tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)
Type: function
Docstring:
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:
- Flattens the filter to a 2-D matrix with shape
[filter_height * filter_width * in_channels, output_channels]. - Extracts image patches from the input tensor to form a virtual tensor of shape
[batch, out_height, out_width, filter_height * filter_width * in_channels]. - For each patch, right-multiplies the filter matrix and the image patch vector.
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].
Args:
input: A Tensor. Must be one of the following types: half, float32. 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].
name: A name for the operation (optional).
Returns:
A Tensor. Has the same type as input.
A 4-D tensor. The dimension order is determined by the value of
data_format, see below for details.
