| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| |
| # paths to dataset and output file |
| path = $1 |
| out_path = $2 |
| |
| data = read(path, format="csv") |
| x_train = data[,1:6] |
| y_train = data[, 7] |
| |
| # Train the model on synthetic dataset for binary classification generated by scikit-learn |
| model = ffTrain(X=x_train, Y=y_train, batch_size=501, epochs=3, learning_rate=0.001, out_activation="sigmoid", loss_fcn="cel", verbose=TRUE, shuffle=TRUE) |
| # Make predictions on the training set to test the model's capability of learning |
| prediction = ffPredict(model=model, X=x_train) |
| |
| # Threshold output of softmax |
| prediction = prediction > 0.5 |
| |
| # calculate accuracy |
| acc = sum(y_train == prediction)/ nrow(y_train) |
| |
| print(acc) |
| write(acc, out_path) |
