MovieReviewClassification.py

import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import imdb
from keras import models
from keras import layers
from keras import optimizers
from keras import losses
from keras import metrics

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

print('____Preparing data____')

(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('____Building model____')

model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))

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

print('____Training model____')

history = model.fit(x_train[10000:],
                    y_train[10000:],
                    epochs=7,
                    batch_size=512,
                    validation_data=(x_train[:10000], y_train[:10000]))

print('____Evaluating model____')

evaluation_results = model.evaluate(x_test, y_test)
print(evaluation_results)

print('____Predicting____')

prediction_results = model.predict(x_test)
print(prediction_results)

#history_dict = history.history
#loss_values = history_dict['loss']
#val_losts_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_losts_values, 'b', label='Validation loss')
#plt.title('Training and validation loss')
#plt.xlabel('Epochs')
#plt.ylabel('Loss')
#plt.legend()

#plt.show()

#plt.clf()
#acc_values = history_dict['binary_accuracy']
#val_acc_values = history_dict['val_binary_accuracy']

#plt.plot(epochs, acc_values, 'bo', label='Training acc') 
#plt.plot(epochs, val_acc_values, 'b', label='Validation acc')
#plt.title('Training and validation accuracy')
#plt.xlabel('Epochs')
#plt.ylabel('Loss')
#plt.legend()

#plt.show()