Hidden layers and units

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler

    from keras.models import Sequential
    from keras.layers import Dense
    from keras.optimizers import SGD

    SEED = 2017

Using TensorFlow backend.



    # Data can be downloaded at https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv


    data = pd.read_csv('Data/winequality-red.csv', sep=';')
    y = data['quality']
    X = data.drop(['quality'], axis=1)


    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)


    scaler = StandardScaler().fit(X_train)
    X_train = pd.DataFrame(scaler.transform(X_train))
    X_test = pd.DataFrame(scaler.transform(X_test))


    model = Sequential()
    model.add(Dense(1024, input_dim=X_train.shape[1], activation='relu'))
    model.add(Dense(1024, activation='relu'))
    model.add(Dense(1024, activation='relu'))
    model.add(Dense(1024, activation='relu'))
    # Output layer
    model.add(Dense(1, activation='linear'))
    # Set optimizer
    opt = SGD(lr=0.01)
    # Compile model
    model.compile(loss='mse', optimizer=opt, metrics=['accuracy'])


    n_epochs = 500
    batch_size = 256

    history = model.fit(X_train.values, y_train, batch_size=batch_size, epochs=n_epochs, validation_split=0.2, verbose=0)


    predictions = model.predict(X_test.values)
    print('Test accuracy: {:f>2}%'.format(np.round(np.sum([y_test==predictions.flatten().round()])/y_test.shape[0]*100, 2)))

Test accuracy: 65.94%



    plt.plot(np.arange(len(history.history['acc'])), history.history['acc'], label='training')
    plt.plot(np.arange(len(history.history['val_acc'])), history.history['val_acc'], label='validation')
    plt.title('Accuracy')
    plt.xlabel('epochs')
    plt.ylabel('accuracy ')
    plt.legend(loc=0)
    plt.show()