在递归循环期间分配给TensorFlow变量

在Tensorflow 1.9中,我想创建一个网络,然后递归地将网络的输出(预测)反馈回网络的输入。在这个循环中,我想将网络所做的预测存储在一个列表中。

以下是我的尝试:

    # Define the number of steps over which to loop the network
    num_steps = 5

    # Define the network weights
    weights_1 = np.random.uniform(0, 1, [1, 10]).astype(np.float32)
    weights_2 = np.random.uniform(0, 1, [10, 1]).astype(np.float32)

    # Create a variable to store the predictions, one for each loop
    predictions = tf.Variable(np.zeros([num_steps, 1]), dtype=np.float32)

    # Define the initial prediction to feed into the loop
    initial_prediction = np.array([[0.1]], dtype=np.float32)
    x = initial_prediction

    # Loop through the predictions
    for step_num in range(num_steps):
        x = tf.matmul(x, weights_1)
        x = tf.matmul(x, weights_2)
        predictions[step_num-1].assign(x)

    # Define the final prediction
    final_prediction = x

    # Start a session
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())

    # Make the predictions
    last_pred, all_preds = sess.run([final_prediction, predictions])
    print(last_pred)
    print(all_preds)

打印出来:

[[48.8769]]

[[0.]
 [0.]
 [0.]
 [0.]
 [0.]]

final_prediction 显示正确,值为 predictions 不是我想的那样。看来 从未实际分配到,尽管行 predictions[step_num-1].assign(x) .