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提交 9cbe7bcc 编写于 作者: Z Zhang Ting 提交者： Aurelius84
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modified error message and API doc for channel_last supported Op (#21002) 

* modified error message for conv and conv_transpose, test=develop

* modified doc of conv and conv_transpose op, test=develop

* modified the expression for error message, test=develop

* modified error message for group_norm op, test=develop

* modified detail of Attr(data_format) or Attr(data_layout)

* add ValueError in API doc for maxout op, test=develop
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Showing with 206 addition and 83 deletion
paddle/fluid/operators/conv_op.cc
浏览文件 @ 9cbe7bcc
......@@ -50,30 +50,28 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
const std::string data_format = ctx->Attrs().Get<std::string>("data_format");
const bool channel_last = (data_format == "NHWC" || data_format == "NDHWC");
PADDLE_ENFORCE_EQ(in_dims.size() == 4 || in_dims.size() == 5, true,
"ShapeError: Conv input should be 4-D or 5-D tensor. But "
"received: %u-D Tensor,"
"the shape of Conv input is [%s]",
PADDLE_ENFORCE_EQ(
in_dims.size() == 4 || in_dims.size() == 5, true,
"ShapeError: the input of Op(conv) should be 4-D or 5-D Tensor. But "
"received: %u-D Tensor, the shape of input is [%s].",
in_dims.size(), in_dims);
PADDLE_ENFORCE_EQ(
in_dims.size(), filter_dims.size(),
"ShapeError: Conv input dimension and filter dimension should be the "
"equal."
"But received: the shape of Conv input is [%s], input dimension of Conv "
"input is [%d],"
"the shape of filter is [%s], the filter dimension of Conv is [%d]",
"ShapeError: the input's dimension size and filter's dimension size of "
"Op(conv) should be equal. But received: the shape of input is [%s], "
"the dimension size of input is [%d], the shape of filter is [%s], "
"the dimension size of filter is [%d].",
in_dims, in_dims.size(), filter_dims, filter_dims.size());
int in_sub_stride_size = in_dims.size() - strides.size();
PADDLE_ENFORCE_EQ(in_dims.size() - strides.size() == 2U, true,
"ShapeError: the dimension of input minus the dimension of "
"stride must be euqal to 2."
"But received: the dimension of input minus the dimension "
"of stride is [%d], the"
"input dimension of Conv is [%d], the shape of Conv input "
"is [%s], the stride"
"dimension of Conv is [%d]",
"ShapeError: the dimension size of input minus the size of "
"Attr(stride) must be euqal to 2 for Op(conv)."
"But received: the dimension size of input minus the size "
"of Attr(stride) is [%d], the "
"input's dimension size is [%d], the shape of input "
"is [%s], the Attr(stride)'s size is [%d].",
in_sub_stride_size, in_dims.size(), in_dims,
strides.size());
......@@ -83,16 +81,19 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_EQ(
input_channels, filter_dims[1] * groups,
"ShapeError: The number of input channels should be equal to filter "
"channels * groups. But received: the input channels is [%d], the shape"
"of input is [%s], the filter channel is [%d], the shape of filter is "
"[%s],"
"the groups is [%d]",
in_dims[1], in_dims, filter_dims[1], filter_dims, groups);
"channels * groups for Op(conv). But received: the input's channels is "
"[%d], the shape "
"of input is [%s], the filter's channel is [%d], the shape of filter is "
"[%s], the groups is [%d], the data_format is %s. The error may come "
"from wrong data_format setting.",
input_channels, in_dims, filter_dims[1], filter_dims, groups,
data_format);
PADDLE_ENFORCE_EQ(
filter_dims[0] % groups, 0,
"ShapeError: The number of output channels should be divided by groups."
"But received: the output channels is [%d], the shape of filter is [%s]"
"(the first dimension of filter is output channel), the groups is [%d]",
"ShapeError: The number of output channels of Op(conv) should be divided "
"by groups. "
"But received: the output channels is [%d], the shape of filter is [%s] "
"(the first dimension of filter is output channel), the groups is [%d].",
filter_dims[0], filter_dims, groups);
framework::DDim in_data_dims;
......
paddle/fluid/operators/conv_transpose_op.cc
浏览文件 @ 9cbe7bcc
......@@ -46,30 +46,48 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
int groups = ctx->Attrs().Get<int>("groups");
std::string padding_algorithm =
ctx->Attrs().Get<std::string>("padding_algorithm");
const DataLayout data_layout = framework::StringToDataLayout(
ctx->Attrs().Get<std::string>("data_format"));
const std::string data_layout_str =
ctx->Attrs().Get<std::string>("data_format");
const framework::DataLayout data_layout =
framework::StringToDataLayout(data_layout_str);
PADDLE_ENFORCE_EQ(in_dims.size() == 4 || in_dims.size() == 5, true,
"ConvTransposeOp intput should be 4-D or 5-D tensor.");
PADDLE_ENFORCE_EQ(in_dims.size(), filter_dims.size(),
"ConvTransposeOp input dimension and filter dimension "
"should be the same.");
"ShapeError: input of Op(conv_transpose) should be 4-D or "
"5-D Tensor. But received: %u-D Tensor, "
"the shape of input is [%s]",
in_dims.size(), in_dims);
PADDLE_ENFORCE_EQ(
in_dims.size(), filter_dims.size(),
"ShapeError: the input's dimension size and filter's dimension size of "
"Op (conv_transpose) should be equal. But received: the shape of input "
"is [%s], the dimension size of input is [%d], the shape of filter is "
"[%s], the dimension size of filter is [%d]. ",
in_dims, in_dims.size(), filter_dims, filter_dims.size());
int in_sub_stride_size = in_dims.size() - strides.size();
PADDLE_ENFORCE_EQ(
in_dims.size() - strides.size(), 2U,
"ConvTransposeOp input dimension and strides dimension should "
"be consistent.");
"ShapeError: the input's dimension size minus Attr(stride)'s size must "
"be euqal to 2 for Op(conv_transpose). But received: [%d], the "
"input's dimension size is [%d], the shape of input "
"is [%s], the Attr(stride)'s size is [%d].",
in_sub_stride_size, in_dims.size(), in_dims, strides.size());
if (output_size.size())
PADDLE_ENFORCE_EQ(output_size.size(), strides.size(),
"ConvTransposeOp output_size dimension and strides "
"dimension should be the same.");
PADDLE_ENFORCE_EQ(
output_size.size(), strides.size(),
"The Attr(output_size) and Attr(stride) of Op(conv_transpose) "
"should be the same.");
const int64_t C =
(data_layout != DataLayout::kNHWC ? in_dims[1]
: in_dims[in_dims.size() - 1]);
PADDLE_ENFORCE_EQ(
C, filter_dims[0],
"The number of input channels of Op(ConvTransposeOp) should "
"be equal to the number of filter's channels.");
"ShapeError: The number of input channels should be equal to filter "
"channels for Op(conv_transpose). But received: the input's channels is "
"[%d], the shape of input is [%s], the filter's channels is [%d], the "
"shape of filter is [%s]. The data_format is %s."
"The error may come from wrong data_format setting.",
C, in_dims, filter_dims[0], filter_dims, data_layout_str);
framework::DDim in_data_dims;
if (data_layout != DataLayout::kNHWC) {
......
paddle/fluid/operators/group_norm_op.cc
浏览文件 @ 9cbe7bcc
......@@ -39,24 +39,57 @@ class GroupNormOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasOutput("Variance"),
"Output(Variance) of GroupNormOp should not be null.");
auto x_dim = ctx->GetInputDim("X");
const DataLayout data_layout = framework::StringToDataLayout(
ctx->Attrs().Get<std::string>("data_layout"));
const std::string data_layout_str =
ctx->Attrs().Get<std::string>("data_layout");
const framework::DataLayout data_layout =
framework::StringToDataLayout(data_layout_str);
const int64_t channel_num =
(data_layout == DataLayout::kNCHW ? x_dim[1] : x_dim[x_dim.size() - 1]);
auto batch_size = x_dim[0];
auto groups = ctx->Attrs().Get<int>("groups");
PADDLE_ENFORCE_LE(
groups, channel_num,
"'groups' must be less equal than the number of channels.");
PADDLE_ENFORCE_GE(groups, 1, "'groups' must be greater equal than 1.");
"ValueError: the Attr(groups) of Op(group_norm) must be less than or "
"equal to the number of channels. "
"But received: groups is [%s], channels is [%s], the Attr(data_layout) "
"is [%s]. The error may come from wrong data_layout setting.",
groups, channel_num, data_layout_str);
PADDLE_ENFORCE_GE(
groups, 1,
"ValueError: the Attr(groups) of Op(group_norm) must be "
"greater than or equal to 1. But received: groups is [%s].",
groups);
if (ctx->HasInput("Scale")) {
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], channel_num);
PADDLE_ENFORCE_EQ(
ctx->GetInputDim("Scale").size(), 1UL,
"ShapeError: the Input(Scale) of Op(group_norm) should be 1-D "
"Tensor. "
"But received: %u-D Tensor, the shape of Input(Scale) is [%s].",
ctx->GetInputDim("Scale").size(), ctx->GetInputDim("Scale"));
PADDLE_ENFORCE_EQ(
ctx->GetInputDim("Scale")[0], channel_num,
"ShapeError: the Input(Scale)'s first dimension size of "
"Op(group_norm) must be equal to the number of channels. "
"But received: the Input(Scale)'s first dimension size is [%s], the "
"channels is [%s], the Attr(data_layout) is [%s]. The error may come "
"from wrong data_layout setting.",
ctx->GetInputDim("Scale")[0], channel_num, data_layout_str);
}
if (ctx->HasInput("Bias")) {
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], channel_num);
PADDLE_ENFORCE_EQ(
ctx->GetInputDim("Bias").size(), 1UL,
"ShapeError: the Input(Bias) of Op(group_norm) should be 1-D Tensor. "
"But received: %u-D Tensor, the shape of Input(Bias) is [%s].",
ctx->GetInputDim("Bias").size(), ctx->GetInputDim("Bias"));
PADDLE_ENFORCE_EQ(
ctx->GetInputDim("Bias")[0], channel_num,
"ShapeError: the Input(Bias)'s first dimension size of "
"Op(group_norm) must be equal to the number of channels. "
"But received: the Input(Bias)'s first dimension size is [%s], the "
"channels is [%s], the Attr(data_layout) is [%s]. The error may come "
"from wrong data_layout setting.",
ctx->GetInputDim("Bias")[0], channel_num, data_layout_str);
}
ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 9cbe7bcc
......@@ -1213,7 +1213,8 @@ def conv2d(input,
name(str|None): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
data_format (str): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
......@@ -1223,6 +1224,19 @@ def conv2d(input,
result, and if act is not None, the tensor variable storing convolution
and non-linearity activation result.
Raises:
ValueError: If the type of `use_cudnn` is not bool.
ValueError: If `data_format` is not "NCHW" or "NHWC".
ValueError: If the channel dimmention of the input is less than or equal to zero.
ValueError: If `padding` is a string, but not "SAME" or "VALID".
ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
or the element corresponding to the input's channel is not 0.
ShapeError: If the input is not 4-D Tensor.
ShapeError: If the input's dimension size and filter's dimension size not equal.
ShapeError: If the dimension size of input minus the size of `stride` is not 2.
ShapeError: If the number of input channels is not equal to filter's channels * groups.
ShapeError: If the number of output channels is not be divided by groups.
Examples:
.. code-block:: python
......@@ -1467,9 +1481,10 @@ def conv3d(input,
name(str|None): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
data_format (str): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_depth, input_height, input_width]`.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
Returns:
A Variable holding Tensor representing the conv3d, whose data type is
......@@ -1477,6 +1492,19 @@ def conv3d(input,
convolution result, and if act is not None, the tensor variable storing
convolution and non-linearity activation result.
Raises:
ValueError: If the type of `use_cudnn` is not bool.
ValueError: If `data_format` is not "NCDHW" or "NDHWC".
ValueError: If the channel dimmention of the input is less than or equal to zero.
ValueError: If `padding` is a string, but not "SAME" or "VALID".
ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
or the element corresponding to the input's channel is not 0.
ShapeError: If the input is not 5-D Tensor.
ShapeError: If the input's dimension size and filter's dimension size not equal.
ShapeError: If the dimension size of input minus the size of `stride` is not 2.
ShapeError: If the number of input channels is not equal to filter's channels * groups.
ShapeError: If the number of output channels is not be divided by groups.
Examples:
.. code-block:: python
......@@ -2426,7 +2454,10 @@ def batch_norm(input,
will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero.
Default: None.
data_layout(str, default NCHW): the data_layout of input, is NCHW or NHWC.
data_layout (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
in_place(bool, Default False): Make the input and output of batch norm reuse memory.
name(str|None): For detailed information, please refer to :ref:`api_guide_Name`.
Usually name is no need to set and None by default.
......@@ -2700,7 +2731,10 @@ def data_norm(input,
act(string, Default None): Activation type, linear|relu|prelu|...
epsilon(float, Default 1e-05):
param_attr(ParamAttr): The parameter attribute for Parameter `scale`.
data_layout(string, default NCHW): NCHW|NHWC
data_layout (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
in_place(bool, Default False): Make the input and output of batch norm reuse memory.
name(string, Default None): A name for this layer(optional). If set None, the layer
will be named automatically.
......@@ -2944,9 +2978,10 @@ def group_norm(input,
Default: None, the default bias parameter attribute is used. For more information, please
refer to :ref:`api_guide_ParamAttr` .
act(str, optional): Activation to be applied to the output of group normalizaiton.
data_layout(str, optional): The data format of the input and output data. An optional string
from: `"NCHW"`, `"NHWC"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, channels, height, width]`. Default: "NCHW".
data_layout(str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
name (str, optional): The default value is None. Normally there is no need for user to set this
property. For more information, please refer to :ref:`api_guide_Name` .
......@@ -2955,6 +2990,12 @@ def group_norm(input,
Raises:
ValueError: If `data_layout` is neither 'NCHW' nor 'NHWC'.
ValueError: If `groups` is greater than the number of input channels.
ValueError: If `groups` is less than 1.
ShapeError: If the param_attr(Scale) is not 1-D Tensor.
ShapeError: If the param_attr(Scale)'s first dimension size is not equal to the input channels.
ShapeError: If the bias_attr(Bias) is not 1-D Tensor.
ShapeError: If the bias_attr(Bias)'s first dimension size is not equal to the input channels.
Examples:
.. code-block:: python
......@@ -3240,9 +3281,10 @@ def conv2d_transpose(input,
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
data_format(str, optional): The data format of the input and output data. An optional string
from: `"NCHW"`, `"NHWC"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. Default: 'NCHW'.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
Returns:
A Variable holding Tensor representing the conv2d_transpose, whose
......@@ -3253,8 +3295,17 @@ def conv2d_transpose(input,
result.
Raises:
ValueError: If the shapes of output, input, filter_size, stride, padding and
groups mismatch.
ValueError: If the type of `use_cudnn` is not bool.
ValueError: If `data_format` is not "NCHW" or "NHWC".
ValueError: If `padding` is a string, but not "SAME" or "VALID".
ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
or the element corresponding to the input's channel is not 0.
ValueError: If `output_size` and filter_size are None at the same time.
ShapeError: If the input is not 4-D Tensor.
ShapeError: If the input's dimension size and filter's dimension size not equal.
ShapeError: If the dimension size of input minus the size of `stride` is not 2.
ShapeError: If the number of input channels is not equal to filter's channels.
ShapeError: If the size of `output_size` is not equal to that of `stride`.
Examples:
.. code-block:: python
......@@ -3519,9 +3570,10 @@ def conv3d_transpose(input,
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
data_format(str, optional):The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`.
Default: 'NCDHW'.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
Returns:
A Variable holding Tensor representing the conv3d_transpose, whose data
......@@ -3531,8 +3583,17 @@ def conv3d_transpose(input,
variable storing transposed convolution and non-linearity activation result.
