I am trying to use Keras to make a neural network. The data I am using is https://archive.ics.uci.edu/ml/datasets/Yacht+Hydrodynamics. My code is as follows:
import numpy as np
from keras.layers import Dense, Activation
from keras.models import Sequential
from sklearn.model_selection import train_test_split
data = np.genfromtxt(r"""file location""", delimiter=',')
model = Sequential()
model.add(Dense(32, activation = 'relu', input_dim = 6))
model.add(Dense(1,))
model.compile(optimizer='adam', loss='mean_squared_error', metrics = ['accuracy'])
Y = data[:,-1]
X = data[:, :-1]
From here I have tried using model.fit(X, Y), but the accuracy of the model appears to remain at 0. I am new to Keras so this is probably an easy solution, apologies in advance.
My question is what is the best way to add regression to the model so that the accuracy increases?
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 StandardScaler
sc = 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
So our classifier becomes:
# Initialising the ANN
model = Sequential()
# Adding the input layer and the first hidden layer
model.add(Dense(32, activation = 'relu', input_dim = 6))
# Adding the second hidden layer
model.add(Dense(units = 32, activation = 'relu'))
# Adding the third hidden layer
model.add(Dense(units = 32, activation = 'relu'))
# Adding the output layer
model.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 the full code
import numpy as np
from keras.layers import Dense, Activation
from keras.models import Sequential
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
# Importing the dataset
dataset = np.genfromtxt("data.txt", delimiter='')
X = dataset[:, :-1]
y = dataset[:, -1]
# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.08, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Initialising the ANN
model = Sequential()
# Adding the input layer and the first hidden layer
model.add(Dense(32, activation = 'relu', input_dim = 6))
# Adding the second hidden layer
model.add(Dense(units = 32, activation = 'relu'))
# Adding the third hidden layer
model.add(Dense(units = 32, activation = 'relu'))
# Adding the output layer
model.add(Dense(units = 1))
#model.add(Dense(1))
# Compiling the ANN
model.compile(optimizer = 'adam', loss = 'mean_squared_error')
# Fitting the ANN to the Training set
model.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()