How to do it...

1. Import libraries as follows:

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

1. Load the dataset:

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

1. Split the dataset into training and testing:

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

1. Normalize the input data:

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

1. Define the model and optimizer and compile:

model = Sequential()
model.add(Dense(1024, input_dim=X_train.shape[1], activation='relu')) 
model.add(Dense(1024, activation='relu')) 
model.add(Dense(512, activation='relu')) 
model.add(Dense(512, activation='relu')) 
# Output layer
model.add(Dense(1, activation='linear'))
# Set optimizer
opt = SGD()
# Compile model
model.compile(loss='mse', optimizer=opt, metrics=['accuracy'])

1. Set the hyperparameters and train the model:

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)

1. Predict on the test set:

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)))

1. Plot the training and validation accuracy:

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()

The following graph is obtained: