operators¶

Module webdnn.graph.operators contains WebDNN Operator IR classes. All classes inherit Operator base class.

All operator class instances are callable. When it is called, input and output variables are registered with corresponding names. In this document, the registered name of each operator is described in Signature section.

For almost of all users, it is not need to understand this section. If you want to implement your custom converter/generator handler, this section may be helpful.

Abs¶

class webdnn.graph.operators.abs.Abs(name)[source]¶

Elementwise absolute value operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Absolute value of x. Its shape and order is same as x0.

This operator also can be called by abs().

y = abs(x0)

Acos¶

class webdnn.graph.operators.acos.Acos(name)[source]¶

Elementwise acos operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

Acosh¶

class webdnn.graph.operators.acosh.Acosh(name)[source]¶

Elementwise acosh operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

Asin¶

class webdnn.graph.operators.asin.Asin(name)[source]¶

Elementwise asin operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

Asinh¶

class webdnn.graph.operators.asinh.Asinh(name)[source]¶

Elementwise asinh operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

Atan¶

class webdnn.graph.operators.atan.Atan(name)[source]¶

Elementwise atan operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

Atanh¶

class webdnn.graph.operators.atanh.Atanh(name)[source]¶

Elementwise atanh operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

AveragePooling2D¶

class webdnn.graph.operators.average_pooling_2d.AveragePooling2D(name, ksize, stride, padding, cover_all=True)[source]¶

Spatial average pooling operator.

Parameters:

name (str) – Operator name.
ksize (int or tuple of int) – Kernel size.
stride (int or tuple of int) – Stride size.
padding (int or tuple of int) – Padding size.
cover_all (bool, optional) – If True, all input pixels are pooled into some output pixels.
divide_without_padding – divides sum of input by the number of elements not of padding. Used in tensorflow.
https (See) – //github.com/mil-tokyo/webdnn/issues/694

Signature

y, = op(x)

x - Input variable.
y - Output value. Its order is same as x.

AxiswiseBias¶

class webdnn.graph.operators.axiswise_bias.AxiswiseBias(name, axis)[source]¶

Adds a bias value along to specified axis.

Parameters:	name (str) – Operator name. axis (`Axis`) – target axis

Signature

y, = op(x, b)

x - Data variable. It must has axis axis, and whose size must be same as b.
b - Bias variable. It must be 1D variable and that size must be same as x.shape_dict[axis]
y - Output variable. Its order and shape is same as x.

Deprecated since version v1.2: Use ElementwiseAdd instead.

AxiswiseScale¶

class webdnn.graph.operators.axiswise_scale.AxiswiseScale(name, axis)[source]¶

Multiply a scale value along to specified axis.

Parameters:	name (str) – Operator name. axis (`Axis`) – target axis

Signature

y, = op(x, s)

x - Data variable. It must has axis axis, and whose size must be same as s.
s - Scale variable. It must be 1D variable and that size must be same as x.shape_dict[axis]
y - Output variable. Its order and shape is same as x.

Deprecated since version v1.2: Use ElementwiseMul instead.

Broadcast¶

class webdnn.graph.operators.broadcast.Broadcast(name, out_shape, out_order)[source]¶

Broadcast variable to specified shape. Each elementwise operator supports implicit broadcast feature. Therefore you don’t need to use this operator explicitly.

Parameters:	name (str) – Operator name. out_shape (list of int) – target shape out_order (`Order`) – target order

Signature

y, = op(x)

x - Input variable.
y - Output variable.

ClippedRelu¶

class webdnn.graph.operators.clipped_relu.ClippedRelu(name, cap)[source]¶

Clipped relu operator.

\[y = min(max(x, 0), cap)\]

Parameters:	name (str) – Operator name. cap (float) – clipping threshold.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Col2Im¶

class webdnn.graph.operators.col2im.Col2Im(name: typing.Union[str, NoneType], ksize: typing.Union[_ForwardRef('IntOrTuple'), int, typing.Tuple[int, int]], stride: typing.Union[_ForwardRef('IntOrTuple'), int, typing.Tuple[int, int]], padding: typing.Union[_ForwardRef('IntOrTuple'), int, typing.Tuple[int, int]])[source]¶

Concat¶

class webdnn.graph.operators.concat.Concat(name, axis)[source]¶

Concatenate multiple variables into one variable along to specified axis.

