python - 在 tensorflow 中重新分配非变量张量

Question

我有一个要求，我想使用 x 的更新值作为 RNN 的输入。下面的代码片段可能会详细说明您。

x = tf.placeholder("float", shape=[None,1])
RNNcell = tf.nn.rnn_cell.BasicRNNCell(....)
outputs, _ = tf.dynamic_rnn(RNNCell, tf.reshape(x, [1,-1,1]))
x = outputs[-1] * (tf.Varaibles(...) * tf.Constants(...)) 

Answer 1

@Vlad 的答案是正确的，但由于我是新成员，所以无法投票。下面的代码片段是带有 RNN 单元的 Vlads 的更新版本。

x = tf.placeholder("float", shape=[None,1])
model = tf.nn.rnn_cell.BasicRNNCell(num_units=1, activation=None)

outputs, state = tf.nn.dynamic_rnn(model, tf.reshape(x, [-1,1, 1]), dtype=tf.float32)

# output1 = model.output 

# output1 = outputs[-1]
output1 = outputs[:,-1,:]
# output1 = outputs

some_value = tf.constant([9.0],     # <-- Some tensor the output will be multiplied by
                         dtype=tf.float32)
output1 *= some_value                # <-- The output had been multiplied by `some_value`
                                     #     (with broadcasting in case of
                                     #     more than one input samples)

with tf.control_dependencies([output1]): # <-- Not necessary, but explicit control
    output2, state2 = model(output1,state)