我可以在不应用输入的情况下获得张量相对于输入的梯度吗? [英] Can I get the gradient of a tensor with respect to the input without applying the input?

问题描述

例如，我需要计算 cross_entropy 相对于 x 的梯度，但我需要对梯度函数应用另一个值.

For example, I need to compute the gradient of the cross_entropy with respect to x, but I need to apply another value to the gradient function.

即:

f'(x)|x = x_t

我认为 tf.gradients() 函数只会给出 x = x 处的梯度.那么 tensorflow 是否提供任何这种功能?

I think tf.gradients() function will only give the gradient at x = x. So does tensorflow provide any of this feature?

推荐答案

的结果tf.gradients 是一个 张量(一般的张量列表)，而不是一个 float 值.在某种程度上，这个张量是一个函数:它可以在任何点进行计算.客户端只需要提供所需的输入值.

The result of tf.gradients is a tensor (list of tensors in general), not a float value. In a way, this tensor is a function: it can be evaluated in any point. The client only needs to feed the desired input value.

示例:

features = 3
n_samples = 10
hidden = 1

X = tf.placeholder(dtype=tf.float32, shape=[n_samples, features])
Y = tf.placeholder(dtype=tf.float32, shape=[n_samples])

W = tf.Variable(np.ones([features, hidden]), dtype=tf.float32, name="weight")
b = tf.Variable(np.ones([hidden]), dtype=tf.float32, name="bias")

pred = tf.add(tf.matmul(X, W), b)
cost = tf.reduce_mean(tf.pow(pred - Y, 2))

dc_dw, dc_db = tf.gradients(cost, [W, b])

session.run(tf.global_variables_initializer())

# Let's compute `dc_dw` at `ones` matrix.
print(dc_dw.eval(feed_dict={X: np.ones([n_samples, features]), 
                            Y: np.ones([n_samples])}))

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