Parameters:	name (str) – Operator name. ( (axis) – obj:~`webdnn.Axis`): target axis

Signature

y, = op(x0, x1, ...)

x0, x1, ... - Input variables. All variables has same shape except the specified axis.
y - Output variable. Its order is same as x0.

Convolution2D¶

class webdnn.graph.operators.convolution2d.Convolution2D(name, ksize, stride, padding, dilation_rate=1)[source]¶

Spatial convolution operator.

Parameters:	name (str) – Operator name. ksize (int or tuple of int) – Kernel size. stride (int or tuple of int) – Stride size. padding (int or tuple of int) – Padding size. dilation_rate (int or tuple of int) – Dilation rate. 1 means ordinary convolution. Input pixels are shifted by (dilation_rate - 1) pixels.

Signature

y, = op(x, w)

x - Input variables. It must has 4 axes, N, C, H, and W.
w - Kernel variable. It must has N, C, H, W. Its size of H and W must be same as kernel size. Its size of C must be same as x.
y - Output variable. Its order is same as x.

Cos¶

class webdnn.graph.operators.cos.Cos(name)[source]¶

Elementwise cos operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

Cosh¶

class webdnn.graph.operators.cosh.Cosh(name)[source]¶

Elementwise hyperbolic cos operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

Deconvolution2D¶

class webdnn.graph.operators.deconvolution2d.Deconvolution2D(name, ksize, stride, padding)[source]¶

Spatial deconvolution operator.

Parameters:	name (str) – Operator name. ksize (int or tuple of int) – Kernel size. stride (int or tuple of int) – Stride size. padding (int or tuple of int) – Padding size.

Signature

y, = op(x, w)

x - Input variables. It must has 4 axes, N, C, H, and W.
w - Kernel variable. It must has N, C, H, W. Its size of H and W must be same as kernel size. Its size of C must be same as x.
y - Output variable. Its order is same as x.

Depth2Space¶

class webdnn.graph.operators.depth2space.Depth2Space(name, r)[source]¶

Depth to space transformation.

Parameters:	name (str) – Operator name. r (int) – Upscaling factor.

Signature

y, = op(x, r)

x - Input variable.
y - Output variable. Its order is same as x.

Elementwise¶

class webdnn.graph.operators.elementwise.Elementwise(name)[source]¶

Elementwise operator base class.

Operation “Elementwise” is defined as follows:

It outputs only one variable.
All input variables and the output variable are same shape (not need to be same order).
Each element of output variable can be computed with only the elements at the same position of input variables.

\[y[p] = f(x_0[p], x_1[p], ..., x_n[p]),\]

where \(p\) means the position of the element.

All input variables are registered with name like x0, x1, ... x{index}, and the output variable is registered with name y.

This operator has Elementwise attribute.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0, x1, ...)

x0, x1, ... - Input variables.
y - Output variable. Its shape and order is same as x0.

ElementwiseAdd¶

class webdnn.graph.operators.elementwise_add.ElementwiseAdd(name)[source]¶

Elementwise add

Parameters:	name (str) – Operator name.

Signature

y, = op(x0, x1)

x0, x1 - Input variable. They must be same shape.
y - Output variable. Its order and shape is same as x0.

This operator also can be called by +.

y = x0 + x1

ElementwiseDiv¶

class webdnn.graph.operators.elementwise_div.ElementwiseDiv(name: typing.Union[str, NoneType])[source]¶

ElementwiseAdd(name)

Elementwise divide

Parameters:	name (str) – Operator name.

Signature

y, = op(x0, x1)

x0, x1 - Input variable. They must be same shape.
y - Output variable. Its order and shape is same as x0.

This operator also can be called by /.

y = x0 / x1

ElementwiseMul¶

class webdnn.graph.operators.elementwise_mul.ElementwiseMul(name)[source]¶

Elementwise multiply

Parameters:	name (str) – Operator name.

Signature

y, = op(x0, x1)

x0, x1 - Input variable. They must be same shape.
y - Output variable. Its order and shape is same as x0.

