from __future__ import print_function
import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score
from load_csv_data import load_data
from sknn.mlp import Classifier, Layer
# configurations # default
num_of_trials = 10 #10
test_size = 0.2 #0.2
iteration = 25 #25
learning_rate = 0.001 #0.001
units = [5,10,15,20,25,30]
# load data
X_train, X_test, y_train, y_test = load_data(test_size=test_size)
# arrays for storing results
scores_train = [[] for i in range(num_of_trials)]
scores_test = [[] for i in range(num_of_trials)]
for n in range(num_of_trials):
print('============== Trial: {} ==============='.format(n+1))
for i, unit in enumerate(units):
clf = Classifier(
layers=[
Layer('Rectifier', units=unit),
Layer("Softmax")],
learning_rate=learning_rate,
n_iter=iteration)
clf.fit(X_train, y_train)
# coding: utf-8
#======================================
# Breast cancer tumor classification
# with sklearn MLPClassifier module
# (c) Keishi Ishihara
#======================================
from __future__ import print_function
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score
from load_csv_data import load_data
X_train, X_test, y_train, y_test = load_data()
clf = MLPClassifier(hidden_layer_sizes=(30),
activation='relu',
alpha=1e-5,
learning_rate_init=0.001,
learning_rate='constant',
solver='sgd',
random_state=0,
verbose=True,
tol=1e-4,
max_iter=10000)
clf.fit(X_train, y_train)
print('Training set score: {}'.format(clf.score(X_train, y_train)))
print('Test set score: {}'.format(clf.score(X_test, y_test)))