from numpy import loadtxt, ones, zeros, where import numpy as np from sklearn import datasets from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score import sys, traceback from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from nn import NeuralNet # load the data set filename = 'data/digitsX.dat' data = loadtxt(filename, delimiter=',') X = data[:,:] filename = 'data/digitsY.dat' data1 = loadtxt(filename, delimiter=',') y = data1 layers = np.array([25]) clf = NeuralNet(layers = layers, learningRate = 2.0, numEpochs = 10) clf.fit(X,y) predicted = clf.predict(X) # print predicted print np.mean(predicted == y)
X = X[idx]
y = y[idx]
# split the data
Xtrain = X[:nTrain, :]
ytrain = y[:nTrain]
Xtest = X[nTrain:, :]
ytest = y[nTrain:]
# train the decision tree
modelDT = DecisionTreeClassifier()
modelDT.fit(Xtrain, ytrain)
# train the naive Bayes
layers = np.array(([25]))
modelNN = NeuralNet(layers=layers, learningRate =2, numEpochs=500, epsilon=.62)
modelNN.fit(Xtrain, ytrain)
ypred_NNtrain = modelNN.predict(Xtrain)
# output predictions on the remaining data
ypred_NN = modelNN.predict(Xtest)
# compute the training accuracy of the model
accuracyNT = accuracy_score(ytrain, ypred_NNtrain)
accuracyNN = accuracy_score(ytest, ypred_NN)
print "Training = "+str(accuracyNT)
print "Neural Net accuracy = "+str(accuracyNN)
modelNN.visualizeHiddenNodes("visualizeHiddenNodes.bmp")
import numpy as np from numpy import loadtxt from sklearn import datasets from sklearn.metrics import accuracy_score from nn import NeuralNet # learning rate parameters to be trained by hand numEpochs = 100 learningRate = 0.3 epsilon = 0.12 regularization_parameter = 0.001 nodes_in_hidden_layers = [25] # load the data filenameX = "data/digitsX.dat" dataX = loadtxt(filenameX, delimiter=",") filenameY = "data/digitsY.dat" dataY = loadtxt(filenameY, delimiter=",") n, d = dataX.shape # create NeuralNet class modelNN = NeuralNet(nodes_in_hidden_layers, epsilon, learningRate, numEpochs) # train neural network on digits data modelNN.fit(dataX, dataY) # find the training accuracy # report the training accuracy
import numpy as np from sklearn import datasets from sklearn.metrics import accuracy_score from nn import NeuralNet filename_X = 'data/digitsX.dat' filename_y = 'data/digitsY.dat' X = np.loadtxt(filename_X, delimiter=',') y = np.loadtxt(filename_y, delimiter=',') # takes roughly 1s for each epoch clf_NN = NeuralNet(layers=np.array([25]), learningRate=2.0, numEpochs=450) clf_NN.fit(X, y) y_predict = clf_NN.predict(X) accuracy_NN = accuracy_score(y_predict, y) print "Accuracy: \t" + str(accuracy_NN)
'''
AUTHOR Wenqi Xian
'''
from numpy import loadtxt
import numpy as np
from nn import NeuralNet
X_train = loadtxt('data/digitsX.dat', delimiter=',')
y_train = loadtxt('data/digitsY.dat', delimiter=',')
layers = np.array([25])
NN = NeuralNet(layers = layers, learningRate = 1.8, numEpochs = 700)
NN.fit(X_train,y_train)
predicted = NN.predict(X_train)
accuracy = 100.0 * (predicted == y_train).sum() / y_train.shape[0]
print accuracy
# neural nets doesnt really make sense import math import numpy as np import sys from import_train import import_training_file from import_train import rmsle from sklearn.neural_network import BernoulliRBM from nn import NeuralNet if __name__ == '__main__': (X, y) = import_training_file(sys.argv[1], True) hidden_layers = [5] learningRate = 1.6 epsil = 0.12 eps = 1000 neural_network = NeuralNet(hidden_layers, learningRate, epsilon=epsil, numEpochs=eps) neural_network.fit(X, y) nn_predict = neural_network.predict(X)
X = digitsX[:]
y = digitsY[:]
n, d = X.shape
nTrain = 0.2 * n #training on 50% of the data
# shuffle the data
# idx = np.arange(n)
# np.random.seed(13)
# np.random.shuffle(idx)
# X = X[idx]
# y = y[idx]
# split the data
Xtrain = X[:nTrain, :]
ytrain = y[:nTrain]
# Xtest = X[nTrain:,:]
# ytest = y[nTrain:]
model = NeuralNet(
np.array([25]), .80, 0.12,
600) # 100 @ 2.5 = 0.885, 400 @ 1.6 = 0.88, 1000 @ 1 = 0.8542,
model.fit(X, y)
ypred = model.predict(Xtrain)
accuracy = accuracy_score(ytrain, ypred)
print "NeuralNet Accuracy = " + str(accuracy)
# model.visualizeHiddenNodes('hiddenLayers.png')
# neural nets doesnt really make sense
import math
import numpy as np
import sys
from import_train import import_training_file
from import_train import rmsle
from sklearn.neural_network import BernoulliRBM
from nn import NeuralNet
if __name__ == '__main__':
(X, y) = import_training_file(sys.argv[1], True)
hidden_layers = [5]
learningRate = 1.6
epsil = 0.12
eps = 1000
neural_network = NeuralNet(hidden_layers,
learningRate,
epsilon=epsil,
numEpochs=eps)
neural_network.fit(X, y)
nn_predict = neural_network.predict(X)
# dataset = datasets.load_digits()
X = digitsX[:]
y = digitsY[:]
n,d = X.shape
nTrain = 0.2*n #training on 50% of the data
# shuffle the data
# idx = np.arange(n)
# np.random.seed(13)
# np.random.shuffle(idx)
# X = X[idx]
# y = y[idx]
# split the data
Xtrain = X[:nTrain,:]
ytrain = y[:nTrain]
# Xtest = X[nTrain:,:]
# ytest = y[nTrain:]
model = NeuralNet(np.array([25]), .80, 0.12, 600) # 100 @ 2.5 = 0.885, 400 @ 1.6 = 0.88, 1000 @ 1 = 0.8542,
model.fit(X,y)
ypred = model.predict(Xtrain)
accuracy = accuracy_score(ytrain, ypred)
print "NeuralNet Accuracy = "+str(accuracy)
# model.visualizeHiddenNodes('hiddenLayers.png')
