sklearn_ann.py

# Train a neural network in just 3 lines of code!
#
# the notes for this class can be found at: 
# https://deeplearningcourses.com/c/data-science-deep-learning-in-python
# https://www.udemy.com/data-science-deep-learning-in-python
from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future


import sys
sys.path.append('../ann_logistic_extra')
from process import get_data

from sklearn.neural_network import MLPClassifier
from sklearn.utils import shuffle

# get the data
X, Y, dummy1, dummy2 = get_data()

# split into train and test
X, Y = shuffle(X, Y)
Ntrain = int(0.7*len(X))
Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]
Xtest, Ytest = X[Ntrain:], Y[Ntrain:]

# create the neural network
model = MLPClassifier(hidden_layer_sizes=(20, 20), max_iter=2000)

# train the neural network
model.fit(Xtrain, Ytrain)

# print the train and test accuracy
train_accuracy = model.score(Xtrain, Ytrain)
test_accuracy = model.score(Xtest, Ytest)
print("train accuracy:", train_accuracy, "test accuracy:", test_accuracy)