Python深度学习笔记09--使用Keras建立循环神经网络

6.2 理解循环神经网络

6.2.1 Keras中的循环层：

from keras.layers import SimpleRNN

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

# model = Sequential()
# model.add(Embedding(10000, 32))
# model.add(SimpleRNN(32))
# print(model.summary())
'''
embedding层的参数计算：10000 * 32
imple_rnn层的参数计算：x*h + h*h + y*h + x + y
x是输入向量的维度，本例中是32(第7行定义的32)
h是循环层的向量维度，本例中是32(第8行定义的32)
y是0，本例中没有定义输出
因此计算方式为：32*32 + 32*32 + 0*32 + 32 + 0 = 2080
'''
# model = Sequential()
# model.add(Embedding(10000, 32))
# model.add(SimpleRNN(32, return_sequences=True))
# print(model.summary())


# model = Sequential()
# model.add(Embedding(10000, 32))
# model.add(SimpleRNN(32, return_sequences=True))
# model.add(SimpleRNN(32, return_sequences=True))
# model.add(SimpleRNN(32, return_sequences=True)) # 返回完整状态序列
# model.add(SimpleRNN(32))  # 最后一层只返回最终输出
# print(model.summary())


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

max_features = 10000  # number of words to consider as features
maxlen = 500  # cut texts after this number of words (among top max_features most common words)
batch_size = 32

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

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


from keras.layers import Dense

model = Sequential()
model.add(Embedding(max_features, 32))
model.add(SimpleRNN(32))
model.add(Dense(1, activation='sigmoid'))
# #print(model.summary())


model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
history = model.fit(input_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(len(acc))

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()

# loss: 0.0107 - acc: 0.9970 - val_loss: 0.6327 - val_acc: 0.8286