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from keras import models
from keras import layers

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

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

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# Preprocessing training data
train_images = train_images.reshape((60000, 28*28))
train_images = train_images.astype('float32') / 255

# Preprocessing test data
test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype('float32') / 225

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from keras.utils import to_categorical

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

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# Fit the model to its training data
#         Epochs = 5
#         Batch Size = 128
network.fit(train_images, train_labels, epochs=5, batch_size=128)

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# Calculate Test loss and Test Accuracy
test_loss, test_acc = network.evaluate(test_images, test_labels)

# Print Test loss and Test Accuracy
print(f"Test Loss: {test_loss}\nTest Accuracy : {test_acc * 100} %")

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from ann_visualizer.visualize import ann_viz
ann_viz(network, title="MNIST network", filename="MNIST.gv")

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