76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
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from sacred import Experiment
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from sacred.observers import MongoObserver
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import pandas as pd
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import numpy as np
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from tensorflow import keras
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from sklearn.metrics import accuracy_score
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from tensorflow.keras import layers
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ex = Experiment("437622-mongo", interactive=False, save_git_info=False)
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#ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
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@ex.config
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def my_config():
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epochs = 15
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batch_size = 16
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@ex.capture
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def prepare_model(epochs, batch_size, _run):
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model_name = "model"
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train = pd.read_csv('train.csv', header=None, skiprows=1)
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indexNames = train[train[1] == 2].index
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train.drop(indexNames, inplace=True)
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cols = [0, 2, 3]
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X = train[cols].to_numpy()
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y = train[1].to_numpy()
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X = np.asarray(X).astype('float32')
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model = keras.Sequential(name="winner")
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model.add(keras.Input(shape=(3), name="game_info"))
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model.add(layers.Dense(4, activation="relu", name="layer1"))
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model.add(layers.Dense(8, activation="relu", name="layer2"))
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model.add(layers.Dense(8, activation="relu", name="layer3"))
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model.add(layers.Dense(5, activation="relu", name="layer4"))
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model.add(layers.Dense(1, activation="relu", name="output"))
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model.compile(
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optimizer=keras.optimizers.RMSprop(),
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loss=keras.losses.MeanSquaredError(),
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)
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history = model.fit(
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X,
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y,
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batch_size=batch_size,
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epochs=epochs, )
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model.save(model_name)
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test = pd.read_csv('test.csv', header=None, skiprows=1)
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cols = [0, 2, 3]
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indexNames = test[test[1] == 2].index
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test.drop(indexNames, inplace=True)
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X_test = test[cols].to_numpy()
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y_test = test[1].to_numpy()
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X_test = np.asarray(X_test).astype('float32')
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predictions = model.predict(X_test)
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pd.DataFrame(predictions).to_csv('results.csv', sep='\t', index=False, header=False)
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acc = accuracy_score(y_test, predictions)
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print('Accuracy: ', acc)
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return acc
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@ex.automain
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def my_main(epochs, batch_size):
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print(prepare_model())
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ex.run()
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ex.add_artifact('model')
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