imdb.py

import numpy as np
import matplotlib.pyplot as plt
#from Tkinter import *
from keras.datasets import imdb
from keras import models
from keras import layers

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


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

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

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

print(max([max(sequence) for sequence in train_data]))

word_index = imdb.get_word_index()

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

decoded_review = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])

print(decoded_review)

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

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

#from keras import losses
#from keras import metrics
#from keras import optimizers
#model.compile(optimizer=optimizers.RMSprop(lr=0.001), loss=losses.binary_crossentropy, metrics=[metrics.binary_accuracy])

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

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

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
'''
history = model.fit(partial_x_train,partial_y_train,epochs=20,batch_size=512,validation_data=(x_val, y_val))

history_dict = history.history
#print(history_dict.keys())
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']

epochs = range(1, len(loss_values) + 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()
'''

model.fit(x_train, y_train, epochs=4, batch_size=512)
results = model.evaluate(x_test, y_test)

print(results)