python - 值错误: No variables to optimize

Question

我正在尝试计算两个图像之间的l2_loss并获取它们的梯度。这里给出了我的代码片段:

with tf.name_scope("train"):

    X = tf.placeholder(tf.float32, [1, None, None, None], name='X')
    y = tf.placeholder(tf.float32, [1, None, None, None], name='y')
    Z = tf.nn.l2_loss(X - y, name="loss")
    step_loss = tf.reduce_mean(Z)
    optimizer = tf.train.AdamOptimizer()
    training_op = optimizer.minimize(step_loss)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    init.run()
    content = tf.gfile.FastGFile('cat.0.jpg', 'rb').read()
    noise = tf.gfile.FastGFile('color_img.jpg', 'rb').read()
    loss_append = []
    for epoch in range(10):
        for layer in layers:
            c = sess.run(layer, feed_dict={input_img: content})
            n = sess.run(layer, feed_dict={input_img: noise})
            sess.run(training_op, feed_dict={X: c, y: n})

但它给出了以下错误:

    Traceback (most recent call last):
   File "/home/noise_image.py",     line 68, in <module>
    training_op = optimizer.minimize(lossss)
   File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training /optimizer.py", line 315, in minimize
    grad_loss=grad_loss)
   File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training   /optimizer.py", line 380, in compute_gradients
    raise ValueError("No variables to optimize.")
ValueError: No variables to optimize. 

如何摆脱它？

Answer 1

X 和 y 具有从 feed_dict 馈送的值，而 Z 是这些值的函数，因此TensorFlow 无法训练它们。

不要将X设置为占位符，而是将其分配给其张量值(layer)。对 y 执行相同的操作。

您的最终代码应类似于:

for epoch in range(10):
    sess.run(training_op, feed_dict={input_image_content: content, input_image_noise: noise})