This operator also can be called by *.

y = x0 * x1

ElementwisePow¶

class webdnn.graph.operators.elementwise_pow.ElementwisePow(name)[source]¶

Elementwise power

Parameters:	name (str) – Operator name.

Signature

y, = op(x0, x1)

x0, x1 - Input variable. They must be same shape.
y - Output variable. Its order and shape is same as x0.

This operator also can be called by **.

y = x0 ** x1

ElementwiseSum¶

class webdnn.graph.operators.elementwise_sum.ElementwiseSum(*args, **kwargs)[source]¶: Deprecated since version v1.2: Use ElementwiseAdd instead.

Elu¶

class webdnn.graph.operators.elu.Elu(name)[source]¶

Exponential Linear Unit operator.

\[\begin{split}f(x) = \left \{ \begin{array}{ll} x & {\rm if}~ x \ge 0 \\ \exp(x) - 1 & {\rm if}~ x < 0, \end{array} \right.\end{split}\]

More detail, please see https://arxiv.org/abs/1511.07289

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Embedding¶

class webdnn.graph.operators.embedding.Embedding(name)[source]¶

Word embedding operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x, w)

x - Input variables. It must has 2 axes, N, T.
w - Dictionary variable. It must has N, and C. Its size of C must be same as the vocabulary size. Its size of N must be same as the embed feature size.
y - Output variable. Its order is OrderNTC.

Exp¶

class webdnn.graph.operators.exp.Exp(name)[source]¶

Exponential operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Greater¶

class webdnn.graph.operators.greater.Greater(name)[source]¶

return 1 if x0 is greater than x1 elementwisely

\[f(x) = x0 > x1 ? 1 : 0;\]

Signature

y, = op(x0, x1)

x0, x1 - Input variables.
y - Output variable.

GreaterEqual¶

class webdnn.graph.operators.greater_equal.GreaterEqual(name)[source]¶

return 1 if x0 is greater than or equal to x1 elementwisely

\[f(x) = x0 >= x1 ? 1 : 0;\]

Signature

y, = op(x0, x1)

x0, x1 - Input variables.
y - Output variable.

HardSigmoid¶

class webdnn.graph.operators.hard_sigmoid.HardSigmoid(name)[source]¶

Hard sigmoid operator

\[\begin{split}f(x) = \left \{ \begin{array}{ll} 0 & {\rm if}~ x < -2.5 \\ 0.2 x + 0.5 & {\rm if}~ -2.5 < x < 2.5 \\ 1 & {\rm if}~ 2.5 < x. \end{array} \right.\end{split}\]

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Im2Col¶

class webdnn.graph.operators.im2col.Im2Col(name: typing.Union[str, NoneType], ksize: typing.Union[_ForwardRef('IntOrTuple'), int, typing.Tuple[int, int]], stride: typing.Union[_ForwardRef('IntOrTuple'), int, typing.Tuple[int, int]], padding: typing.Union[_ForwardRef('IntOrTuple'), int, typing.Tuple[int, int]], dilation_rate: typing.Union[_ForwardRef('IntOrTuple'), int, typing.Tuple[int, int]])[source]¶

WH¶: Input window height considering dilation. Returns:

WW¶: Input window width considering dilation. Returns:

LeakyRelu¶

class webdnn.graph.operators.leaky_relu.LeakyRelu(name, slope)[source]¶

Leaky relu operator

\[f(x) = max(x, ax)\]

where \(a\) is slope value.

Parameters:	slope (float) – slope value

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Linear¶

class webdnn.graph.operators.linear.Linear(name)[source]¶

Linear operator a.k.a. Dense, Fully-Connected operator.

This operator compute dot product with input and weight variables. Except N, all axes are used for calculation. If x is OrderNC and w is OrderCN, this operation is just same as np.tensor_dot(x, w, (1, 0)) in numpy’s tensordot.

Also, 4D variable is acceptable for input and weight variable. If x is OrderNCHW and w is OrderNHWC, this operation is just same as np.tensor_dot(x, w, ((1,2,3), (3,1,2)) in numpy’s tensordot.

Input and weight variables must be same dimension. Otherwise, an assertion error is raised.

