# ngraph package¶

ngraph module namespace, exposing factory functions for all ops and other classes.

## ngraph.exceptions module¶

ngraph exceptions hierarchy. All exceptions are descendants of NgraphError.

exception ngraph.exceptions.NgraphError[source]

Bases: Exception

Base class for Ngraph exceptions.

exception ngraph.exceptions.NgraphTypeError[source]

Bases: ngraph.exceptions.NgraphError, TypeError

Type mismatch error.

exception ngraph.exceptions.UserInputError[source]

User provided unexpected input.

## ngraph.ops module¶

Factory functions for all ngraph ops.

ngraph.ops.absolute(node, name=None)[source]

Return node which applies f(x) = abs(x) to the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. New node with Abs operation applied on it.
ngraph.ops.acos(node, name=None)[source]

Apply inverse cosine function on the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. New node with arccos operation applied on it.
ngraph.ops.add(left_node, right_node, name=None)[source]

Return node which applies f(x) = A+B to the input nodes element-wise.

ngraph.ops.argmax(data, axis=0)[source]

Return a node which performs ArgMax index reduction operation.

Parameters: data – Input data. axis – Reduction Axis. The new node which performs ArgMax
ngraph.ops.argmin(data, axis=0)[source]

Return a node which performs ArgMin index reduction operation.

Parameters: data – Input data. axis – Reduction Axis. The new node which performs ArgMin
ngraph.ops.asin(node, name=None)[source]

Apply inverse sine function on the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. New node with arcsin operation applied on it.
ngraph.ops.atan(node, name=None)[source]

Apply inverse tangent function on the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. New node with arctan operation applied on it.
ngraph.ops.avg_pool(data_batch, window_shape, window_strides=None, padding_below=None, padding_above=None, include_padding=False, name=None)[source]

Return average pooling node.

Parameters: data_batch – The input node providing data. window_shape – The pooling window shape. window_strides – The window movement strides. padding_below – The input data optional padding below filled with zeros. padding_above – The input data optional padding below filled with zeros. include_padding – Whether or not to include zero padding in average computations. name – Optional name for the new output node. New node with AvgPool operation applied on its data.
ngraph.ops.batch_norm(eps, gamma, beta, data, mean=None, variance=None, name=None)[source]

Return batch normalization node.

ngraph.ops.broadcast(node, new_shape, broadcast_axes, name=None)[source]

Create a node which broadcasts the input node’s values along specified axes to a desired shape.

Parameters: node – The node with input tensor data. new_shape – The new shape we want to broadcast tensor to. broadcast_axes – The axis positions (0-based) in the result that are being broadcast. name – Optional new name for output node. New node with broadcast shape.
ngraph.ops.broadcast_to(node, new_shape, axis=None, name=None)[source]

Create a node which broadcasts the input node’s values to a desired shape.

The optional axis parameter specifies the starting axis position (0-based) in the output shape from which the current shape of the tensor matches the desired new shape.

e.g. current_shape: [4, 5], new_shape: [2, 3, 4, 5, 6], axis: 2

By using the axis parameter you can control which output axis to broadcast along.

Example:

>>> input_node = ng.constant([1, 2, 3])
>>> current_shape = [3]
>>> new_shape = [3, 3]
array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3]])

>>> ng.broadcast_to(input_node, new_shape, axis=0)
array([[1, 1, 1],
[2, 2, 2],
[3, 3, 3]])


If the axis parameter is not specified, broadcast_to will attempt to match shapes, assuming the current shape matches the rightmost positions of the desired new shape. This behaviour is similar to NumPy’s broadcasting.

i.e. default axis = len(new_shape) - len(current_shape)

Parameters: node – The node with input tensor data. new_shape – The new shape we want to broadcast tensor to. axis – The axis along which we perform broadcasting. name – Optional new name for output node. New node with broadcast shape.
ngraph.ops.ceiling(node, name=None)[source]

Return node which applies ceiling to the input node element-wise.

Parameters: node – The node providing data to ceiling operation. name – Optional name for output node. The node performing element-wise ceiling.
ngraph.ops.clamp(data, min_value, max_value, name=None)[source]

Perform clamp element-wise on data from input node.

