# Reshape¶

Versioned name : Reshape-1

Category : Shape manipulation

Short description : Reshape operation changes dimensions of the input tensor according to the specified order. Input tensor volume is equal to output tensor volume, where volume is the product of dimensions.

Detailed description :

Reshape takes two input tensors: data to be resized and shape of the new output. The values in the shape could be -1, 0 and any positive integer number. The two special values -1 and 0 :

• 0 means “copy the respective dimension *(left aligned)* of the input tensor” if special_zero is set to true; otherwise it is a normal dimension and is applicable to empty tensors.

• -1 means that this dimension is calculated to keep the overall elements count the same as in the input tensor. Not more than one -1 can be used in a reshape operation.

If special_zero is set to true index of 0 cannot be larger than the rank of the input tensor.

Attributes :

• special_zero

• Description : special_zero controls how zero values in shape are interpreted. If special_zero is false, then 0 is interpreted as-is which means that output shape will contain a zero dimension at the specified location. Input and output tensors are empty in this case. If special_zero is true, then all zeros in shape implies the copying of corresponding dimensions from data.shape into the output shape *(left aligned)*.

• Range of values : false or true

• Type : boolean

• Required : yes

Inputs :

• 1 : data a tensor of type T and arbitrary shape. Required.

• 2 : shape 1D tensor of type T_SHAPE describing output shape. Required.

Outputs :

• 1 : Output tensor of type T with the same content as data input tensor but with shape defined by shape input tensor.

Types

• T : any numeric type.

• T_SHAPE : any supported integer type.

Examples

Example 1: reshape empty tensor

<layer ... type="Reshape" ...>
<data special_zero="false"/>
<input>
<port id="0">
<dim>2</dim>
<dim>5</dim>
<dim>5</dim>
<dim>0</dim>
</port>
<port id="1">
<dim>2</dim>   <!--The tensor contains 2 elements: 0, 4 -->
</port>
</input>
<output>
<port id="2">
<dim>0</dim>
<dim>4</dim>
</port>
</output>
</layer>

Example 2: reshape tensor - preserve first dim, calculate second and fix value for third dim

<layer ... type="Reshape" ...>
<data special_zero="true"/>
<input>
<port id="0">
<dim>2</dim>
<dim>5</dim>
<dim>5</dim>
<dim>24</dim>
</port>
<port id="1">
<dim>3</dim>   <!--The tensor contains 3 elements: 0, -1, 4 -->
</port>
</input>
<output>
<port id="2">
<dim>2</dim>
<dim>150</dim>
<dim>4</dim>
</port>
</output>
</layer>

Example 3: reshape tensor - preserve first two dims, fix value for third dim and calculate fourth

<layer ... type="Reshape" ...>
<data special_zero="true"/>
<input>
<port id="0">
<dim>2</dim>
<dim>2</dim>
<dim>3</dim>
</port>
<port id="1">
<dim>4</dim>   <!--The tensor contains 4 elements: 0, 0, 1, -1 -->
</port>
</input>
<output>
<port id="2">
<dim>2</dim>
<dim>2</dim>
<dim>1</dim>
<dim>3</dim>
</port>
</output>
</layer>

Example 4: reshape tensor - calculate first dim and preserve second dim

<layer ... type="Reshape" ...>
<data special_zero="true"/>
<input>
<port id="0">
<dim>3</dim>
<dim>1</dim>
<dim>1</dim>
</port>
<port id="1">
<dim>2</dim>   <!--The tensor contains 2 elements: -1, 0 -->
</port>
</input>
<output>
<port id="2">
<dim>3</dim>
<dim>1</dim>
</port>
</output>
</layer>

Example 5: reshape tensor - preserve first dim and calculate second dim

<layer ... type="Reshape" ...>
<data special_zero="true"/>
<input>
<port id="0">
<dim>3</dim>
<dim>1</dim>
<dim>1</dim>
</port>
<port id="1">
<dim>2</dim>   <!--The tensor contains 2 elements: 0, -1 -->
</port>
</input>
<output>
<port id="2">
<dim>3</dim>
<dim>1</dim>
</port>
</output>
</layer>