本文整理汇总了Python中tensorflow.python.ops.gen_nn_ops._avg_pool_grad方法的典型用法代码示例。如果您正苦于以下问题：Python gen_nn_ops._avg_pool_grad方法的具体用法？Python gen_nn_ops._avg_pool_grad怎么用？Python gen_nn_ops._avg_pool_grad使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.gen_nn_ops的用法示例。
在下文中一共展示了gen_nn_ops._avg_pool_grad方法的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: _AvgPoolGrad
# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import _avg_pool_grad [as 别名]
def _AvgPoolGrad(op, grad):
return gen_nn_ops._avg_pool_grad(
array_ops.shape(op.inputs[0]),
grad,
op.get_attr("ksize"),
op.get_attr("strides"),
op.get_attr("padding"),
data_format=op.get_attr("data_format")) 示例2: testDirectNotUseOverlapping
# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import _avg_pool_grad [as 别名]
def testDirectNotUseOverlapping(self):
for num_batches in [1, 3]:
for row_window_size in [2, 5]:
for col_window_size in [2, 4]:
num_rows = row_window_size * 5
num_cols = col_window_size * 7
for num_channels in [1, 2]:
input_shape = (num_batches, num_rows, num_cols, num_channels)
with self.test_session() as _:
input_tensor = tf.constant(self._GenerateRandomInputTensor(
input_shape).astype(np.float32))
window_size = [1, row_window_size, col_window_size, 1]
stride_size = [1, row_window_size, col_window_size, 1]
padding = "VALID"
output_tensor = tf.nn.avg_pool(input_tensor, window_size,
stride_size, padding)
output_data = output_tensor.eval()
num_elements = 1
for dim_size in output_data.shape:
num_elements *= dim_size
output_backprop = (self._PRNG.rand(num_elements) *
1000).reshape(output_data.shape)
input_backprop_tensor = gen_nn_ops._avg_pool_grad(
input_tensor.get_shape(), output_backprop, window_size,
stride_size, padding)
input_backprop = input_backprop_tensor.eval()
row_seq = list(range(0, num_rows + 1, row_window_size))
col_seq = list(range(0, num_cols + 1, col_window_size))
fap_input_backprop_tensor = gen_nn_ops._fractional_avg_pool_grad(
input_tensor.get_shape(),
output_backprop,
row_seq,
col_seq,
overlapping=False)
fap_input_backprop = fap_input_backprop_tensor.eval()
self.assertShapeEqual(input_backprop, fap_input_backprop_tensor)
self.assertAllClose(input_backprop, fap_input_backprop) 示例3: testDirectUseOverlapping
# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import _avg_pool_grad [as 别名]
def testDirectUseOverlapping(self):
for num_batches in [1, 3]:
for row_window_size in [2, 5]:
for col_window_size in [2, 4]:
num_rows = (row_window_size - 1) * 5 + 1
num_cols = (col_window_size - 1) * 7 + 1
for num_channels in [1, 2]:
input_shape = (num_batches, num_rows, num_cols, num_channels)
with self.test_session() as _:
input_tensor = tf.constant(self._GenerateRandomInputTensor(
input_shape).astype(np.float32))
window_size = [1, row_window_size, col_window_size, 1]
stride_size = [1, row_window_size - 1, col_window_size - 1, 1]
padding = "VALID"
output_tensor = tf.nn.avg_pool(input_tensor, window_size,
stride_size, padding)
output_data = output_tensor.eval()
num_elements = 1
for dim_size in output_data.shape:
num_elements *= dim_size
output_backprop = (self._PRNG.rand(num_elements) *
1000).reshape(output_data.shape)
input_backprop_tensor = gen_nn_ops._avg_pool_grad(
input_tensor.get_shape(), output_backprop, window_size,
stride_size, padding)
input_backprop = input_backprop_tensor.eval()
row_seq = list(range(0, num_rows, row_window_size - 1))
col_seq = list(range(0, num_cols, col_window_size - 1))
row_seq[-1] += 1
col_seq[-1] += 1
fap_input_backprop_tensor = gen_nn_ops._fractional_avg_pool_grad(
input_tensor.get_shape(),
output_backprop,
row_seq,
col_seq,
overlapping=True)
fap_input_backprop = fap_input_backprop_tensor.eval()
self.assertShapeEqual(input_backprop, fap_input_backprop_tensor)
self.assertAllClose(input_backprop, fap_input_backprop)