tf.nn.conv2d_backprop_input( input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu=True, data_format='NHWC', dilations=[1, 1, 1, 1], name=None )
Defined in tensorflow/python/ops/gen_nn_ops.py
.
See the guide: Neural Network > Convolution
Computes the gradients of convolution with respect to the input.
input_sizes
: A Tensor
of type int32
. An integer vector representing the shape of input
, where input
is a 4-D [batch, height, width, channels]
tensor.filter
: A Tensor
. Must be one of the following types: half
, bfloat16
, float32
, float64
. 4-D with shape [filter_height, filter_width, in_channels, out_channels]
.out_backprop
: A Tensor
. Must have the same type as filter
. 4-D with shape [batch, out_height, out_width, out_channels]
. Gradients w.r.t. the output of the convolution.strides
: A list of ints
. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.padding
: A string
from: "SAME", "VALID"
. The type of padding algorithm to use.use_cudnn_on_gpu
: An optional bool
. Defaults to True
.data_format
: An optional string
from: "NHWC", "NCHW"
. Defaults to "NHWC"
. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].dilations
: An optional list of ints
. Defaults to [1, 1, 1, 1]
. 1-D tensor of length 4. The dilation factor for each dimension of input
. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format
, see above for details. Dilations in the batch and depth dimensions must be 1.name
: A name for the operation (optional).A Tensor
. Has the same type as filter
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_backprop_input