Performs a clipping operation on an input value between a pair of boundary values.

For each element in data, if the element’s value is lower than min_value, it will be replaced with min_value. If the value is higher than max_value, it will be replaced by max_value. Intermediate values of data are returned without change.

Clamp uses the following logic:

if data < min_value:
data=min_value
elif data > max_value:
data=max_value

Parameters: data – Input tensor. One of: input node, array or scalar. min_value – The lower bound of the range. Scalar value. max_value – The upper bound of the range. Scalar value. name – Optional output node name. The new node performing a clamp operation on its input data element-wise.
ngraph.ops.concat(nodes, axis, name=None)[source]

Concatenate input nodes into single new node along specified axis.

Parameters: nodes – The nodes we want concatenate into single new node. axis – The axis along which we want to concatenate input nodes. name – The optional new name for output node. Return new node that is a concatenation of input nodes.
ngraph.ops.constant(value, dtype=None, name=None)[source]

Create a Constant node from provided value.

Parameters: value – One of: array of values or scalar to initialize node with. dtype – The data type of provided data. name – Optional name for output node. The Constant node initialized with provided data.
ngraph.ops.convert(node, new_type, name=None)[source]

Return node which casts input node values to specified type.

ngraph.ops.convolution(data_batch, filter_weights, filter_strides=None, filter_dilation_strides=None, padding_below=None, padding_above=None, data_dilation_strides=None, name=None)[source]

Return node performing batched convolution operation.

Parameters: data_batch – The node providing data batch tensor. filter_weights – The node providing filters tensor. filter_strides – The kernel window movement strides. filter_dilation_strides – The filters dilation strides. padding_below – The number of zero padding elements to add on each axis below 0 coordinate. padding_above – The number of zero padding elements to add on each axis above max coordinate. data_dilation_strides – The data batch dilation strides. name – The optional new name for output node. New node performing batched convolution operation.
ngraph.ops.convolution_backprop_data(data_batch_shape, filters, output_delta, window_movement_strides_forward=None, window_dilation_strides_forward=None, padding_below_forward=None, padding_above_forward=None, data_dilation_strides_forward=None, name=None)[source]

Return node performing a batched-convolution data batch-backprop operation.

Parameters: data_batch_shape – The shape of the data batch from forward-prop. filters – The node producing the filters from forward-prop. output_delta – The node producing output delta. window_movement_strides_forward – The window movement strides from forward-prop. window_dilation_strides_forward – The window dilation strides from forward-prop. padding_below_forward – The padding-below sizes from forward-prop. padding_above_forward – The padding-above sizes from forward-prop. data_dilation_strides_forward – The data dilation strides from forward-prop.
ngraph.ops.cos(node, name=None)[source]

Apply cosine function on the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. New node with cos operation applied on it.
ngraph.ops.cosh(node, name=None)[source]

Apply hyperbolic cosine function on the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. New node with cosh operation applied on it.
ngraph.ops.depth_to_space(node, mode, block_size, name=None)[source]

Rearranges input tensor from depth into blocks of spatial data.

Values from the height and width dimensions are moved to the depth dimension.

Input tensor has shape [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.

Output node produces a tensor with shape:

[N, C * block_size * block_size, H / block_size, W / block_size]

Parameters: node – The node with input tensor data. mode – Specifies how the input depth dimension is split to block coordinates blocks_first: The input is divided to [block_size, …, block_size, new_depth] depth_first: The input is divided to [new_depth, block_size, …, block_size] block_size – The size of the spatial block of values describing how the tensor’s data is to be rearranged. name – Optional output node name. The new node performing an DepthToSpace operation on its input tensor.
ngraph.ops.dequantize(data, scale, zero_point, element_type, axes, name=None)[source]

Perform dequantize operation on data from input node.

Computes dequantize on the input tensor:

$output = (input - zero\_point) * scale$
Parameters: data – The node with data tensor. scale – Scale used for mapping. zero_point – Zero point used for mapping. element_type – Output element type. name – Optional output node name. The new node performing a dequantize operation on input tensor.
ngraph.ops.divide(left_node, right_node, name=None)[source]

Return node which applies f(x) = A/B to the input nodes element-wise.

Parameters: left_node – The node providing dividend data. right_node – The node providing divisor data. name – Optional name for output node. The node performing element-wise division.
ngraph.ops.dot(left_node, right_node, reduction_axes_count=None, name=None)[source]

Return node which performs generalized dot product of two input nodes.

This operation is capable of performing scalar-tensor, matrix-vector product and matrix multiplication.

Parameters: left_node – The node providing left hand side data. right_node – The node providing right hand side data. reduction_axes_count – The number of axes to reduce during dot-product. name – The optional name for output node. The new node performing dot-product on input two nodes.
ngraph.ops.elu(data, alpha, name=None)[source]

Perform Exponential Linear Unit operation element-wise on data from input node.

Computes exponential linear: alpha * (exp(data) - 1) if < 0, data otherwise.

For more information refer to: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Parameters: data – Input tensor. One of: input node, array or scalar. alpha – Scalar multiplier for negative values. name – Optional output node name. The new node performing an ELU operation on its input data element-wise.
ngraph.ops.equal(left_node, right_node, name=None)[source]

Return node which checks if input nodes are equal element-wise.

Parameters: left_node – The first input node for equal operation. right_node – The second input node for equal operation. name – The optional name for output new node. The node performing element-wise equality check.
ngraph.ops.exp(node, name=None)[source]

Return node which applies exp to the input node element-wise.

Parameters: node – The node providing data for operation. name – The optional name for new output node. The new node performing natural exponential operation.
ngraph.ops.fake_quantize(data, input_low, input_high, output_low, output_high, levels, name=None)[source]

Perform an element-wise linear quantization on input data.

Input floating point values are quantized into a discrete set of floating point values.

Fake quantize uses the following logic:

$output = \dfrac{round( \dfrac{data - input\_low}{(input\_high - input\_low)\cdot (levels-1)})} {(levels-1)\cdot (output\_high - output\_low)} + output\_low$
Parameters: data – The node with data tensor. input_low – The node with the minimum for input values. input_high – The node with the maximum for input values. output_low – The node with the minimum quantized value. output_high – The node with the maximum quantized value. levels – The number of quantization levels. Integer value. New node with quantized value.
ngraph.ops.floor(node, name=None)[source]

Return node which applies floor to the input node element-wise.

Parameters: node – The input node providing data. name – The optional name for new output node. The node performing element-wise floor operation.
ngraph.ops.gelu(node, name=None)[source]

Perform Gaussian Error Linear Unit operation element-wise on data from input node.

Computes GELU function:

$f(x) = 0.5\cdot x\cdot(1 + erf( \dfrac{x}{\sqrt{2}})$

Parameters: node – Input tensor. One of: input node, array or scalar. name – Optional output node name. The new node performing a GELU operation on its input data element-wise.
ngraph.ops.gemm(A, B, C, alpha, beta, transA, transB, name=None)[source]

Perform General matrix-matrix multiplication on input tensors A, B and C.

Computes:

$Y = alpha\cdot A'\cdot B' + beta\cdot C$

A' is the transpose of matrix A with shape (M, K), if transA is True, otherwise A with shape (K, N).

B' is the transpose of matrix B with shape (K, N), if transB is True, otherwise B with shape (N, K).

C: Matrix broadcastable to shape (M, N).

Y: Matrix with shape (M, N).

Parameters: A – The node with input tensor A. B – The node with input tensor B. C – The node with input tensor C. alpha – Scalar multiplier for the product of input tensors A * B. beta – Scalar multiplier for input tensor C. transA – Whether A should be transposed. Boolean value. transB – Whether B should be transposed. Boolean value. name – Optional name for the output node. Return node with tensor of shape (M, N).
ngraph.ops.get_output_element(data, index)[source]

Return the n-th element of the input tuple.

ngraph.ops.greater(left_node, right_node, name=None)[source]

Return node which checks if left input node is greater than the right node element-wise.

Parameters: left_node – The first input node providing data. right_node – The second input node providing data. name – The optional new name for output node. The node performing element-wise check whether left_node is greater than right_node.
ngraph.ops.greater_eq(left_node, right_node, name=None)[source]

Return node which checks if left node is greater or equal to the right node element-wise.

