<filename>src/deeplearning_with_python/chapter02/00_mnist_keras.py
from keras.datasets import mnist

from keras import models
from keras import layers

from keras.utils import to_categorical

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

# Uncomment for plotting an example of data
# import matplotlib.pyplot as plt
# digit = train_images[4]
# plt.imshow(digit, cmap=plt.cm.binary)
# plt.show()
# import pdb; pdb.set_trace()

# Model definition
network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))

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

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

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

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

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

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

<filename>src/deeplearning_with_python/chapter02/00_mnist_keras.py
from keras.datasets import mnist

from keras import models
from keras import layers

from keras.utils import to_categorical

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

# Uncomment for plotting an example of data
# import matplotlib.pyplot as plt
# digit = train_images[4]
# plt.imshow(digit, cmap=plt.cm.binary)
# plt.show()
# import pdb; pdb.set_trace()

# Model definition
network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))

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

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

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

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

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

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

# Uncomment for plotting an example of data # import matplotlib.pyplot as plt # digit = train_images[4] # plt.imshow(digit, cmap=plt.cm.binary) # plt.show() # import pdb; pdb.set_trace() # Model definition