movie_reviews.py

from keras.datasets import imdb
from keras import models, layers, optimizers, losses, metrics
import numpy as np
import matplotlib.pyplot as plt
from vector_utils import vectorize_sequences
from plotting_utils import plot_metric

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

# Turn every review into a 10,0000 dimension vector
x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)

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

# Set aside some examples to measure the training accuracy during training
x_val = x_train[:10000]
partial_x_train = x_train[10000:]

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

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

model.compile(optimizer=optimizers.RMSprop(lr=0.001),
              loss=losses.mean_squared_error,
              metrics=[metrics.binary_accuracy])

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

print(results)

print(model.predict(x_test)[:20])



"""
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()

plt.clf()
acc = history_dict['binary_accuracy']
val_acc = history_dict['val_binary_accuracy']

plt.plot(epochs, acc, 'bo', label='Training Accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

plt.show()
"""