch 49/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4641 - accuracy: 0.7880 - val_loss: 0.5086 - val_accuracy: 0.7652
Epoch 50/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4598 - accuracy: 0.8052 - val_loss: 0.4543 - val_accuracy: 0.8036
Epoch 51/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4084 - accuracy: 0.8344 - val_loss: 0.4255 - val_accuracy: 0.8240
Epoch 52/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3922 - accuracy: 0.8332 - val_loss: 0.4263 - val_accuracy: 0.8188
Epoch 53/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3988 - accuracy: 0.8376 - val_loss: 0.4253 - val_accuracy: 0.8240
Epoch 54/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3971 - accuracy: 0.8416 - val_loss: 0.4041 - val_accuracy: 0.8356
Epoch 55/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3949 - accuracy: 0.8428 - val_loss: 0.4586 - val_accuracy: 0.8116
Epoch 56/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4201 - accuracy: 0.8240 - val_loss: 0.4205 - val_accuracy: 0.8300
Epoch 57/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3922 - accuracy: 0.8360 - val_loss: 0.4084 - val_accuracy: 0.8288
Epoch 58/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4639 - accuracy: 0.8056 - val_loss: 0.4101 - val_accuracy: 0.8352
Epoch 59/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3755 - accuracy: 0.8492 - val_loss: 0.3841 - val_accuracy: 0.8512
Epoch 60/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3641 - accuracy: 0.8568 - val_loss: 0.3904 - val_accuracy: 0.8528
Epoch 61/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3600 - accuracy: 0.8576 - val_loss: 0.4801 - val_accuracy: 0.8112
Epoch 62/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4689 - accuracy: 0.8104 - val_loss: 0.4442 - val_accuracy: 0.8112
Epoch 63/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4199 - accuracy: 0.8260 - val_loss: 0.4236 - val_accuracy: 0.8212
Epoch 64/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4023 - accuracy: 0.8320 - val_loss: 0.4236 - val_accuracy: 0.8184
Epoch 65/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3846 - accuracy: 0.8368 - val_loss: 0.4025 - val_accuracy: 0.8364
Epoch 66/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3789 - accuracy: 0.8452 - val_loss: 0.4051 - val_accuracy: 0.8356
Epoch 67/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3765 - accuracy: 0.8524 - val_loss: 0.3683 - val_accuracy: 0.8592
Epoch 68/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3581 - accuracy: 0.8600 - val_loss: 0.3453 - val_accuracy: 0.8680
Epoch 69/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3633 - accuracy: 0.8544 - val_loss: 0.3584 - val_accuracy: 0.8532
Epoch 70/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3495 - accuracy: 0.8632 - val_loss: 0.3408 - val_accuracy: 0.8676
Epoch 71/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3435 - accuracy: 0.8732 - val_loss: 0.3465 - val_accuracy: 0.8696
Epoch 72/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3607 - accuracy: 0.8652 - val_loss: 0.3660 - val_accuracy: 0.8560
Epoch 73/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3500 - accuracy: 0.8644 - val_loss: 0.3522 - val_accuracy: 0.8636
Epoch 74/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3480 - accuracy: 0.8628 - val_loss: 0.3526 - val_accuracy: 0.8628
Epoch 75/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3459 - accuracy: 0.8696 - val_loss: 0.3460 - val_accuracy: 0.8668
Epoch 76/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3489 - accuracy: 0.8672 - val_loss: 0.4227 - val_accuracy: 0.8204
Epoch 77/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3986 - accuracy: 0.8300 - val_loss: 0.4046 - val_accuracy: 0.8208
Epoch 78/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3767 - accuracy: 0.8408 - val_loss: 0.3822 - val_accuracy: 0.8432
Epoch 79/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3647 - accuracy: 0.8528 - val_loss: 0.3791 - val_accuracy: 0.8408
Epoch 80/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3537 - accuracy: 0.8548 - val_loss: 0.3624 - val_accuracy: 0.8496
Epoch 81/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3432 - accuracy: 0.8604 - val_loss: 0.3626 - val_accuracy: 0.8508
Epoch 82/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3415 - accuracy: 0.8632 - val_loss: 0.3865 - val_accuracy: 0.8412
Epoch 83/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3452 - accuracy: 0.8592 - val_loss: 0.3724 - val_accuracy: 0.8492
Epoch 84/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3417 - accuracy: 0.8600 - val_loss: 0.3677 - val_accuracy: 0.8460
Epoch 85/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3497 - accuracy: 0.8620 - val_loss: 0.4046 - val_accuracy: 0.8524
Epoch 86/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3627 - accuracy: 0.8620 - val_loss: 0.3845 - val_accuracy: 0.8520
Epoch 87/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3576 - accuracy: 0.8672 - val_loss: 0.4617 - val_accuracy: 0.8260
Epoch 88/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3878 - accuracy: 0.8536 - val_loss: 0.3513 - val_accuracy: 0.8640
Epoch 89/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3353 - accuracy: 0.8796 - val_loss: 0.3367 - val_accuracy: 0.8736
Epoch 90/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3328 - accuracy: 0.8844 - val_loss: 0.3293 - val_accuracy: 0.8780
Epoch 91/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3300 - accuracy: 0.8888 - val_loss: 0.3723 - val_accuracy: 0.8684
Epoch 92/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3477 - accuracy: 0.8728 - val_loss: 0.3583 - val_accuracy: 0.8676
Epoch 93/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3552 - accuracy: 0.8668 - val_loss: 0.4290 - val_accuracy: 0.8332
Epoch 94/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3486 - accuracy: 0.8668 - val_loss: 0.3927 - val_accuracy: 0.8504
Epoch 95/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3503 - accuracy: 0.8712 - val_loss: 0.3956 - val_accuracy: 0.8524
Epoch 96/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3480 - accuracy: 0.8708 - val_loss: 0.3803 - val_accuracy: 0.8520
Epoch 97/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3204 - accuracy: 0.8824 - val_loss: 0.3470 - val_accuracy: 0.8724
Epoch 98/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3000 - accuracy: 0.8932 - val_loss: 0.3088 - val_accuracy: 0.8904
Epoch 99/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2753 - accuracy: 0.9064 - val_loss: 0.3043 - val_accuracy: 0.8900
Epoch 100/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2736 - accuracy: 0.9036 - val_loss: 0.2935 - val_accuracy: 0.8992
Epoch 101/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2749 - accuracy: 0.9072 - val_loss: 0.3115 - val_accuracy: 0.8896
Epoch 102/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2615 - accuracy: 0.9104 - val_loss: 0.3126 - val_accuracy: 0.8864
Epoch 103/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2684 - accuracy: 0.9100 - val_loss: 0.2951 - val_accuracy: 0.8984
Epoch 104/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2611 - accuracy: 0.9124 - val_loss: 0.2887 - val_accuracy: 0.8976
Epoch 105/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2725 - accuracy: 0.9096 - val_loss: 0.3431 - val_accuracy: 0.8796
Epoch 106/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2825 - accuracy: 0.9020 - val_loss: 0.2868 - val_accuracy: 0.9024
Epoch 107/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2767 - accuracy: 0.9044 - val_loss: 0.3275 - val_accuracy: 0.8832
Epoch 108/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2985 - accuracy: 0.8952 - val_loss: 0.3151 - val_accuracy: 0.8876
Epoch 109/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2815 - accuracy: 0.9044 - val_loss: 0.3018 - val_accuracy: 0.8976
Epoch 110/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2827 - accuracy: 0.9028 - val_loss: 0.2902 - val_accuracy: 0.9048
Epoch 111/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2605 - accuracy: 0.9140 - val_loss: 0.2997 - val_accuracy: 0.8952
Epoch 112/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2540 - accuracy: 0.9160 - val_loss: 0.2777 - val_accuracy: 0.9080
Epoch 113/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2428 - accuracy: 0.9176 - val_loss: 0.2913 - val_accuracy: 0.8980
Epoch 114/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2654 - accuracy: 0.9080 - val_loss: 0.3008 - val_accuracy: 0.8948
Epoch 115/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2483 - accuracy: 0.9152 - val_loss: 0.2795 - val_accuracy: 0.9028
Epoch 116/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2470 - accuracy: 0.9160 - val_loss: 0.2937 - val_accuracy: 0.8988
Epoch 117/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3590 - accuracy: 0.8600 - val_loss: 0.3673 - val_accuracy: 0.8496
Epoch 118/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3585 - accuracy: 0.8576 - val_loss: 0.3815 - val_accuracy: 0.8432
Epoch 119/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3618 - accuracy: 0.8560 - val_loss: 0.3473 - val_accuracy: 0.8648
Epoch 120/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3483 - accuracy: 0.8572 - val_loss: 0.3464 - val_accuracy: 0.8596
Epoch 121/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3480 - accuracy: 0.8584 - val_loss: 0.3421 - val_accuracy: 0.8616
Epoch 122/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3424 - accuracy: 0.8644 - val_loss: 0.3477 - val_accuracy: 0.8536
Epoch 123/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3443 - accuracy: 0.8648 - val_loss: 0.3426 - val_accuracy: 0.8528
Epoch 124/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3376 - accuracy: 0.8664 - val_loss: 0.3468 - val_accuracy: 0.8520
Epoch 125/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3363 - accuracy: 0.8612 - val_loss: 0.3369 - val_accuracy: 0.8592
Epoch 126/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3074 - accuracy: 0.8848 - val_loss: 0.2864 - val_accuracy: 0.8988
Epoch 127/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2740 - accuracy: 0.9056 - val_loss: 0.2777 - val_accuracy: 0.9044
Epoch 128/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2652 - accuracy: 0.9044 - val_loss: 0.2824 - val_accuracy: 0.9016
Epoch 129/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2626 - accuracy: 0.9064 - val_loss: 0.2775 - val_accuracy: 0.9020
Epoch 130/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2618 - accuracy: 0.9044 - val_loss: 0.2631 - val_accuracy: 0.9076
Epoch 131/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2511 - accuracy: 0.9108 - val_loss: 0.2600 - val_accuracy: 0.9156
Epoch 132/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2433 - accuracy: 0.9108 - val_loss: 0.2587 - val_accuracy: 0.9148
Epoch 133/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2551 - accuracy: 0.9084 - val_loss: 0.2811 - val_accuracy: 0.8996
Epoch 134/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2351 - accuracy: 0.9188 - val_loss: 0.2558 - val_accuracy: 0.9180
Epoch 135/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2722 - accuracy: 0.9136 - val_loss: 0.2929 - val_accuracy: 0.9044
Epoch 136/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3085 - accuracy: 0.9020 - val_loss: 0.2946 - val_accuracy: 0.9008
Epoch 137/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2960 - accuracy: 0.8980 - val_loss: 0.3340 - val_accuracy: 0.8880
Epoch 138/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2954 - accuracy: 0.8984 - val_loss: 0.3190 - val_accuracy: 0.8904
Epoch 139/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2780 - accuracy: 0.9036 - val_loss: 0.2865 - val_accuracy: 0.9020
Epoch 140/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2774 - accuracy: 0.9012 - val_loss: 0.3152 - val_accuracy: 0.8872
Epoch 141/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2738 - accuracy: 0.9024 - val_loss: 0.4013 - val_accuracy: 0.8472
Epoch 142/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2945 - accuracy: 0.8860 - val_loss: 0.3022 - val_accuracy: 0.8928
Epoch 143/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2758 - accuracy: 0.8980 - val_loss: 0.2889 - val_accuracy: 0.8976
Epoch 144/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2514 - accuracy: 0.9108 - val_loss: 0.2634 - val_accuracy: 0.9100
Epoch 145/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2428 - accuracy: 0.9152 - val_loss: 0.3172 - val_accuracy: 0.8828
Epoch 146/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3130 - accuracy: 0.8796 - val_loss: 0.3579 - val_accuracy: 0.8748
Epoch 147/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3286 - accuracy: 0.8768 - val_loss: 0.3573 - val_accuracy: 0.8692
Epoch 148/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3233 - accuracy: 0.8712 - val_loss: 0.3673 - val_accuracy: 0.8608
Epoch 149/200
79/79 [==============================] - 0s 6ms/step - loss: 0.3389 - accuracy: 0.8708 - val_loss: 0.3232 - val_accuracy: 0.8768
Epoch 150/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2951 - accuracy: 0.8892 - val_loss: 0.2972 - val_accuracy: 0.8940
Epoch 151/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2731 - accuracy: 0.9028 - val_loss: 0.2910 - val_accuracy: 0.8988
Epoch 152/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2837 - accuracy: 0.8960 - val_loss: 0.2896 - val_accuracy: 0.9000
Epoch 153/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2645 - accuracy: 0.9060 - val_loss: 0.2656 - val_accuracy: 0.9052
Epoch 154/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2674 - accuracy: 0.9060 - val_loss: 0.2817 - val_accuracy: 0.8948
Epoch 155/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2627 - accuracy: 0.9004 - val_loss: 0.2649 - val_accuracy: 0.9096
Epoch 156/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2335 - accuracy: 0.9136 - val_loss: 0.2634 - val_accuracy: 0.9152
Epoch 157/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2248 - accuracy: 0.9188 - val_loss: 0.2377 - val_accuracy: 0.9204
Epoch 158/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2385 - accuracy: 0.9156 - val_loss: 0.2319 - val_accuracy: 0.9252
Epoch 159/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2186 - accuracy: 0.9264 - val_loss: 0.2438 - val_accuracy: 0.9228
Epoch 160/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2174 - accuracy: 0.9232 - val_loss: 0.2436 - val_accuracy: 0.9128
Epoch 161/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2391 - accuracy: 0.9180 - val_loss: 0.2516 - val_accuracy: 0.9128
Epoch 162/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2556 - accuracy: 0.9036 - val_loss: 0.2540 - val_accuracy: 0.9088
Epoch 163/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2490 - accuracy: 0.9044 - val_loss: 0.2756 - val_accuracy: 0.9000
Epoch 164/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2661 - accuracy: 0.8964 - val_loss: 0.2696 - val_accuracy: 0.9040
Epoch 165/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2556 - accuracy: 0.9008 - val_loss: 0.2776 - val_accuracy: 0.8924
Epoch 166/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2363 - accuracy: 0.9112 - val_loss: 0.2491 - val_accuracy: 0.9100
Epoch 167/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2443 - accuracy: 0.9072 - val_loss: 0.2796 - val_accuracy: 0.9028
Epoch 168/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2390 - accuracy: 0.9080 - val_loss: 0.2637 - val_accuracy: 0.9100
Epoch 169/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2500 - accuracy: 0.9116 - val_loss: 0.2620 - val_accuracy: 0.9072
Epoch 170/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2543 - accuracy: 0.9076 - val_loss: 0.2640 - val_accuracy: 0.9072
Epoch 171/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2407 - accuracy: 0.9176 - val_loss: 0.3020 - val_accuracy: 0.8964
Epoch 172/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2992 - accuracy: 0.8896 - val_loss: 0.2675 - val_accuracy: 0.9076
Epoch 173/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2341 - accuracy: 0.9220 - val_loss: 0.2504 - val_accuracy: 0.9132
Epoch 174/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2303 - accuracy: 0.9220 - val_loss: 0.2278 - val_accuracy: 0.9256
Epoch 175/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2244 - accuracy: 0.9284 - val_loss: 0.2255 - val_accuracy: 0.9248
Epoch 176/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2026 - accuracy: 0.9328 - val_loss: 0.2065 - val_accuracy: 0.9332
Epoch 177/200
79/79 [==============================] - 0s 6ms/step - loss: 0.1963 - accuracy: 0.9372 - val_loss: 0.2063 - val_accuracy: 0.9296
Epoch 178/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2004 - accuracy: 0.9288 - val_loss: 0.2070 - val_accuracy: 0.9320
Epoch 179/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2023 - accuracy: 0.9356 - val_loss: 0.2043 - val_accuracy: 0.9364
Epoch 180/200
79/79 [==============================] - 0s 6ms/step - loss: 0.1972 - accuracy: 0.9364 - val_loss: 0.1977 - val_accuracy: 0.9360
Epoch 181/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2088 - accuracy: 0.9320 - val_loss: 0.2822 - val_accuracy: 0.9024
Epoch 182/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2404 - accuracy: 0.9160 - val_loss: 0.2308 - val_accuracy: 0.9224
Epoch 183/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2220 - accuracy: 0.9260 - val_loss: 0.2359 - val_accuracy: 0.9180
Epoch 184/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2148 - accuracy: 0.9248 - val_loss: 0.2289 - val_accuracy: 0.9208
Epoch 185/200
79/79 [==============================] - 0s 6ms/step - loss: 0.1943 - accuracy: 0.9332 - val_loss: 0.2050 - val_accuracy: 0.9304
Epoch 186/200
79/79 [==============================] - 0s 6ms/step - loss: 0.1820 - accuracy: 0.9384 - val_loss: 0.2460 - val_accuracy: 0.9156
Epoch 187/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2313 - accuracy: 0.9192 - val_loss: 0.2650 - val_accuracy: 0.9128
Epoch 188/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2283 - accuracy: 0.9176 - val_loss: 0.2776 - val_accuracy: 0.9100
Epoch 189/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2688 - accuracy: 0.8980 - val_loss: 0.3051 - val_accuracy: 0.8956
Epoch 190/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2721 - accuracy: 0.9004 - val_loss: 0.2945 - val_accuracy: 0.9008
Epoch 191/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2764 - accuracy: 0.8976 - val_loss: 0.2676 - val_accuracy: 0.9076
Epoch 192/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2493 - accuracy: 0.9128 - val_loss: 0.2671 - val_accuracy: 0.9052
Epoch 193/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2263 - accuracy: 0.9160 - val_loss: 0.2581 - val_accuracy: 0.9108
Epoch 194/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2231 - accuracy: 0.9200 - val_loss: 0.2770 - val_accuracy: 0.9044
Epoch 195/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2348 - accuracy: 0.9184 - val_loss: 0.2392 - val_accuracy: 0.9176
Epoch 196/200
79/79 [==============================] - 0s 6ms/step - loss: 0.1979 - accuracy: 0.9292 - val_loss: 0.2347 - val_accuracy: 0.9224
Epoch 197/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2099 - accuracy: 0.9264 - val_loss: 0.2498 - val_accuracy: 0.9144
Epoch 198/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2169 - accuracy: 0.9180 - val_loss: 0.2435 - val_accuracy: 0.9176
Epoch 199/200
79/79 [==============================] - 0s 6ms/step - loss: 0.2105 - accuracy: 0.9240 - val_loss: 0.3108 - val_accuracy: 0.8824
Epoch 200/200
79/79 [==============================] - 0s 6ms/step - loss: 0.4865 - accuracy: 0.7912 - val_loss: 0.3834 - val_accuracy: 0.8304

# Plot the loss
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()

<matplotlib.legend.Legend at 0x7fec38311eb8>

# Plot the accuracy too
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.legend()

<matplotlib.legend.Legend at 0x7fec1fb28978>

# Make the problem harder by making T larger
T = 20
D = 1
X = []
Y = []

for t in range(5000):
  x = np.random.randn(T)
  X.append(x)
  y = get_label(x, 0, 1, 2) # long distance
  Y.append(y)

X = np.array(X)
Y = np.array(Y)
N = len(X)

# Now test our Simple RNN again
inputs = np.expand_dims(X, -1)

# make the RNN
i = Input(shape=(T, D))

# method 1
x = SimpleRNN(5)(i)

x = Dense(1, activation='sigmoid')(x)
model = Model(i, x)
model.compile(
  loss='binary_crossentropy',
  optimizer=Adam(lr=0.01),
  metrics=['accuracy'],
)