Parameters:	name (str) – Operator name.

Signature

y, = op(x, w)

x - Input variable.
w - Weight variable.
y - Output variable. Its order is OrderNC. Its N size is same as x.shape_dict[Axis.N], and its C size is same as w.shape_dict[Axis.N]

LocalResponseNormalization¶

class webdnn.graph.operators.local_response_normalization.LocalResponseNormalization(name, n, k, alpha, beta)[source]¶

Operator same as local response normalization layer in Caffe. Only cross channel mode is supported; normalization is done for channel axis.

For more detail, see: http://caffe.berkeleyvision.org/tutorial/layers/lrn.html

Parameters:	name (str) – Operator name. n (float) – Parameter n. k (float) – Parameter k. alpha (float) – Parameter alpha. beta (float) – Parameter beta.

Signature

y, = op(x)

x - Input variable.
y - Output variable. Its order and shape is same as x.

Log¶

class webdnn.graph.operators.log.Log(name)[source]¶

Logarithm operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

LSTM¶

class webdnn.graph.operators.lstm.LSTM(name: typing.Union[str, NoneType], use_bias: bool, return_sequences: bool, use_initial_c: bool, use_initial_h: bool, activation: str, recurrent_activation: str)[source]¶

Long-short term memory layer.

Currently, outputs value of hidden layer after final input is consumed. Details are corresponding to Keras’s implementation (layers/recurrent.py)

Parameters:	name (str) – Operator name.

__call__(x: webdnn.graph.variable.Variable, w_input: webdnn.graph.variable.Variable, w_hidden: webdnn.graph.variable.Variable, b: typing.Union[webdnn.graph.variable.Variable, NoneType] = None, initial_c: typing.Union[webdnn.graph.variable.Variable, NoneType] = None, initial_h: typing.Union[webdnn.graph.variable.Variable, NoneType] = None)[source]¶

Parameters:	x (`Variable`) – Input (sequence OrderNTC) w_input (`Variable`) – Weight for input w_hidden (`Variable`) – Weight for hidden state b (`Variable`) – Bias initial_c (`Variable`) – Initial cell state initial_h (`Variable`) – Initial hidden state
Returns:	Output (OrderNC) final_c (`Variable`): Last cell state (OrderNC)
Return type:	y (`Variable`)

Max¶

class webdnn.graph.operators.max.Max(name, axis)[source]¶

return elements of maximum value along to specified axis

Parameters:	name (str) – Operator name. axis (`Axis`) – axis which will be reduced.

Signature

y, = op(x)

x - Input variables.
y - Output variable.

MaxPooling2D¶

class webdnn.graph.operators.max_pooling_2d.MaxPooling2D(name, ksize, stride, padding, cover_all=False)[source]¶

Spatial max pooling operator.

Parameters:	name (str) – Operator name. ksize (int or tuple of int) – Kernel size. stride (int or tuple of int) – Stride size. padding (int or tuple of int) – Padding size. cover_all (bool, optional) – If True, all input pixels are pooled into some output pixels.

Signature

y, = op(x)
x - Input variable.

y - Output value. Its order is same as x.

Pooling2D¶

class webdnn.graph.operators.pooling_2d.Pooling2D(name, ksize, stride, padding)[source]¶

Spatial pooling base operator.

Parameters:	name (str) – Operator name. ksize (int or tuple of int) – Kernel size. stride (int or tuple of int) – Stride size. padding (int or tuple of int) – Padding size. cover_all (bool, optional) – If True, all input pixels are pooled into some output pixels.

Signature

y, = op(x)
x - Input variable.

y - Output value. Its order is same as x.

Prod¶

class webdnn.graph.operators.prod.Prod(name, axis)[source]¶

return product of the input tensor along to specified axis

Parameters:	name (str) – Operator name. axis (`Axis`) – axis which will be reduced.

Signature

y, = op(x)

x - Input variables.
y - Output variable.

Reduce¶

class webdnn.graph.operators.reduce.Reduce(name, axis)[source]¶

This operator reduces an axis into single element. This operator does NOT consider about the direction of reduction. For all axis without reduction axis, both input and output variables have same size.

