class BiCLSTMCell(tf.keras.layers.Layer): def __init__(self, units, **kwargs): self.units = units self.state_size = [units, units] super(BiCLSTMCell, self).__init__(**kwargs) def build(self, input_shape): self.kernel = self.add_weight(shape=(input_shape[-1], self.units * 4), initializer='glorot_uniform', name='kernel') self.recurrent_kernel = self.add_weight(shape=(self.units, self.units * 4), initializer='orthogonal', name='recurrent_kernel') self.bias = self.add_weight(shape=(self.units * 4,), initializer='zeros', name='bias') self.ca_dense1 = Dense(self.units, activation='relu') self.ca_dense2 = Dense(self.units, activation='sigmoid') super(BiCLSTMCell, self).build(input_shape) def call(self, inputs, states): h_tm1, c_tm1 = states z = tf.keras.backend.dot(inputs, self.kernel) z += tf.keras.backend.dot(h_tm1, self.recurrent_kernel) z = tf.keras.backend.bias_add(z, self.bias) z0, z1, z2, z3 = tf.split(z, 4, axis=-1) # Channel Attention Mechanism ca = self.ca_dense1(c_tm1) ca = self.ca_dense2(ca) # LSTM Gates input_gate = tf.keras.activations.sigmoid(z0) forget_gate = tf.keras.activations.sigmoid(z1) * ca output_gate = tf.keras.activations.sigmoid(z2) cell_state = tf.keras.activations.tanh(z3) c = forget_gate * c_tm1 + input_gate * cell_state h = output_gate * tf.keras.activations.tanh(c) return h, [h, c] def get_config(self): config = super(BiCLSTMCell, self).get_config() config.update({'units': self.units}) return config

这是一个自定义的双向LSTM单元类，继承了Keras的Layer类。在初始化方法中，定义了该单元的神经元个数和状态大小。在build方法中，定义了该单元的权重矩阵和偏置向量，并定义了两个全连接层，用于通道注意力机制。在call方法中，实现了双向LSTM单元的计算过程，包括输入和遗忘门的计算、细胞状态的更新、输出门的计算和输出的计算。同时，还加入了通道注意力机制来增强模型的表达能力。最后，在get_config方法中，返回了该单元的参数配置信息。