| #------------------------------------------------------------- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| # |
| #------------------------------------------------------------- |
| |
| source("src/test/scripts/functions/paramserv/mnist_lenet_paramserv_minimum_version.dml") as mnist_lenet |
| source("scripts/nn/layers/cross_entropy_loss.dml") as cross_entropy_loss |
| |
| # Generate the training data |
| [images, labels, C, Hin, Win] = mnist_lenet::generate_dummy_data() |
| n = nrow(images) |
| |
| # Generate the training data |
| [X, Y, C, Hin, Win] = mnist_lenet::generate_dummy_data() |
| |
| # Split into training and validation |
| val_size = n * 0.1 |
| X = images[(val_size+1):n,] |
| X_val = images[1:val_size,] |
| Y = labels[(val_size+1):n,] |
| Y_val = labels[1:val_size,] |
| |
| # Arguments |
| epochs = 10 |
| workers = 2 |
| batchsize = 64 |
| |
| # Train |
| [W1, b1, W2, b2, W3, b3, W4, b4] = mnist_lenet::train(X, Y, X_val, Y_val, C, Hin, Win, epochs, workers) |
| |
| # Compute validation loss & accuracy |
| probs_val = mnist_lenet::predict(X_val, C, Hin, Win, batchsize, W1, b1, W2, b2, W3, b3, W4, b4) |
| loss_val = cross_entropy_loss::forward(probs_val, Y_val) |
| accuracy_val = mean(rowIndexMax(probs_val) == rowIndexMax(Y_val)) |
| |
| # Output results |
| print("Val Loss: " + loss_val + ", Val Accuracy: " + accuracy_val) |