本文整理汇总了Python中tensorflow.python.ops.gen_nn_ops.bias_add_grad函数的典型用法代码示例。如果您正苦于以下问题：Python bias_add_grad函数的具体用法？Python bias_add_grad怎么用？Python bias_add_grad使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了bias_add_grad函数的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _BiasAddGrad
def _BiasAddGrad(op, received_grad):
"""Return the gradients for the 2 inputs of bias_op.
The first input of unused_bias_op is the tensor t, and its gradient is
just the gradient the unused_bias_op received.
The second input of unused_bias_op is the bias vector which has one fewer
dimension than "received_grad" (the batch dimension.) Its gradient is the
received gradient Summed on the batch dimension, which is the first dimension.
Args:
op: The BiasOp for which we need to generate gradients.
received_grad: Tensor. The gradients passed to the BiasOp.
Returns:
Two tensors, the first one for the "tensor" input of the BiasOp,
the second one for the "bias" input of the BiasOp.
"""
try:
data_format = op.get_attr("data_format")
except ValueError:
data_format = None
return (received_grad,
gen_nn_ops.bias_add_grad(
out_backprop=received_grad, data_format=data_format))示例2: testBiasAddGrad
def testBiasAddGrad(self):
self._testUnary(
gen_nn_ops.bias_add_grad,
np.array([[1., 2.], [3., 4.]], dtype=np.float32),
expected=np.array([4., 6.], dtype=np.float32))
self._testUnary(lambda x: gen_nn_ops.bias_add_grad(x, data_format="NCHW"),
np.array([[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]],
dtype=np.float32),
expected=np.array([10., 26.], dtype=np.float32))示例3: testBiasAddGrad
def testBiasAddGrad(self):
self._assertOpOutputMatchesExpected(
gen_nn_ops.bias_add_grad,
np.array([[1., 2.], [3., 4.]], dtype=np.float32),
expected=np.array([4., 6.], dtype=np.float32))
self._assertOpOutputMatchesExpected(
lambda x: gen_nn_ops.bias_add_grad(x, data_format="NCHW"),
np.array(
[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]], dtype=np.float32),
expected=np.array([14., 22.], dtype=np.float32))