tf.nn.conv2d( input, filter, 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 a 2-D convolution given 4-D input
and filter
tensors.
Given an input tensor of shape [batch, in_height, in_width, in_channels]
and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels]
, this op performs the following:
[filter_height * filter_width * in_channels, output_channels]
.[batch, out_height, out_width, filter_height * filter_width * in_channels]
.In detail, with the default NHWC format,
output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]
Must have strides[0] = strides[3] = 1
. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1]
.
input
: A Tensor
. Must be one of the following types: half
, bfloat16
, float32
, float64
. A 4-D tensor. The dimension order is interpreted according to the value of data_format
, see below for details.filter
: A Tensor
. Must have the same type as input
. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
strides
: A list of ints
. 1-D tensor of length 4. The stride of the sliding window for each dimension of input
. The dimension order is determined by the value of data_format
, see below for details.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, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, 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 input
.
© 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