# train the RNN
r = model.fit(
  inputs, Y,
  epochs=200,
  validation_split=0.5,
)

Epoch 1/200
79/79 [==============================] - 1s 18ms/step - loss: 0.7027 - accuracy: 0.4816 - val_loss: 0.6930 - val_accuracy: 0.5052
Epoch 2/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6940 - accuracy: 0.5000 - val_loss: 0.6946 - val_accuracy: 0.5068
Epoch 3/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6920 - accuracy: 0.5136 - val_loss: 0.6973 - val_accuracy: 0.5020
Epoch 4/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6942 - accuracy: 0.5048 - val_loss: 0.6945 - val_accuracy: 0.4984
Epoch 5/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6927 - accuracy: 0.5068 - val_loss: 0.6942 - val_accuracy: 0.5088
Epoch 6/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6934 - accuracy: 0.5076 - val_loss: 0.6945 - val_accuracy: 0.5036
Epoch 7/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6924 - accuracy: 0.5204 - val_loss: 0.6951 - val_accuracy: 0.5004
Epoch 8/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6931 - accuracy: 0.5120 - val_loss: 0.6952 - val_accuracy: 0.5104
Epoch 9/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6921 - accuracy: 0.5272 - val_loss: 0.6949 - val_accuracy: 0.4976
Epoch 10/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6916 - accuracy: 0.5252 - val_loss: 0.6957 - val_accuracy: 0.5100
Epoch 11/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6925 - accuracy: 0.5172 - val_loss: 0.6950 - val_accuracy: 0.5052
Epoch 12/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6922 - accuracy: 0.5268 - val_loss: 0.6953 - val_accuracy: 0.4996
Epoch 13/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6919 - accuracy: 0.5252 - val_loss: 0.6955 - val_accuracy: 0.5100
Epoch 14/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6910 - accuracy: 0.5280 - val_loss: 0.6965 - val_accuracy: 0.5016
Epoch 15/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6924 - accuracy: 0.5296 - val_loss: 0.6960 - val_accuracy: 0.5060
Epoch 16/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6916 - accuracy: 0.5276 - val_loss: 0.6968 - val_accuracy: 0.4972
Epoch 17/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6905 - accuracy: 0.5388 - val_loss: 0.6971 - val_accuracy: 0.4988
Epoch 18/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6928 - accuracy: 0.5200 - val_loss: 0.6969 - val_accuracy: 0.4908
Epoch 19/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6908 - accuracy: 0.5384 - val_loss: 0.6991 - val_accuracy: 0.4932
Epoch 20/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6907 - accuracy: 0.5480 - val_loss: 0.6964 - val_accuracy: 0.5024
Epoch 21/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6912 - accuracy: 0.5296 - val_loss: 0.6976 - val_accuracy: 0.4924
Epoch 22/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6924 - accuracy: 0.5304 - val_loss: 0.6980 - val_accuracy: 0.4976
Epoch 23/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6907 - accuracy: 0.5408 - val_loss: 0.6993 - val_accuracy: 0.5024
Epoch 24/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6906 - accuracy: 0.5368 - val_loss: 0.6974 - val_accuracy: 0.5044
Epoch 25/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6892 - accuracy: 0.5424 - val_loss: 0.6965 - val_accuracy: 0.5112
Epoch 26/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6882 - accuracy: 0.5452 - val_loss: 0.6970 - val_accuracy: 0.4920
Epoch 27/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6896 - accuracy: 0.5436 - val_loss: 0.6984 - val_accuracy: 0.4944
Epoch 28/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6897 - accuracy: 0.5428 - val_loss: 0.6984 - val_accuracy: 0.4944
Epoch 29/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6887 - accuracy: 0.5392 - val_loss: 0.6974 - val_accuracy: 0.5076
Epoch 30/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6901 - accuracy: 0.5380 - val_loss: 0.6968 - val_accuracy: 0.5000
Epoch 31/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6895 - accuracy: 0.5420 - val_loss: 0.6970 - val_accuracy: 0.5016
Epoch 32/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6891 - accuracy: 0.5508 - val_loss: 0.7012 - val_accuracy: 0.4816
Epoch 33/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6901 - accuracy: 0.5316 - val_loss: 0.6957 - val_accuracy: 0.5072
Epoch 34/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6916 - accuracy: 0.5228 - val_loss: 0.6947 - val_accuracy: 0.5016
Epoch 35/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6880 - accuracy: 0.5452 - val_loss: 0.6961 - val_accuracy: 0.5084
Epoch 36/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6933 - accuracy: 0.5080 - val_loss: 0.6973 - val_accuracy: 0.5136
Epoch 37/200
79/79 [==============================] - 1s 18ms/step - loss: 0.6946 - accuracy: 0.5060 - val_loss: 0.6949 - val_accuracy: 0.5072
Epoch 38/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6929 - accuracy: 0.5168 - val_loss: 0.6961 - val_accuracy: 0.5128
Epoch 39/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6927 - accuracy: 0.5176 - val_loss: 0.6916 - val_accuracy: 0.5384
Epoch 40/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6929 - accuracy: 0.5164 - val_loss: 0.6925 - val_accuracy: 0.5152
Epoch 41/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6933 - accuracy: 0.5176 - val_loss: 0.6920 - val_accuracy: 0.5288
Epoch 42/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6931 - accuracy: 0.5304 - val_loss: 0.6919 - val_accuracy: 0.5264
Epoch 43/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6913 - accuracy: 0.5156 - val_loss: 0.6940 - val_accuracy: 0.5264
Epoch 44/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6920 - accuracy: 0.5380 - val_loss: 0.6931 - val_accuracy: 0.5292
Epoch 45/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6918 - accuracy: 0.5336 - val_loss: 0.6919 - val_accuracy: 0.5276
Epoch 46/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6913 - accuracy: 0.5380 - val_loss: 0.6929 - val_accuracy: 0.4956
Epoch 47/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6921 - accuracy: 0.5256 - val_loss: 0.6917 - val_accuracy: 0.5308
Epoch 48/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6906 - accuracy: 0.5348 - val_loss: 0.6926 - val_accuracy: 0.5300
Epoch 49/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6903 - accuracy: 0.5400 - val_loss: 0.6926 - val_accuracy: 0.5184
Epoch 50/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6901 - accuracy: 0.5412 - val_loss: 0.6920 - val_accuracy: 0.5312
Epoch 51/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6902 - accuracy: 0.5416 - val_loss: 0.6933 - val_accuracy: 0.5308
Epoch 52/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6889 - accuracy: 0.5412 - val_loss: 0.6945 - val_accuracy: 0.5316
Epoch 53/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6894 - accuracy: 0.5456 - val_loss: 0.6920 - val_accuracy: 0.5324
Epoch 54/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6905 - accuracy: 0.5420 - val_loss: 0.6942 - val_accuracy: 0.5328
Epoch 55/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6891 - accuracy: 0.5452 - val_loss: 0.6933 - val_accuracy: 0.5324
Epoch 56/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6903 - accuracy: 0.5436 - val_loss: 0.6931 - val_accuracy: 0.5308
Epoch 57/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6894 - accuracy: 0.5420 - val_loss: 0.6925 - val_accuracy: 0.5340
Epoch 58/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6882 - accuracy: 0.5404 - val_loss: 0.6930 - val_accuracy: 0.5332
Epoch 59/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6909 - accuracy: 0.5404 - val_loss: 0.6931 - val_accuracy: 0.5152
Epoch 60/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6908 - accuracy: 0.5328 - val_loss: 0.6930 - val_accuracy: 0.5256
Epoch 61/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6908 - accuracy: 0.5388 - val_loss: 0.6929 - val_accuracy: 0.5324
Epoch 62/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6909 - accuracy: 0.5292 - val_loss: 0.6923 - val_accuracy: 0.5328
Epoch 63/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6899 - accuracy: 0.5396 - val_loss: 0.6932 - val_accuracy: 0.5204
Epoch 64/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6904 - accuracy: 0.5416 - val_loss: 0.6921 - val_accuracy: 0.5244
Epoch 65/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6912 - accuracy: 0.5332 - val_loss: 0.6913 - val_accuracy: 0.5396
Epoch 66/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6938 - accuracy: 0.5248 - val_loss: 0.7026 - val_accuracy: 0.4960
Epoch 67/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6948 - accuracy: 0.5192 - val_loss: 0.6984 - val_accuracy: 0.5024
Epoch 68/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6944 - accuracy: 0.5088 - val_loss: 0.6966 - val_accuracy: 0.4920
Epoch 69/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6936 - accuracy: 0.5156 - val_loss: 0.6955 - val_accuracy: 0.5000
Epoch 70/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6914 - accuracy: 0.5328 - val_loss: 0.6915 - val_accuracy: 0.5332
Epoch 71/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6908 - accuracy: 0.5444 - val_loss: 0.6909 - val_accuracy: 0.5380
Epoch 72/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6906 - accuracy: 0.5408 - val_loss: 0.6921 - val_accuracy: 0.5360
Epoch 73/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6915 - accuracy: 0.5428 - val_loss: 0.6911 - val_accuracy: 0.5356
Epoch 74/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6902 - accuracy: 0.5456 - val_loss: 0.6911 - val_accuracy: 0.5356
Epoch 75/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6911 - accuracy: 0.5392 - val_loss: 0.6921 - val_accuracy: 0.5396
Epoch 76/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6900 - accuracy: 0.5448 - val_loss: 0.6911 - val_accuracy: 0.5352
Epoch 77/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6917 - accuracy: 0.5384 - val_loss: 0.6918 - val_accuracy: 0.5348
Epoch 78/200
79/79 [==============================] - 1s 18ms/step - loss: 0.6886 - accuracy: 0.5484 - val_loss: 0.6914 - val_accuracy: 0.5348
Epoch 79/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6939 - accuracy: 0.5084 - val_loss: 0.6937 - val_accuracy: 0.5024
Epoch 80/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6942 - accuracy: 0.4972 - val_loss: 0.6940 - val_accuracy: 0.5084
Epoch 81/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6947 - accuracy: 0.5064 - val_loss: 0.6944 - val_accuracy: 0.4936
Epoch 82/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6940 - accuracy: 0.5048 - val_loss: 0.6940 - val_accuracy: 0.5016
Epoch 83/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6945 - accuracy: 0.5036 - val_loss: 0.6940 - val_accuracy: 0.5060
Epoch 84/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6948 - accuracy: 0.4968 - val_loss: 0.6940 - val_accuracy: 0.5052
Epoch 85/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6942 - accuracy: 0.4948 - val_loss: 0.6940 - val_accuracy: 0.4992
Epoch 86/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6938 - accuracy: 0.5044 - val_loss: 0.6948 - val_accuracy: 0.5064
Epoch 87/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6937 - accuracy: 0.5116 - val_loss: 0.6954 - val_accuracy: 0.5060
Epoch 88/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6948 - accuracy: 0.5100 - val_loss: 0.6943 - val_accuracy: 0.4996
Epoch 89/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6943 - accuracy: 0.5136 - val_loss: 0.6951 - val_accuracy: 0.4948
Epoch 90/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6958 - accuracy: 0.4892 - val_loss: 0.6947 - val_accuracy: 0.4976
Epoch 91/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6939 - accuracy: 0.5016 - val_loss: 0.6944 - val_accuracy: 0.5016
Epoch 92/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6941 - accuracy: 0.5028 - val_loss: 0.6953 - val_accuracy: 0.4940
Epoch 93/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6931 - accuracy: 0.5112 - val_loss: 0.6955 - val_accuracy: 0.5056
Epoch 94/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6932 - accuracy: 0.5056 - val_loss: 0.6952 - val_accuracy: 0.4956
Epoch 95/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6942 - accuracy: 0.5064 - val_loss: 0.6950 - val_accuracy: 0.4992
Epoch 96/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6947 - accuracy: 0.5012 - val_loss: 0.6952 - val_accuracy: 0.4980
Epoch 97/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6929 - accuracy: 0.5092 - val_loss: 0.7003 - val_accuracy: 0.4932
Epoch 98/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6956 - accuracy: 0.4948 - val_loss: 0.6956 - val_accuracy: 0.4876
Epoch 99/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6935 - accuracy: 0.5076 - val_loss: 0.6955 - val_accuracy: 0.4872
Epoch 100/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6927 - accuracy: 0.5036 - val_loss: 0.6973 - val_accuracy: 0.4996
Epoch 101/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6936 - accuracy: 0.5088 - val_loss: 0.6957 - val_accuracy: 0.4860
Epoch 102/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6939 - accuracy: 0.4972 - val_loss: 0.6959 - val_accuracy: 0.4928
Epoch 103/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6938 - accuracy: 0.4980 - val_loss: 0.6958 - val_accuracy: 0.4940
Epoch 104/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6929 - accuracy: 0.5208 - val_loss: 0.6985 - val_accuracy: 0.5020
Epoch 105/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6942 - accuracy: 0.5188 - val_loss: 0.6963 - val_accuracy: 0.4852
Epoch 106/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6940 - accuracy: 0.5036 - val_loss: 0.6962 - val_accuracy: 0.4908
Epoch 107/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6926 - accuracy: 0.5136 - val_loss: 0.6973 - val_accuracy: 0.4940
Epoch 108/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6933 - accuracy: 0.5100 - val_loss: 0.6961 - val_accuracy: 0.4920
Epoch 109/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6927 - accuracy: 0.5124 - val_loss: 0.6963 - val_accuracy: 0.4932
Epoch 110/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6928 - accuracy: 0.5132 - val_loss: 0.6964 - val_accuracy: 0.4916
Epoch 111/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6927 - accuracy: 0.5224 - val_loss: 0.6963 - val_accuracy: 0.4952
Epoch 112/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6935 - accuracy: 0.5080 - val_loss: 0.6976 - val_accuracy: 0.4972
Epoch 113/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6952 - accuracy: 0.5036 - val_loss: 0.6938 - val_accuracy: 0.4956
Epoch 114/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6944 - accuracy: 0.4964 - val_loss: 0.6938 - val_accuracy: 0.4980
Epoch 115/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6948 - accuracy: 0.4900 - val_loss: 0.6963 - val_accuracy: 0.5040
Epoch 116/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6963 - accuracy: 0.4868 - val_loss: 0.6966 - val_accuracy: 0.5044
Epoch 117/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6966 - accuracy: 0.4960 - val_loss: 0.6944 - val_accuracy: 0.4992
Epoch 118/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6954 - accuracy: 0.4972 - val_loss: 0.6948 - val_accuracy: 0.5048
Epoch 119/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6956 - accuracy: 0.4964 - val_loss: 0.6938 - val_accuracy: 0.5048
Epoch 120/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6961 - accuracy: 0.4980 - val_loss: 0.6965 - val_accuracy: 0.4988
Epoch 121/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6948 - accuracy: 0.5016 - val_loss: 0.6941 - val_accuracy: 0.5040
Epoch 122/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6946 - accuracy: 0.5012 - val_loss: 0.6941 - val_accuracy: 0.4944
Epoch 123/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6948 - accuracy: 0.4932 - val_loss: 0.6953 - val_accuracy: 0.5024
Epoch 124/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6937 - accuracy: 0.5020 - val_loss: 0.6942 - val_accuracy: 0.4936
Epoch 125/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6948 - accuracy: 0.4956 - val_loss: 0.6975 - val_accuracy: 0.4972
Epoch 126/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6956 - accuracy: 0.4936 - val_loss: 0.6952 - val_accuracy: 0.4888
Epoch 127/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6946 - accuracy: 0.5008 - val_loss: 0.6954 - val_accuracy: 0.4856
Epoch 128/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6932 - accuracy: 0.5124 - val_loss: 0.6959 - val_accuracy: 0.4892
Epoch 129/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6937 - accuracy: 0.5072 - val_loss: 0.6971 - val_accuracy: 0.4960
Epoch 130/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6939 - accuracy: 0.5052 - val_loss: 0.6967 - val_accuracy: 0.4984
Epoch 131/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6938 - accuracy: 0.5012 - val_loss: 0.6962 - val_accuracy: 0.4948
Epoch 132/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6961 - accuracy: 0.5032 - val_loss: 0.6959 - val_accuracy: 0.4908
Epoch 133/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6939 - accuracy: 0.4920 - val_loss: 0.6958 - val_accuracy: 0.4884
Epoch 134/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6935 - accuracy: 0.4932 - val_loss: 0.6975 - val_accuracy: 0.4980
Epoch 135/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6938 - accuracy: 0.4996 - val_loss: 0.6961 - val_accuracy: 0.4956
Epoch 136/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6936 - accuracy: 0.4968 - val_loss: 0.6952 - val_accuracy: 0.5000
Epoch 137/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6928 - accuracy: 0.5248 - val_loss: 0.6963 - val_accuracy: 0.4888
Epoch 138/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6928 - accuracy: 0.5112 - val_loss: 0.6959 - val_accuracy: 0.4904
Epoch 139/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6930 - accuracy: 0.4996 - val_loss: 0.6970 - val_accuracy: 0.4996
Epoch 140/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6949 - accuracy: 0.5024 - val_loss: 0.6956 - val_accuracy: 0.4960
Epoch 141/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6943 - accuracy: 0.5040 - val_loss: 0.6969 - val_accuracy: 0.4856
Epoch 142/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6936 - accuracy: 0.5048 - val_loss: 0.6966 - val_accuracy: 0.4812
Epoch 143/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6930 - accuracy: 0.5048 - val_loss: 0.6971 - val_accuracy: 0.4892
Epoch 144/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6924 - accuracy: 0.5196 - val_loss: 0.6970 - val_accuracy: 0.4808
Epoch 145/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6934 - accuracy: 0.5040 - val_loss: 0.6967 - val_accuracy: 0.4896
Epoch 146/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6932 - accuracy: 0.5024 - val_loss: 0.6959 - val_accuracy: 0.4872
Epoch 147/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6938 - accuracy: 0.5028 - val_loss: 0.6958 - val_accuracy: 0.4972
Epoch 148/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6930 - accuracy: 0.5140 - val_loss: 0.6971 - val_accuracy: 0.4856
Epoch 149/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6933 - accuracy: 0.5088 - val_loss: 0.6978 - val_accuracy: 0.5008
Epoch 150/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6948 - accuracy: 0.5036 - val_loss: 0.6964 - val_accuracy: 0.4940
Epoch 151/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6949 - accuracy: 0.4984 - val_loss: 0.6959 - val_accuracy: 0.4832
Epoch 152/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6925 - accuracy: 0.5108 - val_loss: 0.6976 - val_accuracy: 0.4940
Epoch 153/200
79/79 [==============================] - 1s 17ms/step - loss: 0.6931 - accuracy: 0.5144 - val_loss: 0.6967 - val_accuracy: 0.4924
Epoch 154/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6925 - accuracy: 0.5168 - val_loss: 0.6970 - val_accuracy: 0.4916
Epoch 155/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6925 - accuracy: 0.5156 - val_loss: 0.6972 - val_accuracy: 0.4924
Epoch 156/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6930 - accuracy: 0.5200 - val_loss: 0.6979 - val_accuracy: 0.4812
Epoch 157/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6928 - accuracy: 0.4996 - val_loss: 0.7001 - val_accuracy: 0.4912
Epoch 158/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6921 - accuracy: 0.5140 - val_loss: 0.7003 - val_accuracy: 0.4912
Epoch 159/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6940 - accuracy: 0.5104 - val_loss: 0.6973 - val_accuracy: 0.4984
Epoch 160/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6930 - accuracy: 0.5108 - val_loss: 0.6975 - val_accuracy: 0.4776
Epoch 161/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6924 - accuracy: 0.5096 - val_loss: 0.6968 - val_accuracy: 0.4948
Epoch 162/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6925 - accuracy: 0.5172 - val_loss: 0.6981 - val_accuracy: 0.4848
Epoch 163/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6944 - accuracy: 0.5112 - val_loss: 0.6986 - val_accuracy: 0.4816
Epoch 164/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6941 - accuracy: 0.5028 - val_loss: 0.6976 - val_accuracy: 0.4792
Epoch 165/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6929 - accuracy: 0.5188 - val_loss: 0.6971 - val_accuracy: 0.4892
Epoch 166/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6925 - accuracy: 0.5132 - val_loss: 0.6994 - val_accuracy: 0.4840
Epoch 167/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6929 - accuracy: 0.5120 - val_loss: 0.6980 - val_accuracy: 0.4964
Epoch 168/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6951 - accuracy: 0.5156 - val_loss: 0.6974 - val_accuracy: 0.4840
Epoch 169/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6929 - accuracy: 0.5172 - val_loss: 0.6975 - val_accuracy: 0.4836
Epoch 170/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6930 - accuracy: 0.5128 - val_loss: 0.6982 - val_accuracy: 0.4820
Epoch 171/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6936 - accuracy: 0.5176 - val_loss: 0.6980 - val_accuracy: 0.4812
Epoch 172/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6936 - accuracy: 0.4972 - val_loss: 0.6972 - val_accuracy: 0.4904
Epoch 173/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6937 - accuracy: 0.5036 - val_loss: 0.6981 - val_accuracy: 0.4964
Epoch 174/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6917 - accuracy: 0.5152 - val_loss: 0.6992 - val_accuracy: 0.4964
Epoch 175/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6931 - accuracy: 0.5096 - val_loss: 0.6977 - val_accuracy: 0.4880
Epoch 176/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6931 - accuracy: 0.5144 - val_loss: 0.6969 - val_accuracy: 0.4976
Epoch 177/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6922 - accuracy: 0.5112 - val_loss: 0.6981 - val_accuracy: 0.4848
Epoch 178/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6934 - accuracy: 0.5112 - val_loss: 0.6968 - val_accuracy: 0.4888
Epoch 179/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6918 - accuracy: 0.5084 - val_loss: 0.6981 - val_accuracy: 0.5016
Epoch 180/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6919 - accuracy: 0.5268 - val_loss: 0.7017 - val_accuracy: 0.4892
Epoch 181/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6925 - accuracy: 0.5192 - val_loss: 0.6972 - val_accuracy: 0.4888
Epoch 182/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6939 - accuracy: 0.5052 - val_loss: 0.6991 - val_accuracy: 0.4936
Epoch 183/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6937 - accuracy: 0.5052 - val_loss: 0.6970 - val_accuracy: 0.4960
Epoch 184/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6941 - accuracy: 0.5016 - val_loss: 0.6965 - val_accuracy: 0.4872
Epoch 185/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6930 - accuracy: 0.5092 - val_loss: 0.6961 - val_accuracy: 0.4856
Epoch 186/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6934 - accuracy: 0.5116 - val_loss: 0.6959 - val_accuracy: 0.4956
Epoch 187/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6925 - accuracy: 0.5108 - val_loss: 0.6976 - val_accuracy: 0.4960
Epoch 188/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6932 - accuracy: 0.5152 - val_loss: 0.6958 - val_accuracy: 0.4932
Epoch 189/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6930 - accuracy: 0.5168 - val_loss: 0.6961 - val_accuracy: 0.4928
Epoch 190/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6933 - accuracy: 0.5000 - val_loss: 0.6965 - val_accuracy: 0.4960
Epoch 191/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6930 - accuracy: 0.5140 - val_loss: 0.7008 - val_accuracy: 0.4932
Epoch 192/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6973 - accuracy: 0.4968 - val_loss: 0.6977 - val_accuracy: 0.4908
Epoch 193/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6942 - accuracy: 0.5104 - val_loss: 0.6978 - val_accuracy: 0.4908
Epoch 194/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6929 - accuracy: 0.5052 - val_loss: 0.6965 - val_accuracy: 0.4872
Epoch 195/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6938 - accuracy: 0.4968 - val_loss: 0.6968 - val_accuracy: 0.4868
Epoch 196/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6923 - accuracy: 0.5156 - val_loss: 0.6972 - val_accuracy: 0.4936
Epoch 197/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6940 - accuracy: 0.5112 - val_loss: 0.6971 - val_accuracy: 0.4964
Epoch 198/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6939 - accuracy: 0.5036 - val_loss: 0.6979 - val_accuracy: 0.4952
Epoch 199/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6925 - accuracy: 0.5272 - val_loss: 0.6964 - val_accuracy: 0.4904
Epoch 200/200
79/79 [==============================] - 1s 16ms/step - loss: 0.6920 - accuracy: 0.5256 - val_loss: 0.6953 - val_accuracy: 0.4952

# Plot the loss
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()

<matplotlib.legend.Legend at 0x7fec382d6550>

# Plot the accuracy too
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.legend()

<matplotlib.legend.Legend at 0x7fec260c87f0>

# Now test our LSTM
inputs = np.expand_dims(X, -1)

# make the RNN
i = Input(shape=(T, D))

# method 1
x = LSTM(5)(i)

x = Dense(1, activation='sigmoid')(x)
model = Model(i, x)
model.compile(
  loss='binary_crossentropy',
  optimizer=Adam(lr=0.01),
  metrics=['accuracy'],
)

# train the RNN
r = model.fit(
  inputs, Y,
  epochs=200,
  validation_split=0.5,
)

Epoch 1/200
79/79 [==============================] - 1s 10ms/step - loss: 0.6950 - accuracy: 0.5128 - val_loss: 0.6957 - val_accuracy: 0.4920
Epoch 2/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6926 - accuracy: 0.5152 - val_loss: 0.6974 - val_accuracy: 0.4920
Epoch 3/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6929 - accuracy: 0.5192 - val_loss: 0.6955 - val_accuracy: 0.4916
Epoch 4/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6924 - accuracy: 0.5248 - val_loss: 0.6950 - val_accuracy: 0.4952
Epoch 5/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6917 - accuracy: 0.5232 - val_loss: 0.6951 - val_accuracy: 0.4948
Epoch 6/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6910 - accuracy: 0.5232 - val_loss: 0.6979 - val_accuracy: 0.4968
Epoch 7/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6915 - accuracy: 0.5240 - val_loss: 0.6976 - val_accuracy: 0.4952
Epoch 8/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6915 - accuracy: 0.5228 - val_loss: 0.6959 - val_accuracy: 0.4944
Epoch 9/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6921 - accuracy: 0.5220 - val_loss: 0.6959 - val_accuracy: 0.4952
Epoch 10/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6917 - accuracy: 0.5212 - val_loss: 0.6945 - val_accuracy: 0.4984
Epoch 11/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6928 - accuracy: 0.5288 - val_loss: 0.6964 - val_accuracy: 0.4948
Epoch 12/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6913 - accuracy: 0.5256 - val_loss: 0.6964 - val_accuracy: 0.4980
Epoch 13/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6908 - accuracy: 0.5212 - val_loss: 0.6970 - val_accuracy: 0.4976
Epoch 14/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6911 - accuracy: 0.5296 - val_loss: 0.6945 - val_accuracy: 0.5048
Epoch 15/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6913 - accuracy: 0.5344 - val_loss: 0.6958 - val_accuracy: 0.4952
Epoch 16/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6914 - accuracy: 0.5260 - val_loss: 0.6966 - val_accuracy: 0.5020
Epoch 17/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6903 - accuracy: 0.5264 - val_loss: 0.6979 - val_accuracy: 0.4964
Epoch 18/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6911 - accuracy: 0.5236 - val_loss: 0.6947 - val_accuracy: 0.5028
Epoch 19/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6910 - accuracy: 0.5280 - val_loss: 0.6941 - val_accuracy: 0.5076
Epoch 20/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6895 - accuracy: 0.5248 - val_loss: 0.6970 - val_accuracy: 0.4944
Epoch 21/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6906 - accuracy: 0.5232 - val_loss: 0.6933 - val_accuracy: 0.5184
Epoch 22/200
79/79 [==============================] - 1s 9ms/step - loss: 0.6915 - accuracy: 0.5300 - val_loss: 0.6946 - val_accuracy: 0.5040
Epoch 23/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6897 - accuracy: 0.5268 - val_loss: 0.6984 - val_accuracy: 0.4928
Epoch 24/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6902 - accuracy: 0.5220 - val_loss: 0.6979 - val_accuracy: 0.4928
Epoch 25/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6906 - accuracy: 0.5356 - val_loss: 0.6967 - val_accuracy: 0.4996
Epoch 26/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6892 - accuracy: 0.5260 - val_loss: 0.6973 - val_accuracy: 0.5056
Epoch 27/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6907 - accuracy: 0.5264 - val_loss: 0.6952 - val_accuracy: 0.5012
Epoch 28/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6888 - accuracy: 0.5312 - val_loss: 0.6958 - val_accuracy: 0.5036
Epoch 29/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6908 - accuracy: 0.5228 - val_loss: 0.6958 - val_accuracy: 0.4976
Epoch 30/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6903 - accuracy: 0.5180 - val_loss: 0.6970 - val_accuracy: 0.5072
Epoch 31/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6903 - accuracy: 0.5264 - val_loss: 0.6950 - val_accuracy: 0.5020
Epoch 32/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6912 - accuracy: 0.5392 - val_loss: 0.6963 - val_accuracy: 0.4960
Epoch 33/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6904 - accuracy: 0.5352 - val_loss: 0.6961 - val_accuracy: 0.4952
Epoch 34/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6913 - accuracy: 0.5300 - val_loss: 0.6978 - val_accuracy: 0.4980
Epoch 35/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6911 - accuracy: 0.5424 - val_loss: 0.6949 - val_accuracy: 0.4928
Epoch 36/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6895 - accuracy: 0.5336 - val_loss: 0.6994 - val_accuracy: 0.4976
Epoch 37/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6898 - accuracy: 0.5392 - val_loss: 0.7006 - val_accuracy: 0.4932
Epoch 38/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6908 - accuracy: 0.5372 - val_loss: 0.6993 - val_accuracy: 0.4900
Epoch 39/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6898 - accuracy: 0.5380 - val_loss: 0.6987 - val_accuracy: 0.4912
Epoch 40/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6903 - accuracy: 0.5412 - val_loss: 0.6995 - val_accuracy: 0.4960
Epoch 41/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6909 - accuracy: 0.5336 - val_loss: 0.6954 - val_accuracy: 0.5040
Epoch 42/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6917 - accuracy: 0.5352 - val_loss: 0.7014 - val_accuracy: 0.4920
Epoch 43/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6893 - accuracy: 0.5476 - val_loss: 0.6988 - val_accuracy: 0.4920
Epoch 44/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6890 - accuracy: 0.5420 - val_loss: 0.7003 - val_accuracy: 0.4876
Epoch 45/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6883 - accuracy: 0.5484 - val_loss: 0.7000 - val_accuracy: 0.4900
Epoch 46/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6871 - accuracy: 0.5492 - val_loss: 0.7006 - val_accuracy: 0.4912
Epoch 47/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6870 - accuracy: 0.5580 - val_loss: 0.7034 - val_accuracy: 0.4868
Epoch 48/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6869 - accuracy: 0.5492 - val_loss: 0.7030 - val_accuracy: 0.4868
Epoch 49/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6855 - accuracy: 0.5504 - val_loss: 0.7034 - val_accuracy: 0.4988
Epoch 50/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6852 - accuracy: 0.5448 - val_loss: 0.7031 - val_accuracy: 0.4992
Epoch 51/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6874 - accuracy: 0.5544 - val_loss: 0.7023 - val_accuracy: 0.4940
Epoch 52/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6857 - accuracy: 0.5536 - val_loss: 0.7048 - val_accuracy: 0.4868
Epoch 53/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6847 - accuracy: 0.5608 - val_loss: 0.7034 - val_accuracy: 0.4828
Epoch 54/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6851 - accuracy: 0.5548 - val_loss: 0.7062 - val_accuracy: 0.4940
Epoch 55/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6853 - accuracy: 0.5564 - val_loss: 0.7041 - val_accuracy: 0.4964
Epoch 56/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6812 - accuracy: 0.5648 - val_loss: 0.7075 - val_accuracy: 0.4884
Epoch 57/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6835 - accuracy: 0.5684 - val_loss: 0.7108 - val_accuracy: 0.4896
Epoch 58/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6863 - accuracy: 0.5604 - val_loss: 0.7074 - val_accuracy: 0.4996
Epoch 59/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6879 - accuracy: 0.5484 - val_loss: 0.7055 - val_accuracy: 0.4880
Epoch 60/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6865 - accuracy: 0.5476 - val_loss: 0.7043 - val_accuracy: 0.4912
Epoch 61/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6833 - accuracy: 0.5628 - val_loss: 0.7042 - val_accuracy: 0.4972
Epoch 62/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6821 - accuracy: 0.5628 - val_loss: 0.7035 - val_accuracy: 0.4888
Epoch 63/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6825 - accuracy: 0.5600 - val_loss: 0.7084 - val_accuracy: 0.4936
Epoch 64/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6828 - accuracy: 0.5652 - val_loss: 0.7069 - val_accuracy: 0.4880
Epoch 65/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6809 - accuracy: 0.5664 - val_loss: 0.7080 - val_accuracy: 0.4972
Epoch 66/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6824 - accuracy: 0.5536 - val_loss: 0.7075 - val_accuracy: 0.4940
Epoch 67/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6819 - accuracy: 0.5640 - val_loss: 0.7092 - val_accuracy: 0.4956
Epoch 68/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6825 - accuracy: 0.5644 - val_loss: 0.7102 - val_accuracy: 0.5000
Epoch 69/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6798 - accuracy: 0.5692 - val_loss: 0.7109 - val_accuracy: 0.4916
Epoch 70/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6840 - accuracy: 0.5624 - val_loss: 0.7090 - val_accuracy: 0.5016
Epoch 71/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6847 - accuracy: 0.5580 - val_loss: 0.7095 - val_accuracy: 0.4928
Epoch 72/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6828 - accuracy: 0.5640 - val_loss: 0.7064 - val_accuracy: 0.4980
Epoch 73/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6808 - accuracy: 0.5680 - val_loss: 0.7112 - val_accuracy: 0.4912
Epoch 74/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6827 - accuracy: 0.5624 - val_loss: 0.7086 - val_accuracy: 0.4960
Epoch 75/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6865 - accuracy: 0.5500 - val_loss: 0.7074 - val_accuracy: 0.4996
Epoch 76/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6852 - accuracy: 0.5508 - val_loss: 0.7075 - val_accuracy: 0.4856
Epoch 77/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6807 - accuracy: 0.5648 - val_loss: 0.7109 - val_accuracy: 0.4880
Epoch 78/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6812 - accuracy: 0.5656 - val_loss: 0.7070 - val_accuracy: 0.4960
Epoch 79/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6792 - accuracy: 0.5696 - val_loss: 0.7127 - val_accuracy: 0.4900
Epoch 80/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6843 - accuracy: 0.5616 - val_loss: 0.7108 - val_accuracy: 0.5008
Epoch 81/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6813 - accuracy: 0.5672 - val_loss: 0.7063 - val_accuracy: 0.5052
Epoch 82/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6851 - accuracy: 0.5524 - val_loss: 0.7084 - val_accuracy: 0.4928
Epoch 83/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6839 - accuracy: 0.5564 - val_loss: 0.7019 - val_accuracy: 0.4904
Epoch 84/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6914 - accuracy: 0.5332 - val_loss: 0.6978 - val_accuracy: 0.4976
Epoch 85/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6913 - accuracy: 0.5312 - val_loss: 0.6962 - val_accuracy: 0.4892
Epoch 86/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6908 - accuracy: 0.5304 - val_loss: 0.6967 - val_accuracy: 0.4964
Epoch 87/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6916 - accuracy: 0.5272 - val_loss: 0.7025 - val_accuracy: 0.4876
Epoch 88/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6898 - accuracy: 0.5372 - val_loss: 0.7023 - val_accuracy: 0.4896
Epoch 89/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6843 - accuracy: 0.5424 - val_loss: 0.7034 - val_accuracy: 0.4896
Epoch 90/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6820 - accuracy: 0.5524 - val_loss: 0.7054 - val_accuracy: 0.4904
Epoch 91/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6838 - accuracy: 0.5628 - val_loss: 0.7118 - val_accuracy: 0.4940
Epoch 92/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6814 - accuracy: 0.5612 - val_loss: 0.7041 - val_accuracy: 0.4948
Epoch 93/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6815 - accuracy: 0.5548 - val_loss: 0.7158 - val_accuracy: 0.4928
Epoch 94/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6828 - accuracy: 0.5540 - val_loss: 0.7145 - val_accuracy: 0.4968
Epoch 95/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6817 - accuracy: 0.5660 - val_loss: 0.7119 - val_accuracy: 0.4988
Epoch 96/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6814 - accuracy: 0.5616 - val_loss: 0.7085 - val_accuracy: 0.4924
Epoch 97/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6783 - accuracy: 0.5660 - val_loss: 0.7101 - val_accuracy: 0.4836
Epoch 98/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6785 - accuracy: 0.5740 - val_loss: 0.7114 - val_accuracy: 0.4920
Epoch 99/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6781 - accuracy: 0.5732 - val_loss: 0.7108 - val_accuracy: 0.5024
Epoch 100/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6798 - accuracy: 0.5660 - val_loss: 0.7075 - val_accuracy: 0.5036
Epoch 101/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6772 - accuracy: 0.5716 - val_loss: 0.7097 - val_accuracy: 0.5076
Epoch 102/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6769 - accuracy: 0.5672 - val_loss: 0.7078 - val_accuracy: 0.4948
Epoch 103/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6770 - accuracy: 0.5756 - val_loss: 0.7110 - val_accuracy: 0.4924
Epoch 104/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6851 - accuracy: 0.5656 - val_loss: 0.7182 - val_accuracy: 0.4900
Epoch 105/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6867 - accuracy: 0.5504 - val_loss: 0.7089 - val_accuracy: 0.4904
Epoch 106/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6828 - accuracy: 0.5616 - val_loss: 0.7127 - val_accuracy: 0.4940
Epoch 107/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6832 - accuracy: 0.5604 - val_loss: 0.7105 - val_accuracy: 0.4936
Epoch 108/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6829 - accuracy: 0.5648 - val_loss: 0.7085 - val_accuracy: 0.5072
Epoch 109/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6809 - accuracy: 0.5544 - val_loss: 0.7111 - val_accuracy: 0.4932
Epoch 110/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6786 - accuracy: 0.5724 - val_loss: 0.7088 - val_accuracy: 0.5012
Epoch 111/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6811 - accuracy: 0.5664 - val_loss: 0.7108 - val_accuracy: 0.5048
Epoch 112/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6837 - accuracy: 0.5660 - val_loss: 0.7079 - val_accuracy: 0.4864
Epoch 113/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6769 - accuracy: 0.5636 - val_loss: 0.7140 - val_accuracy: 0.4952
Epoch 114/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6745 - accuracy: 0.5728 - val_loss: 0.7131 - val_accuracy: 0.4852
Epoch 115/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6784 - accuracy: 0.5720 - val_loss: 0.7142 - val_accuracy: 0.4932
Epoch 116/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6758 - accuracy: 0.5760 - val_loss: 0.7186 - val_accuracy: 0.4848
Epoch 117/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6799 - accuracy: 0.5664 - val_loss: 0.7121 - val_accuracy: 0.5036
Epoch 118/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6773 - accuracy: 0.5712 - val_loss: 0.7136 - val_accuracy: 0.4928
Epoch 119/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6805 - accuracy: 0.5564 - val_loss: 0.7085 - val_accuracy: 0.4932
Epoch 120/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6805 - accuracy: 0.5696 - val_loss: 0.7131 - val_accuracy: 0.4940
Epoch 121/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6741 - accuracy: 0.5768 - val_loss: 0.7176 - val_accuracy: 0.4932
Epoch 122/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6787 - accuracy: 0.5628 - val_loss: 0.7120 - val_accuracy: 0.5040
Epoch 123/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6794 - accuracy: 0.5704 - val_loss: 0.7113 - val_accuracy: 0.4804
Epoch 124/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6757 - accuracy: 0.5660 - val_loss: 0.7138 - val_accuracy: 0.4992
Epoch 125/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6780 - accuracy: 0.5732 - val_loss: 0.7164 - val_accuracy: 0.4828
Epoch 126/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6807 - accuracy: 0.5684 - val_loss: 0.7191 - val_accuracy: 0.5000
Epoch 127/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6875 - accuracy: 0.5444 - val_loss: 0.7013 - val_accuracy: 0.4972
Epoch 128/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6853 - accuracy: 0.5520 - val_loss: 0.7029 - val_accuracy: 0.4948
Epoch 129/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6843 - accuracy: 0.5580 - val_loss: 0.7058 - val_accuracy: 0.4928
Epoch 130/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6816 - accuracy: 0.5588 - val_loss: 0.7081 - val_accuracy: 0.4976
Epoch 131/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6789 - accuracy: 0.5708 - val_loss: 0.7078 - val_accuracy: 0.4968
Epoch 132/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6753 - accuracy: 0.5728 - val_loss: 0.7179 - val_accuracy: 0.5016
Epoch 133/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6739 - accuracy: 0.5764 - val_loss: 0.7149 - val_accuracy: 0.4844
Epoch 134/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6778 - accuracy: 0.5736 - val_loss: 0.7147 - val_accuracy: 0.4948
Epoch 135/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6738 - accuracy: 0.5780 - val_loss: 0.7169 - val_accuracy: 0.4920
Epoch 136/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6772 - accuracy: 0.5724 - val_loss: 0.7114 - val_accuracy: 0.4948
Epoch 137/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6768 - accuracy: 0.5712 - val_loss: 0.7168 - val_accuracy: 0.4920
Epoch 138/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6744 - accuracy: 0.5664 - val_loss: 0.7164 - val_accuracy: 0.4912
Epoch 139/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6806 - accuracy: 0.5696 - val_loss: 0.7174 - val_accuracy: 0.4876
Epoch 140/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6798 - accuracy: 0.5688 - val_loss: 0.7134 - val_accuracy: 0.4912
Epoch 141/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6789 - accuracy: 0.5668 - val_loss: 0.7169 - val_accuracy: 0.4884
Epoch 142/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6832 - accuracy: 0.5728 - val_loss: 0.7132 - val_accuracy: 0.4864
Epoch 143/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6819 - accuracy: 0.5568 - val_loss: 0.7127 - val_accuracy: 0.4908
Epoch 144/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6875 - accuracy: 0.5420 - val_loss: 0.7054 - val_accuracy: 0.4872
Epoch 145/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6829 - accuracy: 0.5488 - val_loss: 0.7082 - val_accuracy: 0.4840
Epoch 146/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6781 - accuracy: 0.5668 - val_loss: 0.7158 - val_accuracy: 0.4892
Epoch 147/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6786 - accuracy: 0.5696 - val_loss: 0.7123 - val_accuracy: 0.4840
Epoch 148/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6804 - accuracy: 0.5632 - val_loss: 0.7165 - val_accuracy: 0.4936
Epoch 149/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6764 - accuracy: 0.5764 - val_loss: 0.7140 - val_accuracy: 0.4896
Epoch 150/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6780 - accuracy: 0.5700 - val_loss: 0.7158 - val_accuracy: 0.4940
Epoch 151/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6779 - accuracy: 0.5720 - val_loss: 0.7150 - val_accuracy: 0.4940
Epoch 152/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6754 - accuracy: 0.5696 - val_loss: 0.7141 - val_accuracy: 0.4920
Epoch 153/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6744 - accuracy: 0.5748 - val_loss: 0.7144 - val_accuracy: 0.4956
Epoch 154/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6754 - accuracy: 0.5728 - val_loss: 0.7138 - val_accuracy: 0.4996
Epoch 155/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6763 - accuracy: 0.5736 - val_loss: 0.7185 - val_accuracy: 0.4948
Epoch 156/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6787 - accuracy: 0.5664 - val_loss: 0.7184 - val_accuracy: 0.4964
Epoch 157/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6756 - accuracy: 0.5700 - val_loss: 0.7147 - val_accuracy: 0.4976
Epoch 158/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6770 - accuracy: 0.5720 - val_loss: 0.7144 - val_accuracy: 0.5036
Epoch 159/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6740 - accuracy: 0.5744 - val_loss: 0.7171 - val_accuracy: 0.4924
Epoch 160/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6726 - accuracy: 0.5796 - val_loss: 0.7143 - val_accuracy: 0.5036
Epoch 161/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6801 - accuracy: 0.5696 - val_loss: 0.7124 - val_accuracy: 0.5004
Epoch 162/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6783 - accuracy: 0.5664 - val_loss: 0.7123 - val_accuracy: 0.4916
Epoch 163/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6739 - accuracy: 0.5740 - val_loss: 0.7151 - val_accuracy: 0.5008
Epoch 164/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6759 - accuracy: 0.5708 - val_loss: 0.7175 - val_accuracy: 0.4952
Epoch 165/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6760 - accuracy: 0.5756 - val_loss: 0.7178 - val_accuracy: 0.4952
Epoch 166/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6767 - accuracy: 0.5712 - val_loss: 0.7180 - val_accuracy: 0.4896
Epoch 167/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6779 - accuracy: 0.5812 - val_loss: 0.7132 - val_accuracy: 0.5048
Epoch 168/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6794 - accuracy: 0.5560 - val_loss: 0.7107 - val_accuracy: 0.4932
Epoch 169/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6743 - accuracy: 0.5752 - val_loss: 0.7113 - val_accuracy: 0.4936
Epoch 170/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6805 - accuracy: 0.5592 - val_loss: 0.7030 - val_accuracy: 0.4968
Epoch 171/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6835 - accuracy: 0.5532 - val_loss: 0.7062 - val_accuracy: 0.4908
Epoch 172/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6796 - accuracy: 0.5536 - val_loss: 0.7065 - val_accuracy: 0.4956
Epoch 173/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6781 - accuracy: 0.5632 - val_loss: 0.7105 - val_accuracy: 0.4988
Epoch 174/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6777 - accuracy: 0.5668 - val_loss: 0.7129 - val_accuracy: 0.4916
Epoch 175/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6781 - accuracy: 0.5560 - val_loss: 0.7157 - val_accuracy: 0.4956
Epoch 176/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6735 - accuracy: 0.5756 - val_loss: 0.7128 - val_accuracy: 0.4848
Epoch 177/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6764 - accuracy: 0.5776 - val_loss: 0.7170 - val_accuracy: 0.4972
Epoch 178/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6800 - accuracy: 0.5668 - val_loss: 0.7169 - val_accuracy: 0.4964
Epoch 179/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6730 - accuracy: 0.5804 - val_loss: 0.7211 - val_accuracy: 0.4944
Epoch 180/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6750 - accuracy: 0.5796 - val_loss: 0.7169 - val_accuracy: 0.4928
Epoch 181/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6766 - accuracy: 0.5712 - val_loss: 0.7121 - val_accuracy: 0.4884
Epoch 182/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6724 - accuracy: 0.5820 - val_loss: 0.7210 - val_accuracy: 0.4832
Epoch 183/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6677 - accuracy: 0.5844 - val_loss: 0.7196 - val_accuracy: 0.4900
Epoch 184/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6702 - accuracy: 0.5776 - val_loss: 0.7210 - val_accuracy: 0.4912
Epoch 185/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6717 - accuracy: 0.5808 - val_loss: 0.7222 - val_accuracy: 0.4828
Epoch 186/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6698 - accuracy: 0.5836 - val_loss: 0.7229 - val_accuracy: 0.5016
Epoch 187/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6737 - accuracy: 0.5796 - val_loss: 0.7133 - val_accuracy: 0.4928
Epoch 188/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6708 - accuracy: 0.5784 - val_loss: 0.7147 - val_accuracy: 0.4884
Epoch 189/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6691 - accuracy: 0.5688 - val_loss: 0.7199 - val_accuracy: 0.4948
Epoch 190/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6674 - accuracy: 0.5800 - val_loss: 0.7200 - val_accuracy: 0.4912
Epoch 191/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6714 - accuracy: 0.5800 - val_loss: 0.7199 - val_accuracy: 0.4880
Epoch 192/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6718 - accuracy: 0.5776 - val_loss: 0.7248 - val_accuracy: 0.4900
Epoch 193/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6680 - accuracy: 0.5828 - val_loss: 0.7194 - val_accuracy: 0.4928
Epoch 194/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6706 - accuracy: 0.5704 - val_loss: 0.7193 - val_accuracy: 0.4980
Epoch 195/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6704 - accuracy: 0.5712 - val_loss: 0.7215 - val_accuracy: 0.4984
Epoch 196/200
79/79 [==============================] - 1s 7ms/step - loss: 0.6731 - accuracy: 0.5708 - val_loss: 0.7238 - val_accuracy: 0.4948
Epoch 197/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6682 - accuracy: 0.5788 - val_loss: 0.7185 - val_accuracy: 0.4968
Epoch 198/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6705 - accuracy: 0.5724 - val_loss: 0.7211 - val_accuracy: 0.4916
Epoch 199/200
79/79 [==============================] - 0s 6ms/step - loss: 0.6686 - accuracy: 0.5800 - val_loss: 0.7203 - val_accuracy: 0.5032
Epoch 200/200
79/79 [==============================] - 1s 6ms/step - loss: 0.6706 - accuracy: 0.5860 - val_loss: 0.7207 - val_accuracy: 0.5036

# Plot the loss
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()

<matplotlib.legend.Legend at 0x7fec1fc08f60>

# Plot the accuracy too
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.legend()

<matplotlib.legend.Legend at 0x7fec1f79cc50>

# Now test our GRU
inputs = np.expand_dims(X, -1)

# make the RNN
i = Input(shape=(T, D))

# method 1
x = GRU(5)(i)

x = Dense(1, activation='sigmoid')(x)
model = Model(i, x)
model.compile(
  loss='binary_crossentropy',
  optimizer=Adam(lr=0.01),
  metrics=['accuracy'],
)

