tf.raw_ops.Conv2D

Computes a 2-D convolution given 4-D input and filter tensors.

tf.raw_ops.Conv2D(
    input,
    filter,
    strides,
    padding,
    use_cudnn_on_gpu=True,
    explicit_paddings=[],
    data_format='NHWC',
    dilations=[1, 1, 1, 1],
    name=None
)

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:

  1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels].
  2. 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].
  3. 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].

input A Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32. 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", "EXPLICIT". The type of padding algorithm to use.
use_cudnn_on_gpu An optional bool. Defaults to True.
explicit_paddings An optional list of ints. Defaults to []. If padding is "EXPLICIT", the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is explicit_paddings[2 * i] and explicit_paddings[2 * i + 1], respectively. If padding is not "EXPLICIT", explicit_paddings must be empty.
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.