Parameters:	name (str) – Operator name. axis (`Axis`) –

Signature

y, = op(x)

x - Input variables.
y - Output variable.

ReinterpretAxis¶

class webdnn.graph.operators.reinterpret_axis.ReinterpretAxis(name, in_order, out_order)[source]¶

Re-interpret an axis as another semantics. Shape is not changed.

In case of in_order is OrderNC and out_order is OrderNT, if OrderCN variable is input, ~webdnn.graph.order.OrderTN variable is output.

Parameters:	name (str) – Operator name. in_order (`Order`) – Input order out_order (`Order`) – Output order

Signature

y, = op(x)

x - Input variable.
y - Output variable. Its shape is same as x.

Relu¶

class webdnn.graph.operators.relu.Relu(name, slope)[source]¶

Rectified Linear Unit

\[f(x) = max(0, x)\]

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Reshape¶

class webdnn.graph.operators.reshape.Reshape(name, in_order, out_order, out_shape)[source]¶

Reshape array assuming it is C-order. Removing / inserting axis with length 1

When in_order: NHWC, out_order: NTC, out_shape: (2, 6, 10) and input variable is (2, 3, 4, 5), the semantic procedure is as follows. 1. Interpret input variable as NTHWC (2, 1, 3, 4, 5) with inserting axis with length 1 2. Reshape it with assuming C-order and length of axis being removed is 1; NTHWC (2, 6, 1, 1, 10) 3. Remove axes; NTC (2, 6, 10)

Swapping axes is prohibited because it is ambiguous. If in_order and out_order match the actual input / output variable order, kernel does not have to do anything.

Parameters:	name (str) – Operator name. in_order (`Order`) – input order out_order (`Order`) – output order out_shape (list of int or `Placeholder`) – output shape

Signature

y, = op(x)

x - Input variable.
y - Output variable.

Rsqrt¶

class webdnn.graph.operators.rsqrt.Rsqrt(name)[source]¶

Reciprocal of square root operator.

\[f(x) = 1 / sqrt(x)\]

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

ScalarAdd¶

class webdnn.graph.operators.scalar_add.ScalarAdd(name, value)[source]¶

Elementwise add with scalar value

Parameters:	name (str) – Operator name. value (int or float) – the value

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

This operator also can be called by +.

y = x0 + value

ScalarAffine¶

class webdnn.graph.operators.scalar_affine.ScalarAffine(name, value)[source]¶

Affine transform operator with scalar scale and value

Parameters:	name (str) – Operator name. scale (int or float) – the scale value bias (int or float) – the bias value

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

ScalarMul¶

class webdnn.graph.operators.scalar_mul.ScalarMul(name, value)[source]¶

Elementwise multiply with scalar value

Parameters:	name (str) – Operator name. value (int or float) – the value

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

This operator also can be called by *.

y = x0 * value

ScalarPow¶

class webdnn.graph.operators.scalar_pow.ScalarPow(name, value)[source]¶

Elementwise power with scalar value

Parameters:	name (str) – Operator name. value (int or float) – the value

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

This operator also can be called by **.

y = x0 ** value

Select¶

class webdnn.graph.operators.select.Select(name: typing.Union[str, NoneType])[source]¶

GreaterEqual(name)

return x1 if x0 is 1, otherwise x2.

\[f(x) = x0 ? x1 : x2;\]

Signature

y, = op(x0, x1, x2)

x0 - Condition variable.
x1, x2 - Input variables.
y - Output variable.

Sigmoid¶

class webdnn.graph.operators.sigmoid.Sigmoid(name)[source]¶

Sigmoid operator.

\[f(x) = \frac{1}{1+exp(-x)}\]

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Sin¶

class webdnn.graph.operators.sin.Sin(name)[source]¶

Elementwise sin operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

Sinh¶

class webdnn.graph.operators.sinh.Sinh(name)[source]¶

Elementwise hyperbolic sin operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

Slice¶

class webdnn.graph.operators.slice.Slice(name, indices)[source]¶

Slice original variable to sub variable.

Parameters:	name (str) – Operator name. indices (`AxisKeyDict` of slice, int, or None) – slicing indices

Signature

y = x[indices]

x - Input variable.
y - Sliced variable.

