stock_supervised.py 文件源码

def createRNN(self):
        with self.sess.graph.as_default():
            self.prob = tf.placeholder("float", name="keep_prob")
            # input layer #
            with tf.name_scope("input"):
                self.s = tf.placeholder("float", [None, DAYS_RANGE, INPUT_DIM], name='input_state')
                s_tran = tf.transpose(self.s, [1, 0, 2])
                s_re = tf.reshape(s_tran, [-1, INPUT_DIM])
                s_list = tf.split(0, DAYS_RANGE, s_re) ## split s to DAYS_RANGE tensor of shape [BATCH, INPUT_DIM]

            lstm_cell = rnn_cell.LSTMCell(1024, use_peepholes=True, forget_bias=1.0, state_is_tuple=True)
            lstm_drop = rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob=self.prob)
            lstm_stack = rnn_cell.MultiRNNCell([lstm_cell]*3, state_is_tuple=True)

            lstm_output, hidden_states = rnn.rnn(lstm_stack, s_list, dtype='float', scope='LSTMStack') # out: [timestep, batch, hidden], state: [cell, c+h, batch, hidden]

            h_fc1 = self.FC_layer(lstm_output[-1], [1024, 1024], name='h_fc1', activate=True)
            h_fc1_d = tf.nn.dropout(h_fc1, keep_prob=self.prob, name='h_fc1_drop')
            h_fc2 = self.FC_layer(h_fc1_d, [1024, ACTIONS], name='h_fc2', activate=False)

            # output layer #
            self.pred_action = tf.nn.softmax(h_fc2)