本文整理匯總了Python中tensorflow.python.ops.nn.depthwise_conv2d方法的典型用法代碼示例。如果您正苦於以下問題：Python nn.depthwise_conv2d方法的具體用法？Python nn.depthwise_conv2d怎麽用？Python nn.depthwise_conv2d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.nn的用法示例。
在下文中一共展示了nn.depthwise_conv2d方法的2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: _ssim_per_channel
# 需要導入模塊: from tensorflow.python.ops import nn [as 別名]
# 或者: from tensorflow.python.ops.nn import depthwise_conv2d [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 示例2: depthwise_conv2d
# 需要導入模塊: from tensorflow.python.ops import nn [as 別名]
# 或者: from tensorflow.python.ops.nn import depthwise_conv2d [as 別名]
def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
"""2D convolution with separable filters.
Arguments:
x: input tensor
depthwise_kernel: convolution kernel for the depthwise convolution.
strides: strides tuple (length 2).
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
dilation_rate: tuple of integers,
dilation rates for the separable convolution.
Returns:
Output tensor.
Raises:
ValueError: if `data_format` is neither `channels_last`
or `channels_first`.
"""
if data_format is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format ' + str(data_format))
x = _preprocess_conv2d_input(x, data_format)
padding = _preprocess_padding(padding)
strides = (1,) + strides + (1,)
x = nn.depthwise_conv2d(x, depthwise_kernel,
strides=strides,
padding=padding,
rate=dilation_rate)
return _postprocess_conv2d_output(x, data_format) 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:36，代碼來源:backend.py