我一直在尝试自定义一个LSTM层以进行进一步改进。但是在我的自定义LSTM之后的池化层出现了一个看似正常的错误。
我的环境是：
- Windows 10
- Keras 2.2.0
- Python 3.6
Traceback (most recent call last):
File “E:/PycharmProjects/dialogResearch/dialog/classifier.py”, line 60, in
model = build_model(word_dict, args.max_len, args.max_sents, args.embedding_dim)
File “E:\PycharmProjects\dialogResearch\dialog\model\keras_himodel.py”, line 177, in build_model
l_dense = TimeDistributed(Dense(200))(l_lstm)
File “C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\topology.py”, line 592, in call
self.build(input_shapes[0])
File “C:\ProgramData\Anaconda3\lib\site-packages\keras\layers\wrappers.py”, line 162, in build
assert len(input_shape) >= 3
AssertionError
我的自定义LSTM的代码如下：
class CustomLSTM(Layer): def __init__(self, output_dim, return_sequences, **kwargs): self.init = initializers.get('normal') # self.input_spec = [InputSpec(ndim=3)] self.output_dim = output_dim self.return_sequences = return_sequences super(CustomLSTM, self).__init__(**kwargs) def build(self, input_shape): assert len(input_shape) == 3 self.original_shape = input_shape self.Wi = self.add_weight('Wi', (input_shape[-1], self.output_dim), initializer=self.init, trainable=True) self.Wf = self.add_weight('Wf', (input_shape[-1], self.output_dim), initializer=self.init, trainable=True) self.Wo = self.add_weight('Wo', (input_shape[-1], self.output_dim), initializer=self.init, trainable=True) self.Wu = self.add_weight('Wu', (input_shape[-1], self.output_dim), initializer=self.init, trainable=True) self.Ui = self.add_weight('Ui', (self.output_dim, self.output_dim), initializer=self.init, trainable=True) self.Uf = self.add_weight('Uf', (self.output_dim, self.output_dim), initializer=self.init, trainable=True) self.Uo = self.add_weight('Uo', (self.output_dim, self.output_dim), initializer=self.init, trainable=True) self.Uu = self.add_weight('Uu', (self.output_dim, self.output_dim), initializer=self.init, trainable=True) self.bi = self.add_weight('bi', (self.output_dim,), initializer=self.init, trainable=True) self.bf = self.add_weight('bf', (self.output_dim,), initializer=self.init, trainable=True) self.bo = self.add_weight('bo', (self.output_dim,), initializer=self.init, trainable=True) self.bu = self.add_weight('bu', (self.output_dim,), initializer=self.init, trainable=True) super(CustomLSTM, self).build(input_shape) def step_op(self, step_in, states): i = K.softmax(K.dot(step_in, self.Wi) + K.dot(states[0], self.Ui) + self.bi) f = K.softmax(K.dot(step_in, self.Wf) + K.dot(states[0], self.Uf) + self.bf) o = K.softmax(K.dot(step_in, self.Wo) + K.dot(states[0], self.Uo) + self.bo) u = K.tanh(K.dot(step_in, self.Wu) + K.dot(states[0], self.Uu) + self.bu) c = i * u + f * states[1] h = o * K.tanh(c) return h, [h, c] def call(self, x, mask=None): init_states = [K.zeros((K.shape(x)[0], self.output_dim)), K.zeros((K.shape(x)[0], self.output_dim))] outputs = K.rnn(self.step_op, x, init_states) if self.return_sequences: return outputs[1] else: return outputs[0] def compute_output_shape(self, input_shape): return input_shape[0], input_shape[-1]模型代码如下：
def build_model(words, max_len, max_sents, embedding_dim): sentence_input = Input(shape=(max_len,), dtype='int32') embedding_layer = Embedding(len(words) + 1, embedding_dim, input_length=max_len, trainable=True) embedded_sequences = embedding_layer(sentence_input) l_lstm = CustomLSTM(200, return_sequences=True)(embedded_sequences) print(l_lstm.get_shape()) l_dense = TimeDistributed(Dense(200))(l_lstm) l_att = AttLayer()(l_dense) sentEncoder = Model(sentence_input, l_att) review_input = Input(shape=(max_sents, max_len), dtype='int32') review_encoder = TimeDistributed(sentEncoder)(review_input) l_lstm_sent = CustomLSTM(200, return_sequences=True)(review_encoder) l_dense_sent = TimeDistributed(Dense(200))(l_lstm_sent) l_att_sent = AttLayer()(l_dense_sent) preds = Dense(3, activation='softmax')(l_att_sent) model = Model(review_input, preds) optimizer = Nadam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, schedule_decay=0.004) model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=[precision, recall, f1, 'acc']) return model感谢您的帮助。
回答：
我认为错误发生的原因是当return_sequences=True时，compute_output_shape返回的形状不正确。我会尝试以下方法：
def compute_output_shape(self, input_shape): if self.return_sequences: return input_shape return (input_shape[0], input_shape[-1])