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| #include "micrograd.hpp" | ||
| #include "value.hpp" | ||
| #include "mlp.hpp" | ||
| using namespace microgradCpp; | ||
| /* | ||
| --file_name ./data/iris.csv | ||
| --encode variety | ||
| */ | ||
| int main(int argc, char *argv[]) | ||
| { | ||
| // DatasetType dataset = get_iris(); | ||
| DataFrame df; | ||
| df.from_csv("./data/wine.csv", true, ';'); | ||
| // df.normalize( ); | ||
| df.encode_column("quality"); | ||
| df.print(); | ||
| df.shuffle(); | ||
| df.print(); | ||
| double TRAIN_SIZE{0.8}; | ||
| // Create MLP model | ||
| // Input: 4 features, hidden layers: [7,7], output: 3 classes | ||
| // Define the model and hyperparameters | ||
| // MLP model(4, {10, 10, 3}); | ||
| MLP model(4, {16, 16, 10}); | ||
| auto params = model.parameters(); | ||
| double learning_rate = 0.01; | ||
| int epochs; // = 100; | ||
| std::cout << "Epoch : ?"; | ||
| std::cin >> epochs; | ||
| AdamOptimizer optimizer(params, learning_rate); | ||
| train_eval(df, TRAIN_SIZE, model, optimizer, epochs); | ||
| return 0; | ||
| } |
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| // MIT License | ||
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| // Copyright (c) [2024] Sermet Pekin | ||
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| // Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| // of this software and associated documentation files (the "Software"), to deal | ||
| // in the Software without restriction, including without limitation the rights | ||
| // to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| // copies of the Software, and to permit persons to whom the Software is | ||
| // furnished to do so, subject to the following conditions: | ||
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| // The above copyright notice and this permission notice shall be included in | ||
| // all copies or substantial portions of the Software. | ||
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| // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN | ||
| // THE SOFTWARE. | ||
| // */ | ||
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| #include "micrograd.hpp" | ||
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| #include "value.hpp" | ||
| #include "mlp.hpp" | ||
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| using namespace microgradCpp; | ||
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| int main() | ||
| { | ||
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| // DatasetType dataset = get_iris(); | ||
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| DataFrame df; | ||
| df.from_csv("./data/iris.csv"); | ||
| df.normalize(); | ||
| df.encode_column("variety"); | ||
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| df.print(); | ||
| df.shuffle(); | ||
| df.print(); | ||
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| // return 0; | ||
| // stop(); | ||
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| // return 0; | ||
| // shuffle(dataset); | ||
| double TRAIN_SIZE{0.8}; | ||
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| // Create MLP model | ||
| // Input: 4 features, hidden layers: [7,7], output: 3 classes | ||
| // Define the model and hyperparameters | ||
| // MLP model(4, {10, 10, 3}); | ||
| MLP model(4, {16, 16, 3}); | ||
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| auto params = model.parameters(); | ||
| double learning_rate = 0.01; | ||
| int epochs = 100; | ||
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| int epochs; // = 100; | ||
| std::cout << "Epoch : ?"; | ||
| std::cin >> epochs; | ||
| // Initialize Adam optimizer | ||
| AdamOptimizer optimizer(params, learning_rate); | ||
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| // Train and evaluate the model | ||
| // train_eval(dataset, TRAIN_SIZE, model, learning_rate, epochs); | ||
| // train_eval(dataset, TRAIN_SIZE, model, optimizer, epochs); | ||
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| // Train and evaluate the model | ||
| train_eval(df, TRAIN_SIZE, model, optimizer, epochs); | ||
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| return 0; | ||
| } | ||
| /* | ||
| */ | ||
| /* | ||
| Notes | ||
| ----------- | ||
| g++ -std=c++17 -Iinclude -O2 -o main easy_df.cpp | ||
| g++ -std=c++17 -Iinclude -O2 -o main easy_df_adam.cpp | ||
| // or | ||
| make run | ||
| */ | ||
| */ |
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