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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() |
