/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://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. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author GS // #include namespace sd { namespace ops { CUSTOM_OP_IMPL(relu_layer, 3, 1, false, 0, 0) { auto x = INPUT_VARIABLE(0); auto w = INPUT_VARIABLE(1); auto b = INPUT_VARIABLE(2); REQUIRE_TRUE(x->isMatrix(), 0, "relu_layer: x argument should be a 2D tensor, but got rank %i instead!", x->rankOf()); REQUIRE_TRUE(w->isMatrix(), 0, "relu_layer: weights argument should be a 2D tensor, but got rank %i instead!", w->rankOf()); REQUIRE_TRUE(b->isVector(), 0, "relu_layer: biases argument should be a 1D tensor, but got rank %i instead!", b->rankOf()); REQUIRE_TRUE(b->lengthOf() == w->sizeAt(1), 0, "relu_layer: biases array length should match to columns of weights matrix, however got length = %i and columns = %i!", b->lengthOf(), w->sizeAt(1)); REQUIRE_TRUE(x->sizeAt(1) == w->sizeAt(0), 0, "relu_layer: number of x columns should match to row number of weights matrix, but got x_columns = %i and weights_rows = %i!", x->sizeAt(1), w->sizeAt(0)); auto output = OUTPUT_VARIABLE(0); sd::ops::xw_plus_b op; auto status = op.execute({x, w, b}, {output}); REQUIRE_TRUE(Status::OK() == status, 0, "relu_layer: xw_plus_b op failed on input data."); auto scalar = block.numT() > 0 ? block.getTArguments()->at(0) : 0.0; output->applyScalar(sd::scalar::RELU, scalar, *output); return Status::OK(); } DECLARE_SHAPE_FN(relu_layer) { auto inShape = inputShape->at(0); auto weightsShape = inputShape->at(1); auto outputShape = ShapeUtils::matrixProductShape(inShape, weightsShape, false, false, ArrayOptions::dataType(inShape), block.getWorkspace()); return SHAPELIST(outputShape); } DECLARE_TYPES(relu_layer) { getOpDescriptor() ->setAllowedInputTypes(sd::DataType::ANY) // ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedOutputTypes({ALL_FLOATS}); } } }