How to create a neural network for regression?

First of all, you have to split your dataset into training set and test set using train_test_split class from sklearn.model_selection library.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.08, random_state = 0)

Also, you have to scale your values using StandardScaler class.

from sklearn.preprocessing import StandardScalersc = StandardScaler()X_train = sc.fit_transform(X_train)X_test = sc.transform(X_test)

Then, you should add more layers in order to get better results.

Note

Usually it's a good practice to apply following formula in order to find out the total number of hidden layers needed.

Nh = Ns/(α∗ (Ni + No))

where

• Ni = number of input neurons.
• No = number of output neurons.
• Ns = number of samples in training data set.
• α = an arbitrary scaling factor usually 2-10.

So our classifier becomes:

# Initialising the ANNmodel = Sequential()# Adding the input layer and the first hidden layermodel.add(Dense(32, activation = 'relu', input_dim = 6))# Adding the second hidden layermodel.add(Dense(units = 32, activation = 'relu'))# Adding the third hidden layermodel.add(Dense(units = 32, activation = 'relu'))# Adding the output layermodel.add(Dense(units = 1))

The metric that you use- metrics=['accuracy'] corresponds to a classification problem. If you want to do regression, remove metrics=['accuracy']. That is, just use

model.compile(optimizer = 'adam',loss = 'mean_squared_error')

Here is a list of keras metrics for regression and classification

Also, you have to define the batch_size and epochs values for fit method.

model.fit(X_train, y_train, batch_size = 10, epochs = 100)

After you trained your network you can predict the results for X_test using model.predict method.

y_pred = model.predict(X_test)

Now, you can compare the y_pred that we obtained from neural network prediction and y_test which is real data. For this, you can create a plot using matplotlib library.

plt.plot(y_test, color = 'red', label = 'Real data')plt.plot(y_pred, color = 'blue', label = 'Predicted data')plt.title('Prediction')plt.legend()plt.show()

It seems that our neural network learns very good

Here is how the plot looks.

Here is the full code

import numpy as npfrom keras.layers import Dense, Activationfrom keras.models import Sequentialfrom sklearn.model_selection import train_test_splitimport matplotlib.pyplot as plt# Importing the datasetdataset = np.genfromtxt("data.txt", delimiter='')X = dataset[:, :-1]y = dataset[:, -1]# Splitting the dataset into the Training set and Test setX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.08, random_state = 0)# Feature Scalingfrom sklearn.preprocessing import StandardScalersc = StandardScaler()X_train = sc.fit_transform(X_train)X_test = sc.transform(X_test)# Initialising the ANNmodel = Sequential()# Adding the input layer and the first hidden layermodel.add(Dense(32, activation = 'relu', input_dim = 6))# Adding the second hidden layermodel.add(Dense(units = 32, activation = 'relu'))# Adding the third hidden layermodel.add(Dense(units = 32, activation = 'relu'))# Adding the output layermodel.add(Dense(units = 1))#model.add(Dense(1))# Compiling the ANNmodel.compile(optimizer = 'adam', loss = 'mean_squared_error')# Fitting the ANN to the Training setmodel.fit(X_train, y_train, batch_size = 10, epochs = 100)y_pred = model.predict(X_test)plt.plot(y_test, color = 'red', label = 'Real data')plt.plot(y_pred, color = 'blue', label = 'Predicted data')plt.title('Prediction')plt.legend()plt.show()