void fullConnect(Tensor &out, const char *params, const Tensor &in)
{
- // TODO call actual function
+ const float *input = in.getData();
+ Dims<4> input_d = shapeToDims(in.getShape());
+ Kernel kernel = deserializeKernel(params);
+ Shape out_s = deserializeShape(params);
+
+ out.reShape(out_s);
+
+ FullyConnected(input, input_d,
+ kernel.data, kernel.dims,
+ out.getData(), shapeToDims(out_s));
}
void cappedRelu(Tensor &out, const char *params, const Tensor &in)
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+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.
+==============================================================================*/
+
+inline void FullyConnected(const float* input_data, const Dims<4>& input_dims,
+ const float* weights_data,
+ const Dims<4>& weights_dims,
+ float* output_data, const Dims<4>& output_dims) {
+ const int input_rows = ArraySize(weights_dims, 0);
+ const auto input_matrix_map =
+ MapAsMatrixWithGivenNumberOfRows(input_data, input_dims, input_rows);
+ const auto filter_matrix_map =
+ MapAsMatrixWithFirstDimAsRows(weights_data, weights_dims);
+ auto output_matrix_map =
+ MapAsMatrixWithFirstDimAsRows(output_data, output_dims);
+
+ Gemm(filter_matrix_map.transpose(), input_matrix_map, &output_matrix_map);
+}