# train the RNN
r = model.fit(
  inputs, Y,
  epochs=400,
  validation_split=0.5,
)

Epoch 1/400
79/79 [==============================] - 1s 9ms/step - loss: 0.6950 - accuracy: 0.4860 - val_loss: 0.6950 - val_accuracy: 0.4904
Epoch 2/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6942 - accuracy: 0.5080 - val_loss: 0.6976 - val_accuracy: 0.4940
Epoch 3/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6943 - accuracy: 0.5056 - val_loss: 0.6958 - val_accuracy: 0.4956
Epoch 4/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6938 - accuracy: 0.4996 - val_loss: 0.6962 - val_accuracy: 0.4996
Epoch 5/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6933 - accuracy: 0.5180 - val_loss: 0.6959 - val_accuracy: 0.4964
Epoch 6/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6939 - accuracy: 0.4900 - val_loss: 0.6958 - val_accuracy: 0.4916
Epoch 7/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6939 - accuracy: 0.4936 - val_loss: 0.6952 - val_accuracy: 0.4928
Epoch 8/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6934 - accuracy: 0.4980 - val_loss: 0.6952 - val_accuracy: 0.4920
Epoch 9/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6933 - accuracy: 0.4936 - val_loss: 0.6950 - val_accuracy: 0.4868
Epoch 10/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6936 - accuracy: 0.5080 - val_loss: 0.6955 - val_accuracy: 0.4892
Epoch 11/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6934 - accuracy: 0.4944 - val_loss: 0.6948 - val_accuracy: 0.4908
Epoch 12/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6935 - accuracy: 0.5056 - val_loss: 0.6948 - val_accuracy: 0.4904
Epoch 13/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6940 - accuracy: 0.5032 - val_loss: 0.6946 - val_accuracy: 0.4848
Epoch 14/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6930 - accuracy: 0.5016 - val_loss: 0.6954 - val_accuracy: 0.4940
Epoch 15/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6932 - accuracy: 0.4996 - val_loss: 0.6945 - val_accuracy: 0.4880
Epoch 16/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6933 - accuracy: 0.5044 - val_loss: 0.6948 - val_accuracy: 0.4868
Epoch 17/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6938 - accuracy: 0.4896 - val_loss: 0.6953 - val_accuracy: 0.4852
Epoch 18/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6927 - accuracy: 0.5164 - val_loss: 0.6951 - val_accuracy: 0.4920
Epoch 19/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6928 - accuracy: 0.5092 - val_loss: 0.6952 - val_accuracy: 0.4888
Epoch 20/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6929 - accuracy: 0.5172 - val_loss: 0.6958 - val_accuracy: 0.4904
Epoch 21/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6924 - accuracy: 0.5132 - val_loss: 0.6953 - val_accuracy: 0.4876
Epoch 22/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6923 - accuracy: 0.5124 - val_loss: 0.6958 - val_accuracy: 0.4812
Epoch 23/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6922 - accuracy: 0.5084 - val_loss: 0.6968 - val_accuracy: 0.4844
Epoch 24/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6912 - accuracy: 0.5188 - val_loss: 0.6979 - val_accuracy: 0.4936
Epoch 25/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6936 - accuracy: 0.5180 - val_loss: 0.6977 - val_accuracy: 0.4884
Epoch 26/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6922 - accuracy: 0.5224 - val_loss: 0.6965 - val_accuracy: 0.5000
Epoch 27/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6906 - accuracy: 0.5252 - val_loss: 0.6994 - val_accuracy: 0.4940
Epoch 28/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6923 - accuracy: 0.5348 - val_loss: 0.6968 - val_accuracy: 0.4888
Epoch 29/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6899 - accuracy: 0.5288 - val_loss: 0.6997 - val_accuracy: 0.4904
Epoch 30/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6894 - accuracy: 0.5280 - val_loss: 0.7009 - val_accuracy: 0.4960
Epoch 31/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6906 - accuracy: 0.5288 - val_loss: 0.7015 - val_accuracy: 0.4860
Epoch 32/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6881 - accuracy: 0.5376 - val_loss: 0.6992 - val_accuracy: 0.4892
Epoch 33/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6859 - accuracy: 0.5480 - val_loss: 0.7075 - val_accuracy: 0.4868
Epoch 34/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6895 - accuracy: 0.5420 - val_loss: 0.7045 - val_accuracy: 0.4872
Epoch 35/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6865 - accuracy: 0.5432 - val_loss: 0.7005 - val_accuracy: 0.5000
Epoch 36/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6863 - accuracy: 0.5384 - val_loss: 0.7020 - val_accuracy: 0.4920
Epoch 37/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6852 - accuracy: 0.5436 - val_loss: 0.7076 - val_accuracy: 0.4964
Epoch 38/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6861 - accuracy: 0.5520 - val_loss: 0.7036 - val_accuracy: 0.4956
Epoch 39/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6876 - accuracy: 0.5412 - val_loss: 0.7031 - val_accuracy: 0.4976
Epoch 40/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6829 - accuracy: 0.5528 - val_loss: 0.7050 - val_accuracy: 0.4852
Epoch 41/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6808 - accuracy: 0.5508 - val_loss: 0.7074 - val_accuracy: 0.4924
Epoch 42/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6825 - accuracy: 0.5540 - val_loss: 0.7150 - val_accuracy: 0.4892
Epoch 43/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6826 - accuracy: 0.5600 - val_loss: 0.7037 - val_accuracy: 0.4964
Epoch 44/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6846 - accuracy: 0.5484 - val_loss: 0.7099 - val_accuracy: 0.4920
Epoch 45/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6820 - accuracy: 0.5512 - val_loss: 0.7057 - val_accuracy: 0.4976
Epoch 46/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6797 - accuracy: 0.5572 - val_loss: 0.7067 - val_accuracy: 0.5048
Epoch 47/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6816 - accuracy: 0.5552 - val_loss: 0.7072 - val_accuracy: 0.4924
Epoch 48/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6789 - accuracy: 0.5652 - val_loss: 0.7108 - val_accuracy: 0.4888
Epoch 49/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6871 - accuracy: 0.5492 - val_loss: 0.7064 - val_accuracy: 0.4904
Epoch 50/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6853 - accuracy: 0.5500 - val_loss: 0.7052 - val_accuracy: 0.4992
Epoch 51/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6806 - accuracy: 0.5544 - val_loss: 0.7067 - val_accuracy: 0.4944
Epoch 52/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6794 - accuracy: 0.5600 - val_loss: 0.7082 - val_accuracy: 0.4916
Epoch 53/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6785 - accuracy: 0.5640 - val_loss: 0.7079 - val_accuracy: 0.4964
Epoch 54/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6822 - accuracy: 0.5588 - val_loss: 0.7126 - val_accuracy: 0.4868
Epoch 55/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6784 - accuracy: 0.5576 - val_loss: 0.7119 - val_accuracy: 0.4916
Epoch 56/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6746 - accuracy: 0.5680 - val_loss: 0.7108 - val_accuracy: 0.4888
Epoch 57/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6762 - accuracy: 0.5708 - val_loss: 0.7140 - val_accuracy: 0.4888
Epoch 58/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6775 - accuracy: 0.5628 - val_loss: 0.7142 - val_accuracy: 0.4888
Epoch 59/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6749 - accuracy: 0.5664 - val_loss: 0.7183 - val_accuracy: 0.4972
Epoch 60/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6804 - accuracy: 0.5608 - val_loss: 0.7118 - val_accuracy: 0.4996
Epoch 61/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6873 - accuracy: 0.5540 - val_loss: 0.7166 - val_accuracy: 0.4948
Epoch 62/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6927 - accuracy: 0.5248 - val_loss: 0.7052 - val_accuracy: 0.4804
Epoch 63/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6926 - accuracy: 0.5168 - val_loss: 0.7017 - val_accuracy: 0.4920
Epoch 64/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6949 - accuracy: 0.5060 - val_loss: 0.6979 - val_accuracy: 0.4944
Epoch 65/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6912 - accuracy: 0.5208 - val_loss: 0.6999 - val_accuracy: 0.4868
Epoch 66/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6896 - accuracy: 0.5296 - val_loss: 0.6996 - val_accuracy: 0.5008
Epoch 67/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6920 - accuracy: 0.5228 - val_loss: 0.6984 - val_accuracy: 0.4740
Epoch 68/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6939 - accuracy: 0.5020 - val_loss: 0.6944 - val_accuracy: 0.5024
Epoch 69/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6936 - accuracy: 0.5144 - val_loss: 0.6962 - val_accuracy: 0.4984
Epoch 70/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6949 - accuracy: 0.5048 - val_loss: 0.6949 - val_accuracy: 0.5056
Epoch 71/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6938 - accuracy: 0.5096 - val_loss: 0.6965 - val_accuracy: 0.5000
Epoch 72/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6953 - accuracy: 0.4960 - val_loss: 0.6938 - val_accuracy: 0.5044
Epoch 73/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6940 - accuracy: 0.5128 - val_loss: 0.6938 - val_accuracy: 0.5092
Epoch 74/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6936 - accuracy: 0.5108 - val_loss: 0.6935 - val_accuracy: 0.5128
Epoch 75/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6938 - accuracy: 0.5036 - val_loss: 0.6929 - val_accuracy: 0.5064
Epoch 76/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6955 - accuracy: 0.4864 - val_loss: 0.6932 - val_accuracy: 0.5016
Epoch 77/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6949 - accuracy: 0.4744 - val_loss: 0.6938 - val_accuracy: 0.4960
Epoch 78/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6934 - accuracy: 0.4928 - val_loss: 0.6940 - val_accuracy: 0.4960
Epoch 79/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6942 - accuracy: 0.5060 - val_loss: 0.6936 - val_accuracy: 0.5064
Epoch 80/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6924 - accuracy: 0.5132 - val_loss: 0.6932 - val_accuracy: 0.5036
Epoch 81/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6926 - accuracy: 0.5204 - val_loss: 0.6924 - val_accuracy: 0.5108
Epoch 82/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6910 - accuracy: 0.5316 - val_loss: 0.6921 - val_accuracy: 0.5192
Epoch 83/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6917 - accuracy: 0.5284 - val_loss: 0.6917 - val_accuracy: 0.5288
Epoch 84/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6909 - accuracy: 0.5368 - val_loss: 0.6894 - val_accuracy: 0.5384
Epoch 85/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6892 - accuracy: 0.5384 - val_loss: 0.6890 - val_accuracy: 0.5424
Epoch 86/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6922 - accuracy: 0.5216 - val_loss: 0.6940 - val_accuracy: 0.5144
Epoch 87/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6903 - accuracy: 0.5336 - val_loss: 0.6966 - val_accuracy: 0.5168
Epoch 88/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6940 - accuracy: 0.5236 - val_loss: 0.6966 - val_accuracy: 0.5088
Epoch 89/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6940 - accuracy: 0.5020 - val_loss: 0.6943 - val_accuracy: 0.5016
Epoch 90/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6934 - accuracy: 0.5084 - val_loss: 0.6949 - val_accuracy: 0.4908
Epoch 91/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6949 - accuracy: 0.5104 - val_loss: 0.6941 - val_accuracy: 0.5068
Epoch 92/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6931 - accuracy: 0.5088 - val_loss: 0.6956 - val_accuracy: 0.4920
Epoch 93/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6941 - accuracy: 0.5028 - val_loss: 0.6952 - val_accuracy: 0.4936
Epoch 94/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6935 - accuracy: 0.5036 - val_loss: 0.6936 - val_accuracy: 0.4920
Epoch 95/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6938 - accuracy: 0.5124 - val_loss: 0.6935 - val_accuracy: 0.4964
Epoch 96/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6930 - accuracy: 0.5144 - val_loss: 0.6941 - val_accuracy: 0.5132
Epoch 97/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6929 - accuracy: 0.5132 - val_loss: 0.6943 - val_accuracy: 0.5096
Epoch 98/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6927 - accuracy: 0.5148 - val_loss: 0.6938 - val_accuracy: 0.5092
Epoch 99/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6920 - accuracy: 0.5180 - val_loss: 0.6938 - val_accuracy: 0.5140
Epoch 100/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6912 - accuracy: 0.5328 - val_loss: 0.6930 - val_accuracy: 0.5176
Epoch 101/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6908 - accuracy: 0.5344 - val_loss: 0.6937 - val_accuracy: 0.5240
Epoch 102/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6911 - accuracy: 0.5324 - val_loss: 0.6922 - val_accuracy: 0.5236
Epoch 103/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6894 - accuracy: 0.5380 - val_loss: 0.6928 - val_accuracy: 0.5296
Epoch 104/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6916 - accuracy: 0.5328 - val_loss: 0.6922 - val_accuracy: 0.5356
Epoch 105/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6906 - accuracy: 0.5488 - val_loss: 0.6903 - val_accuracy: 0.5352
Epoch 106/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6903 - accuracy: 0.5304 - val_loss: 0.6894 - val_accuracy: 0.5328
Epoch 107/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6894 - accuracy: 0.5364 - val_loss: 0.6925 - val_accuracy: 0.5300
Epoch 108/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6891 - accuracy: 0.5404 - val_loss: 0.6910 - val_accuracy: 0.5400
Epoch 109/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6929 - accuracy: 0.5204 - val_loss: 0.6921 - val_accuracy: 0.5204
Epoch 110/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6913 - accuracy: 0.5324 - val_loss: 0.6913 - val_accuracy: 0.5344
Epoch 111/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6901 - accuracy: 0.5484 - val_loss: 0.6911 - val_accuracy: 0.5384
Epoch 112/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6895 - accuracy: 0.5476 - val_loss: 0.6919 - val_accuracy: 0.5260
Epoch 113/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6875 - accuracy: 0.5532 - val_loss: 0.6918 - val_accuracy: 0.5296
Epoch 114/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6871 - accuracy: 0.5484 - val_loss: 0.6921 - val_accuracy: 0.5380
Epoch 115/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6878 - accuracy: 0.5408 - val_loss: 0.6925 - val_accuracy: 0.5208
Epoch 116/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6865 - accuracy: 0.5484 - val_loss: 0.6903 - val_accuracy: 0.5364
Epoch 117/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6853 - accuracy: 0.5528 - val_loss: 0.6894 - val_accuracy: 0.5372
Epoch 118/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6835 - accuracy: 0.5688 - val_loss: 0.6915 - val_accuracy: 0.5312
Epoch 119/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6834 - accuracy: 0.5680 - val_loss: 0.6895 - val_accuracy: 0.5488
Epoch 120/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6807 - accuracy: 0.5732 - val_loss: 0.6923 - val_accuracy: 0.5396
Epoch 121/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6870 - accuracy: 0.5492 - val_loss: 0.6869 - val_accuracy: 0.5532
Epoch 122/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6868 - accuracy: 0.5484 - val_loss: 0.6855 - val_accuracy: 0.5600
Epoch 123/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6871 - accuracy: 0.5564 - val_loss: 0.6847 - val_accuracy: 0.5568
Epoch 124/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6831 - accuracy: 0.5676 - val_loss: 0.6837 - val_accuracy: 0.5624
Epoch 125/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6835 - accuracy: 0.5664 - val_loss: 0.6830 - val_accuracy: 0.5680
Epoch 126/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6801 - accuracy: 0.5716 - val_loss: 0.6814 - val_accuracy: 0.5740
Epoch 127/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6792 - accuracy: 0.5772 - val_loss: 0.6786 - val_accuracy: 0.5792
Epoch 128/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6772 - accuracy: 0.5888 - val_loss: 0.6780 - val_accuracy: 0.5784
Epoch 129/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6759 - accuracy: 0.5888 - val_loss: 0.6759 - val_accuracy: 0.5772
Epoch 130/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6715 - accuracy: 0.5900 - val_loss: 0.6708 - val_accuracy: 0.5920
Epoch 131/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6653 - accuracy: 0.5992 - val_loss: 0.6661 - val_accuracy: 0.6016
Epoch 132/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6691 - accuracy: 0.5964 - val_loss: 0.6636 - val_accuracy: 0.6092
Epoch 133/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6598 - accuracy: 0.6020 - val_loss: 0.6593 - val_accuracy: 0.6076
Epoch 134/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6520 - accuracy: 0.6056 - val_loss: 0.6446 - val_accuracy: 0.6276
Epoch 135/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6388 - accuracy: 0.6184 - val_loss: 0.6400 - val_accuracy: 0.6132
Epoch 136/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6322 - accuracy: 0.6208 - val_loss: 0.6191 - val_accuracy: 0.6404
Epoch 137/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6021 - accuracy: 0.6452 - val_loss: 0.6042 - val_accuracy: 0.6336
Epoch 138/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5900 - accuracy: 0.6408 - val_loss: 0.5920 - val_accuracy: 0.6360
Epoch 139/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5946 - accuracy: 0.6268 - val_loss: 0.5867 - val_accuracy: 0.6284
Epoch 140/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5686 - accuracy: 0.6468 - val_loss: 0.5758 - val_accuracy: 0.6464
Epoch 141/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5621 - accuracy: 0.6496 - val_loss: 0.5707 - val_accuracy: 0.6364
Epoch 142/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5562 - accuracy: 0.6496 - val_loss: 0.5659 - val_accuracy: 0.6428
Epoch 143/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5560 - accuracy: 0.6516 - val_loss: 0.5618 - val_accuracy: 0.6484
Epoch 144/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5496 - accuracy: 0.6544 - val_loss: 0.5625 - val_accuracy: 0.6468
Epoch 145/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5465 - accuracy: 0.6568 - val_loss: 0.5568 - val_accuracy: 0.6544
Epoch 146/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5427 - accuracy: 0.6592 - val_loss: 0.5825 - val_accuracy: 0.6384
Epoch 147/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5469 - accuracy: 0.6588 - val_loss: 0.5518 - val_accuracy: 0.6524
Epoch 148/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5413 - accuracy: 0.6608 - val_loss: 0.5475 - val_accuracy: 0.6512
Epoch 149/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5380 - accuracy: 0.6660 - val_loss: 0.5513 - val_accuracy: 0.6480
Epoch 150/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5360 - accuracy: 0.6648 - val_loss: 0.5449 - val_accuracy: 0.6592
Epoch 151/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5345 - accuracy: 0.6608 - val_loss: 0.5473 - val_accuracy: 0.6616
Epoch 152/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5334 - accuracy: 0.6676 - val_loss: 0.5457 - val_accuracy: 0.6520
Epoch 153/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5287 - accuracy: 0.6740 - val_loss: 0.5434 - val_accuracy: 0.6572
Epoch 154/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5241 - accuracy: 0.6708 - val_loss: 0.5523 - val_accuracy: 0.6556
Epoch 155/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5290 - accuracy: 0.6728 - val_loss: 0.5411 - val_accuracy: 0.6656
Epoch 156/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5232 - accuracy: 0.6764 - val_loss: 0.5415 - val_accuracy: 0.6664
Epoch 157/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5294 - accuracy: 0.6868 - val_loss: 0.5385 - val_accuracy: 0.6556
Epoch 158/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5276 - accuracy: 0.6740 - val_loss: 0.5327 - val_accuracy: 0.6648
Epoch 159/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5212 - accuracy: 0.6784 - val_loss: 0.5280 - val_accuracy: 0.6612
Epoch 160/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5185 - accuracy: 0.6800 - val_loss: 0.5347 - val_accuracy: 0.6644
Epoch 161/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5164 - accuracy: 0.6816 - val_loss: 0.5133 - val_accuracy: 0.6800
Epoch 162/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5066 - accuracy: 0.6896 - val_loss: 0.5063 - val_accuracy: 0.6920
Epoch 163/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5099 - accuracy: 0.6888 - val_loss: 0.5053 - val_accuracy: 0.6976
Epoch 164/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4994 - accuracy: 0.6952 - val_loss: 0.5000 - val_accuracy: 0.7000
Epoch 165/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5113 - accuracy: 0.7108 - val_loss: 0.4923 - val_accuracy: 0.7084
Epoch 166/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5311 - accuracy: 0.6740 - val_loss: 0.5261 - val_accuracy: 0.6888
Epoch 167/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5154 - accuracy: 0.6836 - val_loss: 0.5097 - val_accuracy: 0.6908
Epoch 168/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5073 - accuracy: 0.6976 - val_loss: 0.5069 - val_accuracy: 0.7012
Epoch 169/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4980 - accuracy: 0.7064 - val_loss: 0.5059 - val_accuracy: 0.7060
Epoch 170/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4964 - accuracy: 0.7020 - val_loss: 0.4987 - val_accuracy: 0.7032
Epoch 171/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4890 - accuracy: 0.7100 - val_loss: 0.4979 - val_accuracy: 0.7080
Epoch 172/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4920 - accuracy: 0.7112 - val_loss: 0.4929 - val_accuracy: 0.7068
Epoch 173/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4945 - accuracy: 0.7172 - val_loss: 0.5323 - val_accuracy: 0.7124
Epoch 174/400
79/79 [==============================] - 0s 6ms/step - loss: 0.5101 - accuracy: 0.7104 - val_loss: 0.4942 - val_accuracy: 0.7080
Epoch 175/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4816 - accuracy: 0.7244 - val_loss: 0.4950 - val_accuracy: 0.7164
Epoch 176/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4822 - accuracy: 0.7224 - val_loss: 0.4951 - val_accuracy: 0.7048
Epoch 177/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4773 - accuracy: 0.7248 - val_loss: 0.4978 - val_accuracy: 0.7100
Epoch 178/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4834 - accuracy: 0.7220 - val_loss: 0.4915 - val_accuracy: 0.7148
Epoch 179/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4780 - accuracy: 0.7192 - val_loss: 0.4916 - val_accuracy: 0.7228
Epoch 180/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4803 - accuracy: 0.7252 - val_loss: 0.4898 - val_accuracy: 0.7164
Epoch 181/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4768 - accuracy: 0.7228 - val_loss: 0.4940 - val_accuracy: 0.7140
Epoch 182/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4748 - accuracy: 0.7256 - val_loss: 0.4916 - val_accuracy: 0.7168
Epoch 183/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4799 - accuracy: 0.7228 - val_loss: 0.4883 - val_accuracy: 0.7144
Epoch 184/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4753 - accuracy: 0.7268 - val_loss: 0.4849 - val_accuracy: 0.7172
Epoch 185/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4737 - accuracy: 0.7248 - val_loss: 0.4904 - val_accuracy: 0.7204
Epoch 186/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4682 - accuracy: 0.7288 - val_loss: 0.4914 - val_accuracy: 0.7136
Epoch 187/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4657 - accuracy: 0.7300 - val_loss: 0.4925 - val_accuracy: 0.7216
Epoch 188/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4694 - accuracy: 0.7284 - val_loss: 0.4925 - val_accuracy: 0.7148
Epoch 189/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4743 - accuracy: 0.7268 - val_loss: 0.4870 - val_accuracy: 0.7216
Epoch 190/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4759 - accuracy: 0.7336 - val_loss: 0.4797 - val_accuracy: 0.7240
Epoch 191/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4677 - accuracy: 0.7336 - val_loss: 0.4749 - val_accuracy: 0.7184
Epoch 192/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4645 - accuracy: 0.7320 - val_loss: 0.4803 - val_accuracy: 0.7260
Epoch 193/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4638 - accuracy: 0.7376 - val_loss: 0.4966 - val_accuracy: 0.7268
Epoch 194/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4659 - accuracy: 0.7340 - val_loss: 0.4818 - val_accuracy: 0.7308
Epoch 195/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4668 - accuracy: 0.7304 - val_loss: 0.4776 - val_accuracy: 0.7312
Epoch 196/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4604 - accuracy: 0.7356 - val_loss: 0.4731 - val_accuracy: 0.7304
Epoch 197/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4588 - accuracy: 0.7420 - val_loss: 0.4618 - val_accuracy: 0.7356
Epoch 198/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4508 - accuracy: 0.7452 - val_loss: 0.4548 - val_accuracy: 0.7432
Epoch 199/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4599 - accuracy: 0.7452 - val_loss: 0.4670 - val_accuracy: 0.7332
Epoch 200/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4463 - accuracy: 0.7540 - val_loss: 0.4577 - val_accuracy: 0.7496
