lstm.py 文件源码

def _init(self, inputs, num_outputs, options):
        use_tf100_api = (distutils.version.LooseVersion(tf.VERSION) >=
                         distutils.version.LooseVersion("1.0.0"))

        self.x = x = inputs
        for i in range(4):
            x = tf.nn.elu(conv2d(x, 32, "l{}".format(i + 1), [3, 3], [2, 2]))
        # Introduce a "fake" batch dimension of 1 after flatten so that we can
        # do LSTM over the time dim.
        x = tf.expand_dims(flatten(x), [0])

        size = 256
        if use_tf100_api:
            lstm = rnn.BasicLSTMCell(size, state_is_tuple=True)
        else:
            lstm = rnn.rnn_cell.BasicLSTMCell(size, state_is_tuple=True)
        step_size = tf.shape(self.x)[:1]

        c_init = np.zeros((1, lstm.state_size.c), np.float32)
        h_init = np.zeros((1, lstm.state_size.h), np.float32)
        self.state_init = [c_init, h_init]
        c_in = tf.placeholder(tf.float32, [1, lstm.state_size.c])
        h_in = tf.placeholder(tf.float32, [1, lstm.state_size.h])
        self.state_in = [c_in, h_in]

        if use_tf100_api:
            state_in = rnn.LSTMStateTuple(c_in, h_in)
        else:
            state_in = rnn.rnn_cell.LSTMStateTuple(c_in, h_in)
        lstm_out, lstm_state = tf.nn.dynamic_rnn(lstm, x,
                                                 initial_state=state_in,
                                                 sequence_length=step_size,
                                                 time_major=False)
        lstm_c, lstm_h = lstm_state
        x = tf.reshape(lstm_out, [-1, size])
        logits = linear(x, num_outputs, "action", normc_initializer(0.01))
        self.state_out = [lstm_c[:1, :], lstm_h[:1, :]]
        return logits, x