67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
import sys
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from tensorflow.keras import layers, Sequential
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# from keras.layers import Flatten,Dense,Dropout, GlobalAveragePooling2D
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from tensorflow.keras.optimizers import Adam
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from tensorflow import convert_to_tensor
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import numpy as np
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import pandas as pd
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from sklearn.metrics import mean_squared_error
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from sacred.observers import FileStorageObserver, MongoObserver
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from sacred import Experiment
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from sacred.observers import MongoObserver
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from datetime import datetime
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import mlflow
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ex = Experiment("434684", 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.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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movies_train = pd.read_csv('movies_train.csv')
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x_train = movies_train.copy()
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y_train = x_train.pop('rottentomatoes_audience_score')
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x_train.pop('Unnamed: 0')
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model = Sequential()
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model.add(layers.Input(shape=(22,)))
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model.add(layers.Dense(64))
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model.add(layers.Dense(64))
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model.add(layers.Dense(32))
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model.add(layers.Dense(1))
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mlflow.log_param("learning_rate", learning_rate)
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model.compile(loss='mean_absolute_error', optimizer=Adam(learning_rate))
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_run.info["train model"] = str(datetime.now())
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history = model.fit(
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x = convert_to_tensor(x_train, np.float32),
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y = y_train,
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verbose=0, epochs=99)
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loss = history.history['loss'][-1]
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_run.info["Loss"] = str(loss)
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mlflow.log_metric("Loss", loss)
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model.save('model_movies.h5')
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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("model_movies.h5")
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