Epoch 201/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4490 - accuracy: 0.7572 - val_loss: 0.4565 - val_accuracy: 0.7564
Epoch 202/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4400 - accuracy: 0.7680 - val_loss: 0.4427 - val_accuracy: 0.7744
Epoch 203/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4340 - accuracy: 0.7816 - val_loss: 0.4418 - val_accuracy: 0.7820
Epoch 204/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4214 - accuracy: 0.7848 - val_loss: 0.4242 - val_accuracy: 0.7752
Epoch 205/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4140 - accuracy: 0.7900 - val_loss: 0.4061 - val_accuracy: 0.7996
Epoch 206/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4110 - accuracy: 0.8032 - val_loss: 0.4196 - val_accuracy: 0.7864
Epoch 207/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4019 - accuracy: 0.8092 - val_loss: 0.4060 - val_accuracy: 0.8024
Epoch 208/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3966 - accuracy: 0.8056 - val_loss: 0.4329 - val_accuracy: 0.7864
Epoch 209/400
79/79 [==============================] - 0s 6ms/step - loss: 0.4044 - accuracy: 0.8108 - val_loss: 0.4098 - val_accuracy: 0.8000
Epoch 210/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3890 - accuracy: 0.8208 - val_loss: 0.3925 - val_accuracy: 0.8220
Epoch 211/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3767 - accuracy: 0.8268 - val_loss: 0.3883 - val_accuracy: 0.8180
Epoch 212/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3725 - accuracy: 0.8320 - val_loss: 0.3958 - val_accuracy: 0.8180
Epoch 213/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3733 - accuracy: 0.8284 - val_loss: 0.3906 - val_accuracy: 0.8224
Epoch 214/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3736 - accuracy: 0.8304 - val_loss: 0.3767 - val_accuracy: 0.8264
Epoch 215/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3653 - accuracy: 0.8404 - val_loss: 0.3746 - val_accuracy: 0.8288
Epoch 216/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3565 - accuracy: 0.8388 - val_loss: 0.3837 - val_accuracy: 0.8244
Epoch 217/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3645 - accuracy: 0.8364 - val_loss: 0.3668 - val_accuracy: 0.8352
Epoch 218/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3478 - accuracy: 0.8392 - val_loss: 0.3597 - val_accuracy: 0.8404
Epoch 219/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3459 - accuracy: 0.8336 - val_loss: 0.3646 - val_accuracy: 0.8356
Epoch 220/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3489 - accuracy: 0.8388 - val_loss: 0.3582 - val_accuracy: 0.8344
Epoch 221/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3411 - accuracy: 0.8456 - val_loss: 0.3456 - val_accuracy: 0.8360
Epoch 222/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3370 - accuracy: 0.8488 - val_loss: 0.3502 - val_accuracy: 0.8384
Epoch 223/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3319 - accuracy: 0.8476 - val_loss: 0.3502 - val_accuracy: 0.8420
Epoch 224/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3363 - accuracy: 0.8472 - val_loss: 0.3430 - val_accuracy: 0.8436
Epoch 225/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3778 - accuracy: 0.8284 - val_loss: 0.4149 - val_accuracy: 0.8112
Epoch 226/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3839 - accuracy: 0.8228 - val_loss: 0.3709 - val_accuracy: 0.8284
Epoch 227/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3425 - accuracy: 0.8468 - val_loss: 0.3601 - val_accuracy: 0.8340
Epoch 228/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3409 - accuracy: 0.8440 - val_loss: 0.3505 - val_accuracy: 0.8432
Epoch 229/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3295 - accuracy: 0.8484 - val_loss: 0.3356 - val_accuracy: 0.8508
Epoch 230/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3588 - accuracy: 0.8436 - val_loss: 0.3536 - val_accuracy: 0.8308
Epoch 231/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3324 - accuracy: 0.8468 - val_loss: 0.3320 - val_accuracy: 0.8432
Epoch 232/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3206 - accuracy: 0.8556 - val_loss: 0.3609 - val_accuracy: 0.8368
Epoch 233/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3172 - accuracy: 0.8536 - val_loss: 0.3313 - val_accuracy: 0.8484
Epoch 234/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3154 - accuracy: 0.8528 - val_loss: 0.3404 - val_accuracy: 0.8504
Epoch 235/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3129 - accuracy: 0.8564 - val_loss: 0.3338 - val_accuracy: 0.8492
Epoch 236/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3169 - accuracy: 0.8620 - val_loss: 0.3350 - val_accuracy: 0.8488
Epoch 237/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3091 - accuracy: 0.8556 - val_loss: 0.3287 - val_accuracy: 0.8496
Epoch 238/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3036 - accuracy: 0.8608 - val_loss: 0.3361 - val_accuracy: 0.8496
Epoch 239/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3015 - accuracy: 0.8616 - val_loss: 0.3231 - val_accuracy: 0.8540
Epoch 240/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3041 - accuracy: 0.8676 - val_loss: 0.3379 - val_accuracy: 0.8528
Epoch 241/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3002 - accuracy: 0.8636 - val_loss: 0.3311 - val_accuracy: 0.8476
Epoch 242/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3036 - accuracy: 0.8636 - val_loss: 0.3258 - val_accuracy: 0.8544
Epoch 243/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3242 - accuracy: 0.8584 - val_loss: 0.3431 - val_accuracy: 0.8444
Epoch 244/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3018 - accuracy: 0.8628 - val_loss: 0.3199 - val_accuracy: 0.8536
Epoch 245/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3061 - accuracy: 0.8636 - val_loss: 0.3290 - val_accuracy: 0.8496
Epoch 246/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3023 - accuracy: 0.8604 - val_loss: 0.3210 - val_accuracy: 0.8520
Epoch 247/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2966 - accuracy: 0.8700 - val_loss: 0.3544 - val_accuracy: 0.8288
Epoch 248/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3162 - accuracy: 0.8492 - val_loss: 0.3154 - val_accuracy: 0.8520
Epoch 249/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3012 - accuracy: 0.8644 - val_loss: 0.3147 - val_accuracy: 0.8584
Epoch 250/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2968 - accuracy: 0.8632 - val_loss: 0.3246 - val_accuracy: 0.8500
Epoch 251/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2947 - accuracy: 0.8676 - val_loss: 0.3142 - val_accuracy: 0.8568
Epoch 252/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2884 - accuracy: 0.8688 - val_loss: 0.3040 - val_accuracy: 0.8576
Epoch 253/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2885 - accuracy: 0.8696 - val_loss: 0.3189 - val_accuracy: 0.8620
Epoch 254/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2943 - accuracy: 0.8696 - val_loss: 0.3055 - val_accuracy: 0.8516
Epoch 255/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2916 - accuracy: 0.8640 - val_loss: 0.3133 - val_accuracy: 0.8532
Epoch 256/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2931 - accuracy: 0.8664 - val_loss: 0.3389 - val_accuracy: 0.8536
Epoch 257/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2836 - accuracy: 0.8752 - val_loss: 0.3070 - val_accuracy: 0.8572
Epoch 258/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2816 - accuracy: 0.8720 - val_loss: 0.3079 - val_accuracy: 0.8592
Epoch 259/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2950 - accuracy: 0.8692 - val_loss: 0.3066 - val_accuracy: 0.8620
Epoch 260/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2838 - accuracy: 0.8736 - val_loss: 0.3033 - val_accuracy: 0.8620
Epoch 261/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2801 - accuracy: 0.8760 - val_loss: 0.3058 - val_accuracy: 0.8568
Epoch 262/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2814 - accuracy: 0.8772 - val_loss: 0.3037 - val_accuracy: 0.8636
Epoch 263/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2987 - accuracy: 0.8668 - val_loss: 0.3168 - val_accuracy: 0.8512
Epoch 264/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2747 - accuracy: 0.8816 - val_loss: 0.2969 - val_accuracy: 0.8680
Epoch 265/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2730 - accuracy: 0.8816 - val_loss: 0.3008 - val_accuracy: 0.8692
Epoch 266/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2710 - accuracy: 0.8812 - val_loss: 0.2961 - val_accuracy: 0.8620
Epoch 267/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2667 - accuracy: 0.8804 - val_loss: 0.2989 - val_accuracy: 0.8628
Epoch 268/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2685 - accuracy: 0.8832 - val_loss: 0.2945 - val_accuracy: 0.8676
Epoch 269/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2664 - accuracy: 0.8848 - val_loss: 0.2975 - val_accuracy: 0.8612
Epoch 270/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2732 - accuracy: 0.8800 - val_loss: 0.3056 - val_accuracy: 0.8532
Epoch 271/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3005 - accuracy: 0.8776 - val_loss: 0.2920 - val_accuracy: 0.8684
Epoch 272/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2830 - accuracy: 0.8696 - val_loss: 0.2983 - val_accuracy: 0.8684
Epoch 273/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2739 - accuracy: 0.8800 - val_loss: 0.2991 - val_accuracy: 0.8580
Epoch 274/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2663 - accuracy: 0.8816 - val_loss: 0.2911 - val_accuracy: 0.8652
Epoch 275/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2614 - accuracy: 0.8856 - val_loss: 0.2876 - val_accuracy: 0.8660
Epoch 276/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2728 - accuracy: 0.8820 - val_loss: 0.3010 - val_accuracy: 0.8620
Epoch 277/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2796 - accuracy: 0.8736 - val_loss: 0.3075 - val_accuracy: 0.8648
Epoch 278/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2701 - accuracy: 0.8836 - val_loss: 0.2892 - val_accuracy: 0.8632
Epoch 279/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2818 - accuracy: 0.8812 - val_loss: 0.2953 - val_accuracy: 0.8656
Epoch 280/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3780 - accuracy: 0.8300 - val_loss: 0.3273 - val_accuracy: 0.8580
Epoch 281/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2866 - accuracy: 0.8720 - val_loss: 0.2984 - val_accuracy: 0.8660
Epoch 282/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2721 - accuracy: 0.8780 - val_loss: 0.2901 - val_accuracy: 0.8684
Epoch 283/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3003 - accuracy: 0.8636 - val_loss: 0.3686 - val_accuracy: 0.8172
Epoch 284/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3474 - accuracy: 0.8428 - val_loss: 0.3232 - val_accuracy: 0.8496
Epoch 285/400
79/79 [==============================] - 0s 6ms/step - loss: 0.3059 - accuracy: 0.8640 - val_loss: 0.3046 - val_accuracy: 0.8572
Epoch 286/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2884 - accuracy: 0.8696 - val_loss: 0.2977 - val_accuracy: 0.8656
Epoch 287/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2780 - accuracy: 0.8772 - val_loss: 0.3672 - val_accuracy: 0.8384
Epoch 288/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2969 - accuracy: 0.8676 - val_loss: 0.2954 - val_accuracy: 0.8624
Epoch 289/400
79/79 [==============================] - 1s 6ms/step - loss: 0.2803 - accuracy: 0.8736 - val_loss: 0.2911 - val_accuracy: 0.8624
Epoch 290/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2810 - accuracy: 0.8760 - val_loss: 0.2936 - val_accuracy: 0.8616
Epoch 291/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2767 - accuracy: 0.8800 - val_loss: 0.2882 - val_accuracy: 0.8660
Epoch 292/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2697 - accuracy: 0.8780 - val_loss: 0.2849 - val_accuracy: 0.8668
Epoch 293/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2753 - accuracy: 0.8748 - val_loss: 0.2933 - val_accuracy: 0.8648
Epoch 294/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2767 - accuracy: 0.8748 - val_loss: 0.2799 - val_accuracy: 0.8668
Epoch 295/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2623 - accuracy: 0.8824 - val_loss: 0.2881 - val_accuracy: 0.8616
Epoch 296/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2602 - accuracy: 0.8816 - val_loss: 0.2886 - val_accuracy: 0.8652
Epoch 297/400
79/79 [==============================] - 1s 6ms/step - loss: 0.2612 - accuracy: 0.8788 - val_loss: 0.2838 - val_accuracy: 0.8652
Epoch 298/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2593 - accuracy: 0.8868 - val_loss: 0.2771 - val_accuracy: 0.8688
Epoch 299/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2582 - accuracy: 0.8800 - val_loss: 0.2817 - val_accuracy: 0.8668
Epoch 300/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2604 - accuracy: 0.8856 - val_loss: 0.2763 - val_accuracy: 0.8716
Epoch 301/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2640 - accuracy: 0.8848 - val_loss: 0.3153 - val_accuracy: 0.8596
Epoch 302/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2773 - accuracy: 0.8804 - val_loss: 0.2828 - val_accuracy: 0.8680
Epoch 303/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2748 - accuracy: 0.8816 - val_loss: 0.2749 - val_accuracy: 0.8716
Epoch 304/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2664 - accuracy: 0.8816 - val_loss: 0.2797 - val_accuracy: 0.8740
Epoch 305/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2570 - accuracy: 0.8840 - val_loss: 0.2722 - val_accuracy: 0.8760
Epoch 306/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2559 - accuracy: 0.8900 - val_loss: 0.2786 - val_accuracy: 0.8700
Epoch 307/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2547 - accuracy: 0.8896 - val_loss: 0.2692 - val_accuracy: 0.8756
Epoch 308/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2573 - accuracy: 0.8868 - val_loss: 0.2748 - val_accuracy: 0.8720
Epoch 309/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2520 - accuracy: 0.8900 - val_loss: 0.2758 - val_accuracy: 0.8728
Epoch 310/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2533 - accuracy: 0.8864 - val_loss: 0.2676 - val_accuracy: 0.8748
Epoch 311/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2516 - accuracy: 0.8948 - val_loss: 0.2699 - val_accuracy: 0.8784
Epoch 312/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2489 - accuracy: 0.8948 - val_loss: 0.2641 - val_accuracy: 0.8796
Epoch 313/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2505 - accuracy: 0.8972 - val_loss: 0.2711 - val_accuracy: 0.8760
Epoch 314/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2379 - accuracy: 0.9004 - val_loss: 0.2585 - val_accuracy: 0.8832
Epoch 315/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2383 - accuracy: 0.9008 - val_loss: 0.2604 - val_accuracy: 0.8856
Epoch 316/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2286 - accuracy: 0.9076 - val_loss: 0.2660 - val_accuracy: 0.8840
Epoch 317/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2282 - accuracy: 0.9100 - val_loss: 0.2567 - val_accuracy: 0.8888
Epoch 318/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2291 - accuracy: 0.9096 - val_loss: 0.2530 - val_accuracy: 0.8948
Epoch 319/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2284 - accuracy: 0.9108 - val_loss: 0.2332 - val_accuracy: 0.9012
Epoch 320/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2151 - accuracy: 0.9176 - val_loss: 0.2388 - val_accuracy: 0.8984
Epoch 321/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2130 - accuracy: 0.9188 - val_loss: 0.2076 - val_accuracy: 0.9264
Epoch 322/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1967 - accuracy: 0.9292 - val_loss: 0.2096 - val_accuracy: 0.9312
Epoch 323/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1843 - accuracy: 0.9388 - val_loss: 0.1982 - val_accuracy: 0.9344
Epoch 324/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1722 - accuracy: 0.9468 - val_loss: 0.1952 - val_accuracy: 0.9396
Epoch 325/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1616 - accuracy: 0.9528 - val_loss: 0.1722 - val_accuracy: 0.9496
Epoch 326/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1605 - accuracy: 0.9532 - val_loss: 0.1745 - val_accuracy: 0.9448
Epoch 327/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1689 - accuracy: 0.9468 - val_loss: 0.1750 - val_accuracy: 0.9468
Epoch 328/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1570 - accuracy: 0.9536 - val_loss: 0.1766 - val_accuracy: 0.9492
Epoch 329/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1527 - accuracy: 0.9616 - val_loss: 0.1581 - val_accuracy: 0.9576
Epoch 330/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1371 - accuracy: 0.9660 - val_loss: 0.1693 - val_accuracy: 0.9536
Epoch 331/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1348 - accuracy: 0.9648 - val_loss: 0.1552 - val_accuracy: 0.9568
Epoch 332/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1453 - accuracy: 0.9612 - val_loss: 0.1445 - val_accuracy: 0.9608
Epoch 333/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1447 - accuracy: 0.9636 - val_loss: 0.1648 - val_accuracy: 0.9560
Epoch 334/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1431 - accuracy: 0.9652 - val_loss: 0.1450 - val_accuracy: 0.9632
Epoch 335/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2000 - accuracy: 0.9468 - val_loss: 0.1380 - val_accuracy: 0.9644
Epoch 336/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1285 - accuracy: 0.9676 - val_loss: 0.1403 - val_accuracy: 0.9620
Epoch 337/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1197 - accuracy: 0.9700 - val_loss: 0.1930 - val_accuracy: 0.9472
Epoch 338/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1395 - accuracy: 0.9640 - val_loss: 0.1366 - val_accuracy: 0.9636
Epoch 339/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1173 - accuracy: 0.9704 - val_loss: 0.1253 - val_accuracy: 0.9668
Epoch 340/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1209 - accuracy: 0.9724 - val_loss: 0.1151 - val_accuracy: 0.9704
Epoch 341/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1031 - accuracy: 0.9772 - val_loss: 0.1122 - val_accuracy: 0.9724
Epoch 342/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1015 - accuracy: 0.9776 - val_loss: 0.1129 - val_accuracy: 0.9720
Epoch 343/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1069 - accuracy: 0.9752 - val_loss: 0.1121 - val_accuracy: 0.9728
Epoch 344/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1031 - accuracy: 0.9752 - val_loss: 0.0995 - val_accuracy: 0.9756
Epoch 345/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1038 - accuracy: 0.9736 - val_loss: 0.0965 - val_accuracy: 0.9756
Epoch 346/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0845 - accuracy: 0.9800 - val_loss: 0.0894 - val_accuracy: 0.9776
Epoch 347/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0977 - accuracy: 0.9764 - val_loss: 0.1004 - val_accuracy: 0.9752
Epoch 348/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0964 - accuracy: 0.9748 - val_loss: 0.1017 - val_accuracy: 0.9728
Epoch 349/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0760 - accuracy: 0.9820 - val_loss: 0.0827 - val_accuracy: 0.9796
Epoch 350/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0864 - accuracy: 0.9792 - val_loss: 0.0825 - val_accuracy: 0.9832
Epoch 351/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0752 - accuracy: 0.9832 - val_loss: 0.1226 - val_accuracy: 0.9684
Epoch 352/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1088 - accuracy: 0.9716 - val_loss: 0.1066 - val_accuracy: 0.9740
Epoch 353/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1056 - accuracy: 0.9728 - val_loss: 0.0994 - val_accuracy: 0.9776
Epoch 354/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0875 - accuracy: 0.9788 - val_loss: 0.0836 - val_accuracy: 0.9816
Epoch 355/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0811 - accuracy: 0.9824 - val_loss: 0.0956 - val_accuracy: 0.9756
Epoch 356/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0750 - accuracy: 0.9836 - val_loss: 0.0742 - val_accuracy: 0.9828
Epoch 357/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1014 - accuracy: 0.9764 - val_loss: 0.1630 - val_accuracy: 0.9560
Epoch 358/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1405 - accuracy: 0.9652 - val_loss: 0.1010 - val_accuracy: 0.9752
Epoch 359/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1292 - accuracy: 0.9672 - val_loss: 0.1304 - val_accuracy: 0.9672
Epoch 360/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1048 - accuracy: 0.9740 - val_loss: 0.0815 - val_accuracy: 0.9812
Epoch 361/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0782 - accuracy: 0.9816 - val_loss: 0.0712 - val_accuracy: 0.9852
Epoch 362/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0658 - accuracy: 0.9848 - val_loss: 0.0721 - val_accuracy: 0.9812
Epoch 363/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0638 - accuracy: 0.9848 - val_loss: 0.0672 - val_accuracy: 0.9836
Epoch 364/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0647 - accuracy: 0.9844 - val_loss: 0.0713 - val_accuracy: 0.9828
Epoch 365/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0654 - accuracy: 0.9844 - val_loss: 0.0715 - val_accuracy: 0.9820
Epoch 366/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0677 - accuracy: 0.9820 - val_loss: 0.0689 - val_accuracy: 0.9828
Epoch 367/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0583 - accuracy: 0.9860 - val_loss: 0.0653 - val_accuracy: 0.9836
Epoch 368/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0557 - accuracy: 0.9892 - val_loss: 0.0648 - val_accuracy: 0.9820
Epoch 369/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0545 - accuracy: 0.9872 - val_loss: 0.0668 - val_accuracy: 0.9828
Epoch 370/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0600 - accuracy: 0.9864 - val_loss: 0.0649 - val_accuracy: 0.9848
Epoch 371/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0551 - accuracy: 0.9864 - val_loss: 0.0674 - val_accuracy: 0.9832
Epoch 372/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0639 - accuracy: 0.9856 - val_loss: 0.0781 - val_accuracy: 0.9808
Epoch 373/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0665 - accuracy: 0.9812 - val_loss: 0.0711 - val_accuracy: 0.9824
Epoch 374/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0638 - accuracy: 0.9844 - val_loss: 0.0605 - val_accuracy: 0.9864
Epoch 375/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0620 - accuracy: 0.9864 - val_loss: 0.0632 - val_accuracy: 0.9856
Epoch 376/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0673 - accuracy: 0.9828 - val_loss: 0.0635 - val_accuracy: 0.9848
Epoch 377/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0660 - accuracy: 0.9824 - val_loss: 0.0627 - val_accuracy: 0.9860
Epoch 378/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0504 - accuracy: 0.9872 - val_loss: 0.0597 - val_accuracy: 0.9860
Epoch 379/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0549 - accuracy: 0.9868 - val_loss: 0.0649 - val_accuracy: 0.9856
Epoch 380/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0523 - accuracy: 0.9860 - val_loss: 0.1208 - val_accuracy: 0.9768
Epoch 381/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1911 - accuracy: 0.9576 - val_loss: 0.1913 - val_accuracy: 0.9484
Epoch 382/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1130 - accuracy: 0.9720 - val_loss: 0.0887 - val_accuracy: 0.9820
Epoch 383/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0736 - accuracy: 0.9836 - val_loss: 0.0687 - val_accuracy: 0.9840
Epoch 384/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0636 - accuracy: 0.9844 - val_loss: 0.0674 - val_accuracy: 0.9856
Epoch 385/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0595 - accuracy: 0.9828 - val_loss: 0.0779 - val_accuracy: 0.9832
Epoch 386/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0703 - accuracy: 0.9844 - val_loss: 0.0651 - val_accuracy: 0.9852
Epoch 387/400
79/79 [==============================] - 1s 8ms/step - loss: 0.0707 - accuracy: 0.9832 - val_loss: 0.0796 - val_accuracy: 0.9828
Epoch 388/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2051 - accuracy: 0.9564 - val_loss: 0.4431 - val_accuracy: 0.8876
Epoch 389/400
79/79 [==============================] - 0s 6ms/step - loss: 0.2905 - accuracy: 0.9260 - val_loss: 0.1163 - val_accuracy: 0.9708
Epoch 390/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1224 - accuracy: 0.9688 - val_loss: 0.0957 - val_accuracy: 0.9780
Epoch 391/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1141 - accuracy: 0.9712 - val_loss: 0.1079 - val_accuracy: 0.9764
Epoch 392/400
79/79 [==============================] - 0s 6ms/step - loss: 0.1019 - accuracy: 0.9752 - val_loss: 0.0845 - val_accuracy: 0.9808
Epoch 393/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0949 - accuracy: 0.9772 - val_loss: 0.0862 - val_accuracy: 0.9820
Epoch 394/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0836 - accuracy: 0.9784 - val_loss: 0.0897 - val_accuracy: 0.9816
Epoch 395/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0785 - accuracy: 0.9796 - val_loss: 0.0960 - val_accuracy: 0.9808
Epoch 396/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0785 - accuracy: 0.9804 - val_loss: 0.0907 - val_accuracy: 0.9800
Epoch 397/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0561 - accuracy: 0.9876 - val_loss: 0.0772 - val_accuracy: 0.9844
Epoch 398/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0550 - accuracy: 0.9876 - val_loss: 0.0745 - val_accuracy: 0.9844
Epoch 399/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0525 - accuracy: 0.9872 - val_loss: 0.0661 - val_accuracy: 0.9848
Epoch 400/400
79/79 [==============================] - 0s 6ms/step - loss: 0.0503 - accuracy: 0.9880 - val_loss: 0.0654 - val_accuracy: 0.9864

