import tensorflow as tf
tf.InteractiveSession()
@tf.custom_gradient
def custom_multiply(a, x):
# Define your own forward step
y = a * x
# Define your own backward step
def grads(dy): return dy * x, dy * a + 100
# Return the forward result and the backward function
return y, grads
a, x = tf.constant(2), tf.constant(3)
y = custom_multiply(a, x)
dy_dx = tf.gradients(y, x)[0]
# It will print `dy/dx = 102` instead of 2 if the gradient is not customized
print('dy/dx =', dy_dx.eval())
您可以使用tf.custom_gradient在单个方法中定义自己的前进和后退步骤。下面是一个简单的例子:
如果您想customize your own layer,只需将
tf.layers.Dropout.call
中使用的核心函数替换为您自己的核心函数相关问题 更多 >
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