本文整理匯總了Python中tensorflow.python.ops.array_ops.shape_n方法的典型用法代碼示例。如果您正苦於以下問題：Python array_ops.shape_n方法的具體用法？Python array_ops.shape_n怎麽用？Python array_ops.shape_n使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.python.ops.array_ops的用法示例。
在下文中一共展示了array_ops.shape_n方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _Conv2DGrad
# 需要導入模塊: from tensorflow.python.ops import array_ops [as 別名]
# 或者: from tensorflow.python.ops.array_ops import shape_n [as 別名]
def _Conv2DGrad(op, grad):
strides = op.get_attr("strides")
padding = op.get_attr("padding")
use_cudnn_on_gpu = op.get_attr("use_cudnn_on_gpu")
data_format = op.get_attr("data_format")
shape_0, shape_1 = array_ops.shape_n([op.inputs[0], op.inputs[1]])
return [nn_ops.conv2d_backprop_input(shape_0,
op.inputs[1],
grad,
strides,
padding,
use_cudnn_on_gpu,
data_format),
nn_ops.conv2d_backprop_filter(op.inputs[0],
shape_1,
grad,
strides,
padding,
use_cudnn_on_gpu,
data_format)] 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:22，代碼來源:nn_grad.py
示例2: _verify_compatible_image_shapes
# 需要導入模塊: from tensorflow.python.ops import array_ops [as 別名]
# 或者: from tensorflow.python.ops.array_ops import shape_n [as 別名]
def _verify_compatible_image_shapes(img1, img2):
"""Checks if two image tensors are compatible for applying SSIM or PSNR.
This function checks if two sets of images have ranks at least 3, and if the
last three dimensions match.
Args:
img1: Tensor containing the first image batch.
img2: Tensor containing the second image batch.
Returns:
A tuple containing: the first tensor shape, the second tensor shape, and a
list of control_flow_ops.Assert() ops implementing the checks.
Raises:
ValueError: When static shape check fails.
"""
shape1 = img1.get_shape().with_rank_at_least(3)
shape2 = img2.get_shape().with_rank_at_least(3)
shape1[-3:].assert_is_compatible_with(shape2[-3:])
if shape1.ndims is not None and shape2.ndims is not None:
for dim1, dim2 in zip(reversed(shape1.dims[:-3]),
reversed(shape2.dims[:-3])):
if not (dim1 == 1 or dim2 == 1 or dim1.is_compatible_with(dim2)):
raise ValueError('Two images are not compatible: %s and %s' % (shape1, shape2))
shape1, shape2 = array_ops.shape_n([img1, img2])
checks = []
checks.append(control_flow_ops.Assert(
math_ops.greater_equal(array_ops.size(shape1), 3),
[shape1, shape2], summarize=10))
checks.append(control_flow_ops.Assert(
math_ops.reduce_all(math_ops.equal(shape1[-3:], shape2[-3:])),
[shape1, shape2], summarize=10))
return shape1, shape2, checks 示例3: _ssim_per_channel
# 需要導入模塊: from tensorflow.python.ops import array_ops [as 別名]
# 或者: from tensorflow.python.ops.array_ops import shape_n [as 別名]
def _ssim_per_channel(img1, img2, max_val=1.0):
filter_size = constant_op.constant(11, dtype=dtypes.int32)
filter_sigma = constant_op.constant(1.5, dtype=img1.dtype)
shape1, shape2 = array_ops.shape_n([img1, img2])
checks = [
control_flow_ops.Assert(math_ops.reduce_all(math_ops.greater_equal(
shape1[-3:-1], filter_size)), [shape1, filter_size], summarize=8),
control_flow_ops.Assert(math_ops.reduce_all(math_ops.greater_equal(
shape2[-3:-1], filter_size)), [shape2, filter_size], summarize=8)]
# Enforce the check to run before computation.
with ops.control_dependencies(checks):
img1 = array_ops.identity(img1)
kernel = _fspecial_gauss(filter_size, filter_sigma)
kernel = array_ops.tile(kernel, multiples=[1, 1, shape1[-1], 1])
compensation = 1.0
def reducer(x):
shape = array_ops.shape(x)
x = array_ops.reshape(x, shape=array_ops.concat([[-1], shape[-3:]], 0))
y = nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID')
return array_ops.reshape(y, array_ops.concat([shape[:-3],
array_ops.shape(y)[1:]], 0))
luminance, cs = _ssim_helper(img1, img2, reducer, max_val, compensation)
# Average over the second and the third from the last: height, width.
axes = constant_op.constant([-3, -2], dtype=dtypes.int32)
ssim_val = math_ops.reduce_mean(luminance * cs, axes)
cs = math_ops.reduce_mean(cs, axes)
luminance = math_ops.reduce_mean(luminance, axes)
return ssim_val, cs, luminance