tf.compat.v1.nn.conv2d_backprop_input
Computes the gradients of convolution with respect to the input.
tf.compat.v1.nn.conv2d_backprop_input(
input_sizes, filter=None, out_backprop=None, strides=None, padding=None,
use_cudnn_on_gpu=True, data_format='NHWC', dilations=[1, 1, 1, 1], name=None,
filters=None
)
| Args | ||
|---|---|---|
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 |
Either the string"SAME"or"VALID"indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is"NHWC", this should be in the form[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is"NCHW", this should be in the form[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]. </td> </tr><tr> <td>use_cudnn_on_gpu</td> <td> An optionalbool. Defaults toTrue. </td> </tr><tr> <td>data_format</td> <td> An optionalstringfrom:"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]. </td> </tr><tr> <td>dilations</td> <td> An optional list ofints. Defaults to[1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension ofinput. 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 ofdata_format, see above for details. Dilations in the batch and depth dimensions must be 1. </td> </tr><tr> <td>name</td> <td> A name for the operation (optional). </td> </tr><tr> <td>filters` |
Alias for filter. |
| Returns | |
|---|---|
A Tensor. Has the same type as filter. |
© 2020 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/versions/r2.3/api_docs/python/tf/compat/v1/nn/conv2d_backprop_input