# Plot the loss
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()

<matplotlib.legend.Legend at 0x7fec1f98f400>

# Plot the accuracy too
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.legend()

<matplotlib.legend.Legend at 0x7fec262e1d30>

# Make the problem harder by making T larger
T = 30
D = 1
X = []
Y = []

for t in range(5000):
  x = np.random.randn(T)
  X.append(x)
  y = get_label(x, 0, 1, 2) # long distance
  Y.append(y)

X = np.array(X)
Y = np.array(Y)
N = len(X)

# Now test our LSTM
inputs = np.expand_dims(X, -1)

# make the RNN
i = Input(shape=(T, D))

# method 1
x = LSTM(15)(i)

x = Dense(1, activation='sigmoid')(x)
model = Model(i, x)
model.compile(
  loss='binary_crossentropy',
  optimizer=Adam(lr=0.01),
  metrics=['accuracy'],
)

# train the RNN
r = model.fit(
  inputs, Y,
  epochs=400,
  validation_split=0.5,
)

Epoch 1/400
79/79 [==============================] - 1s 10ms/step - loss: 0.6938 - accuracy: 0.5124 - val_loss: 0.6950 - val_accuracy: 0.4940
Epoch 2/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6925 - accuracy: 0.5200 - val_loss: 0.6945 - val_accuracy: 0.4964
Epoch 3/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6927 - accuracy: 0.5204 - val_loss: 0.6955 - val_accuracy: 0.4960
Epoch 4/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6916 - accuracy: 0.5224 - val_loss: 0.6943 - val_accuracy: 0.4956
Epoch 5/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6922 - accuracy: 0.5228 - val_loss: 0.6952 - val_accuracy: 0.4960
Epoch 6/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6920 - accuracy: 0.5232 - val_loss: 0.6945 - val_accuracy: 0.4960
Epoch 7/400
79/79 [==============================] - 0s 6ms/step - loss: 0.6921 - accuracy: 0.5252 - val_loss: 0.6949 - val_accuracy: 0.4912
Epoch 8/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6917 - accuracy: 0.5276 - val_loss: 0.6943 - val_accuracy: 0.4908
Epoch 9/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6908 - accuracy: 0.5244 - val_loss: 0.6962 - val_accuracy: 0.4892
Epoch 10/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6929 - accuracy: 0.5300 - val_loss: 0.6971 - val_accuracy: 0.4964
Epoch 11/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6913 - accuracy: 0.5284 - val_loss: 0.6950 - val_accuracy: 0.4960
Epoch 12/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6909 - accuracy: 0.5248 - val_loss: 0.6954 - val_accuracy: 0.4940
Epoch 13/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6911 - accuracy: 0.5284 - val_loss: 0.6941 - val_accuracy: 0.4964
Epoch 14/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6912 - accuracy: 0.5344 - val_loss: 0.6959 - val_accuracy: 0.4968
Epoch 15/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6904 - accuracy: 0.5332 - val_loss: 0.6952 - val_accuracy: 0.5016
Epoch 16/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6912 - accuracy: 0.5372 - val_loss: 0.6958 - val_accuracy: 0.5020
Epoch 17/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6907 - accuracy: 0.5324 - val_loss: 0.6962 - val_accuracy: 0.5052
Epoch 18/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6924 - accuracy: 0.5300 - val_loss: 0.6953 - val_accuracy: 0.5056
Epoch 19/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6893 - accuracy: 0.5352 - val_loss: 0.6994 - val_accuracy: 0.4976
Epoch 20/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6899 - accuracy: 0.5268 - val_loss: 0.6960 - val_accuracy: 0.4988
Epoch 21/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6898 - accuracy: 0.5432 - val_loss: 0.6958 - val_accuracy: 0.5104
Epoch 22/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6879 - accuracy: 0.5376 - val_loss: 0.6995 - val_accuracy: 0.5052
Epoch 23/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6891 - accuracy: 0.5356 - val_loss: 0.7014 - val_accuracy: 0.5012
Epoch 24/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6875 - accuracy: 0.5416 - val_loss: 0.7036 - val_accuracy: 0.5016
Epoch 25/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6885 - accuracy: 0.5388 - val_loss: 0.6955 - val_accuracy: 0.4996
Epoch 26/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6861 - accuracy: 0.5396 - val_loss: 0.7011 - val_accuracy: 0.5016
Epoch 27/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6859 - accuracy: 0.5472 - val_loss: 0.7008 - val_accuracy: 0.4956
Epoch 28/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6856 - accuracy: 0.5528 - val_loss: 0.7030 - val_accuracy: 0.5012
Epoch 29/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6843 - accuracy: 0.5496 - val_loss: 0.7024 - val_accuracy: 0.4944
Epoch 30/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6826 - accuracy: 0.5508 - val_loss: 0.7150 - val_accuracy: 0.4960
Epoch 31/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6811 - accuracy: 0.5580 - val_loss: 0.7058 - val_accuracy: 0.5004
Epoch 32/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6882 - accuracy: 0.5332 - val_loss: 0.7025 - val_accuracy: 0.4940
Epoch 33/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6927 - accuracy: 0.5188 - val_loss: 0.6988 - val_accuracy: 0.5000
Epoch 34/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6897 - accuracy: 0.5416 - val_loss: 0.7027 - val_accuracy: 0.5052
Epoch 35/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6892 - accuracy: 0.5388 - val_loss: 0.6926 - val_accuracy: 0.5076
Epoch 36/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6703 - accuracy: 0.5680 - val_loss: 0.6594 - val_accuracy: 0.5656
Epoch 37/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6364 - accuracy: 0.6020 - val_loss: 0.6418 - val_accuracy: 0.5920
Epoch 38/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6196 - accuracy: 0.6136 - val_loss: 0.6510 - val_accuracy: 0.5844
Epoch 39/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6257 - accuracy: 0.6184 - val_loss: 0.6371 - val_accuracy: 0.5816
Epoch 40/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6065 - accuracy: 0.6308 - val_loss: 0.6372 - val_accuracy: 0.5924
Epoch 41/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6144 - accuracy: 0.6324 - val_loss: 0.6255 - val_accuracy: 0.5948
Epoch 42/400
79/79 [==============================] - 1s 7ms/step - loss: 0.6017 - accuracy: 0.6336 - val_loss: 0.6380 - val_accuracy: 0.5960
Epoch 43/400
79/79 [==============================] - 1s 6ms/step - loss: 0.6007 - accuracy: 0.6372 - val_loss: 0.6215 - val_accuracy: 0.6032
Epoch 44/400
79/79 [==============================] - 1s 6ms/step - loss: 0.5848 - accuracy: 0.6604 - val_loss: 0.5784 - val_accuracy: 0.6684
Epoch 45/400
79/79 [==============================] - 1s 6ms/step - loss: 0.5135 - accuracy: 0.7432 - val_loss: 0.4908 - val_accuracy: 0.7532
Epoch 46/400
79/79 [==============================] - 1s 7ms/step - loss: 0.4700 - accuracy: 0.7776 - val_loss: 0.5146 - val_accuracy: 0.7540
Epoch 47/400
79/79 [==============================] - 1s 7ms/step - loss: 0.4460 - accuracy: 0.7956 - val_loss: 0.4578 - val_accuracy: 0.7732
Epoch 48/400
79/79 [==============================] - 1s 6ms/step - loss: 0.4209 - accuracy: 0.8076 - val_loss: 0.4344 - val_accuracy: 0.7916
Epoch 49/400
79/79 [==============================] - 1s 6ms/step - loss: 0.3859 - accuracy: 0.8280 - val_loss: 0.3907 - val_accuracy: 0.8176
Epoch 50/400
79/79 [==============================] - 1s 7ms/step - loss: 0.2989 - accuracy: 0.8860 - val_loss: 0.2240 - val_accuracy: 0.9068
Epoch 51/400
79/79 [==============================] - 1s 6ms/step - loss: 0.1341 - accuracy: 0.9544 - val_loss: 0.1162 - val_accuracy: 0.9628
Epoch 52/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0979 - accuracy: 0.9668 - val_loss: 0.0890 - val_accuracy: 0.9664
Epoch 53/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0632 - accuracy: 0.9812 - val_loss: 0.0899 - val_accuracy: 0.9660
Epoch 54/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0643 - accuracy: 0.9800 - val_loss: 0.1283 - val_accuracy: 0.9576
Epoch 55/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0563 - accuracy: 0.9800 - val_loss: 0.0561 - val_accuracy: 0.9788
Epoch 56/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0444 - accuracy: 0.9836 - val_loss: 0.0495 - val_accuracy: 0.9824
Epoch 57/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0373 - accuracy: 0.9892 - val_loss: 0.0891 - val_accuracy: 0.9708
Epoch 58/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0411 - accuracy: 0.9844 - val_loss: 0.0512 - val_accuracy: 0.9828
Epoch 59/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0312 - accuracy: 0.9896 - val_loss: 0.0501 - val_accuracy: 0.9808
Epoch 60/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0293 - accuracy: 0.9916 - val_loss: 0.0605 - val_accuracy: 0.9748
Epoch 61/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0300 - accuracy: 0.9868 - val_loss: 0.0444 - val_accuracy: 0.9836
Epoch 62/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0214 - accuracy: 0.9924 - val_loss: 0.0561 - val_accuracy: 0.9856
Epoch 63/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0158 - accuracy: 0.9948 - val_loss: 0.0404 - val_accuracy: 0.9868
Epoch 64/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0212 - accuracy: 0.9920 - val_loss: 0.0402 - val_accuracy: 0.9852
Epoch 65/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0233 - accuracy: 0.9924 - val_loss: 0.0302 - val_accuracy: 0.9876
Epoch 66/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0317 - accuracy: 0.9876 - val_loss: 0.0469 - val_accuracy: 0.9836
Epoch 67/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0213 - accuracy: 0.9900 - val_loss: 0.0292 - val_accuracy: 0.9900
Epoch 68/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0099 - accuracy: 0.9976 - val_loss: 0.0289 - val_accuracy: 0.9880
Epoch 69/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0165 - accuracy: 0.9940 - val_loss: 0.0329 - val_accuracy: 0.9868
Epoch 70/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0244 - accuracy: 0.9932 - val_loss: 0.0339 - val_accuracy: 0.9852
Epoch 71/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0256 - accuracy: 0.9908 - val_loss: 0.0401 - val_accuracy: 0.9872
Epoch 72/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0263 - accuracy: 0.9912 - val_loss: 0.0332 - val_accuracy: 0.9880
Epoch 73/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0125 - accuracy: 0.9960 - val_loss: 0.0553 - val_accuracy: 0.9832
Epoch 74/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0204 - accuracy: 0.9932 - val_loss: 0.0300 - val_accuracy: 0.9888
Epoch 75/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0091 - accuracy: 0.9976 - val_loss: 0.0419 - val_accuracy: 0.9876
Epoch 76/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0178 - accuracy: 0.9952 - val_loss: 0.0442 - val_accuracy: 0.9856
Epoch 77/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0151 - accuracy: 0.9948 - val_loss: 0.0257 - val_accuracy: 0.9908
Epoch 78/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0091 - accuracy: 0.9972 - val_loss: 0.0352 - val_accuracy: 0.9892
Epoch 79/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0140 - accuracy: 0.9940 - val_loss: 0.0460 - val_accuracy: 0.9856
Epoch 80/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0125 - accuracy: 0.9964 - val_loss: 0.0349 - val_accuracy: 0.9884
Epoch 81/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0070 - accuracy: 0.9972 - val_loss: 0.0329 - val_accuracy: 0.9908
Epoch 82/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0069 - accuracy: 0.9972 - val_loss: 0.0338 - val_accuracy: 0.9904
Epoch 83/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0289 - accuracy: 0.9964 - val_loss: 0.0410 - val_accuracy: 0.9868
Epoch 84/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0840 - accuracy: 0.9744 - val_loss: 0.0600 - val_accuracy: 0.9792
Epoch 85/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0278 - accuracy: 0.9892 - val_loss: 0.0368 - val_accuracy: 0.9872
Epoch 86/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0108 - accuracy: 0.9972 - val_loss: 0.0358 - val_accuracy: 0.9868
Epoch 87/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0072 - accuracy: 0.9976 - val_loss: 0.0401 - val_accuracy: 0.9868
Epoch 88/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0059 - accuracy: 0.9984 - val_loss: 0.0444 - val_accuracy: 0.9856
Epoch 89/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0145 - accuracy: 0.9956 - val_loss: 0.0383 - val_accuracy: 0.9880
Epoch 90/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0131 - accuracy: 0.9952 - val_loss: 0.0456 - val_accuracy: 0.9852
Epoch 91/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0079 - accuracy: 0.9976 - val_loss: 0.0293 - val_accuracy: 0.9884
Epoch 92/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0057 - accuracy: 0.9976 - val_loss: 0.0328 - val_accuracy: 0.9864
Epoch 93/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0030 - accuracy: 0.9996 - val_loss: 0.0243 - val_accuracy: 0.9924
Epoch 94/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0041 - accuracy: 0.9992 - val_loss: 0.0367 - val_accuracy: 0.9872
Epoch 95/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.0438 - val_accuracy: 0.9884
Epoch 96/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0044 - accuracy: 0.9992 - val_loss: 0.0483 - val_accuracy: 0.9864
Epoch 97/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0026 - accuracy: 0.9988 - val_loss: 0.0479 - val_accuracy: 0.9884
Epoch 98/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0059 - accuracy: 0.9988 - val_loss: 0.0357 - val_accuracy: 0.9884
Epoch 99/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0145 - accuracy: 0.9960 - val_loss: 0.0373 - val_accuracy: 0.9884
Epoch 100/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0154 - accuracy: 0.9948 - val_loss: 0.0376 - val_accuracy: 0.9852
Epoch 101/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0075 - accuracy: 0.9980 - val_loss: 0.0364 - val_accuracy: 0.9884
Epoch 102/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0075 - accuracy: 0.9980 - val_loss: 0.0261 - val_accuracy: 0.9920
Epoch 103/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0108 - accuracy: 0.9964 - val_loss: 0.0284 - val_accuracy: 0.9900
Epoch 104/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0113 - accuracy: 0.9968 - val_loss: 0.0232 - val_accuracy: 0.9908
Epoch 105/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0271 - val_accuracy: 0.9896
Epoch 106/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.0290 - val_accuracy: 0.9892
Epoch 107/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9992 - val_loss: 0.0292 - val_accuracy: 0.9896
Epoch 108/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.0302 - val_accuracy: 0.9912
Epoch 109/400
79/79 [==============================] - 1s 7ms/step - loss: 9.9011e-04 - accuracy: 0.9996 - val_loss: 0.0255 - val_accuracy: 0.9920
Epoch 110/400
79/79 [==============================] - 1s 7ms/step - loss: 7.9221e-04 - accuracy: 1.0000 - val_loss: 0.0396 - val_accuracy: 0.9896
Epoch 111/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0195 - accuracy: 0.9964 - val_loss: 0.0237 - val_accuracy: 0.9916
Epoch 112/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0077 - accuracy: 0.9976 - val_loss: 0.0388 - val_accuracy: 0.9864
Epoch 113/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0068 - accuracy: 0.9976 - val_loss: 0.0319 - val_accuracy: 0.9892
Epoch 114/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0050 - accuracy: 0.9988 - val_loss: 0.0298 - val_accuracy: 0.9904
Epoch 115/400
79/79 [==============================] - 1s 6ms/step - loss: 0.0065 - accuracy: 0.9984 - val_loss: 0.0315 - val_accuracy: 0.9892
Epoch 116/400
79/79 [==============================] - 1s 7ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 0.0335 - val_accuracy: 0.9900
Epoch 117/400
79/79 [==============================] - 1s 7ms/step - loss: 9.3726e-04 - accuracy: 1.0000 - val_loss: 0.0297 - val_accuracy: 0.9908
Epoch 118/400
79/79 [==============================] - 1s 7ms/step - loss: 6.0662e-04 - accuracy: 1.0000 - val_loss: 0.0312 - val_accuracy: 0.9908
Epoch 119/400
79/79 [==============================] - 1s 7ms/step - loss: 5.9509e-04 - accuracy: 1.0000 - val_loss: 0.0352 - val_accuracy: 0.9904
Epoch 120/400
79/79 [==============================] - 1s 7ms/step - loss: 5.5597e-04 - accuracy: 1.0000 - val_loss: 0.0334 - val_accuracy: 0.9912
Epoch 121/400
79/79 [==============================] - 1s 7ms/step - loss: 5.2154e-04 - accuracy: 1.0000 - val_loss: 0.0347 - val_accuracy: 0.9908
Epoch 122/400
79/79 [==============================] - 1s 6ms/step - loss: 3.3155e-04 - accuracy: 1.0000 - val_loss: 0.0393 - val_accuracy: 0.9908
Epoch 123/400
79/79 [==============================] - 1s 6ms/step - loss: 2.8362e-04 - accuracy: 1.0000 - val_loss: 0.0393 - val_accuracy: 0.9912
Epoch 124/400
79/79 [==============================] - 1s 7ms/step - loss: 2.2577e-04 - accuracy: 1.0000 - val_loss: 0.0416 - val_accuracy: 0.9908
Epoch 125/400
79/79 [==============================] - 1s 7ms/step - loss: 1.9810e-04 - accuracy: 1.0000 - val_loss: 0.0404 - val_accuracy: 0.9912
Epoch 126/400
79/79 [==============================] - 1s 7ms/step - loss: 1.7869e-04 - accuracy: 1.0000 - val_loss: 0.0429 - val_accuracy: 0.9924
Epoch 127/400
79/79 [==============================] - 1s 6ms/step - loss: 1.5033e-04 - accuracy: 1.0000 - val_loss: 0.0429 - val_accuracy: 0.9924
Epoch 128/400
79/79 [==============================] - 1s 7ms/step - loss: 1.1684e-04 - accuracy: 1.0000 - val_loss: 0.0446 - val_accuracy: 0.9924
Epoch 129/400
79/79 [==============================] - 1s 7ms/step - loss: 1.0989e-04 - accuracy: 1.0000 - val_loss: 0.0461 - val_accuracy: 0.9924
Epoch 130/400
79/79 [==============================] - 1s 6ms/step - loss: 9.0361e-05 - accuracy: 1.0000 - val_loss: 0.0465 - val_accuracy: 0.9924
Epoch 131/400
79/79 [==============================] - 1s 7ms/step - loss: 7.7742e-05 - accuracy: 1.0000 - val_loss: 0.0473 - val_accuracy: 0.9924
Epoch 132/400
79/79 [==============================] - 1s 6ms/step - loss: 7.2091e-05 - accuracy: 1.0000 - val_loss: 0.0480 - val_accuracy: 0.9924
Epoch 133/400
79/79 [==============================] - 1s 7ms/step - loss: 6.4350e-05 - accuracy: 1.0000 - val_loss: 0.0493 - val_accuracy: 0.9924
Epoch 134/400
79/79 [==============================] - 1s 7ms/step - loss: 5.8189e-05 - accuracy: 1.0000 - val_loss: 0.0493 - val_accuracy: 0.9924
Epoch 135/400
79/79 [==============================] - 1s 6ms/step - loss: 4.9459e-05 - accuracy: 1.0000 - val_loss: 0.0501 - val_accuracy: 0.9924
Epoch 136/400
79/79 [==============================] - 1s 6ms/step - loss: 4.5237e-05 - accuracy: 1.0000 - val_loss: 0.0508 - val_accuracy: 0.9928
Epoch 137/400
79/79 [==============================] - 1s 7ms/step - loss: 4.1174e-05 - accuracy: 1.0000 - val_loss: 0.0510 - val_accuracy: 0.9928
Epoch 138/400
79/79 [==============================] - 1s 7ms/step - loss: 3.6626e-05 - accuracy: 1.0000 - val_loss: 0.0515 - val_accuracy: 0.9924
Epoch 139/400
79/79 [==============================] - 1s 7ms/step - loss: 3.3935e-05 - accuracy: 1.0000 - val_loss: 0.0522 - val_accuracy: 0.9928
Epoch 140/400
79/79 [==============================] - 1s 7ms/step - loss: 3.0910e-05 - accuracy: 1.0000 - val_loss: 0.0526 - val_accuracy: 0.9928
Epoch 141/400
79/79 [==============================] - 1s 7ms/step - loss: 2.8376e-05 - accuracy: 1.0000 - val_loss: 0.0529 - val_accuracy: 0.9928
Epoch 142/400
79/79 [==============================] - 1s 6ms/step - loss: 2.6438e-05 - accuracy: 1.0000 - val_loss: 0.0534 - val_accuracy: 0.9928
Epoch 143/400
79/79 [==============================] - 1s 6ms/step - loss: 2.4154e-05 - accuracy: 1.0000 - val_loss: 0.0538 - val_accuracy: 0.9928
Epoch 144/400
79/79 [==============================] - 1s 7ms/step - loss: 2.2308e-05 - accuracy: 1.0000 - val_loss: 0.0543 - val_accuracy: 0.9924
Epoch 145/400
79/79 [==============================] - 1s 7ms/step - loss: 2.1110e-05 - accuracy: 1.0000 - val_loss: 0.0547 - val_accuracy: 0.9928
Epoch 146/400
79/79 [==============================] - 1s 6ms/step - loss: 1.9753e-05 - accuracy: 1.0000 - val_loss: 0.0552 - val_accuracy: 0.9924
Epoch 147/400
79/79 [==============================] - 1s 6ms/step - loss: 1.9095e-05 - accuracy: 1.0000 - val_loss: 0.0557 - val_accuracy: 0.9920
Epoch 148/400
79/79 [==============================] - 1s 6ms/step - loss: 1.7381e-05 - accuracy: 1.0000 - val_loss: 0.0561 - val_accuracy: 0.9924
Epoch 149/400
79/79 [==============================] - 1s 7ms/step - loss: 1.6414e-05 - accuracy: 1.0000 - val_loss: 0.0565 - val_accuracy: 0.9924
Epoch 150/400
79/79 [==============================] - 1s 6ms/step - loss: 1.5528e-05 - accuracy: 1.0000 - val_loss: 0.0570 - val_accuracy: 0.9924
Epoch 151/400
79/79 [==============================] - 1s 7ms/step - loss: 1.4698e-05 - accuracy: 1.0000 - val_loss: 0.0576 - val_accuracy: 0.9924
Epoch 152/400
79/79 [==============================] - 1s 7ms/step - loss: 1.4516e-05 - accuracy: 1.0000 - val_loss: 0.0581 - val_accuracy: 0.9924
Epoch 153/400
79/79 [==============================] - 1s 7ms/step - loss: 1.3769e-05 - accuracy: 1.0000 - val_loss: 0.0585 - val_accuracy: 0.9920
Epoch 154/400
79/79 [==============================] - 0s 6ms/step - loss: 1.2615e-05 - accuracy: 1.0000 - val_loss: 0.0588 - val_accuracy: 0.9920
Epoch 155/400
79/79 [==============================] - 1s 7ms/step - loss: 1.2033e-05 - accuracy: 1.0000 - val_loss: 0.0592 - val_accuracy: 0.9920
Epoch 156/400
79/79 [==============================] - 1s 6ms/step - loss: 1.1480e-05 - accuracy: 1.0000 - val_loss: 0.0598 - val_accuracy: 0.9920
Epoch 157/400
79/79 [==============================] - 1s 6ms/step - loss: 1.1004e-05 - accuracy: 1.0000 - val_loss: 0.0604 - val_accuracy: 0.9920
Epoch 158/400
79/79 [==============================] - 1s 6ms/step - loss: 1.0427e-05 - accuracy: 1.0000 - val_loss: 0.0609 - val_accuracy: 0.9912
Epoch 159/400
79/79 [==============================] - 1s 7ms/step - loss: 1.0029e-05 - accuracy: 1.0000 - val_loss: 0.0613 - val_accuracy: 0.9912
Epoch 160/400
79/79 [==============================] - 1s 7ms/step - loss: 9.6064e-06 - accuracy: 1.0000 - val_loss: 0.0618 - val_accuracy: 0.9912
Epoch 161/400
79/79 [==============================] - 1s 7ms/step - loss: 9.2735e-06 - accuracy: 1.0000 - val_loss: 0.0624 - val_accuracy: 0.9912
Epoch 162/400
79/79 [==============================] - 0s 6ms/step - loss: 8.9280e-06 - accuracy: 1.0000 - val_loss: 0.0629 - val_accuracy: 0.9908
Epoch 163/400
79/79 [==============================] - 1s 7ms/step - loss: 8.5592e-06 - accuracy: 1.0000 - val_loss: 0.0634 - val_accuracy: 0.9908
Epoch 164/400
79/79 [==============================] - 1s 7ms/step - loss: 8.1564e-06 - accuracy: 1.0000 - val_loss: 0.0637 - val_accuracy: 0.9908
Epoch 165/400
79/79 [==============================] - 1s 7ms/step - loss: 7.7459e-06 - accuracy: 1.0000 - val_loss: 0.0642 - val_accuracy: 0.9908
Epoch 166/400
79/79 [==============================] - 1s 7ms/step - loss: 7.4394e-06 - accuracy: 1.0000 - val_loss: 0.0647 - val_accuracy: 0.9908
Epoch 167/400
79/79 [==============================] - 1s 6ms/step - loss: 7.3749e-06 - accuracy: 1.0000 - val_loss: 0.0652 - val_accuracy: 0.9908
Epoch 168/400
79/79 [==============================] - 1s 7ms/step - loss: 6.8616e-06 - accuracy: 1.0000 - val_loss: 0.0657 - val_accuracy: 0.9908
Epoch 169/400
79/79 [==============================] - 1s 7ms/step - loss: 6.6022e-06 - accuracy: 1.0000 - val_loss: 0.0662 - val_accuracy: 0.9908
Epoch 170/400
79/79 [==============================] - 1s 7ms/step - loss: 6.3705e-06 - accuracy: 1.0000 - val_loss: 0.0667 - val_accuracy: 0.9908
Epoch 171/400
79/79 [==============================] - 1s 7ms/step - loss: 6.0982e-06 - accuracy: 1.0000 - val_loss: 0.0671 - val_accuracy: 0.9908
Epoch 172/400
79/79 [==============================] - 1s 7ms/step - loss: 5.8814e-06 - accuracy: 1.0000 - val_loss: 0.0675 - val_accuracy: 0.9908
Epoch 173/400
79/79 [==============================] - 1s 7ms/step - loss: 5.6631e-06 - accuracy: 1.0000 - val_loss: 0.0682 - val_accuracy: 0.9908
Epoch 174/400
79/79 [==============================] - 1s 7ms/step - loss: 5.6681e-06 - accuracy: 1.0000 - val_loss: 0.0688 - val_accuracy: 0.9908
Epoch 175/400
79/79 [==============================] - 1s 7ms/step - loss: 5.4060e-06 - accuracy: 1.0000 - val_loss: 0.0691 - val_accuracy: 0.9904
Epoch 176/400
79/79 [==============================] - 1s 7ms/step - loss: 5.0593e-06 - accuracy: 1.0000 - val_loss: 0.0698 - val_accuracy: 0.9904
Epoch 177/400
79/79 [==============================] - 1s 7ms/step - loss: 4.9132e-06 - accuracy: 1.0000 - val_loss: 0.0703 - val_accuracy: 0.9904
Epoch 178/400
79/79 [==============================] - 1s 7ms/step - loss: 4.8269e-06 - accuracy: 1.0000 - val_loss: 0.0707 - val_accuracy: 0.9904
Epoch 179/400
79/79 [==============================] - 1s 7ms/step - loss: 4.5163e-06 - accuracy: 1.0000 - val_loss: 0.0713 - val_accuracy: 0.9908
Epoch 180/400
79/79 [==============================] - 1s 7ms/step - loss: 4.3722e-06 - accuracy: 1.0000 - val_loss: 0.0718 - val_accuracy: 0.9908
Epoch 181/400
79/79 [==============================] - 1s 7ms/step - loss: 4.1614e-06 - accuracy: 1.0000 - val_loss: 0.0724 - val_accuracy: 0.9904
Epoch 182/400
79/79 [==============================] - 1s 7ms/step - loss: 4.0555e-06 - accuracy: 1.0000 - val_loss: 0.0728 - val_accuracy: 0.9904
Epoch 183/400
79/79 [==============================] - 1s 7ms/step - loss: 3.8702e-06 - accuracy: 1.0000 - val_loss: 0.0734 - val_accuracy: 0.9904
Epoch 184/400
79/79 [==============================] - 1s 6ms/step - loss: 3.7382e-06 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 0.9904
Epoch 185/400
79/79 [==============================] - 1s 7ms/step - loss: 3.6140e-06 - accuracy: 1.0000 - val_loss: 0.0745 - val_accuracy: 0.9904
Epoch 186/400
79/79 [==============================] - 1s 6ms/step - loss: 3.4893e-06 - accuracy: 1.0000 - val_loss: 0.0750 - val_accuracy: 0.9904
Epoch 187/400
79/79 [==============================] - 1s 7ms/step - loss: 3.4553e-06 - accuracy: 1.0000 - val_loss: 0.0757 - val_accuracy: 0.9900
Epoch 188/400
79/79 [==============================] - 1s 6ms/step - loss: 3.2320e-06 - accuracy: 1.0000 - val_loss: 0.0767 - val_accuracy: 0.9900
Epoch 189/400
79/79 [==============================] - 1s 7ms/step - loss: 3.0963e-06 - accuracy: 1.0000 - val_loss: 0.0772 - val_accuracy: 0.9900
Epoch 190/400
79/79 [==============================] - 1s 6ms/step - loss: 2.9725e-06 - accuracy: 1.0000 - val_loss: 0.0776 - val_accuracy: 0.9900
Epoch 191/400
79/79 [==============================] - 1s 7ms/step - loss: 2.8801e-06 - accuracy: 1.0000 - val_loss: 0.0783 - val_accuracy: 0.9900
Epoch 192/400
79/79 [==============================] - 0s 6ms/step - loss: 2.7571e-06 - accuracy: 1.0000 - val_loss: 0.0788 - val_accuracy: 0.9900
Epoch 193/400
79/79 [==============================] - 1s 7ms/step - loss: 2.6647e-06 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 0.9896
Epoch 194/400
79/79 [==============================] - 1s 6ms/step - loss: 2.5738e-06 - accuracy: 1.0000 - val_loss: 0.0803 - val_accuracy: 0.9896
Epoch 195/400
79/79 [==============================] - 1s 7ms/step - loss: 2.4619e-06 - accuracy: 1.0000 - val_loss: 0.0809 - val_accuracy: 0.9896
Epoch 196/400
79/79 [==============================] - 0s 6ms/step - loss: 2.4338e-06 - accuracy: 1.0000 - val_loss: 0.0815 - val_accuracy: 0.9896
Epoch 197/400
79/79 [==============================] - 1s 7ms/step - loss: 2.2797e-06 - accuracy: 1.0000 - val_loss: 0.0822 - val_accuracy: 0.9896
Epoch 198/400
79/79 [==============================] - 1s 6ms/step - loss: 2.2387e-06 - accuracy: 1.0000 - val_loss: 0.0827 - val_accuracy: 0.9896
Epoch 199/400
79/79 [==============================] - 1s 7ms/step - loss: 2.1369e-06 - accuracy: 1.0000 - val_loss: 0.0833 - val_accuracy: 0.9896
Epoch 200/400
79/79 [==============================] - 1s 6ms/step - loss: 2.0409e-06 - accuracy: 1.0000 - val_loss: 0.0842 - val_accuracy: 0.9896
Epoch 201/400
79/79 [==============================] - 1s 7ms/step - loss: 2.0086e-06 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 0.9896
Epoch 202/400
79/79 [==============================] - 1s 6ms/step - loss: 1.9156e-06 - accuracy: 1.0000 - val_loss: 0.0853 - val_accuracy: 0.9896
Epoch 203/400
79/79 [==============================] - 1s 7ms/step - loss: 1.9042e-06 - accuracy: 1.0000 - val_loss: 0.0859 - val_accuracy: 0.9896
Epoch 204/400
79/79 [==============================] - 1s 6ms/step - loss: 1.7815e-06 - accuracy: 1.0000 - val_loss: 0.0865 - val_accuracy: 0.9896
