def anncurvefit_train():
print 'Loading the data...'
# Import cancerData
train_in, train_out, test_in, test_out = loadfile.readartmfcc()
# Import bodyfat (optional)
#train_in, train_out, test_in, test_out = loadfile.readbody()
# Import building (optional)
#train_in, train_out, test_in, test_out = loadfile.readbuilding()
print 'Assigning the data variables...'
# Generate initial values.
values = SetValues(inputs=train_in,
outputs=train_out,
learningRate=0.001,
momentum=0.9,
epochNum=100,
hiddenUnits=50,
W=None,
b=None)
ihMatrix,hoMatrix = values.genWeight()
hBiasMatrix, oBiasMatrix = values.genBias()
inX, outY = values.genMatrices()
batchSize = 10
print 'Constructing ANN_model...'
models = ANN_model(inX=inX,
outY=outY,
ihMatrix=ihMatrix,
hoMatrix=hoMatrix,
hBiasMatrix=hBiasMatrix,
oBiasMatrix=oBiasMatrix,
learningRate=0.001)
outputStorage, outputActivation = models.fnn(methods='curvefitting')
cost, updates = models.sgd2(outputStorage)
train = theano.function(inputs=[inX,outY],outputs=cost,updates=updates,allow_input_downcast=True)
predict = theano.function(inputs=[inX],outputs=outputActivation,allow_input_downcast=True)
print 'Training the data...'
total_epoches = range(values.epoches())
input_epoches = len(train_in)/batchSize
for epoch in total_epoches: # 총 몇번인가.. 그랜드 에포크 한 20번정도? 여기는 모든 인풋데이터가 한번 도는걸 의미함
error_history = []
# Shuffling inputs and outputs # 인풋 데이터 돌리기전에 한번 순서들을 섞어줘야함
rand_num = np.random.permutation(len(train_in))
train_in = train_in[rand_num]
train_out = train_out[rand_num]
#total_inputs = range(train_in.shape[0])
for batch_in,batch_out in zip(range(0,train_in.shape[0],batchSize),range(batchSize,train_in.shape[0],batchSize)):
#for iter in total_inputs:
error = train(train_in[batch_in:batch_out],train_out[batch_in:batch_out])
error_history.append(error)
error_sum = np.mean(error_history)
# print error rate and prediction correctness
#error_sum = np.mean(error_history)
print 'Epoch: {}, error: {}.'.format(epoch+1,error_sum)
#accuracy = np.mean(np.argmax(test_out,axis=1) == predict(test_in))
#print '\tAccuracy: {} percent'.format(accuracy)
print '\nprocess finished.\n'
def ann_train():
print 'Loading the data...'
# Import cancerData
train_in, train_out, test_in, test_out = loadfile.readcancer()
# Import MNIST (optional)
#train_in, train_out, test_in, test_out = loadfile.readmnist()
print 'Assigning the data variables...'
# Generate initial values.
values = SetValues(inputs=train_in,
outputs=train_out,
learningRate=0.001,
momentum=0.9,
epochNum=1000,
hiddenUnits=50,
W=None,
b=None)
ihMatrix,hoMatrix = values.genWeight()
hBiasMatrix, oBiasMatrix = values.genBias()
inX, outY = values.genMatrices()
batchSize = 10
print 'Constructing ANN_model...'
models = ANN_model(inX=inX,
outY=outY,
ihMatrix=ihMatrix,
hoMatrix=hoMatrix,
hBiasMatrix=hBiasMatrix,
oBiasMatrix=oBiasMatrix,
learningRate=0.001)
outputStorage, outputActivation = models.fnn(methods='classification')
cost, updates = models.sgd(outputStorage)
train = theano.function(inputs=[inX,outY],outputs=cost,updates=updates,allow_input_downcast=True)
predict = theano.function(inputs=[inX],outputs=outputActivation,allow_input_downcast=True)
print 'Training the data...'
total_epoches = range(values.epoches())
error_history = []
for epoch in total_epoches:
# Shuffling inputs and outputs
#total_inputs = range(train_in.shape[0])
for batch_in,batch_out in zip(range(0,train_in.shape[0],batchSize),range(batchSize,train_in.shape[0],batchSize)):
#for iter in total_inputs:
error = train(train_in[batch_in:batch_out],train_out[batch_in:batch_out])
error_history.append(error)
# print error rate and prediction correctness
error_sum = np.mean(error_history)
print 'Epoch: {}, error: {}.'.format(epoch+1,error_sum)
accuracy = np.mean(np.argmax(test_out,axis=1) == predict(test_in))
print '\tAccuracy: {} percent'.format(accuracy)
print '\nprocess finished.\n'