diff --git a/develop/doc/api/v2/data.html b/develop/doc/api/v2/data.html index 1dff7c81242bdf09d5c8d4b76a5627961167051c..697dfc899ec775c85b0514bc9f20ff946e72c4b8 100644 --- a/develop/doc/api/v2/data.html +++ b/develop/doc/api/v2/data.html @@ -185,12 +185,50 @@

Data Reader Interface and DataSets

DataTypes

+
+
+paddle.v2.data_type.dense_array(dim, seq_type=0)
+

Dense Array. It means the input feature is dense array with float type. +For example, if the input is an image with 28*28 pixels, the input of +Paddle neural network could be a dense vector with dimension 784 or a +numpy array with shape (28, 28).

+

For the 2-D convolution operation, each sample in one mini-batch must have +the similarly size in PaddlePaddle now. But, it supports variable-dimension +feature across mini-batch. For the variable-dimension, the param dim is not +used. While the data reader must yield numpy array and the data feeder will +set the data shape correctly.

+ +++ + + + + + + + +
Parameters:
    +
  • dim (int) – dimension of this vector.
    • +
  • seq_type (int) – sequence type of input.
    • +
+
Returns:

An input type object.

+
Return type:

InputType

+
+
+
paddle.v2.data_type.dense_vector(dim, seq_type=0)
-

Dense Vector. It means the input feature is dense float vector. For example, -if the input is an image with 28*28 pixels, the input of Paddle neural -network should be a dense vector with dimension 784.

+

Dense Array. It means the input feature is dense array with float type. +For example, if the input is an image with 28*28 pixels, the input of +Paddle neural network could be a dense vector with dimension 784 or a +numpy array with shape (28, 28).

+

For the 2-D convolution operation, each sample in one mini-batch must have +the similarly size in PaddlePaddle now. But, it supports variable-dimension +feature across mini-batch. For the variable-dimension, the param dim is not +used. While the data reader must yield numpy array and the data feeder will +set the data shape correctly.

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\ No newline at end of file diff --git a/develop/doc_cn/api/v2/data.html b/develop/doc_cn/api/v2/data.html index 712d36f0062c829d8b7f46a6a059c10f3e584ed0..c0d4b4679f72728ffe16e8cba6340ac8d0aa356b 100644 --- a/develop/doc_cn/api/v2/data.html +++ b/develop/doc_cn/api/v2/data.html @@ -192,12 +192,50 @@

Data Reader Interface and DataSets

DataTypes

+
+
+paddle.v2.data_type.dense_array(dim, seq_type=0)
+

Dense Array. It means the input feature is dense array with float type. +For example, if the input is an image with 28*28 pixels, the input of +Paddle neural network could be a dense vector with dimension 784 or a +numpy array with shape (28, 28).

+

For the 2-D convolution operation, each sample in one mini-batch must have +the similarly size in PaddlePaddle now. But, it supports variable-dimension +feature across mini-batch. For the variable-dimension, the param dim is not +used. While the data reader must yield numpy array and the data feeder will +set the data shape correctly.

+
+++ + + + + + + + +
参数:
    +
  • dim (int) – dimension of this vector.
    • +
  • seq_type (int) – sequence type of input.
    • +
+
返回:

An input type object.

+
返回类型:

InputType

+
+
+
paddle.v2.data_type.dense_vector(dim, seq_type=0)
-

Dense Vector. It means the input feature is dense float vector. For example, -if the input is an image with 28*28 pixels, the input of Paddle neural -network should be a dense vector with dimension 784.

+

Dense Array. It means the input feature is dense array with float type. +For example, if the input is an image with 28*28 pixels, the input of +Paddle neural network could be a dense vector with dimension 784 or a +numpy array with shape (28, 28).

+

For the 2-D convolution operation, each sample in one mini-batch must have +the similarly size in PaddlePaddle now. But, it supports variable-dimension +feature across mini-batch. For the variable-dimension, the param dim is not +used. While the data reader must yield numpy array and the data feeder will +set the data shape correctly.

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