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- import numpy
- import pandas
- from keras.models import Sequential
- from keras.layers import Dense
- from keras.wrappers.scikit_learn import KerasRegressor
- from sklearn.model_selection import cross_val_score
- from sklearn.model_selection import KFold
- from sklearn.preprocessing import StandardScaler
- from sklearn.pipeline import Pipeline
- dataframe = pandas.read_csv('data/tmdb/train_processed.csv')
- dataframe.drop('id', axis=1, inplace=True)
- Y = dataframe['revenue'].values
- dataframe.drop(columns=['revenue'], inplace=True)
- X = dataframe.values
- def baseline_model():
- model = Sequential()
- model.add(Dense(13, input_dim=3, kernel_initializer='normal', activation='relu'))
- model.add(Dense(1, kernel_initializer='normal'))
- model.compile(loss='mean_squared_error', optimizer='adam')
- return model
- seed = 7
- numpy.random.seed(seed)
- estimators = []
- estimators.append(('standardize', StandardScaler()))
- estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=100, batch_size=5, verbose=1)))
- pipeline = Pipeline(estimators)
- kfold = KFold(n_splits=10, random_state=seed)
- results = cross_val_score(pipeline, X, Y, cv=kfold)
- print("Result: %.2f (%.2f) MSE" % (results.mean(), results.std()))
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