Softmax¶

class webdnn.graph.operators.softmax.Softmax(name, axis)[source]¶

Softmax operator.

Parameters:	name (str) – Operator name. axis (`Axis`) –

Signature

y, = op(x)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Softplus¶

class webdnn.graph.operators.softplus.Softplus(name, beta)[source]¶

Softplus operator.

\[f(x) = \frac{1}{beta} log(1 + exp(beta * x))\]

Parameters:	name (str) – Operator name. beta (float) – coefficient.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Softsign¶

class webdnn.graph.operators.softsign.Softsign(name)[source]¶

Softsign operator.

For more detail, see https://www.tensorflow.org/api_docs/python/tf/nn/softsign

\[f(x) = \frac{x}{abs(x) + 1}\]

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Space2Depth¶

class webdnn.graph.operators.space2depth.Space2Depth(name, r)[source]¶

Space to depth transformation.

Parameters:	name (str) – Operator name. r (int) – Downscaling factor.

Signature

y, = op(x, r)

x - Input variable.
y - Output variable. Its order is same as x.

Tan¶

class webdnn.graph.operators.tan.Tan(name)[source]¶

Elementwise tan operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable.

Tanh¶

class webdnn.graph.operators.tanh.Tanh(name)[source]¶

Tanh operator.

Parameters:	name (str) – Operator name.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Tensordot¶

class webdnn.graph.operators.tensordot.Tensordot(name, axes)[source]¶

Tensordot operator

Parameters:	name (str) – Operator name. axes (`Axis`, list of `Axis`, list of list of `Axis`) – axes which are reduced If axes is an `Axis` instance,

Signature

C, = op(A, B)

A, B - Input variables.
C - Output variable.

ThresholdRelu¶

class webdnn.graph.operators.threshold_relu.ThresholdRelu(name)[source]¶

Relu operator with threshold.

\[f(x) = x > a ? x : 0\]

Parameters:	name (str) – Operator name. threshold (float) – the threshold value.

Signature

y, = op(x0)

x0 - Input variable.
y - Output variable. Its order and shape is same as x0.

Tile¶

class webdnn.graph.operators.tile.Tile(name)[source]¶

Repeat input variable.

Parameters:	name (str) – Operator name. multiplier (tuple of int) – number of repeat

Signature

y, = op(x)

x - Input variable.
y - Output variable.

Transpose¶

class webdnn.graph.operators.transpose.Transpose(name: typing.Union[str, NoneType])[source]¶

Transposition. Doing nothing in frontend level, and do memory transposition in backend if input / output variable order differs. This layer is inserted in optimizer to support layers which accepts certain order.

Parameters:	name (str) – Operator name.

Unpooling2D¶

class webdnn.graph.operators.unpooling_2d.Unpooling2D(name, ksize, stride, padding, outsize)[source]¶

Inverse operation of pooling for 2d array. This function acts similarly to Deconvolution2D, but it spreads input 2d array’s value without any parameter instead of computing the inner products.

Parameters:	name (str) – Operator name. ksize (int or tuple of int) – Kernel size. stride (int or tuple of int) – Stride size. padding (int or tuple of int) – Padding size. outsize (int or tuple of int) – Output size.

Signature

y, = op(x)

x - Input variable.
y - Output value. Its order is same as x.

ZeroPadding1D¶

class webdnn.graph.operators.zero_padding_1d.ZeroPadding1D(name, padding)[source]¶

Zero padding 1D operator

Add padding to time-series data (n, t, c) -> (n, left + t + right, c)

Parameters:	name (str) – Operator name. padding (int or tuple of int) – Padding size. [left, right]

Signature

y, = op(x)

x - Input variable.
y - Output variable. Its order and shape is same as x.

ZeroPadding2D¶

class webdnn.graph.operators.zero_padding_2d.ZeroPadding2D(name, padding)[source]¶

Zero padding 2D operator

Supposed to be merged into convolution in optimization

Parameters:	name (str) – Operator name. padding (int or tuple of int) – Padding size. [top, left]

Signature

y, = op(x)

x - Input variable.
y - Output variable. Its order and shape is same as x.