TF2 0 ANN MNIST
================ by Jawad Haider
# 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__)
|████████████████████████████████| 348.9MB 52kB/s
|████████████████████████████████| 3.1MB 49.1MB/s
|████████████████████████████████| 501kB 54.6MB/s
2.0.0-beta1
# Load in the data
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
print("x_train.shape:", x_train.shape)
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
x_train.shape: (60000, 28, 28)
# Build the model
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
WARNING: Logging before flag parsing goes to stderr.
W0718 16:30:06.976897 139639418169216 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 [==============================] - 7s 111us/sample - loss: 0.2949 - accuracy: 0.9134 - val_loss: 0.1339 - val_accuracy: 0.9594
Epoch 2/10
60000/60000 [==============================] - 6s 93us/sample - loss: 0.1452 - accuracy: 0.9564 - val_loss: 0.1063 - val_accuracy: 0.9681
Epoch 3/10
60000/60000 [==============================] - 6s 95us/sample - loss: 0.1100 - accuracy: 0.9664 - val_loss: 0.0879 - val_accuracy: 0.9722
Epoch 4/10
60000/60000 [==============================] - 6s 94us/sample - loss: 0.0902 - accuracy: 0.9721 - val_loss: 0.0737 - val_accuracy: 0.9766
Epoch 5/10
60000/60000 [==============================] - 6s 93us/sample - loss: 0.0765 - accuracy: 0.9758 - val_loss: 0.0747 - val_accuracy: 0.9748
Epoch 6/10
60000/60000 [==============================] - 6s 94us/sample - loss: 0.0672 - accuracy: 0.9789 - val_loss: 0.0699 - val_accuracy: 0.9772
Epoch 7/10
60000/60000 [==============================] - 6s 93us/sample - loss: 0.0586 - accuracy: 0.9820 - val_loss: 0.0729 - val_accuracy: 0.9781
Epoch 8/10
60000/60000 [==============================] - 6s 94us/sample - loss: 0.0544 - accuracy: 0.9815 - val_loss: 0.0697 - val_accuracy: 0.9780
Epoch 9/10
60000/60000 [==============================] - 6s 93us/sample - loss: 0.0505 - accuracy: 0.9834 - val_loss: 0.0690 - val_accuracy: 0.9789
Epoch 10/10
60000/60000 [==============================] - 6s 93us/sample - loss: 0.0447 - accuracy: 0.9850 - val_loss: 0.0692 - val_accuracy: 0.9806
# Plot loss per iteration
import matplotlib.pyplot as plt
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()
<matplotlib.legend.Legend at 0x7effe00f2ac8>
# Plot accuracy per iteration
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.legend()
<matplotlib.legend.Legend at 0x7effe0092518>
10000/10000 [==============================] - 1s 55us/sample - loss: 0.0692 - accuracy: 0.9806
[0.06924617666350968, 0.9806]
# Plot confusion matrix
from sklearn.metrics import confusion_matrix
import numpy as np
import itertools
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
p_test = model.predict(x_test).argmax(axis=1)
cm = confusion_matrix(y_test, p_test)
plot_confusion_matrix(cm, list(range(10)))
# Do these results make sense?
# It's easy to confuse 9 <--> 4, 9 <--> 7, 2 <--> 7, etc.
Confusion matrix, without normalization
[[ 972 0 1 1 0 0 2 1 2 1]
[ 0 1126 2 2 0 0 2 0 3 0]
[ 2 1 1015 3 1 0 1 4 4 1]
[ 1 0 1 996 0 2 0 3 3 4]
[ 1 1 2 1 959 0 5 3 1 9]
[ 2 1 0 12 1 868 2 2 3 1]
[ 5 3 1 1 1 3 939 0 5 0]
[ 2 10 12 3 0 0 0 994 3 4]
[ 6 0 3 2 3 1 0 2 953 4]
[ 2 2 0 3 12 3 0 3 0 984]]
# Show some misclassified examples
misclassified_idx = np.where(p_test != y_test)[0]
i = np.random.choice(misclassified_idx)
plt.imshow(x_test[i], cmap='gray')
plt.title("True label: %s Predicted: %s" % (y_test[i], p_test[i]));
