PYTHON深度学习6.2RNN循环网络

#简单的循环网络

#-*-coding:utf-8 -*-


from keras.datasets import imdb
from keras.preprocessing import sequence

max_fetaures = 10000
maxlen = 500
batch_size = 32
print("Loading data...")
(x_train, y_train), (x_test, y_test) = imdb.load_data(path="/home/duchao/projects(my)/keras/kagge/6/6.1/imdb.npz",num_words=max_fetaures)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

print('Pad sequences (sample 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)



from keras.layers import Dense
from keras.models import Sequential
from keras.layers import Embedding,SimpleRNN

model = Sequential()
model.add(Embedding(max_fetaures, 32))
model.add(SimpleRNN(32))
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
history = model.fit(x_train, y_train, epochs=10, batch_size=128, validation_split=0.2)




import matplotlib.pyplot as plt

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

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

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()

plt.figure()

plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()