Commit 00677c6f authored by tomdol's avatar tomdol

Merge branch 'tomdol/pycapsule' of github.com:NervanaSystems/ngraph into tomdol/pycapsule

parents 00333155 e4bc7014
......@@ -32,6 +32,7 @@ ngraph.ops
cosh
divide
dot
elu
equal
exp
floor
......
......@@ -45,6 +45,7 @@ from ngraph.ops import cos
from ngraph.ops import cosh
from ngraph.ops import divide
from ngraph.ops import dot
from ngraph.ops import elu
from ngraph.ops import equal
from ngraph.ops import exp
from ngraph.ops import floor
......
......@@ -69,6 +69,7 @@ from _pyngraph.op import Cos
from _pyngraph.op import Cosh
from _pyngraph.op import Divide
from _pyngraph.op import Dot
from _pyngraph.op import Elu
from _pyngraph.op import Equal
from _pyngraph.op import Exp
from _pyngraph.op import Floor
......
......@@ -22,7 +22,7 @@ from ngraph.impl import AxisSet, AxisVector, Coordinate, CoordinateDiff, Functio
from ngraph.impl.op import Abs, Acos, Add, And, Asin, ArgMax, ArgMin, Atan, AvgPool, \
BatchNormTraining, BatchNormInference, Broadcast, Ceiling, Concat, Constant, Convert, \
Convolution, ConvolutionBackpropData, Cos, Cosh, Divide, Dot, Equal, Exp, Floor, \
Convolution, ConvolutionBackpropData, Cos, Cosh, Divide, Dot, Elu, Equal, Exp, Floor, \
GetOutputElement, Greater, GreaterEq, Less, LessEq, Log, LRN, Max, Maximum, MaxPool, \
Min, Minimum, Multiply, Negative, Not, NotEqual, OneHot, Or, Pad, Parameter, Product, \
Power, Relu, ReplaceSlice, Reshape, Reverse, Select, Sign, Sin, Sinh, Slice, Softmax, \
......@@ -35,7 +35,7 @@ from ngraph.utils.decorators import nameable_op, binary_op, unary_op
from ngraph.utils.input_validation import assert_list_of_ints
from ngraph.utils.reduction import get_reduction_axes
from ngraph.utils.types import NumericType, NumericData, TensorShape, make_constant_node, \
NodeInput, ScalarData
NodeInput, ScalarData, as_node
from ngraph.utils.types import get_element_type
......@@ -60,6 +60,24 @@ def constant(value, dtype=None, name=None): # type: (NumericData, NumericType,
return make_constant_node(value, dtype)
@nameable_op
def elu(data, alpha, name=None): # type: (NodeInput, NodeInput, str) -> Node
"""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)
<http://arxiv.org/abs/1511.07289>`_
:param data: Input tensor. One of: input node, array or scalar.
:param alpha: Multiplier for negative values. One of: input node or scalar value.
:param name: Optional output node name.
:return: The new node performing an ELU operation on its input data element-wise.
"""
return Elu(as_node(data), as_node(alpha))
# Unary ops
@unary_op
def absolute(node, name=None): # type: (NodeInput, str) -> Node
......
......@@ -14,8 +14,6 @@
// limitations under the License.
//*****************************************************************************
// #include <Python.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
......