Epoch 205/400
79/79 [==============================] - 1s 7ms/step - loss: 1.7301e-06 - accuracy: 1.0000 - val_loss: 0.0860 - val_accuracy: 0.9896
Epoch 206/400
79/79 [==============================] - 1s 7ms/step - loss: 1.6366e-06 - accuracy: 1.0000 - val_loss: 0.0869 - val_accuracy: 0.9896
Epoch 207/400
79/79 [==============================] - 1s 6ms/step - loss: 1.6629e-06 - accuracy: 1.0000 - val_loss: 0.0875 - val_accuracy: 0.9896
Epoch 208/400
79/79 [==============================] - 1s 7ms/step - loss: 1.4986e-06 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9896
Epoch 209/400
79/79 [==============================] - 0s 6ms/step - loss: 1.4416e-06 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 0.9896
Epoch 210/400
79/79 [==============================] - 1s 7ms/step - loss: 1.4065e-06 - accuracy: 1.0000 - val_loss: 0.0896 - val_accuracy: 0.9896
Epoch 211/400
79/79 [==============================] - 1s 7ms/step - loss: 1.3245e-06 - accuracy: 1.0000 - val_loss: 0.0903 - val_accuracy: 0.9896
Epoch 212/400
79/79 [==============================] - 1s 7ms/step - loss: 1.2749e-06 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9896
Epoch 213/400
79/79 [==============================] - 0s 6ms/step - loss: 1.2286e-06 - accuracy: 1.0000 - val_loss: 0.0915 - val_accuracy: 0.9896
Epoch 214/400
79/79 [==============================] - 1s 7ms/step - loss: 1.2243e-06 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9896
Epoch 215/400
79/79 [==============================] - 0s 6ms/step - loss: 1.1306e-06 - accuracy: 1.0000 - val_loss: 0.0929 - val_accuracy: 0.9896
Epoch 216/400
79/79 [==============================] - 1s 7ms/step - loss: 1.0883e-06 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9896
Epoch 217/400
79/79 [==============================] - 1s 6ms/step - loss: 1.0679e-06 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9896
Epoch 218/400
79/79 [==============================] - 1s 7ms/step - loss: 1.0146e-06 - accuracy: 1.0000 - val_loss: 0.0946 - val_accuracy: 0.9896
Epoch 219/400
79/79 [==============================] - 1s 6ms/step - loss: 9.6506e-07 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 0.9896
Epoch 220/400
79/79 [==============================] - 1s 7ms/step - loss: 9.4242e-07 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 0.9896
Epoch 221/400
79/79 [==============================] - 1s 7ms/step - loss: 9.3247e-07 - accuracy: 1.0000 - val_loss: 0.0960 - val_accuracy: 0.9896
Epoch 222/400
79/79 [==============================] - 1s 7ms/step - loss: 9.1268e-07 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9896
Epoch 223/400
79/79 [==============================] - 1s 7ms/step - loss: 8.4512e-07 - accuracy: 1.0000 - val_loss: 0.0969 - val_accuracy: 0.9896
Epoch 224/400
79/79 [==============================] - 1s 7ms/step - loss: 7.8692e-07 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 0.9896
Epoch 225/400
79/79 [==============================] - 1s 7ms/step - loss: 7.5930e-07 - accuracy: 1.0000 - val_loss: 0.0977 - val_accuracy: 0.9896
Epoch 226/400
79/79 [==============================] - 1s 11ms/step - loss: 7.3015e-07 - accuracy: 1.0000 - val_loss: 0.0983 - val_accuracy: 0.9896
Epoch 227/400
79/79 [==============================] - 1s 7ms/step - loss: 7.3000e-07 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9896
Epoch 228/400
79/79 [==============================] - 1s 7ms/step - loss: 6.6488e-07 - accuracy: 1.0000 - val_loss: 0.0993 - val_accuracy: 0.9896
Epoch 229/400
79/79 [==============================] - 1s 7ms/step - loss: 6.4071e-07 - accuracy: 1.0000 - val_loss: 0.0997 - val_accuracy: 0.9896
Epoch 230/400
79/79 [==============================] - 1s 7ms/step - loss: 6.2024e-07 - accuracy: 1.0000 - val_loss: 0.1001 - val_accuracy: 0.9896
Epoch 231/400
79/79 [==============================] - 1s 7ms/step - loss: 5.8943e-07 - accuracy: 1.0000 - val_loss: 0.1007 - val_accuracy: 0.9896
Epoch 232/400
79/79 [==============================] - 1s 7ms/step - loss: 5.6635e-07 - accuracy: 1.0000 - val_loss: 0.1010 - val_accuracy: 0.9896
Epoch 233/400
79/79 [==============================] - 1s 7ms/step - loss: 5.4311e-07 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9896
Epoch 234/400
79/79 [==============================] - 1s 7ms/step - loss: 5.1889e-07 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9896
Epoch 235/400
79/79 [==============================] - 1s 7ms/step - loss: 4.9914e-07 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9896
Epoch 236/400
79/79 [==============================] - 1s 7ms/step - loss: 4.8892e-07 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9896
Epoch 237/400
79/79 [==============================] - 1s 6ms/step - loss: 4.5632e-07 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9896
Epoch 238/400
79/79 [==============================] - 1s 7ms/step - loss: 4.3818e-07 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9900
Epoch 239/400
79/79 [==============================] - 1s 7ms/step - loss: 4.2312e-07 - accuracy: 1.0000 - val_loss: 0.1043 - val_accuracy: 0.9900
Epoch 240/400
79/79 [==============================] - 1s 7ms/step - loss: 4.0240e-07 - accuracy: 1.0000 - val_loss: 0.1048 - val_accuracy: 0.9900
Epoch 241/400
79/79 [==============================] - 1s 6ms/step - loss: 3.8764e-07 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9900
Epoch 242/400
79/79 [==============================] - 1s 7ms/step - loss: 3.7113e-07 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9900
Epoch 243/400
79/79 [==============================] - 1s 6ms/step - loss: 3.5447e-07 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9900
Epoch 244/400
79/79 [==============================] - 1s 7ms/step - loss: 3.4402e-07 - accuracy: 1.0000 - val_loss: 0.1064 - val_accuracy: 0.9900
Epoch 245/400
79/79 [==============================] - 1s 6ms/step - loss: 3.2618e-07 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9900
Epoch 246/400
79/79 [==============================] - 1s 7ms/step - loss: 3.1365e-07 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9900
Epoch 247/400
79/79 [==============================] - 1s 7ms/step - loss: 2.9951e-07 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9900
Epoch 248/400
79/79 [==============================] - 1s 7ms/step - loss: 2.8696e-07 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9900
Epoch 249/400
79/79 [==============================] - 1s 7ms/step - loss: 2.7783e-07 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9900
Epoch 250/400
79/79 [==============================] - 1s 7ms/step - loss: 2.6762e-07 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9900
Epoch 251/400
79/79 [==============================] - 1s 6ms/step - loss: 2.6602e-07 - accuracy: 1.0000 - val_loss: 0.1097 - val_accuracy: 0.9900
Epoch 252/400
79/79 [==============================] - 1s 7ms/step - loss: 2.4770e-07 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9896
Epoch 253/400
79/79 [==============================] - 1s 7ms/step - loss: 2.3165e-07 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9892
Epoch 254/400
79/79 [==============================] - 1s 6ms/step - loss: 2.2150e-07 - accuracy: 1.0000 - val_loss: 0.1114 - val_accuracy: 0.9896
Epoch 255/400
79/79 [==============================] - 1s 6ms/step - loss: 2.1205e-07 - accuracy: 1.0000 - val_loss: 0.1118 - val_accuracy: 0.9896
Epoch 256/400
79/79 [==============================] - 1s 6ms/step - loss: 2.1201e-07 - accuracy: 1.0000 - val_loss: 0.1126 - val_accuracy: 0.9896
Epoch 257/400
79/79 [==============================] - 1s 6ms/step - loss: 1.9501e-07 - accuracy: 1.0000 - val_loss: 0.1123 - val_accuracy: 0.9896
Epoch 258/400
79/79 [==============================] - 1s 7ms/step - loss: 1.8666e-07 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9896
Epoch 259/400
79/79 [==============================] - 1s 7ms/step - loss: 1.7893e-07 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9896
Epoch 260/400
79/79 [==============================] - 1s 7ms/step - loss: 1.7109e-07 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9896
Epoch 261/400
79/79 [==============================] - 1s 6ms/step - loss: 1.6504e-07 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9896
Epoch 262/400
79/79 [==============================] - 1s 7ms/step - loss: 1.5974e-07 - accuracy: 1.0000 - val_loss: 0.1157 - val_accuracy: 0.9896
Epoch 263/400
79/79 [==============================] - 1s 6ms/step - loss: 1.5161e-07 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9896
Epoch 264/400
79/79 [==============================] - 1s 7ms/step - loss: 1.4414e-07 - accuracy: 1.0000 - val_loss: 0.1169 - val_accuracy: 0.9896
Epoch 265/400
79/79 [==============================] - 1s 6ms/step - loss: 1.3805e-07 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9896
Epoch 266/400
79/79 [==============================] - 1s 7ms/step - loss: 1.3314e-07 - accuracy: 1.0000 - val_loss: 0.1183 - val_accuracy: 0.9896
Epoch 267/400
79/79 [==============================] - 1s 7ms/step - loss: 1.2723e-07 - accuracy: 1.0000 - val_loss: 0.1189 - val_accuracy: 0.9896
Epoch 268/400
79/79 [==============================] - 1s 7ms/step - loss: 1.2212e-07 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9896
Epoch 269/400
79/79 [==============================] - 1s 7ms/step - loss: 1.1708e-07 - accuracy: 1.0000 - val_loss: 0.1204 - val_accuracy: 0.9892
Epoch 270/400
79/79 [==============================] - 1s 7ms/step - loss: 1.1297e-07 - accuracy: 1.0000 - val_loss: 0.1210 - val_accuracy: 0.9892
Epoch 271/400
79/79 [==============================] - 1s 6ms/step - loss: 1.4267e-07 - accuracy: 1.0000 - val_loss: 0.1218 - val_accuracy: 0.9892
Epoch 272/400
79/79 [==============================] - 1s 6ms/step - loss: 1.0165e-07 - accuracy: 1.0000 - val_loss: 0.1221 - val_accuracy: 0.9892
Epoch 273/400
79/79 [==============================] - 1s 7ms/step - loss: 9.6930e-08 - accuracy: 1.0000 - val_loss: 0.1238 - val_accuracy: 0.9892
Epoch 274/400
79/79 [==============================] - 1s 6ms/step - loss: 9.3161e-08 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9892
Epoch 275/400
79/79 [==============================] - 1s 7ms/step - loss: 8.9359e-08 - accuracy: 1.0000 - val_loss: 0.1236 - val_accuracy: 0.9892
Epoch 276/400
79/79 [==============================] - 1s 7ms/step - loss: 8.5516e-08 - accuracy: 1.0000 - val_loss: 0.1257 - val_accuracy: 0.9892
Epoch 277/400
79/79 [==============================] - 1s 7ms/step - loss: 8.1970e-08 - accuracy: 1.0000 - val_loss: 0.1253 - val_accuracy: 0.9892
Epoch 278/400
79/79 [==============================] - 1s 7ms/step - loss: 7.9408e-08 - accuracy: 1.0000 - val_loss: 0.1266 - val_accuracy: 0.9892
Epoch 279/400
79/79 [==============================] - 1s 7ms/step - loss: 7.9157e-08 - accuracy: 1.0000 - val_loss: 0.1276 - val_accuracy: 0.9892
Epoch 280/400
79/79 [==============================] - 1s 7ms/step - loss: 7.2570e-08 - accuracy: 1.0000 - val_loss: 0.1279 - val_accuracy: 0.9892
Epoch 281/400
79/79 [==============================] - 1s 7ms/step - loss: 6.9062e-08 - accuracy: 1.0000 - val_loss: 0.1283 - val_accuracy: 0.9892
Epoch 282/400
79/79 [==============================] - 1s 6ms/step - loss: 6.6571e-08 - accuracy: 1.0000 - val_loss: 0.1295 - val_accuracy: 0.9892
Epoch 283/400
79/79 [==============================] - 1s 7ms/step - loss: 6.3675e-08 - accuracy: 1.0000 - val_loss: 0.1295 - val_accuracy: 0.9892
Epoch 284/400
79/79 [==============================] - 1s 6ms/step - loss: 6.1242e-08 - accuracy: 1.0000 - val_loss: 0.1303 - val_accuracy: 0.9892
Epoch 285/400
79/79 [==============================] - 1s 7ms/step - loss: 5.8799e-08 - accuracy: 1.0000 - val_loss: 0.1310 - val_accuracy: 0.9892
Epoch 286/400
79/79 [==============================] - 1s 7ms/step - loss: 5.6264e-08 - accuracy: 1.0000 - val_loss: 0.1314 - val_accuracy: 0.9892
Epoch 287/400
79/79 [==============================] - 1s 7ms/step - loss: 5.6734e-08 - accuracy: 1.0000 - val_loss: 0.1321 - val_accuracy: 0.9892
Epoch 288/400
79/79 [==============================] - 1s 7ms/step - loss: 5.2411e-08 - accuracy: 1.0000 - val_loss: 0.1322 - val_accuracy: 0.9892
Epoch 289/400
79/79 [==============================] - 1s 7ms/step - loss: 4.9885e-08 - accuracy: 1.0000 - val_loss: 0.1331 - val_accuracy: 0.9892
Epoch 290/400
79/79 [==============================] - 1s 6ms/step - loss: 4.8236e-08 - accuracy: 1.0000 - val_loss: 0.1334 - val_accuracy: 0.9892
Epoch 291/400
79/79 [==============================] - 1s 7ms/step - loss: 4.6550e-08 - accuracy: 1.0000 - val_loss: 0.1341 - val_accuracy: 0.9892
Epoch 292/400
79/79 [==============================] - 1s 7ms/step - loss: 4.4213e-08 - accuracy: 1.0000 - val_loss: 0.1346 - val_accuracy: 0.9892
Epoch 293/400
79/79 [==============================] - 1s 7ms/step - loss: 4.2812e-08 - accuracy: 1.0000 - val_loss: 0.1348 - val_accuracy: 0.9892
Epoch 294/400
79/79 [==============================] - 1s 7ms/step - loss: 4.2498e-08 - accuracy: 1.0000 - val_loss: 0.1352 - val_accuracy: 0.9892
Epoch 295/400
79/79 [==============================] - 1s 7ms/step - loss: 3.9641e-08 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9892
Epoch 296/400
79/79 [==============================] - 1s 7ms/step - loss: 3.8775e-08 - accuracy: 1.0000 - val_loss: 0.1343 - val_accuracy: 0.9896
Epoch 297/400
79/79 [==============================] - 1s 7ms/step - loss: 4.9838e-08 - accuracy: 1.0000 - val_loss: 0.1330 - val_accuracy: 0.9896
Epoch 298/400
79/79 [==============================] - 1s 7ms/step - loss: 3.5308e-08 - accuracy: 1.0000 - val_loss: 0.1376 - val_accuracy: 0.9892
Epoch 299/400
79/79 [==============================] - 1s 7ms/step - loss: 5.0419e-08 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9896
Epoch 300/400
79/79 [==============================] - 1s 7ms/step - loss: 3.4353e-08 - accuracy: 1.0000 - val_loss: 0.1311 - val_accuracy: 0.9896
Epoch 301/400
79/79 [==============================] - 1s 7ms/step - loss: 3.2750e-08 - accuracy: 1.0000 - val_loss: 0.1323 - val_accuracy: 0.9896
Epoch 302/400
79/79 [==============================] - 1s 7ms/step - loss: 3.0650e-08 - accuracy: 1.0000 - val_loss: 0.1337 - val_accuracy: 0.9896
Epoch 303/400
79/79 [==============================] - 1s 7ms/step - loss: 2.9290e-08 - accuracy: 1.0000 - val_loss: 0.1348 - val_accuracy: 0.9896
Epoch 304/400
79/79 [==============================] - 1s 7ms/step - loss: 2.8035e-08 - accuracy: 1.0000 - val_loss: 0.1355 - val_accuracy: 0.9896
Epoch 305/400
79/79 [==============================] - 1s 6ms/step - loss: 2.6969e-08 - accuracy: 1.0000 - val_loss: 0.1362 - val_accuracy: 0.9896
Epoch 306/400
79/79 [==============================] - 1s 7ms/step - loss: 2.5546e-08 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9896
Epoch 307/400
79/79 [==============================] - 1s 7ms/step - loss: 2.4577e-08 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9896
Epoch 308/400
79/79 [==============================] - 1s 7ms/step - loss: 2.3482e-08 - accuracy: 1.0000 - val_loss: 0.1369 - val_accuracy: 0.9896
Epoch 309/400
79/79 [==============================] - 1s 7ms/step - loss: 2.2726e-08 - accuracy: 1.0000 - val_loss: 0.1374 - val_accuracy: 0.9896
Epoch 310/400
79/79 [==============================] - 1s 7ms/step - loss: 2.1791e-08 - accuracy: 1.0000 - val_loss: 0.1379 - val_accuracy: 0.9900
Epoch 311/400
79/79 [==============================] - 1s 7ms/step - loss: 2.0993e-08 - accuracy: 1.0000 - val_loss: 0.1385 - val_accuracy: 0.9900
Epoch 312/400
79/79 [==============================] - 1s 7ms/step - loss: 2.0128e-08 - accuracy: 1.0000 - val_loss: 0.1393 - val_accuracy: 0.9896
Epoch 313/400
79/79 [==============================] - 1s 7ms/step - loss: 1.9871e-08 - accuracy: 1.0000 - val_loss: 0.1402 - val_accuracy: 0.9896
Epoch 314/400
79/79 [==============================] - 1s 7ms/step - loss: 1.8920e-08 - accuracy: 1.0000 - val_loss: 0.1411 - val_accuracy: 0.9896
Epoch 315/400
79/79 [==============================] - 1s 7ms/step - loss: 1.8015e-08 - accuracy: 1.0000 - val_loss: 0.1421 - val_accuracy: 0.9896
Epoch 316/400
79/79 [==============================] - 1s 7ms/step - loss: 1.7445e-08 - accuracy: 1.0000 - val_loss: 0.1429 - val_accuracy: 0.9896
Epoch 317/400
79/79 [==============================] - 1s 7ms/step - loss: 1.7241e-08 - accuracy: 1.0000 - val_loss: 0.1436 - val_accuracy: 0.9896
Epoch 318/400
79/79 [==============================] - 1s 6ms/step - loss: 1.6312e-08 - accuracy: 1.0000 - val_loss: 0.1443 - val_accuracy: 0.9896
Epoch 319/400
79/79 [==============================] - 1s 7ms/step - loss: 1.5645e-08 - accuracy: 1.0000 - val_loss: 0.1450 - val_accuracy: 0.9896
Epoch 320/400
79/79 [==============================] - 1s 7ms/step - loss: 1.5163e-08 - accuracy: 1.0000 - val_loss: 0.1456 - val_accuracy: 0.9896
Epoch 321/400
79/79 [==============================] - 1s 7ms/step - loss: 1.4684e-08 - accuracy: 1.0000 - val_loss: 0.1459 - val_accuracy: 0.9896
Epoch 322/400
79/79 [==============================] - 1s 7ms/step - loss: 1.4123e-08 - accuracy: 1.0000 - val_loss: 0.1465 - val_accuracy: 0.9896
Epoch 323/400
79/79 [==============================] - 1s 7ms/step - loss: 1.3666e-08 - accuracy: 1.0000 - val_loss: 0.1471 - val_accuracy: 0.9892
Epoch 324/400
79/79 [==============================] - 1s 7ms/step - loss: 1.3325e-08 - accuracy: 1.0000 - val_loss: 0.1476 - val_accuracy: 0.9892
Epoch 325/400
79/79 [==============================] - 1s 6ms/step - loss: 1.2906e-08 - accuracy: 1.0000 - val_loss: 0.1479 - val_accuracy: 0.9892
Epoch 326/400
79/79 [==============================] - 1s 7ms/step - loss: 1.2509e-08 - accuracy: 1.0000 - val_loss: 0.1483 - val_accuracy: 0.9892
Epoch 327/400
79/79 [==============================] - 1s 7ms/step - loss: 1.2057e-08 - accuracy: 1.0000 - val_loss: 0.1484 - val_accuracy: 0.9892
Epoch 328/400
79/79 [==============================] - 1s 6ms/step - loss: 1.1698e-08 - accuracy: 1.0000 - val_loss: 0.1487 - val_accuracy: 0.9892
Epoch 329/400
79/79 [==============================] - 1s 7ms/step - loss: 1.1402e-08 - accuracy: 1.0000 - val_loss: 0.1491 - val_accuracy: 0.9892
Epoch 330/400
79/79 [==============================] - 1s 6ms/step - loss: 1.1036e-08 - accuracy: 1.0000 - val_loss: 0.1493 - val_accuracy: 0.9892
Epoch 331/400
79/79 [==============================] - 1s 6ms/step - loss: 1.0763e-08 - accuracy: 1.0000 - val_loss: 0.1496 - val_accuracy: 0.9892
Epoch 332/400
79/79 [==============================] - 1s 7ms/step - loss: 1.0315e-08 - accuracy: 1.0000 - val_loss: 0.1494 - val_accuracy: 0.9896
Epoch 333/400
79/79 [==============================] - 1s 7ms/step - loss: 1.0067e-08 - accuracy: 1.0000 - val_loss: 0.1497 - val_accuracy: 0.9896
Epoch 334/400
79/79 [==============================] - 1s 7ms/step - loss: 9.7468e-09 - accuracy: 1.0000 - val_loss: 0.1497 - val_accuracy: 0.9896
Epoch 335/400
79/79 [==============================] - 1s 7ms/step - loss: 9.4660e-09 - accuracy: 1.0000 - val_loss: 0.1500 - val_accuracy: 0.9896
Epoch 336/400
79/79 [==============================] - 1s 6ms/step - loss: 9.1215e-09 - accuracy: 1.0000 - val_loss: 0.1503 - val_accuracy: 0.9896
Epoch 337/400
79/79 [==============================] - 1s 6ms/step - loss: 8.9241e-09 - accuracy: 1.0000 - val_loss: 0.1504 - val_accuracy: 0.9896
Epoch 338/400
79/79 [==============================] - 1s 7ms/step - loss: 8.6136e-09 - accuracy: 1.0000 - val_loss: 0.1506 - val_accuracy: 0.9896
Epoch 339/400
79/79 [==============================] - 1s 7ms/step - loss: 8.3407e-09 - accuracy: 1.0000 - val_loss: 0.1507 - val_accuracy: 0.9896
Epoch 340/400
79/79 [==============================] - 1s 6ms/step - loss: 8.1220e-09 - accuracy: 1.0000 - val_loss: 0.1510 - val_accuracy: 0.9896
Epoch 341/400
79/79 [==============================] - 1s 7ms/step - loss: 7.9070e-09 - accuracy: 1.0000 - val_loss: 0.1510 - val_accuracy: 0.9896
Epoch 342/400
79/79 [==============================] - 1s 6ms/step - loss: 7.6520e-09 - accuracy: 1.0000 - val_loss: 0.1512 - val_accuracy: 0.9896
Epoch 343/400
79/79 [==============================] - 1s 7ms/step - loss: 7.4509e-09 - accuracy: 1.0000 - val_loss: 0.1514 - val_accuracy: 0.9896
Epoch 344/400
79/79 [==============================] - 1s 7ms/step - loss: 7.2214e-09 - accuracy: 1.0000 - val_loss: 0.1515 - val_accuracy: 0.9896
Epoch 345/400
79/79 [==============================] - 1s 7ms/step - loss: 7.0713e-09 - accuracy: 1.0000 - val_loss: 0.1517 - val_accuracy: 0.9896
Epoch 346/400
79/79 [==============================] - 1s 7ms/step - loss: 6.8703e-09 - accuracy: 1.0000 - val_loss: 0.1519 - val_accuracy: 0.9900
Epoch 347/400
79/79 [==============================] - 1s 7ms/step - loss: 6.6697e-09 - accuracy: 1.0000 - val_loss: 0.1522 - val_accuracy: 0.9900
Epoch 348/400
79/79 [==============================] - 1s 6ms/step - loss: 6.4546e-09 - accuracy: 1.0000 - val_loss: 0.1524 - val_accuracy: 0.9900
Epoch 349/400
79/79 [==============================] - 1s 7ms/step - loss: 6.2903e-09 - accuracy: 1.0000 - val_loss: 0.1525 - val_accuracy: 0.9900
Epoch 350/400
79/79 [==============================] - 0s 6ms/step - loss: 6.1464e-09 - accuracy: 1.0000 - val_loss: 0.1528 - val_accuracy: 0.9900
Epoch 351/400
79/79 [==============================] - 1s 7ms/step - loss: 6.1079e-09 - accuracy: 1.0000 - val_loss: 0.1531 - val_accuracy: 0.9900
Epoch 352/400
79/79 [==============================] - 1s 7ms/step - loss: 5.7918e-09 - accuracy: 1.0000 - val_loss: 0.1534 - val_accuracy: 0.9900
Epoch 353/400
79/79 [==============================] - 1s 7ms/step - loss: 5.6367e-09 - accuracy: 1.0000 - val_loss: 0.1537 - val_accuracy: 0.9900
Epoch 354/400
79/79 [==============================] - 1s 7ms/step - loss: 5.4914e-09 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9900
Epoch 355/400
79/79 [==============================] - 1s 7ms/step - loss: 5.4113e-09 - accuracy: 1.0000 - val_loss: 0.1541 - val_accuracy: 0.9900
Epoch 356/400
79/79 [==============================] - 1s 7ms/step - loss: 5.2781e-09 - accuracy: 1.0000 - val_loss: 0.1543 - val_accuracy: 0.9900
Epoch 357/400
79/79 [==============================] - 1s 7ms/step - loss: 5.0791e-09 - accuracy: 1.0000 - val_loss: 0.1546 - val_accuracy: 0.9900
Epoch 358/400
79/79 [==============================] - 1s 7ms/step - loss: 4.9753e-09 - accuracy: 1.0000 - val_loss: 0.1548 - val_accuracy: 0.9900
Epoch 359/400
79/79 [==============================] - 1s 7ms/step - loss: 4.8996e-09 - accuracy: 1.0000 - val_loss: 0.1549 - val_accuracy: 0.9900
Epoch 360/400
79/79 [==============================] - 1s 7ms/step - loss: 4.7424e-09 - accuracy: 1.0000 - val_loss: 0.1551 - val_accuracy: 0.9900
Epoch 361/400
79/79 [==============================] - 1s 7ms/step - loss: 4.6247e-09 - accuracy: 1.0000 - val_loss: 0.1552 - val_accuracy: 0.9900
Epoch 362/400
79/79 [==============================] - 1s 7ms/step - loss: 4.6962e-09 - accuracy: 1.0000 - val_loss: 0.1554 - val_accuracy: 0.9900
Epoch 363/400
79/79 [==============================] - 1s 7ms/step - loss: 4.3938e-09 - accuracy: 1.0000 - val_loss: 0.1558 - val_accuracy: 0.9900
Epoch 364/400
79/79 [==============================] - 1s 7ms/step - loss: 4.3013e-09 - accuracy: 1.0000 - val_loss: 0.1559 - val_accuracy: 0.9900
Epoch 365/400
79/79 [==============================] - 1s 7ms/step - loss: 4.1931e-09 - accuracy: 1.0000 - val_loss: 0.1562 - val_accuracy: 0.9900
Epoch 366/400
79/79 [==============================] - 1s 7ms/step - loss: 4.1789e-09 - accuracy: 1.0000 - val_loss: 0.1563 - val_accuracy: 0.9900
Epoch 367/400
79/79 [==============================] - 1s 7ms/step - loss: 3.9860e-09 - accuracy: 1.0000 - val_loss: 0.1563 - val_accuracy: 0.9900
Epoch 368/400
79/79 [==============================] - 1s 7ms/step - loss: 3.8786e-09 - accuracy: 1.0000 - val_loss: 0.1566 - val_accuracy: 0.9900
Epoch 369/400
79/79 [==============================] - 1s 7ms/step - loss: 3.7894e-09 - accuracy: 1.0000 - val_loss: 0.1568 - val_accuracy: 0.9900
Epoch 370/400
79/79 [==============================] - 1s 7ms/step - loss: 3.7430e-09 - accuracy: 1.0000 - val_loss: 0.1570 - val_accuracy: 0.9900
Epoch 371/400
79/79 [==============================] - 1s 7ms/step - loss: 3.6241e-09 - accuracy: 1.0000 - val_loss: 0.1571 - val_accuracy: 0.9900
Epoch 372/400
79/79 [==============================] - 1s 7ms/step - loss: 3.5539e-09 - accuracy: 1.0000 - val_loss: 0.1573 - val_accuracy: 0.9900
Epoch 373/400
79/79 [==============================] - 1s 7ms/step - loss: 3.4580e-09 - accuracy: 1.0000 - val_loss: 0.1575 - val_accuracy: 0.9900
Epoch 374/400
79/79 [==============================] - 1s 7ms/step - loss: 3.3896e-09 - accuracy: 1.0000 - val_loss: 0.1574 - val_accuracy: 0.9900
Epoch 375/400
79/79 [==============================] - 1s 7ms/step - loss: 3.3307e-09 - accuracy: 1.0000 - val_loss: 0.1576 - val_accuracy: 0.9900
Epoch 376/400
79/79 [==============================] - 1s 7ms/step - loss: 3.2459e-09 - accuracy: 1.0000 - val_loss: 0.1578 - val_accuracy: 0.9900
Epoch 377/400
79/79 [==============================] - 1s 7ms/step - loss: 3.2133e-09 - accuracy: 1.0000 - val_loss: 0.1580 - val_accuracy: 0.9900
Epoch 378/400
79/79 [==============================] - 1s 7ms/step - loss: 3.1123e-09 - accuracy: 1.0000 - val_loss: 0.1581 - val_accuracy: 0.9900
Epoch 379/400
79/79 [==============================] - 1s 7ms/step - loss: 3.0661e-09 - accuracy: 1.0000 - val_loss: 0.1582 - val_accuracy: 0.9900
Epoch 380/400
79/79 [==============================] - 1s 7ms/step - loss: 3.0002e-09 - accuracy: 1.0000 - val_loss: 0.1583 - val_accuracy: 0.9900
Epoch 381/400
79/79 [==============================] - 1s 7ms/step - loss: 2.9334e-09 - accuracy: 1.0000 - val_loss: 0.1584 - val_accuracy: 0.9900
Epoch 382/400
79/79 [==============================] - 1s 7ms/step - loss: 2.8723e-09 - accuracy: 1.0000 - val_loss: 0.1586 - val_accuracy: 0.9900
Epoch 383/400
79/79 [==============================] - 1s 7ms/step - loss: 2.8174e-09 - accuracy: 1.0000 - val_loss: 0.1588 - val_accuracy: 0.9900
Epoch 384/400
79/79 [==============================] - 1s 7ms/step - loss: 2.7735e-09 - accuracy: 1.0000 - val_loss: 0.1588 - val_accuracy: 0.9900
Epoch 385/400
79/79 [==============================] - 1s 7ms/step - loss: 2.7304e-09 - accuracy: 1.0000 - val_loss: 0.1589 - val_accuracy: 0.9900
Epoch 386/400
79/79 [==============================] - 1s 7ms/step - loss: 2.6646e-09 - accuracy: 1.0000 - val_loss: 0.1590 - val_accuracy: 0.9900
Epoch 387/400
79/79 [==============================] - 1s 7ms/step - loss: 2.6190e-09 - accuracy: 1.0000 - val_loss: 0.1592 - val_accuracy: 0.9900
Epoch 388/400
79/79 [==============================] - 1s 7ms/step - loss: 2.5701e-09 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 0.9900
Epoch 389/400
79/79 [==============================] - 1s 7ms/step - loss: 2.6549e-09 - accuracy: 1.0000 - val_loss: 0.1594 - val_accuracy: 0.9900
Epoch 390/400
79/79 [==============================] - 1s 6ms/step - loss: 2.4742e-09 - accuracy: 1.0000 - val_loss: 0.1593 - val_accuracy: 0.9900
Epoch 391/400
79/79 [==============================] - 1s 7ms/step - loss: 2.4180e-09 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 0.9900
Epoch 392/400
79/79 [==============================] - 1s 7ms/step - loss: 2.3913e-09 - accuracy: 1.0000 - val_loss: 0.1597 - val_accuracy: 0.9900
Epoch 393/400
79/79 [==============================] - 1s 7ms/step - loss: 2.4473e-09 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 0.9900
Epoch 394/400
79/79 [==============================] - 1s 6ms/step - loss: 2.2954e-09 - accuracy: 1.0000 - val_loss: 0.1597 - val_accuracy: 0.9900
Epoch 395/400
79/79 [==============================] - 1s 7ms/step - loss: 2.2595e-09 - accuracy: 1.0000 - val_loss: 0.1598 - val_accuracy: 0.9900
Epoch 396/400
79/79 [==============================] - 1s 7ms/step - loss: 2.2090e-09 - accuracy: 1.0000 - val_loss: 0.1600 - val_accuracy: 0.9900
Epoch 397/400
79/79 [==============================] - 1s 7ms/step - loss: 2.1746e-09 - accuracy: 1.0000 - val_loss: 0.1600 - val_accuracy: 0.9900
Epoch 398/400
79/79 [==============================] - 1s 7ms/step - loss: 2.1427e-09 - accuracy: 1.0000 - val_loss: 0.1600 - val_accuracy: 0.9900
Epoch 399/400
79/79 [==============================] - 1s 7ms/step - loss: 2.1051e-09 - accuracy: 1.0000 - val_loss: 0.1602 - val_accuracy: 0.9900
Epoch 400/400
79/79 [==============================] - 1s 7ms/step - loss: 2.0643e-09 - accuracy: 1.0000 - val_loss: 0.1603 - val_accuracy: 0.9900

