def conv2d(input, filter, strides, padding, use_cudnn_on_gpu=True, data_format="NHWC", name=None):
r"""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.
"""
if not isinstance(strides, (list, tuple)):
raise TypeError(
"Expected list for 'strides' argument to "
"'conv2d' Op, not %r." % strides)
strides = [_execute.make_int(_i, "strides") for _i in strides]
padding = _execute.make_str(padding, "padding")
if use_cudnn_on_gpu is None:
use_cudnn_on_gpu = True
use_cudnn_on_gpu = _execute.make_bool(use_cudnn_on_gpu, "use_cudnn_on_gpu")
if data_format is None:
data_format = "NHWC"
data_format = _execute.make_str(data_format, "data_format")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"Conv2D", input=input, filter=filter, strides=strides,
padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu,
data_format=data_format, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "strides", _op.get_attr("strides"),
"use_cudnn_on_gpu", _op.get_attr("use_cudnn_on_gpu"), "padding",
_op.get_attr("padding"), "data_format",
_op.get_attr("data_format"))
else:
_attr_T, _inputs_T = _execute.args_to_matching_eager([input, filter], _ctx)
(input, filter) = _inputs_T
_attr_T = _attr_T.as_datatype_enum
_inputs_flat = [input, filter]
_attrs = ("T", _attr_T, "strides", strides, "use_cudnn_on_gpu",
use_cudnn_on_gpu, "padding", padding, "data_format",
data_format)
_result = _execute.execute(b"Conv2D", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"Conv2D", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
注意函數(shù)的幾個(gè)參數(shù)
每個(gè)參數(shù)的shape均不相同
最終返回Returns:
A Tensor. Has the same type as input.
