63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
from datetime import datetime
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import mlflow
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import pandas as pd
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from sacred import Experiment
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from sacred.observers import FileStorageObserver, MongoObserver
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import sys
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import tensorflow
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from tensorflow.keras import layers
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ex = Experiment("470607", interactive=False, save_git_info=False)
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ex.observers.append(
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MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
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ex.observers.append(FileStorageObserver('my_runs'))
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@ex.config
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def my_config():
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learning_rate = float(sys.argv[1])
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@ex.capture
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def prepare_train_model(learning_rate, _run):
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with mlflow.start_run():
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_run.info["prepare_model"] = str(datetime.now())
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X_train = pd.read_csv('train.csv')
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X_valid = pd.read_csv('valid.csv')
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Y_train = X_train.pop('stabf')
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Y_train = pd.get_dummies(Y_train)
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Y_valid = X_valid.pop('stabf')
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Y_valid = pd.get_dummies(Y_valid)
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model = tensorflow.keras.Sequential([
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layers.Input(shape=(12,)),
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layers.Dense(32),
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layers.Dense(16),
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layers.Dense(2, activation='softmax')
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])
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model.compile(
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loss=tensorflow.keras.losses.BinaryCrossentropy(),
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optimizer=tensorflow.keras.optimizers.Adam(learning_rate=learning_rate),
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metrics=[tensorflow.keras.metrics.BinaryAccuracy()])
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history = model.fit(X_train, Y_train, epochs=2, validation_data=(X_valid, Y_valid))
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model.save('grid-stability-dense.h5')
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_run.info['history'] = str(history.history['loss'][-1])
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mlflow.log_metric('loss', history.history['loss'][-1])
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mlflow.log_param('learning_rate', learning_rate)
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@ex.main
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def my_main(learning_rate):
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print(prepare_train_model())
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r = ex.run()
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ex.add_artifact('grid-stability-dense.h5')
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