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OpenAI CartPole w/ Keras: Model
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| """ | |
| __name__ = model.py | |
| __author__ = Yash Patel | |
| __description__ = Defines model to be trained on the Cartpole data, | |
| predicting the directioal action to take given 4D observation state | |
| """ | |
| from keras.models import Sequential | |
| from keras.layers import Dense, Dropout | |
| def create_model(): | |
| model = Sequential() | |
| model.add(Dense(128, input_shape=(4,), activation="relu")) | |
| model.add(Dropout(0.6)) | |
| model.add(Dense(256, activation="relu")) | |
| model.add(Dropout(0.6)) | |
| model.add(Dense(512, activation="relu")) | |
| model.add(Dropout(0.6)) | |
| model.add(Dense(256, activation="relu")) | |
| model.add(Dropout(0.6)) | |
| model.add(Dense(128, activation="relu")) | |
| model.add(Dropout(0.6)) | |
| model.add(Dense(2, activation="softmax")) | |
| model.compile( | |
| loss="categorical_crossentropy", | |
| optimizer="adam", | |
| metrics=["accuracy"]) | |
| return model | |
| if __name__ == "__main__": | |
| model = create_model() |
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