Machine-Learning-with-Python/mnist-deep-learning.py at master · devAmoghS/Machine-Learning-with-Python · GitHub

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from keras.datasets import mnist

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# 1. Prepare the Data

from keras.utils import to_categorical

train_images = train_images.reshape((60000, 28 * 28)).astype('float32') / 255

test_images = test_images.reshape((10000, 28 * 28)).astype('float32') / 255

train_labels = to_categorical(train_labels)

test_labels = to_categorical(test_labels)

# 2. Set up network architecture

from keras import models, layers

network = models.Sequential()

network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))

network.add(layers.Dense(10, activation='softmax'))

# 3/4. Pick Optimizer and Loss

network.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

network.fit(train_images, train_labels, epochs=5, batch_size=128)

# 5. Measure on test

test_loss, test_acc = network.evaluate(test_images, test_labels)

print('test_acc:', test_acc)

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