Parameters: left_node – The first input node providing data. right_node – The second input node providing data. name – The optional new name for output node. The node performing element-wise check whether left_node is greater than or equal right_node.
ngraph.ops.grn(data, bias, name=None)[source]

Perform Global Response Normalization with L2 norm (across channels only).

Computes GRN operation on channels for input tensor:

$output_i = \dfrac{input_i}{\sqrt{\sum_{i}^{C} input_i}}$
Parameters: data – The node with data tensor. bias – The bias added to the variance. Scalar value. name – Optional output node name. The new node performing a GRN operation on tensor’s channels.
ngraph.ops.group_convolution(data_batch, filters, window_movement_strides, window_dilation_strides, padding_below, padding_above, data_dilation_strides, groups, pad_type='EXPLICIT', name=None)[source]

Perform Group Convolution operation on data from input node.

ngraph.ops.hard_sigmoid(data, alpha, beta, name=None)[source]

Perform Hard Sigmoid operation element-wise on data from input node.

Hard Sigmoid uses the following logic:

y = max(0, min(1, alpha * data + beta))

Parameters: data – The node with data tensor. alpha – Alpha parameter. Scalar value. beta – Beta parameter. Scalar value. name – Optional output node name. The new node performing a Hard Sigmoid element-wise on input tensor.
ngraph.ops.less(left_node, right_node, name=None)[source]

Return node which checks if left input node is less than the right node element-wise.

Parameters: left_node – The first input node providing data. right_node – The second input node providing data. name – The optional new name for output node. The node performing element-wise check whether left_node is less than the right_node.
ngraph.ops.less_eq(left_node, right_node, name=None)[source]

Return node which checks if left input node is less or equal the right node element-wise.

Parameters: left_node – The first input node providing data. right_node – The second input node providing data. name – The optional new name for output node. The node performing element-wise check whether left_node is less than or equal the right_node.
ngraph.ops.log(node, name=None)[source]

Return node which applies natural logarithm to the input node element-wise.

Parameters: node – The input node providing data for operation. name – The optional new name for output node. The new node performing log operation element-wise.
ngraph.ops.logical_and(left_node, right_node, name=None)[source]

Return node which perform logical and operation on input nodes element-wise.

Parameters: left_node – The first input node providing data. right_node – The second input node providing data. name – The optional new name for output node. The node performing logical and operation on input nodes corresponding elements.
ngraph.ops.logical_not(node, name=None)[source]

Return node which applies logical negation to the input node elementwise.

ngraph.ops.logical_or(left_node, right_node, name=None)[source]

Return node which performs logical or operation on input nodes element-wise.

Parameters: left_node – The first input node providing data. right_node – The second input node providing data. name – The optional new name for output node. The node performing logical or operation on input nodes corresponding elements.
ngraph.ops.lrn(data, alpha=1, beta=0.5, bias=1, size=5, name=None)[source]

Return a node which performs element-wise Local Response Normalization (LRN) operation.

Parameters: data – Input data. alpha – A scale factor (usually positive). beta – An exponent. bias – An offset (usually positive) to avoid dividing by 0. size – Width of the 1-D normalization window. name – An optional name of the output node. The new node which performs LRN.
ngraph.ops.max(node, reduction_axes=None, name=None)[source]

Max-reduction operation on input tensor, eliminating the specified reduction axes.

Parameters: node – The tensor we want to max-reduce. reduction_axes – The axes to eliminate through max operation. name – Optional name for output node.
ngraph.ops.max_pool(x, window_shape, strides=None, padding_above=None, padding_below=None, name=None)[source]

Return max pooling node.

ngraph.ops.maximum(left_node, right_node, name=None)[source]

Return node which applies the maximum operation to input nodes elementwise.

ngraph.ops.min(node, reduction_axes=None, name=None)[source]

Min-reduction operation on input tensor, eliminating the specified reduction axes.

Parameters: node – The tensor we want to min-reduce. reduction_axes – The axes to eliminate through min operation. name – Optional name for output node.
ngraph.ops.minimum(left_node, right_node, name=None)[source]

Return node which applies the minimum operation to input nodes elementwise.

ngraph.ops.multiply(left_node, right_node, name=None)[source]

Return node which applies f(x) = A*B to the input nodes elementwise.

ngraph.ops.mvn(data, axes, normalize_variance, eps, name=None)[source]

Perform Mean Variance Normalization operation on data from input node.

