47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
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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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with mlflow.start_run():
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learning_rate = float(sys.argv[1])
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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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learning_rate = float(sys.argv[1])
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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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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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mlflow.log_metric("Loss", loss)
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model.save('model_movies.h5')
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