我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用keras.layers.time_distributed_dense()。
def preprocess_input(self, x): if self.consume_less == 'cpu': if 0 < self.dropout_W < 1: dropout = self.dropout_W else: dropout = 0 input_shape = self.input_spec[0].shape input_dim = input_shape[2] timesteps = input_shape[1] x_i = time_distributed_dense(x, self.W_i, self.b_i, dropout, input_dim, self.output_dim, timesteps) x_f = time_distributed_dense(x, self.W_f, self.b_f, dropout, input_dim, self.output_dim, timesteps) x_c = time_distributed_dense(x, self.W_c, self.b_c, dropout, input_dim, self.output_dim, timesteps) x_o = time_distributed_dense(x, self.W_o, self.b_o, dropout, input_dim, self.output_dim, timesteps) return K.concatenate([x_i, x_f, x_c, x_o], axis=2) else: return x
def preprocess_input(self, x): print("begin preprocess_input(self, x)") if self.consume_less == 'cpu': if 0 < self.dropout_W < 1: dropout = self.dropout_W else: dropout = 0 input_shape = self.input_spec[0].shape input_dim = input_shape[2] timesteps = input_shape[1] # x_i = time_distributed_dense(x, self.W_i, self.b_i, dropout, # input_dim, self.output_dim, timesteps) # x_f = time_distributed_dense(x, self.W_f, self.b_f, dropout, # input_dim, self.output_dim, timesteps) # x_c = time_distributed_dense(x, self.W_c, self.b_c, dropout, # input_dim, self.output_dim, timesteps) # x_o = time_distributed_dense(x, self.W_o, self.b_o, dropout, # input_dim, self.output_dim, timesteps) # add by Robot Steven ****************************************# x_i = time_distributed_dense(x, self.W_i, self.b_i, dropout, input_dim, self.controller_output_dim, timesteps) x_f = time_distributed_dense(x, self.W_f, self.b_f, dropout, input_dim, self.controller_output_dim, timesteps) x_c = time_distributed_dense(x, self.W_c, self.b_c, dropout, input_dim, self.controller_output_dim, timesteps) x_o = time_distributed_dense(x, self.W_o, self.b_o, dropout, input_dim, self.controller_output_dim, timesteps) # add by Robot Steven ****************************************# print("end preprocess_input(self,x )") return K.concatenate([x_i, x_f, x_c, x_o], axis=2) else: print("end preprocess_input(self,x )\n") return x
def preprocess_input(self, x): if self.consume_less == 'cpu': input_shape = self.input_spec[0].shape input_dim = input_shape[2] timesteps = input_shape[1] return time_distributed_dense(x, self.W, None, self.dropout_W, input_dim, self.output_dim, timesteps) else: return x # override Recurrent's get_initial_states function to load the trainable # initial hidden state
def preprocess_input(self, x): if self.consume_less == 'cpu': input_shape = self.input_spec[0].shape input_dim = input_shape[2] timesteps = input_shape[1] x_t = time_distributed_dense(x, self.W_t, self.b_t, self.dropout_W, input_dim, self.output_dim, timesteps) x_h = time_distributed_dense(x, self.W_h, self.b_h, self.dropout_W, input_dim, self.output_dim, timesteps) return K.concatenate([x_t, x_h], axis=2) else: return x