Computes MVN on the input tensor data (called X) using formula:

$Y = \dfrac{X-EX}{\sqrt{E(X-EX)^2}}$
Parameters: data – The node with data tensor. axes – A list of axes, along which to reduce. Array of integers. normalize_variance – Flag that denotes if mean values are shared across channels. Boolen value. eps – The number added to the variance to avoid division by zero when normalizing the value. Scalar value. name – Optional output node name. The new node performing a MVN operation on input tensor.
ngraph.ops.negative(node, name=None)[source]

Return node which applies f(x) = -x to the input node elementwise.

ngraph.ops.not_equal(left_node, right_node, name=None)[source]

Return node which checks if input nodes are unequal element-wise.

Parameters: left_node – The first input node for not-equal operation. right_node – The second input node for not-equal operation. name – The optional name for output new node. The node performing element-wise inequality check.
ngraph.ops.one_hot(node, shape, one_hot_axis, name=None)[source]

Create node performing one-hot encoding on input data.

Parameters: node – The input node providing data for operation. shape – The output node shape including the new one-hot axis. one_hot_axis – The index within the output shape of the new one-hot axis. name – The optional name for new output node. New node performing one-hot operation.
ngraph.ops.pad(data_batch, value, padding_below=None, padding_above=None, padding_in=None, name=None)[source]

Parameters: data_batch – The input node providing data. value – The node producing the scalar value to be inserted for padding. padding_below – The padding-below widths. padding_above – The padding-above widths. padding_in – The interior-padding widths. name – The optional new name for output node. Return node that represents a padding of input nodes data.
ngraph.ops.parameter(shape, dtype=<class 'numpy.float32'>, name=None)[source]

Return an ngraph Parameter object.

ngraph.ops.power(left_node, right_node, name=None)[source]

Return node which perform element-wise exponentiation operation.

Parameters: left_node – The node providing the base of operation. right_node – The node providing the exponent of operation. name – The optional name for the new output node. The new node performing element-wise exponentiation operation on input nodes.
ngraph.ops.prelu(data, slope, name=None)[source]

Perform Parametrized Relu operation element-wise on data from input node.

PRelu uses the following logic:

if data < 0:
data = data * slope
elif data >= 0:
data = data

Parameters: data – The node with data tensor. slope – The node with the multipliers for negative values. name – Optional output node name. The new node performing a PRelu operation on tensor’s channels.
ngraph.ops.prod(node, reduction_axes=None, name=None)[source]

Product-reduction operation on input tensor, eliminating the specified reduction axes.

Parameters: node – The tensor we want to product-reduce. reduction_axes – The axes to eliminate through product operation. name – Optional name for output node. The new node performing product-reduction operation.
ngraph.ops.quantize(data, scale, zero_point, new_type, axes, round_mode, name=None)[source]

Perform quantize operation on data from input node.

Computes quantize on the input tensor:

$output = ROUND((input / scale) + zero\_point)$
Parameters: data – The node with data tensor. scale – Scale used for mapping. zero_point – Zero point used for mapping. new_type – Output element type. round_mode – Number describes how to perform ROUND function. ROUND_NEAREST_TOWARD_INFINITY: Round to nearest integer. In case of two equidistant integers round away from zero e.g. 2.5 -> 3, -3.5 -> -4 ROUND_NEAREST_TOWARD_ZERO: Round to nearest integer. In case of two equidistant integers round toward zero e.g. 2.5 -> 2, -3.5 -> -3 ROUND_NEAREST_UPWARD: Round to nearest integer. In case of two equidistant integers round up e.g. 2.5 -> 2, -3.5 -> -3 ROUND_NEAREST_DOWNWARD: Round to nearest integer. In case of two equidistant integers round down e.g. 2.5 -> 2, -3.5 -> -4 ROUND_NEAREST_TOWARD_EVEN: Round to nearest integer. In case of two equidistant integers round down e.g. 2.5 -> 2, -3.5 -> -4 ROUND_TOWARD_INFINITY: Round to nearest integer away from zero. ROUND_TOWARD_ZERO: Round to nearest integer toward zero. ROUND_UP: Round to nearest integer toward infinity (ceiling). ROUND_DOWN: Round to nearest integer toward negative infinity (floor). name – Optional output node name. The new node performing a quantize operation on input tensor.
ngraph.ops.quantized_convolution(data, filters, window_movement_strides, window_dilation_strides, padding_below, padding_above, data_dilation_strides, input_scale, input_zero_point, filter_scale, filter_zero_point, output_scale, output_zero_point, output_type, input_axes, filter_axes, output_axes, name=None)[source]

Perform quantized convolution operation on data from input node.

