keras_regression_sample.py

from keras.datasets import boston_housing
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

(x_train, y_train), (x_test, y_test) = boston_housing.load_data(test_split=0.0)

def scale_input(data):
    from sklearn.preprocessing import StandardScaler
    scaler = StandardScaler()
    return scaler.fit_transform(data)
    
x_train = scale_input(x_train)

def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(13, input_dim=13, kernel_initializer='normal', activation='relu'))
    model.add(Dense(1, kernel_initializer='normal'))
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model

def larger_model():
    # create model
    model = Sequential()
    model.add(Dense(13, input_dim=13, kernel_initializer='normal', activation='relu'))
    model.add(Dense(6, kernel_initializer='normal', activation='relu'))
    model.add(Dense(1, kernel_initializer='normal'))
    opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
    # Compile model
    model.compile(loss='mean_squared_error', optimizer=opt)
    return model

def wider_model():
    # create model
    model = Sequential()
    model.add(Dense(20, input_dim=13, kernel_initializer='normal', activation='relu'))
    model.add(Dense(1, kernel_initializer='normal'))
    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model

m = baseline_model()
#m = larger_model()
#m = wider_model()
h = m.fit(x_train, y_train, batch_size=20, epochs=300, verbose=1, validation_split=0.2)

def plot_history(h):
    import matplotlib.pyplot as plt
    plt.plot(h['loss'][-100:])
    plt.plot(h['val_loss'][-100:])
    plt.title('model loss')
    plt.xlabel('epoch')
    plt.ylabel('loss')
    plt.legend(['loss', 'val_loss'], loc='best')
    plt.show()

#print( m.evaluate(x_test, y_test, verbose=1) )
plot_history(h.history)