本文整理汇总了Python中tensorflow.python.ops.nn_ops.conv3d方法的典型用法代码示例。如果您正苦于以下问题：Python nn_ops.conv3d方法的具体用法？Python nn_ops.conv3d怎么用？Python nn_ops.conv3d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.nn_ops的用法示例。
在下文中一共展示了nn_ops.conv3d方法的5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: _Conv3DBackpropInputGrad
# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import conv3d [as 别名]
def _Conv3DBackpropInputGrad(op, grad):
data_format = op.get_attr("data_format")
return [None,
nn_ops.conv3d_backprop_filter_v2(grad,
array_ops.shape(op.inputs[1]),
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format),
nn_ops.conv3d(grad,
op.inputs[1],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format)] 示例2: _Conv3DBackpropFilterGrad
# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import conv3d [as 别名]
def _Conv3DBackpropFilterGrad(op, grad):
data_format = op.get_attr("data_format")
return [nn_ops.conv3d_backprop_input_v2(array_ops.shape(op.inputs[0]),
grad,
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format),
None,
nn_ops.conv3d(op.inputs[0],
grad,
strides=op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format=data_format)] 示例3: _Conv3DBackpropInputGrad
# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import conv3d [as 别名]
def _Conv3DBackpropInputGrad(op, grad):
return [None,
nn_ops.conv3d_backprop_filter_v2(grad,
array_ops.shape(op.inputs[1]),
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding")),
nn_ops.conv3d(grad,
op.inputs[1],
strides=op.get_attr("strides"),
padding=op.get_attr("padding"))] 示例4: _Conv3DBackpropFilterGrad
# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import conv3d [as 别名]
def _Conv3DBackpropFilterGrad(op, grad):
return [nn_ops.conv3d_backprop_input_v2(array_ops.shape(op.inputs[0]),
grad,
op.inputs[2],
strides=op.get_attr("strides"),
padding=op.get_attr("padding")),
None,
nn_ops.conv3d(op.inputs[0],
grad,
strides=op.get_attr("strides"),
padding=op.get_attr("padding"))] 示例5: _test_convolution3d
# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import conv3d [as 别名]
def _test_convolution3d(opname, tensor_in_sizes, filter_in_sizes,
dilations, strides, padding, data_format,
deconv_output_shape=[]):
""" One iteration of 3D convolution with given shapes and attributes """
total_size_1 = np.prod(tensor_in_sizes)
total_size_2 = np.prod(filter_in_sizes)
# Initializes the input tensor with array containing incrementing
# numbers from 1.
data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]
with tf.Graph().as_default():
in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
in_filter = constant_op.constant(
filter_array, shape=filter_in_sizes, dtype='float32')
if data_format == 'NDHWC':
strides = [1] + strides + [1]
dilations = [1] + dilations + [1]
else:
strides = [1, 1] + strides
dilations = [1, 1] + dilations
if opname == 'conv':
nn_ops.conv3d(in_data,
in_filter,
strides=strides,
dilations=dilations,
padding=padding,
data_format=data_format)
compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
'Placeholder:0', 'Conv3D:0', cuda_layout="NCDHW")