Parameters: data – The node producing the input data batch tensor. filters – The node producing the filters tensor. window_movement_strides – The window movement strides. window_dilation_strides – he window dilation strides. padding_below – The padding-below sizes. padding_above – The padding-above sizes. data_dilation_strides – The data dilation strides. input_scale – Scale to transform the input. input_zero_point – Zero point used for mapping. filter_scale – Scale to transform the filters. filter_zero_point – Zero point used for mapping. output_scale – Scale to transform the output. output_zero_point – Zero point used for mapping. output_type – Output element type. input_axes – Input axes set for channel wise quantization. filter_axes – Filter axes set for channel wise quantization. output_type – Output axes set for channel wise quantization. name – Optional output node name. The new node performing a quantized convolution operation on input tensor.
ngraph.ops.quantized_dot(input0, input1, reduction_axes_count, input0_scale, input0_zero_point, input1_scale, input1_zero_point, output_scale, output_zero_point, output_type, input0_axes, input1_axes, output_axes, name=None)[source]

Perform quantized dot operation on data from input node.

Parameters: input0 – The node producing the input data batch tensor. input1 – The node producing the filters tensor. reduction_axes_count – Number of reduction axes. input0_scale – Scale to transform the input. input0_zero_point – Zero point used for mapping. input1_scale – Scale to transform the filters. input1_zero_point – Zero point used for mapping. output_scale – Scale to transform the output. output_zero_point – Zero point used for mapping. output_type – Output element type. input0_axes – Input0 axes set for channel wise quantization input1_axes – Input1 axes set for channel wise quantization output_axes – Output axes set for channel wise quantization name – Optional output node name. The new node performing a quantized dot operation on input tensor.
ngraph.ops.relu(node, name=None)[source]

Perform rectified linear unit operation on input node element-wise.

Parameters: node – One of: input node, array or scalar. name – The optional ouptut node name. The new node performing relu operation on its input element-wise.
ngraph.ops.replace_slice(dest_node, src_node, lower_bounds, upper_bounds, strides=None, name=None)[source]

Return a copy of dest_node with the specified slice overwritten by the src_node data.

Parameters: dest_node – The node providing data to be overwritten by the specified slice. src_node – The node providing data for overwriting. lower_bounds – The (inclusive) lower-bound coordinates for the replaced slice. upper_bounds – The (exclusive) upper-bound coordinates for the replaced slice. strides – The strides for the replaced slice. name – The optional name for the output new node. The new node with copy of dest_node with the specified slice overwritten by the src_node.
ngraph.ops.reshape(node, output_shape, input_order=None, name=None)[source]

Return reshaped node according to provided parameters.

Parameters: node – The tensor we want to reshape. input_order – The order in which to iterate over input axes of input tensor. output_shape – The new shape for input tensor.
ngraph.ops.reverse(node, reversed_axes, name=None)[source]

Perform axis-reverse operation.

Parameters: node – The input node on which operation will be carried out. reversed_axes – The list of indices of axes to be reversed. name – The optional name of the output node. The new node with reversed axes.
ngraph.ops.rnn_cell(X, H_t, W, R, B, hidden_size, activations, activation_alpha, activation_beta, clip, name=None)[source]

Perform RNNCell operation on tensor from input node.

It follows notation and equations defined as in ONNX standard: https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN

Note this class represents only single cell and not whole RNN layer.

