import hw5gendata
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import KFold
# generates data & split it into X (training input) and y (target output)
X, y = hw5gendata.genDataSet(1000)
neurons = 3 # <- number of neurons in the hidden layer
eta = 0.1 # <- the learning rate parameter
# here we create the MLP regressor
mlp = MLPRegressor(hidden_layer_sizes=(neurons, ),
verbose=True,
learning_rate_init=eta)
# here we train the MLP
mlp.fit(X, y)
# E_out in training
print("Training set score: %f" % mlp.score(X, y))
# now we generate new data as testing set and get E_out for testing set
X, y = hw5gendata.genDataSet(1000)
print("Testing set score: %f" % mlp.score(X, y))
ypred = mlp.predict(X)
plt.plot(X[:, 0], X[:, 1], '.')
plt.plot(X[:, 0], y, 'rx')
plt.plot(X[:, 0], ypred, '-k')
plt.show()
import hw5gendata
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import KFold
# number of samples
N = 1000
# generate data & split it into X (training input) and y (target output)
X, y = hw5gendata.genDataSet(N)
# linear regression solution
w = np.linalg.pinv(X.T.dot(X)).dot(X.T).dot(y)
#neurons <- number of neurons in the hidden layer
#eta <- the learning rate parameter
bestNeurons = 0
bestEta = 0
bestScore = float('-inf')
score = 0
for neurons in range(1, 100, 1):
for eta in range(1, 11, 1):
eta = eta / 10.0
kf = KFold(n_splits=10)
cvscore = []
for train, validation in kf.split(X):
X_train, X_validation, y_train, y_validation = X[train, :], X[
validation, :], y[train], y[validation]
# here we create the MLP regressor
