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mnist (unix).py
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mnist (unix).py
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# https://www.kaggle.com/c/digit-recognizer/data
import csvReader as reader
import neuralNetwork as nn
import activationFunctions as F
import lossFunctions as L
import networkPrinter as printer
import numpy as np
import matplotlib.pyplot as plt
def _test(e, Xtest, net, name):
Ytest = []
for x in Xtest:
Ytest.append(net.forward(x))
reader.saveMnistResult(name+'Epoch'+str(e)+'.csv', Ytest)
print('Saved epoch ', e)
def test0():
name = 'test0'
Xtrain, Ytrain = reader.readmnistAsOneLineTraining('mnist/train.csv')
print('Training set ready, size: ', len(Xtrain))
net = nn.NeuralNetwork(momentumSize=0)
net.addLayer(784, 10, F.softmax, True)
net.setCostFunction(L.crossEntropy)
print('Starting training...')
Xtest = reader.readmnistAsOneLineTest('mnist/test.csv')
net.kFoldsTrainAndValidate(Xtrain, Ytrain,
k=6,
epochs=100,
learningRate=1e-2,
batchSize=100,
showError=True,
showNodes=False,
print= lambda e: _test(e, Xtest, net, name),
showEvery=5,
name = name)
def reduceMNIST(n):
Xtrain, Ytrain = reader.readmnistAsOneLineTraining('mnist/train.csv')
Xtest = reader.readmnistAsOneLineTest('mnist/test.csv')
from sklearn.decomposition import PCA
# pca = PCA(n_components = 784)
# Xtrain = np.array(Xtrain).reshape((42000, 784))
# pca = pca.fit(Xtrain)
# cum_var_explained = np.cumsum(pca.explained_variance_)
# plt.figure(10000)
# plt.plot(cum_var_explained)
# plt.show()
pca = PCA(n_components = n)
Xtrain = pca.fit_transform(np.array(Xtrain).reshape((42000, 784)))
np.savetxt('mnist/trainPCA'+str(n)+'.csv', np.round(Xtrain, 5), delimiter=',')
Xtest = np.array(Xtest).reshape((28000, 784))
Xtest = np.round(pca.transform(Xtest), 5)
np.savetxt('mnist/testPCA'+str(n)+'.csv', Xtest, delimiter=',')
def test1():
_, Ytrain = reader.readmnistAsOneLineTraining('mnist/train.csv')
Xtrain = reader.readmnistAsOneLinePCA('mnist/trainPCA300.csv')
Xtest = reader.readmnistAsOneLinePCA('mnist/testPCA300.csv')
name = 'test1'
print('Training set ready, size: ', len(Xtrain))
net = nn.NeuralNetwork(momentumSize=0)
net.addLayer(300, 10, F.softmax, True)
net.setCostFunction(L.crossEntropy)
print('Starting training...')
net.kFoldsTrainAndValidate(Xtrain, Ytrain,
k=6,
epochs=100,
learningRate=1e-2,
batchSize=100,
showError=True,
showNodes=False,
print= lambda e: _test(e, Xtest, net, name),
showEvery=10,
name = name)
def test2():
_, Ytrain = reader.readmnistAsOneLineTraining('mnist/train.csv')
Xtrain = reader.readmnistAsOneLinePCA('mnist/trainPCA300.csv')
Xtest = reader.readmnistAsOneLinePCA('mnist/testPCA300.csv')
name = '2test'
print('Training set ready, size: ', len(Xtrain))
net = nn.NeuralNetwork(momentumSize=0)
net.addLayer(300, 100, F.LReLU, True)
net.addLayer(100, 10, F.softmax, True)
net.setCostFunction(L.crossEntropy)
print('Starting training...')
net.kFoldsTrainAndValidate(Xtrain, Ytrain,
k=6,
epochs=100,
learningRate=1e-2,
batchSize=100,
showError=True,
showNodes=False,
print= lambda e: _test(e, Xtest, net, name),
showEvery=10,
name = name)
def test3():
print('Running test3...')
Xtrain, Ytrain = reader.readmnistAsOneLineTraining('mnist/train.csv')
Xtest = reader.readmnistAsOneLineTest('mnist/test.csv')
name = '3test'
print('Training set ready, size: ', len(Xtrain))
net = nn.NeuralNetwork(momentumSize=0)
net.addLayer(784, 150, F.tanh, True)
net.addLayer(150, 70, F.LReLU, True)
net.addLayer(70, 10, F.softmax, True)
net.setCostFunction(L.crossEntropy)
print('Starting training...')
net.kFoldsTrainAndValidate(Xtrain, Ytrain,
k=6,
epochs=100,
learningRate=1e-2,
batchSize=100,
showError=True,
showNodes=False,
print= lambda e: _test(e, Xtest, net, name),
showEvery=25,
name = name)
def test4():
print('Running test4...')
Xtrain, Ytrain = reader.readmnistAsOneLineTraining('mnist/train.csv')
Xtest = reader.readmnistAsOneLineTest('mnist/test.csv')
name = '4test'
print('Training set ready, size: ', len(Xtrain))
net = nn.NeuralNetwork(momentumSize=0)
net.addLayer(784, 20, F.tanh, True)
net.addLayer(20, 30, F.LReLU, True)
net.addLayer(30, 10, F.softmax, True)
net.setCostFunction(L.crossEntropy)
print('Starting training...')
net.kFoldsTrainAndValidate(Xtrain, Ytrain,
k=6,
epochs=100,
learningRate=1e-2,
batchSize=100,
showError=True,
showNodes=False,
print= lambda e: _test(e, Xtest, net, name),
showEvery=25,
name = name)
def test5():
print('Running test5...')
Xtrain, Ytrain = reader.readmnistAsOneLineTraining('mnist/train.csv')
Xtest = reader.readmnistAsOneLineTest('mnist/test.csv')
name = '5test'
print('Training set ready, size: ', len(Xtrain))
net = nn.NeuralNetwork(momentumSize=0)
net.addLayer(784, 20, F.swish, True)
net.addLayer(20, 30, F.swish, True)
net.addLayer(30, 10, F.softmax, True)
net.setCostFunction(L.crossEntropy)
print('Starting training...')
net.kFoldsTrainAndValidate(Xtrain, Ytrain,
k=6,
epochs=100,
learningRate=1e-2,
batchSize=100,
showError=True,
showNodes=False,
print= lambda e: _test(e, Xtest, net, name),
showEvery=25,
name = name)
if __name__ == "__main__":
# test0()
# reduceMNIST(300)
# test1()
# test2()
test3()
# test4()
# test5()
input('Click enter')