Question

我是tensorflow的新手，我正在构建一个网络，但无法为其计算/应用渐变。我收到错误：

ValueError: No gradients provided for any variable: ((None, tensorflow.python.ops.variables.Variable object at 0x1025436d0), ... (None, tensorflow.python.ops.variables.Variable object at 0x10800b590))

我尝试使用tensorboard graph查看是否有某些内容无法跟踪图表并获得渐变但我看不到任何内容。

这是代码的一部分：

sess = tf.Session()

X = tf.placeholder(type, [batch_size,feature_size])

W = tf.Variable(tf.random_normal([feature_size, elements_size * dictionary_size]), name="W")

target_probabilties = tf.placeholder(type, [batch_size * elements_size, dictionary_size])

lstm = tf.nn.rnn_cell.BasicLSTMCell(lstm_hidden_size)

stacked_lstm = tf.nn.rnn_cell.MultiRNNCell([lstm] * number_of_layers)

initial_state = state = stacked_lstm.zero_state(batch_size, type)

output, state = stacked_lstm(X, state)

pred = tf.matmul(output,W)
pred = tf.reshape(pred, (batch_size * elements_size, dictionary_size))

# instead of calculating this, I will calculate the difference between the target_W and the current W
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(target_probabilties, pred)

cost = tf.reduce_mean(cross_entropy)

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)



sess.run(optimizer, feed_dict={X:my_input, target_probabilties:target_prob})

我将非常感谢您解决这个问题。

Answer 1

我总是使用tf.nn.softmax_cross_entropy_with_logits（），这样我就可以将logits作为第一个参数，将标签作为第二个参数。你能试试吗？

Tensorflow：没有为任何变量提供渐变

1 个答案: