From f7bd8c60f2be281c7e569054f8c8a4e06351902e Mon Sep 17 00:00:00 2001 From: Travis CI Date: Tue, 23 Jan 2018 02:53:14 +0000 Subject: [PATCH] Deploy to GitHub Pages: 9b1a17a8356ce2c9180500eabc4fa6e8b2ac7849 --- develop/doc/api/v2/fluid/layers.html | 90 ++++++++++++++++++------- develop/doc/operators.json | 4 +- develop/doc/searchindex.js | 2 +- develop/doc_cn/api/v2/fluid/layers.html | 90 ++++++++++++++++++------- develop/doc_cn/searchindex.js | 2 +- 5 files changed, 132 insertions(+), 56 deletions(-) diff --git a/develop/doc/api/v2/fluid/layers.html b/develop/doc/api/v2/fluid/layers.html index 6885f3d0d92..94e511190fd 100644 --- a/develop/doc/api/v2/fluid/layers.html +++ b/develop/doc/api/v2/fluid/layers.html @@ -1265,13 +1265,12 @@ are in NCHW format. Where N is batch size, C is the number of channels, H is the of the feature, and W is the width of the feature. The details of convolution layer, please refer UFLDL’s convolution, . If bias attribution and activation type are provided, bias is added to the output of the convolution, -and the corresponding activation function is applied to the final result. -For each input \(X\), the equation is:

+and the corresponding activation function is applied to the final result.

+

For each input \(X\), the equation is:

\[Out = \sigma (W \ast X + b)\]

In the above equation:

-
-
    +
    • \(X\): Input value, a tensor with NCHW format.
    • \(W\): Filter value, a tensor with MCHW format.
    • \(\ast\): Convolution operation.
      • @@ -1279,20 +1278,21 @@ For each input \(X\), the equation is:

    • \(\sigma\): Activation function.
    • \(Out\): Output value, the shape of \(Out\) and \(X\) may be different.
    -

Example

-
-
Input:
-

Input shape: $(N, C_{in}, H_{in}, W_{in})$

-

Filter shape: $(C_{out}, C_{in}, H_f, W_f)$

-
-
Output:
-
Output shape: $(N, C_{out}, H_{out}, W_{out})$
-
+

Where

-\[\begin{split}H_{out}&= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\ -W_{out}&= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1\end{split}\]
+\[\] +

H_{out}&= frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \ +W_{out}&= frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1

@@ -2067,33 +2067,63 @@ to compute the length.
paddle.v2.fluid.layers.conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, dilation=None, param_attr=None, use_cudnn=True, name=None)
-

The transpose of conv2d layer.

-

This layer is also known as deconvolution layer.

+

Convlution2D transpose layer

+

The convolution2D transpose layer calculates the output based on the input, +filter, and dilations, strides, paddings. Input(Input) and output(Output) +are in NCHW format. Where N is batch size, C is the number of channels, +H is the height of the feature, and W is the width of the feature. +Parameters(dilations, strides, paddings) are two elements. These two elements +represent height and width, respectively. The details of convolution transpose +layer, please refer to the following explanation and references therein.

+

For each input \(X\), the equation is:

+
+\[Out = W \ast X\]
+

In the above equation:

+
    +
  • \(X\): Input value, a tensor with NCHW format.
    • +
  • \(W\): Filter value, a tensor with MCHW format.
    • +
  • \(\ast\) : Convolution transpose operation.
    • +
  • \(Out\): Output value, the shape of \(Out\) and \(X\) may be different.
    • +
+

Example

+
    +

  • Input:

    +

    Input shape: $(N, C_{in}, H_{in}, W_{in})$

    +

    Filter shape: $(C_{in}, C_{out}, H_f, W_f)$

    +
    • +

  • Output:

    +

    Output shape: $(N, C_{out}, H_{out}, W_{out})$

    +
    • +
+

Where

+
+\[\begin{split}H_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\ +W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\end{split}\]
- - + +
Parameters:
  • input (Variable) – The input image with [N, C, H, W] format.
    • -
  • num_filters (int) – The number of filter. It is as same as the output +
  • num_filters (int) – The number of the filter. It is as same as the output image channel.
  • output_size (int|tuple|None) – The output image size. If output size is a tuple, it must contain two integers, (image_H, image_W). This parameter only works when filter_size is None.
  • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, it must contain two integers, (filter_size_H, filter_size_W). -Otherwise, the filter will be a square. None if use output size to -calculate filter_size
    • +Otherwise, the filter will be a square. None if use output size to +calculate filter_size.
  • padding (int|tuple) – The padding size. If padding is a tuple, it must contain two integers, (padding_H, padding_W). Otherwise, the -padding_H = padding_W = padding.
    • +padding_H = padding_W = padding. Default: padding = 0.
  • stride (int|tuple) – The stride size. If stride is a tuple, it must contain two integers, (stride_H, stride_W). Otherwise, the -stride_H = stride_W = stride.
    • +stride_H = stride_W = stride. Default: stride = 1.
  • dilation (int|tuple) – The dilation size. If dilation is a tuple, it must contain two integers, (dilation_H, dilation_W). Otherwise, the -dilation_H = dilation_W = dilation.
    • -
  • param_attr – Parameter Attribute.
    • +dilation_H = dilation_W = dilation. Default: dilation = 1. +
  • param_attr (ParamAttr) – The parameters to the Conv2d_transpose Layer. Default: None
  • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: True
  • name (str|None) – A name for this layer(optional). If set None, the layer @@ -2101,14 +2131,22 @@ will be named automatically.
Returns:

Output image.

+
Returns:

The tensor variable storing the convolution transpose result.
Return type:

Variable

+
Return type:

Variable

+
Raises:

ValueError – If the shapes of input, filter_size, stride, padding and groups mismatch.
+

Examples

+
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
+conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3)
+
+
diff --git a/develop/doc/operators.json b/develop/doc/operators.json index 9ea016f5dbe..ddcf2187e9e 100644 --- a/develop/doc/operators.json +++ b/develop/doc/operators.json @@ -649,7 +649,7 @@ } ] },{ "type" : "conv3d_transpose", - "comment" : "\nConvolution3D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the\nnumber of channels, D is the depth of the feature, H is the height of the feature,\nand W is the width of the feature.\nFilter(Input) is in MCDHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, D is the depth of the filter,H is the\nheight of the filter, and W is the width of the filter.\nParameters(strides, paddings) are three elements. These three elements represent\ndepth, height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample: \n Input:\n Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$\n Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$\n Output:\n Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$\n Where\n $$\n D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + D_f \\\\\n H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + H_f \\\\\n W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + W_f\n $$\n", + "comment" : "\nConvolution3D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the\nnumber of channels, D is the depth of the feature, H is the height of the feature,\nand W is the width of the feature.\nFilter(Input) is in MCDHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, D is the depth of the filter,H is the\nheight of the filter, and W is the width of the filter.\nParameters(strides, paddings) are three elements. These three elements represent\ndepth, height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample: \n Input:\n Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$\n Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$\n Output:\n Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$\n Where\n $$\n D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\\n H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\\n W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1\n $$\n", "inputs" : [ { "name" : "Input", @@ -1805,7 +1805,7 @@ "attrs" : [ ] },{ "type" : "conv2d_transpose", - "comment" : "\nConvolution2D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the\nnumber of channels, H is the height of the feature, and W is the width of the feature.\nFilter(Input) is in MCHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, H is the height of the filter,\nand W is the width of the filter.\nParameters(strides, paddings) are two elements. These two elements represent height\nand width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:\n Input:\n Input shape: $(N, C_{in}, H_{in}, W_{in})$\n Filter shape: $(C_{in}, C_{out}, H_f, W_f)$\n Output:\n Output shape: $(N, C_{out}, H_{out}, W_{out})$\n Where\n $$\n H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + H_f \\\\\n W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + W_f\n $$\n", + "comment" : "\nConvolution2D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the\nnumber of channels, H is the height of the feature, and W is the width of the feature.\nFilter(Input) is in MCHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, H is the height of the filter,\nand W is the width of the filter.\nParameters(strides, paddings) are two elements. These two elements represent height\nand width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:\n Input:\n Input shape: $(N, C_{in}, H_{in}, W_{in})$\n Filter shape: $(C_{in}, C_{out}, H_f, W_f)$\n Output:\n Output shape: $(N, C_{out}, H_{out}, W_{out})$\n Where\n $$\n H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\\n W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\n $$\n", "inputs" : [ { "name" : "Input", diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js index 19cc93c04bf..512454786e7 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/develop/doc_cn/api/v2/fluid/layers.html b/develop/doc_cn/api/v2/fluid/layers.html index 0d46dfae261..f32f2df1043 100644 --- a/develop/doc_cn/api/v2/fluid/layers.html +++ b/develop/doc_cn/api/v2/fluid/layers.html @@ -1284,13 +1284,12 @@ are in NCHW format. Where N is batch size, C is the number of channels, H is the of the feature, and W is the width of the feature. The details of convolution layer, please refer UFLDL’s convolution, . If bias attribution and activation type are provided, bias is added to the output of the convolution, -and the corresponding activation function is applied to the final result. -For each input \(X\), the equation is:

+and the corresponding activation function is applied to the final result.

+

For each input \(X\), the equation is:

\[Out = \sigma (W \ast X + b)\]

In the above equation:

-
-
    +
    • \(X\): Input value, a tensor with NCHW format.
    • \(W\): Filter value, a tensor with MCHW format.
    • \(\ast\): Convolution operation.
      • @@ -1298,20 +1297,21 @@ For each input \(X\), the equation is:

    • \(\sigma\): Activation function.
    • \(Out\): Output value, the shape of \(Out\) and \(X\) may be different.
    -

Example

-
-
Input:
-

Input shape: $(N, C_{in}, H_{in}, W_{in})$

-

Filter shape: $(C_{out}, C_{in}, H_f, W_f)$

-
-
Output:
-
Output shape: $(N, C_{out}, H_{out}, W_{out})$
-
+

Where

-\[\begin{split}H_{out}&= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\ -W_{out}&= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1\end{split}\]
+\[\] +

H_{out}&= frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \ +W_{out}&= frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1

@@ -2086,33 +2086,63 @@ to compute the length.
paddle.v2.fluid.layers.conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, dilation=None, param_attr=None, use_cudnn=True, name=None)
-

The transpose of conv2d layer.

-

This layer is also known as deconvolution layer.

+

Convlution2D transpose layer

+

The convolution2D transpose layer calculates the output based on the input, +filter, and dilations, strides, paddings. Input(Input) and output(Output) +are in NCHW format. Where N is batch size, C is the number of channels, +H is the height of the feature, and W is the width of the feature. +Parameters(dilations, strides, paddings) are two elements. These two elements +represent height and width, respectively. The details of convolution transpose +layer, please refer to the following explanation and references therein.

+

For each input \(X\), the equation is:

+
+\[Out = W \ast X\]
+

In the above equation:

+
    +
  • \(X\): Input value, a tensor with NCHW format.
    • +
  • \(W\): Filter value, a tensor with MCHW format.
    • +
  • \(\ast\) : Convolution transpose operation.
    • +
  • \(Out\): Output value, the shape of \(Out\) and \(X\) may be different.
    • +
+

Example

+
    +

  • Input:

    +

    Input shape: $(N, C_{in}, H_{in}, W_{in})$

    +

    Filter shape: $(C_{in}, C_{out}, H_f, W_f)$

    +
    • +

  • Output:

    +

    Output shape: $(N, C_{out}, H_{out}, W_{out})$

    +
    • +
+

Where

+
+\[\begin{split}H_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\ +W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\end{split}\]
- - + +
参数:
  • input (Variable) – The input image with [N, C, H, W] format.
    • -
  • num_filters (int) – The number of filter. It is as same as the output +
  • num_filters (int) – The number of the filter. It is as same as the output image channel.
  • output_size (int|tuple|None) – The output image size. If output size is a tuple, it must contain two integers, (image_H, image_W). This parameter only works when filter_size is None.
  • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, it must contain two integers, (filter_size_H, filter_size_W). -Otherwise, the filter will be a square. None if use output size to -calculate filter_size
    • +Otherwise, the filter will be a square. None if use output size to +calculate filter_size.
  • padding (int|tuple) – The padding size. If padding is a tuple, it must contain two integers, (padding_H, padding_W). Otherwise, the -padding_H = padding_W = padding.
    • +padding_H = padding_W = padding. Default: padding = 0.
  • stride (int|tuple) – The stride size. If stride is a tuple, it must contain two integers, (stride_H, stride_W). Otherwise, the -stride_H = stride_W = stride.
    • +stride_H = stride_W = stride. Default: stride = 1.
  • dilation (int|tuple) – The dilation size. If dilation is a tuple, it must contain two integers, (dilation_H, dilation_W). Otherwise, the -dilation_H = dilation_W = dilation.
    • -
  • param_attr – Parameter Attribute.
    • +dilation_H = dilation_W = dilation. Default: dilation = 1. +
  • param_attr (ParamAttr) – The parameters to the Conv2d_transpose Layer. Default: None
  • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: True
  • name (str|None) – A name for this layer(optional). If set None, the layer @@ -2120,14 +2150,22 @@ will be named automatically.
返回:

Output image.

+
返回:

The tensor variable storing the convolution transpose result.
返回类型:

Variable

+
返回类型:

Variable

+
Raises:

ValueError – If the shapes of input, filter_size, stride, padding and groups mismatch.
+

Examples

+
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
+conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3)
+
+
diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js index 628148b7767..3cfe385e64a 100644 --- a/develop/doc_cn/searchindex.js +++ b/develop/doc_cn/searchindex.js @@ -1 +1 @@ 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