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- model = Sequential()
- #get number of columns in training data
- n_cols = X_train.shape[1]
- #add model layers
- model.add(Dense(8, activation='relu', input_shape=(n_cols,)))
- model.add(Dense(8, activation='relu'))
- model.add(Dropout(rate = 0.05))
- model.add(Dense(8, activation='relu'))
- model.add(Dense(1))
- #adam = optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
- model.compile(optimizer='adam', loss='mae')
- history = model.fit(X_train, y_train, epochs= 200, validation_split=0.2, batch_size=128)
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