//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include "ngraph/op/fused/elu.hpp"
#include "pyngraph/ops/elu.hpp"
namespace py = pybind11;
void regclass_pyngraph_op_Elu(py::module m)
{
py::class_<ngraph::op::Elu, std::shared_ptr<ngraph::op::Elu>, ngraph::op::Op> elu(m, "Elu");
elu.doc() = "ngraph.impl.op.Elu wraps ngraph::op::Elu";
elu.def(py::init<const std::shared_ptr<ngraph::Node>&, const std::shared_ptr<ngraph::Node>&>());
}
//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#pragma once
#include <pybind11/pybind11.h>
namespace py = pybind11;
void regclass_pyngraph_op_Elu(py::module m);
//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include "ngraph/op/fused/elu.hpp"
#include "pyngraph/ops/fused/elu.hpp"
namespace py = pybind11;
void regclass_pyngraph_op_Elu(py::module m)
{
py::class_<ngraph::op::Elu, std::shared_ptr<ngraph::op::Elu>, ngraph::op::Op> elu(m, "Elu");
elu.doc() = "ngraph.impl.op.Elu wraps ngraph::op::Elu";
elu.def(py::init<const std::shared_ptr<ngraph::Node>&, const std::shared_ptr<ngraph::Node>&>());
}
......@@ -49,6 +49,7 @@ void regmodule_pyngraph_op(py::module m_op)
regclass_pyngraph_op_Cosh(m_op);
regclass_pyngraph_op_Divide(m_op);
regclass_pyngraph_op_Dot(m_op);
regclass_pyngraph_op_Elu(m_op);
regclass_pyngraph_op_Equal(m_op);
regclass_pyngraph_op_Exp(m_op);
regclass_pyngraph_op_Floor(m_op);
......
......@@ -39,6 +39,7 @@
#include "pyngraph/ops/cosh.hpp"
#include "pyngraph/ops/divide.hpp"
#include "pyngraph/ops/dot.hpp"
#include "pyngraph/ops/elu.hpp"
#include "pyngraph/ops/equal.hpp"
#include "pyngraph/ops/exp.hpp"
#include "pyngraph/ops/floor.hpp"
......
......@@ -179,6 +179,7 @@ sources = [
'pyngraph/ops/ceiling.cpp',
'pyngraph/ops/divide.cpp',
'pyngraph/ops/dot.cpp',
'pyngraph/ops/elu.cpp',
'pyngraph/ops/equal.cpp',
'pyngraph/ops/exp.cpp',
'pyngraph/ops/floor.cpp',
......
# ******************************************************************************
# Copyright 2017-2019 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
import numpy as np
import ngraph as ng
from test.ngraph.util import get_runtime
def test_elu_operator():
runtime = get_runtime()
data_shape = [2, 2]
alpha_shape = [2]
parameter_data = ng.parameter(data_shape, name='Data', dtype=np.float32)
parameter_alpha = ng.parameter(alpha_shape, name='Alpha', dtype=np.float32)
model = ng.elu(parameter_data, parameter_alpha)
computation = runtime.computation(model, parameter_data, parameter_alpha)
value_data = np.array([[-5, 1], [-2, 3]], dtype=np.float32)
value_alpha = np.array([3, 3], dtype=np.float32)
result = computation(value_data, value_alpha)
expected = np.array([[-2.9797862, 1.], [-2.5939941, 3.]], dtype=np.float32)
assert np.allclose(result, expected)
def test_elu_operator_with_scalar_and_array():
runtime = get_runtime()
data_value = np.array([[-5, 1], [-2, 3]], dtype=np.float32)
alpha_value = np.float32(3)
model = ng.elu(data_value, alpha_value)
computation = runtime.computation(model)
result = computation()
expected = np.array([[-2.9797862, 1.], [-2.5939941, 3.]], dtype=np.float32)
assert np.allclose(result, expected)
def test_elu_operator_with_scalar():
runtime = get_runtime()
data_value = np.array([[-5, 1], [-2, 3]], dtype=np.float32)
alpha_value = np.float32(3)
data_shape = [2, 2]
parameter_data = ng.parameter(data_shape, name='Data', dtype=np.float32)
model = ng.elu(parameter_data, alpha_value)
computation = runtime.computation(model, parameter_data)
result = computation(data_value)
expected = np.array([[-2.9797862, 1.], [-2.5939941, 3.]], dtype=np.float32)
assert np.allclose(result, expected)
......@@ -39,7 +39,7 @@ NodeVector op::Elu::decompose_op() const
auto data = get_argument(0);
auto alpha_node = get_argument(1);
alpha_node = ngraph::op::make_broadcast_node(alpha_node, data->get_shape());
alpha_node = ngraph::op::numpy_style_broadcast(alpha_node, data->get_shape());
shared_ptr<ngraph::Node> zero_node =
builder::make_constant(data->get_element_type(), data->get_shape(), 0);
......
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