Raises:
ValueError: If the shapes of output, input, filter_size, stride, padding and
groups mismatch.
ValueError: If the type of `use_cudnn` is not bool.
ValueError: If `data_format` is not "NCDHW" or "NDHWC".
ValueError: If `padding` is a string, but not "SAME" or "VALID".
ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
or the element corresponding to the input's channel is not 0.
ValueError: If `output_size` and filter_size are None at the same time.
ShapeError: If the input is not 5-D Tensor.
ShapeError: If the input's dimension size and filter's dimension size not equal.
ShapeError: If the dimension size of input minus the size of `stride` is not 2.
ShapeError: If the number of input channels is not equal to filter's channels.
ShapeError: If the size of `output_size` is not equal to that of `stride`.
Examples:
.. code-block:: python
......@@ -5742,9 +5803,11 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None,
beta (float, optional): The exponent, positive. Default:0.75
name (str, optional): The default value is None. Normally there is no need for user to set
this property. For more information, please refer to :ref:`api_guide_Name`
data_format(str, optional): The data format of the input and output data. An optional string
from: `"NCHW"`, `"NHWC"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. Default: 'NCHW'.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
Returns:
Variable: A tensor variable storing the transformation result with the same shape and data type as input.
......@@ -6376,11 +6439,11 @@ def image_resize(input,
align_mode(int) : An optional for bilinear interpolation. can be \'0\'
for src_idx = scale*(dst_indx+0.5)-0.5 , can be \'1\' for
src_idx = scale*dst_index.
data_format(str, optional): NCHW(num_batches, channels, height, width) or
NHWC(num_batches, height, width, channels) for 4-D Tensor,
NCDHW(num_batches, channels, depth, height, width) or
NDHWC(num_batches, depth, height, width, channels) for 5-D Tensor.
Default: 'NCHW'.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`, `"NCDHW"`,
`"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored
in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`.
Returns:
A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels),
......@@ -6696,8 +6759,10 @@ def resize_bilinear(input,
Default: None
align_corners(bool): ${align_corners_comment}
align_mode(bool): ${align_mode_comment}
data_format(str, optional): NCHW(num_batches, channels, height, width) or
NHWC(num_batches, height, width, channels). Default: 'NCHW'.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
......@@ -6858,9 +6923,10 @@ def resize_trilinear(input,
Default: None
align_corners(bool): ${align_corners_comment}
align_mode(bool): ${align_mode_comment}
data_format(str, optional): NCDHW(num_batches, channels, depth, height, width) or
NDHWC(num_batches, depth, height, width, channels).
Default: 'NCDHW'.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCDHW"`, `"NDHWC"`.
The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_depth, input_height, input_width]`.
Returns:
Variable: A 5-D Tensor(NCDHW or NDHWC)
......@@ -7010,9 +7076,10 @@ def resize_nearest(input,
errors would be occured in graph constructing stage.
Default: None
align_corners(bool): ${align_corners_comment}
data_format(str, optional): NCHW(num_batches, channels, height, width) or
NHWC(num_batches, height, width, channels).
Default: 'NCHW'.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
Returns:
Variable: 4-D tensor(NCHW or NHWC).
......@@ -11104,6 +11171,7 @@ def maxout(x, groups, name=None, axis=1):
Raises:
ValueError: If `axis` is not 1, -1 or 3.
ValueError: If the number of input channels can not be divisible by `groups`.
Examples:
.. code-block:: python
......@@ -11264,8 +11332,11 @@ def affine_channel(x,
bias (Variable): 1D input of shape (C), the c-th element is the bias
of the affine transformation for the c-th channel of the input.
The data type is float32 or float64.
data_layout (str, default NCHW): NCHW or NHWC. If input is 2D
tensor, you can ignore data_layout.
data_layout (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. If input is 2D Tensor, you can ignore
data_layout.
name (str, default None): The name of this layer. For more information,
please refer to :ref:`api_guide_Name` .
act (str, default None): Activation to be applied to the output of this layer.
......
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