Parameters: X – The input tensor with shape: [batch_size, input_size]. H_t – The hidden state tensor at current time step with shape: [batch_size, hidden_size]. W – The weight tensor with shape: [hidden_size, input_size]. R – The recurrence weight tensor with shape: [hidden_size, hidden_size]. B – The bias tensor for input gate with shape: [2*hidden_size]. hidden_size – The number of hidden units for recurrent cell. activations – The vector of activation functions used inside recurrent cell. activation_alpha – The vector of alpha parameters for activation functions in order respective to activation list. activation_beta – The vector of beta parameters for activation functions in order respective to activation list. clip – The value defining clipping range [-clip, clip] on input of activation functions. name – Optional output node name. The new node performing a RNNCell operation on tensor from input node.
ngraph.ops.scale_shift(data, scale, shift, name=None)[source]

Perform ScaleShift transformation on input node.

Computes ScaleShift:

$Y = scale\cdot data + shift$
Parameters: data – The node with data tensor. scale – The node with data tensor that scale input data. shift – The node with data tensor that shift input data. name – Optional output node name. The new node performing a ScaleShift operation on input tensor.
ngraph.ops.select(selection_node, input_node1, input_node2, name=None)[source]

Perform an element-wise selection operation on input tensors.

Parameters: selection_node – The node providing selection values of bool type. input_node1 – The node providing data to be selected if respective selection_node item value is True. input_node2 – The node providing data to be selected if respective selection_node item value is False. name – The optional new name for output node. The new node with values selected according to provided arguments.
ngraph.ops.shuffle_channels(data, axis, groups, name=None)[source]

Perform permutation on data in the channel dimension of the input tensor.

The operation is the equivalent with the following transformation of the input tensor data of shape [N, C, H, W]:

data_reshaped = reshape(data, [N, group, C / group, H * W])

data_trnasposed = transpose(data_reshaped, [0, 2, 1, 3])

output = reshape(data_trnasposed, [N, C, H, W])

For example:

Inputs: tensor of shape [1, 6, 2, 2]

data = [[[[ 0.,  1.], [ 2.,  3.]],
[[ 4.,  5.], [ 6.,  7.]],
[[ 8.,  9.], [10., 11.]],
[[12., 13.], [14., 15.]],
[[16., 17.], [18., 19.]],
[[20., 21.], [22., 23.]]]]

axis = 1
groups = 3

Output: tensor of shape [1, 6, 2, 2]

output = [[[[ 0.,  1.], [ 2.,  3.]],
[[ 8.,  9.], [10., 11.]],
[[16., 17.], [18., 19.]],
[[ 4.,  5.], [ 6.,  7.]],
[[12., 13.], [14., 15.]],
[[20., 21.], [22., 23.]]]]

Parameters: data – The node with input tensor. axis – Channel dimension index in the data tensor. A negative value means that the index should be calculated from the back of the input data shape.
:param group:The channel dimension specified by the axis parameter
should be split into this number of groups.
Parameters: name – Optional output node name. The new node performing a permutation on data in the channel dimension of the input tensor.
ngraph.ops.sign(node, name=None)[source]

Perform element-wise sign operation.

Parameters: node – One of: input node, array or scalar. name – The optional new name for ouptut node. The node with mapped elements of the input tensor to -1 (if it is negative), 0 (if it is zero), or 1 (if it is positive).
ngraph.ops.sin(node, name=None)[source]

Apply sine function on the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. New node with sin operation applied on it.
ngraph.ops.sinh(node, name=None)[source]

Apply hyperbolic sine function on the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. New node with sin operation applied on it.
ngraph.ops.slice(node, lower_bounds, upper_bounds, strides=None, name=None)[source]

Take a slice of an input tensor, (sub-tensor) that resides within a bounding box.

Optionally this function may be provided with stride along each axis.

Parameters: node – The tensor we want to slice. lower_bounds – The (inclusive) lower-bound coordinates for the tensor slice. upper_bounds – The (exclusive) upper-bound coordinates for the tensor slice. strides – The strides for the tensor slice. name – Optional name for the output node. Return node that represents a slice of input nodes data.
ngraph.ops.softmax(node, axes, name=None)[source]

Apply softmax operation on each element of input tensor.

Parameters: node – The tensor providing input data. axes – The list of axes indices which are used to calculate divider of the softmax function. name – The optional new name for output node. The new node with softmax operation applied on each element.
ngraph.ops.space_to_depth(data, mode, block_size, name=None)[source]

Perform SpaceToDepth operation on the input tensor.

