我目前正在尝试扩大我在书中找到的时间序列示例。我一直在尝试将其移至功能 API，但遇到了问题。我在功能模型中遇到的错误是：
回溯（最近一次调用）：文件“merge_n.py”，第 57 行，在 lstm = LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True)(inputs) 文件“/Users/pjhampton/Desktop /MTL/lib/python3.5/site-packages/keras/layers/recurrent.py”，第 243 行，调用return super(Recurrent, self)。调用(inputs, **kwargs) 文件“/Users/pjhampton/Desktop/MTL/lib/python3.5/site-packages/keras/engine/topology.py”，第 541 行，调用self.assert_input_compatibility(inputs) 文件"/Users/pjhampton/Desktop/MTL/lib/python3.5/site-packages/keras/engine/topology.py", line 440, in assert_input_compatibility str(K.ndim(x))) ValueError: Input 0 is incompatible层 lstm_1：预期 ndim=3，发现 ndim=4
顺序模型（原始）
########################################################
# main input
########################################################
look_back = 5
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1))
batch_size = 1
model = Sequential()
model.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True))
model.add(Dense(1))
model.compile(loss='mse', optimizer='adam')
for i in range(100):
model.fit(trainX, trainY, epochs=1, batch_size=batch_size, verbose=2, shuffle=False)
model.reset_states()
基于函数式 API 的模型（我尝试过的）
inputs = Input(shape=(batch_size, look_back, 1))
lstm = LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True)(inputs)
dense = Dense(1)(lstm)
model = Model(inputs=inputs, outputs=dense)
model.compile(loss='mse', optimizer='adam')
for i in range(100):
model.fit(trainX, trainY, epochs=1, batch_size=batch_size, verbose=2, shuffle=False)
model.reset_states()
您已指定 RNN 是有状态的，因此您需要batch_shape在输入中指定。
inputs = Input(batch_shape=(batch_size, look_back, 1))
lstm = LSTM(4, stateful=True)(inputs)
dense = Dense(1)(lstm)
model = Model(inputs=inputs, outputs=dense)
model.compile(loss='mse', optimizer='adam')
for i in range(100):
model.fit(trainX, trainY, epochs=1, batch_size=batch_size, verbose=2, shuffle=False)
model.reset_states()
似乎顺序模型正是您正在寻找的。
本文收集自互联网，转载请注明来源。
如有侵权，请联系[email protected] 删除。
我来说两句