imdb_cnn.py

'''This example demonstrates the use of Convolution1D for text classification.

Gets to 0.89 test accuracy after 2 epochs.
90s/epoch on Intel i5 2.4Ghz CPU.
10s/epoch on Tesla K40 GPU.

'''

from __future__ import print_function

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalMaxPooling1D
from keras.datasets import imdb
import matplotlib.pyplot as plt

# set parameters:
max_features = 5000
maxlen = 400
batch_size = 32
embedding_dims = 50
filters = 250
kernel_size = 3
hidden_dims = 250
epochs = 5
dropout = 1.0

print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)


dropouts = [0.2, 0.6, 1.0]
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
accs, val_accs = [], []
for i in range(len(dropouts)):
    print('Build model...')
    model = Sequential()

    # we start off with an efficient embedding layer which maps
    # our vocab indices into embedding_dims dimensions
    model.add(Embedding(max_features,
                        embedding_dims,
                        input_length=maxlen))
    model.add(Dropout(dropouts[i]))

    # we add a Convolution1D, which will learn filters
    # word group filters of size filter_length:
    model.add(Conv1D(filters,
                     kernel_size,
                     padding='valid',
                     activation='relu',
                     strides=1))
    # we use max pooling:
    model.add(GlobalMaxPooling1D())

    # We add a vanilla hidden layer:
    model.add(Dense(hidden_dims))
    model.add(Dropout(dropouts[i]))
    model.add(Activation('relu'))

    # We project onto a single unit output layer, and squash it with a sigmoid:
    model.add(Dense(1))
    model.add(Activation('sigmoid'))

    model.compile(loss='binary_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    history = model.fit(x_train, y_train,
              batch_size=batch_size,
              epochs=epochs,
              validation_data=(x_test, y_test))
    score = model.evaluate(x_test, y_test, verbose=0)
    print('Test loss with dropout %.2f : %.4f' % (dropouts[i], score[0]))
    print('Test accuracy with dropout %.2f : %.4f' % (dropouts[i], score[1]))
    accs.append(history.history['acc'])
    val_accs.append(history.history['val_acc'])
# summarize history for accuracy
for i in range(len(accs)):
    plt.plot(accs[i], '--', label='train dropout '+str(dropouts[i]), color=colors[i])
    plt.plot(val_accs[i], '-', label='test dropout '+str(dropouts[i]), color=colors[i])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend()
plt.show()