# Plot the loss
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()

<matplotlib.legend.Legend at 0x7fec1e9abf60>

# Plot the accuracy too
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.legend()

<matplotlib.legend.Legend at 0x7fec20e9fa20>

# Now try a LSTM with Global Max Pooling
inputs = np.expand_dims(X, -1)

# make the RNN
i = Input(shape=(T, D))

# method 2
x = LSTM(5, return_sequences=True)(i)
x = GlobalMaxPool1D()(x)

x = Dense(1, activation='sigmoid')(x)
model = Model(i, x)
model.compile(
  loss='binary_crossentropy',
  optimizer=Adam(lr=0.01),
  metrics=['accuracy'],
)

# train the RNN
r = model.fit(
  inputs, Y,
  epochs=100,
  validation_split=0.5,
)

Epoch 1/100
79/79 [==============================] - 1s 10ms/step - loss: 0.6922 - accuracy: 0.5128 - val_loss: 0.6952 - val_accuracy: 0.4960
Epoch 2/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6920 - accuracy: 0.5256 - val_loss: 0.6938 - val_accuracy: 0.4916
Epoch 3/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6920 - accuracy: 0.5256 - val_loss: 0.6938 - val_accuracy: 0.4916
Epoch 4/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6916 - accuracy: 0.5188 - val_loss: 0.6934 - val_accuracy: 0.4920
Epoch 5/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6906 - accuracy: 0.5200 - val_loss: 0.7001 - val_accuracy: 0.4960
Epoch 6/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6898 - accuracy: 0.5216 - val_loss: 0.6995 - val_accuracy: 0.4924
Epoch 7/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6900 - accuracy: 0.5120 - val_loss: 0.7016 - val_accuracy: 0.4916
Epoch 8/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6862 - accuracy: 0.5516 - val_loss: 0.6866 - val_accuracy: 0.5744
Epoch 9/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6788 - accuracy: 0.5668 - val_loss: 0.6845 - val_accuracy: 0.5476
Epoch 10/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6640 - accuracy: 0.6072 - val_loss: 0.6606 - val_accuracy: 0.6100
Epoch 11/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6728 - accuracy: 0.5720 - val_loss: 0.6956 - val_accuracy: 0.5208
Epoch 12/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6911 - accuracy: 0.5244 - val_loss: 0.6947 - val_accuracy: 0.5100
Epoch 13/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6892 - accuracy: 0.5428 - val_loss: 0.6953 - val_accuracy: 0.5160
Epoch 14/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6879 - accuracy: 0.5524 - val_loss: 0.6920 - val_accuracy: 0.5348
Epoch 15/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6851 - accuracy: 0.5516 - val_loss: 0.6950 - val_accuracy: 0.5072
Epoch 16/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6837 - accuracy: 0.5624 - val_loss: 0.6968 - val_accuracy: 0.5120
Epoch 17/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6834 - accuracy: 0.5360 - val_loss: 0.6892 - val_accuracy: 0.5148
Epoch 18/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6794 - accuracy: 0.5340 - val_loss: 0.6846 - val_accuracy: 0.5392
Epoch 19/100
79/79 [==============================] - 1s 6ms/step - loss: 0.6782 - accuracy: 0.5444 - val_loss: 0.6825 - val_accuracy: 0.5424
Epoch 20/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6752 - accuracy: 0.5480 - val_loss: 0.6802 - val_accuracy: 0.5260
Epoch 21/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6711 - accuracy: 0.5364 - val_loss: 0.6748 - val_accuracy: 0.5260
Epoch 22/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6654 - accuracy: 0.5416 - val_loss: 0.6752 - val_accuracy: 0.5176
Epoch 23/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6588 - accuracy: 0.5520 - val_loss: 0.6679 - val_accuracy: 0.5388
Epoch 24/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6531 - accuracy: 0.5648 - val_loss: 0.6652 - val_accuracy: 0.5416
Epoch 25/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6462 - accuracy: 0.5728 - val_loss: 0.6525 - val_accuracy: 0.5784
Epoch 26/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6396 - accuracy: 0.5880 - val_loss: 0.6378 - val_accuracy: 0.6028
Epoch 27/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6258 - accuracy: 0.6152 - val_loss: 0.6318 - val_accuracy: 0.6208
Epoch 28/100
79/79 [==============================] - 1s 7ms/step - loss: 0.6056 - accuracy: 0.6636 - val_loss: 0.5923 - val_accuracy: 0.6812
Epoch 29/100
79/79 [==============================] - 1s 7ms/step - loss: 0.5701 - accuracy: 0.7096 - val_loss: 0.5999 - val_accuracy: 0.6684
Epoch 30/100
79/79 [==============================] - 1s 7ms/step - loss: 0.5357 - accuracy: 0.7500 - val_loss: 0.5204 - val_accuracy: 0.7716
Epoch 31/100
79/79 [==============================] - 1s 7ms/step - loss: 0.5005 - accuracy: 0.7652 - val_loss: 0.5802 - val_accuracy: 0.7012
Epoch 32/100
79/79 [==============================] - 1s 7ms/step - loss: 0.5468 - accuracy: 0.7332 - val_loss: 0.5303 - val_accuracy: 0.7600
Epoch 33/100
79/79 [==============================] - 1s 7ms/step - loss: 0.4894 - accuracy: 0.7704 - val_loss: 0.4789 - val_accuracy: 0.8024
Epoch 34/100
79/79 [==============================] - 1s 6ms/step - loss: 0.4361 - accuracy: 0.8068 - val_loss: 0.4265 - val_accuracy: 0.8304
Epoch 35/100
79/79 [==============================] - 1s 7ms/step - loss: 0.3958 - accuracy: 0.8424 - val_loss: 0.4039 - val_accuracy: 0.8512
Epoch 36/100
79/79 [==============================] - 1s 7ms/step - loss: 0.3591 - accuracy: 0.8604 - val_loss: 0.3483 - val_accuracy: 0.8824
Epoch 37/100
79/79 [==============================] - 1s 7ms/step - loss: 0.3223 - accuracy: 0.8788 - val_loss: 0.3191 - val_accuracy: 0.8828
Epoch 38/100
79/79 [==============================] - 1s 7ms/step - loss: 0.3949 - accuracy: 0.8412 - val_loss: 0.3870 - val_accuracy: 0.8552
Epoch 39/100
79/79 [==============================] - 1s 7ms/step - loss: 0.3276 - accuracy: 0.8852 - val_loss: 0.3033 - val_accuracy: 0.9040
Epoch 40/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2861 - accuracy: 0.8992 - val_loss: 0.2872 - val_accuracy: 0.8984
Epoch 41/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2709 - accuracy: 0.9060 - val_loss: 0.2601 - val_accuracy: 0.9104
Epoch 42/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2476 - accuracy: 0.9088 - val_loss: 0.2502 - val_accuracy: 0.9128
Epoch 43/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2438 - accuracy: 0.9152 - val_loss: 0.2460 - val_accuracy: 0.9124
Epoch 44/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2410 - accuracy: 0.9128 - val_loss: 0.2703 - val_accuracy: 0.9072
Epoch 45/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2333 - accuracy: 0.9188 - val_loss: 0.2288 - val_accuracy: 0.9252
Epoch 46/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2147 - accuracy: 0.9244 - val_loss: 0.2322 - val_accuracy: 0.9192
Epoch 47/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2729 - accuracy: 0.8940 - val_loss: 0.3241 - val_accuracy: 0.8808
Epoch 48/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2782 - accuracy: 0.8868 - val_loss: 0.2404 - val_accuracy: 0.9212
Epoch 49/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2247 - accuracy: 0.9220 - val_loss: 0.2149 - val_accuracy: 0.9364
Epoch 50/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2006 - accuracy: 0.9340 - val_loss: 0.1975 - val_accuracy: 0.9432
Epoch 51/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1819 - accuracy: 0.9424 - val_loss: 0.1847 - val_accuracy: 0.9492
Epoch 52/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1808 - accuracy: 0.9400 - val_loss: 0.1770 - val_accuracy: 0.9460
Epoch 53/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1819 - accuracy: 0.9396 - val_loss: 0.1812 - val_accuracy: 0.9496
Epoch 54/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1610 - accuracy: 0.9484 - val_loss: 0.1729 - val_accuracy: 0.9516
Epoch 55/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1583 - accuracy: 0.9480 - val_loss: 0.1687 - val_accuracy: 0.9524
Epoch 56/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1474 - accuracy: 0.9484 - val_loss: 0.1795 - val_accuracy: 0.9452
Epoch 57/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1531 - accuracy: 0.9440 - val_loss: 0.1821 - val_accuracy: 0.9504
Epoch 58/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1545 - accuracy: 0.9488 - val_loss: 0.1570 - val_accuracy: 0.9532
Epoch 59/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1374 - accuracy: 0.9520 - val_loss: 0.1556 - val_accuracy: 0.9528
Epoch 60/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1329 - accuracy: 0.9516 - val_loss: 0.1441 - val_accuracy: 0.9580
Epoch 61/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1282 - accuracy: 0.9556 - val_loss: 0.1637 - val_accuracy: 0.9556
Epoch 62/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1489 - accuracy: 0.9568 - val_loss: 0.1428 - val_accuracy: 0.9516
Epoch 63/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1164 - accuracy: 0.9588 - val_loss: 0.1803 - val_accuracy: 0.9516
Epoch 64/100
79/79 [==============================] - 1s 7ms/step - loss: 0.2297 - accuracy: 0.9368 - val_loss: 0.1882 - val_accuracy: 0.9500
Epoch 65/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1285 - accuracy: 0.9552 - val_loss: 0.1299 - val_accuracy: 0.9576
Epoch 66/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1113 - accuracy: 0.9624 - val_loss: 0.1390 - val_accuracy: 0.9596
Epoch 67/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1054 - accuracy: 0.9660 - val_loss: 0.1181 - val_accuracy: 0.9628
Epoch 68/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1078 - accuracy: 0.9672 - val_loss: 0.1155 - val_accuracy: 0.9640
Epoch 69/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1340 - accuracy: 0.9636 - val_loss: 0.1945 - val_accuracy: 0.9488
Epoch 70/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1208 - accuracy: 0.9564 - val_loss: 0.1208 - val_accuracy: 0.9636
Epoch 71/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1188 - accuracy: 0.9604 - val_loss: 0.1047 - val_accuracy: 0.9664
Epoch 72/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1027 - accuracy: 0.9660 - val_loss: 0.1066 - val_accuracy: 0.9668
Epoch 73/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0941 - accuracy: 0.9736 - val_loss: 0.0951 - val_accuracy: 0.9708
Epoch 74/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0874 - accuracy: 0.9716 - val_loss: 0.0990 - val_accuracy: 0.9668
Epoch 75/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0855 - accuracy: 0.9720 - val_loss: 0.1011 - val_accuracy: 0.9688
Epoch 76/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1028 - accuracy: 0.9692 - val_loss: 0.1240 - val_accuracy: 0.9628
Epoch 77/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0825 - accuracy: 0.9720 - val_loss: 0.1017 - val_accuracy: 0.9736
Epoch 78/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0786 - accuracy: 0.9748 - val_loss: 0.0949 - val_accuracy: 0.9704
Epoch 79/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0777 - accuracy: 0.9764 - val_loss: 0.1001 - val_accuracy: 0.9728
Epoch 80/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0846 - accuracy: 0.9736 - val_loss: 0.0992 - val_accuracy: 0.9672
Epoch 81/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0718 - accuracy: 0.9804 - val_loss: 0.0954 - val_accuracy: 0.9700
Epoch 82/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0698 - accuracy: 0.9788 - val_loss: 0.0893 - val_accuracy: 0.9728
Epoch 83/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0633 - accuracy: 0.9820 - val_loss: 0.0924 - val_accuracy: 0.9732
Epoch 84/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0630 - accuracy: 0.9816 - val_loss: 0.0772 - val_accuracy: 0.9760
Epoch 85/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1032 - accuracy: 0.9664 - val_loss: 0.0859 - val_accuracy: 0.9744
Epoch 86/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0643 - accuracy: 0.9812 - val_loss: 0.0761 - val_accuracy: 0.9744
Epoch 87/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0599 - accuracy: 0.9828 - val_loss: 0.0722 - val_accuracy: 0.9756
Epoch 88/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0601 - accuracy: 0.9812 - val_loss: 0.0712 - val_accuracy: 0.9788
Epoch 89/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0545 - accuracy: 0.9816 - val_loss: 0.0688 - val_accuracy: 0.9780
Epoch 90/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0505 - accuracy: 0.9848 - val_loss: 0.0739 - val_accuracy: 0.9796
Epoch 91/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0502 - accuracy: 0.9848 - val_loss: 0.0864 - val_accuracy: 0.9764
Epoch 92/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0772 - accuracy: 0.9724 - val_loss: 0.0678 - val_accuracy: 0.9780
Epoch 93/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0572 - accuracy: 0.9800 - val_loss: 0.0756 - val_accuracy: 0.9772
Epoch 94/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0530 - accuracy: 0.9804 - val_loss: 0.0852 - val_accuracy: 0.9780
Epoch 95/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0543 - accuracy: 0.9796 - val_loss: 0.0818 - val_accuracy: 0.9776
Epoch 96/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0487 - accuracy: 0.9840 - val_loss: 0.0531 - val_accuracy: 0.9792
Epoch 97/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0526 - accuracy: 0.9852 - val_loss: 0.0644 - val_accuracy: 0.9804
Epoch 98/100
79/79 [==============================] - 1s 7ms/step - loss: 0.0445 - accuracy: 0.9856 - val_loss: 0.0567 - val_accuracy: 0.9796
Epoch 99/100
79/79 [==============================] - 1s 7ms/step - loss: 0.1566 - accuracy: 0.9680 - val_loss: 0.4206 - val_accuracy: 0.8924
Epoch 100/100
79/79 [==============================] - 1s 7ms/step - loss: 1.0968 - accuracy: 0.7592 - val_loss: 1.0851 - val_accuracy: 0.6596

# Plot the loss
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()

<matplotlib.legend.Legend at 0x7fec209687b8>

# Plot the accuracy too
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.legend()

<matplotlib.legend.Legend at 0x7fec20a92780>

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