imdb.py

print('IMDB classification')

from hack import hack

hack()


import keras
print('keras version ' + keras.__version__)

import numpy as np

def vectorize_sequences(sequences, dimension=10000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return results



from keras.datasets import imdb

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)

# print(train_data[0])

x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)

# print(x_train[0])

y_train = np.asarray(train_labels).astype('float32')
y_test = np.asarray(test_labels).astype('float32')


from keras import layers
from keras import models
# from keras import optimizers

model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))
model.add(layers.Dropout(0.3))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dropout(0.3))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

x_val = x_train[:10000]
partial_x_train = x_train[10000:]

y_val = y_train[:10000]
partial_y_train = y_train[10000:]

model.summary()

history = model.fit(partial_x_train,
                    partial_y_train,
                    epochs=5,
                    batch_size=512,
                    validation_data=(x_val, y_val))


history_dict = history.history
print(history_dict.keys())

import matplotlib.pyplot as plt

acc = history.history['acc']
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']

epochs = range(1, len(acc) + 1)

plt.plot(epochs, loss_values, 'bo', label='Training loss')
plt.plot(epochs, val_loss_values, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

# print(model.predict(x_test))