SpaceToDepth rearranges blocks of spatial data into depth. The operator returns a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension.

Parameters: data – The node with data tensor. mode – Specifies how the output depth dimension is gathered from block coordinates. blocks_first: The output depth is gathered from [block_size, …, block_size, C] depth_first: The output depth is gathered from [C, block_size, …, block_size] block_size – The size of the block of values to be moved. Scalar value. name – Optional output node name. The new node performing a SpaceToDepth operation on input tensor.
ngraph.ops.sqrt(node, name=None)[source]

Return node which applies square root to the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. The new node with sqrt operation applied element-wise.
ngraph.ops.squared_difference(x1, x2, name=None)[source]

Perform an element-wise squared difference between two tensors.

$y[i] = (x_1[i] - x_2[i])^2$
Parameters: x1 – The node with first input tensor. x2 – The node with second input tensor. name – Optional new name for output node. The new node performing a squared difference between two tensors.
ngraph.ops.squeeze(data, axes, name=None)[source]

Perform squeeze operation on input tensor.

Remove single-dimensional entries from the shape of a tensor. Takes a parameter axes with a list of axes to squeeze. If axes is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised.

For example:

Inputs: tensor with shape [1, 2, 1, 3, 1, 1], axes=[2, 4]

Result: tensor with shape [1, 2, 3, 1]

Parameters: data – The node with data tensor. axes – List of non-negative integers, indicate the dimensions to squeeze. One of: input node or array. name – Optional new name for output node. The new node performing a squeeze operation on input tensor.
ngraph.ops.subtract(left_node, right_node, name=None)[source]

Return node which applies f(x) = A-B to the input nodes element-wise.

Parameters: left_node – The node providing data for left hand side of operator. right_node – The node providing data for right hand side of operator. name – The optional name for output node. The new output node performing subtraction operation on both tensors element-wise.
ngraph.ops.sum(node, reduction_axes=None, name=None)[source]

Perform element-wise sums of the input tensor, eliminating the specified reduction axes.

Parameters: node – The node providing data for operation. reduction_axes – The axes to eliminate through summation. name – The optional new name for ouptut node. The new node performing summation along reduction_axes element-wise.
ngraph.ops.tan(node, name=None)[source]

Apply tangent function on the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. New node with tan operation applied on it.
ngraph.ops.tanh(node, name=None)[source]

Return node which applies hyperbolic tangent to the input node element-wise.

Parameters: node – One of: input node, array or scalar. name – Optional new name for output node. New node with tanh operation applied on it.
ngraph.ops.topk(data, k, kaxis=-1, cmax=True)[source]

Return a node which performs TopK.

Parameters: data – Input data. kaxis – TopK Axis. k – cmax – Compute TopK largest (True) or smallest (False) The new node which performs TopK (both indices and values)
ngraph.ops.unsqueeze(data, axes, name=None)[source]

Perform unsqueeze operation on input tensor.

Insert single-dimensional entries to the shape of a tensor. Takes one required argument axes, a list of dimensions that will be inserted. Dimension indices in axes are as seen in the output tensor.

For example: Inputs: tensor with shape [3, 4, 5], axes=[0, 4]
Result: tensor with shape [1, 3, 4, 5, 1]
Parameters: data – The node with data tensor. axes – List of non-negative integers, indicate the dimensions to be inserted. One of: input node or array. The new node performing an unsqueeze operation on input tensor.

## ngraph.runtime module¶

Provide a layer of abstraction for the ngraph++ runtime environment.

class ngraph.runtime.Computation(runtime, ng_function)[source]

Bases: object

ngraph callable computation object.

serialize(indent=0)[source]

Serialize function (compute graph) to a JSON string.

Parameters: indent – set indent of serialized output serialized model
class ngraph.runtime.Runtime(backend_name)[source]

Bases: object

Represents the ngraph++ runtime environment.

computation(node_or_function, *inputs)[source]

Return a callable Computation object.

ngraph.runtime.runtime(backend_name='CPU')[source]

Create a Runtime object (helper factory).

Use signature to parameterize runtime as needed.