69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
import sys
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from tensorflow.keras.backend import batch_dot, mean
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import pandas as pd
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import numpy as np
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from six import int2byte
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from sklearn import preprocessing
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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from sklearn.model_selection import train_test_split
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras.layers import Input, Dense, Activation,Dropout
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from tensorflow.keras.models import Model
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from tensorflow.keras.callbacks import EarlyStopping
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from tensorflow.keras.models import Sequential
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import mlflow
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def my_main(epochs, batch_size):
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vgsales=pd.read_csv('vgsales.csv')
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vgsales['Nintendo'] = vgsales['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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Y = vgsales['Nintendo']
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X = vgsales.drop(['Rank','Name','Platform','Year','Genre','Publisher','Nintendo'],axis = 1)
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X_train, X_test, y_train, y_test = train_test_split(X,Y , test_size=0.2,train_size=0.8, random_state=21)
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model = Sequential()
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model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
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model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
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early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
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model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
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model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
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prediction = model.predict(X_test)
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rmse = mean_squared_error(y_test, prediction)
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model.save('vgsales_model.h5')
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return rmse, model, X_train, y_train
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epochs = int(sys.argv[1]) if len(sys.argv) > 1 else 15
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batch_size = int(sys.argv[2]) if len(sys.argv) > 2 else 16
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with mlflow.start_run():
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rmse, model, x_train, y_train = my_main(epochs, batch_size)
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mlflow.log_param("epochs", epochs)
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mlflow.log_param("batch_size", batch_size)
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mlflow.log_metric("rmse", rmse)
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#mlflow.keras.log_model(model, 'vgsales_model.h5')
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mlflow.keras.save_model(model, "my_model", signature=mlflow.models.signature.infer_signature(x_train, y_train), input_example=x_train)
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