import sys from tensorflow.keras.backend import batch_dot, mean import pandas as pd import numpy as np from six import int2byte from sklearn import preprocessing from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split import tensorflow as tf from tensorflow import keras from tensorflow.keras.layers import Input, Dense, Activation,Dropout from tensorflow.keras.models import Model from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.models import Sequential import mlflow from mlflow.tracking import MlflowClient mlflow.set_tracking_uri("http://172.17.0.1:5000") mlflow.set_experiment("s434695") client = MlflowClient() def my_main(epochs, batch_size): vgsales=pd.read_csv('vgsales.csv') vgsales['Nintendo'] = vgsales['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0) Y = vgsales['Nintendo'] X = vgsales.drop(['Rank','Name','Platform','Year','Genre','Publisher','Nintendo'],axis = 1) X_train, X_test, y_train, y_test = train_test_split(X,Y , test_size=0.2,train_size=0.8, random_state=21) model = Sequential() model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu')) model.add(Dense(1,kernel_initializer='normal', activation='sigmoid')) early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test)) prediction = model.predict(X_test) rmse = mean_squared_error(y_test, prediction) model.save('vgsales_model.h5') return rmse, model, X_train, y_train epochs = int(sys.argv[1]) if len(sys.argv) > 1 else 15 batch_size = int(sys.argv[2]) if len(sys.argv) > 2 else 16 with mlflow.start_run(): rmse, model, x_train, y_train = my_main(epochs, batch_size) mlflow.log_param("epochs", epochs) mlflow.log_param("batch_size", batch_size) mlflow.log_metric("rmse", rmse) #mlflow.keras.log_model(model, 'vgsales_model.h5') mlflow.keras.save_model(model, "my_model", signature=mlflow.models.signature.infer_signature(x_train, y_train), input_example=x_train) mlflow.keras.log_model(model, "model",registered_model_name="s434695", signature=mlflow.models.signature.infer_signature(x_train, y_train), input_example=x_train)