def test(args):
print('Welcome to the world of neural networks!')
activation = 'sigmoid'
if (args.dataset == 'MNIST'):
inSize = 28 * 28
else:
inSize = 200 * 200
NoImages = len(os.listdir(args.test))
inputs = np.zeros((NoImages, inSize))
itrNo = 0
for imgName in os.listdir(args.test):
imgName = args.test + '/' + imgName
if (args.dataset == 'Cat-Dog'):
img = util.rgb2gray(mpimg.imread(imgName))
else:
img = mpimg.imread(imgName)
inputs[itrNo, :] = img.reshape(inSize)
itrNo += 1
network = mlp.MLNN()
fileName = '../Model/' + args.dataset + '.npy'
numLayers = network.loadWeights(fileName)
network.forwardProp(inputs)
predict = network.predict(network.nnLayers[numLayers - 1].output)
predicted = np.argmax(predict, axis=1)
print(predicted)
numLayers = 3
numNodes = [featureDim, 10, numClass]
activation = 'sigmoid'
#MLPkeras.MLPkeras(in_train,label_train,in_test,label_test,numNodes,activation)
label_train_in = np.zeros((len(label_train), numClass))
#label_orig = np.ones(3,3)
for i in range(len(label_train)):
label_train_in[i, label_train[i]] = 1
label_test_in = np.zeros((len(label_test), numClass))
for i in range(len(label_test)):
label_test_in[i, label_test[i]] = 1
network = mlp.MLNN()
network.initializeStruct(numLayers, numNodes, activation)
network.initializeWeights()
error, predicted = network.train(in_train, label_train_in)
#saveFileName = '../Model/' + dataset + '_' + '{}'.format(numLayers) + '_' + '{}'.format(numNodes[1]) + '.npy'
#network.saveModel(saveFileName)
network.forwardProp(in_train)
predicted = network.predict(network.nnLayers[numLayers - 1].output)
accuracy = util.getAccuracy(np.argmax(label_train_in, axis=1),
np.argmax(predicted, axis=1))
f1_macro, f1_micro = util.fi_macro_micro(np.argmax(label_train_in, axis=1),
np.argmax(predicted, axis=1),
numClass)
print(accuracy, f1_macro, f1_micro)
def trainAndtest(args):
print('Training and testing in progress...')
if(args.dataset != 'MNIST' and args.dataset != 'Cat-Dog'):
print('Please provide valid dataset (MNIST / Cat-Dog)')
exit(0)
if(args.configuration == None):
print('Please provide configuration of the network')
s = args.configuration[1:-1]
s1 = s.split(' ')
activation = 'sigmoid'
numLayers = len(s1)#3
numNodes = np.zeros(numLayers, dtype=int)
for i in range(numLayers):
numNodes[i] = int(s1[i])
if(args.dataset == 'MNIST'):
numClass = 10
inSize = 28*28
NoImages = 0
for i in range(10):
folderName = args.train + '/'
folderName = folderName + '{}/'.format(i)
NoImages += len(os.listdir(folderName))
#maxImage = 30
#NoImages = maxImage*10
inputs = np.zeros((NoImages,inSize))
labMat = np.eye(numClass,numClass)
labels = np.zeros((NoImages,numClass))
itrNo = 0
for i in range(numClass):
folderName = args.train + '/'
imgNo = 0
folderName = folderName + '{}/'.format(i)
for imgName in os.listdir(folderName):
imgName = folderName+imgName
imgNo += 1
#if(imgNo<=maxImage):
img = mpimg.imread(imgName)
inputs[itrNo,:] = img.reshape(28*28)
labels[itrNo,:] = labMat[i]
itrNo += 1
if(args.dataset == 'Cat-Dog'):
numClass = 2
inSize = 200*200
NoImages = 0
for i in range(numClass):
folderName = args.train + '/'
if (i==0):
folderName = folderName + 'cat/'
else:
folderName = folderName + 'dog/'
NoImages += len(os.listdir(folderName))
#maxImage = 2
#NoImages = maxImage*numClass
inputs = np.zeros((NoImages,inSize))
labMat = np.eye(numClass,numClass)
labels = np.zeros((NoImages,numClass))
itrNo = 0
for i in range(numClass):
folderName = args.train + '/'
imgNo = 0
if (i==0):
folderName = folderName + 'cat/'
else:
folderName = folderName + 'dog/'
for imgName in os.listdir(folderName):
imgName = folderName+imgName
imgNo += 1
#if(imgNo<=maxImage):
img = util.rgb2gray(mpimg.imread(imgName))
inputs[itrNo,:] = img.reshape(inSize)
labels[itrNo,:] = labMat[i]
itrNo += 1
in_train, in_test, label_train, label_test = train_test_split(inputs, labels, test_size=0.1)
scaler = StandardScaler()
scaler.fit(in_train)
in_train = scaler.transform(in_train)
in_test = scaler.transform(in_test)
if(numNodes[0] != inSize):
print('Input neurons specified does not match image size')
exit(0)
if(numNodes[-1] != numClass):
print('Output neurons specified does not match number of classes')
exit(0)
network = mlp.MLNN()
network.initializeStruct(numLayers,numNodes,activation)
network.initializeWeights()
error, predicted = network.train(in_train,label_train)
saveFileName = '../Model/' + args.dataset + '.npy'
network.saveModel(saveFileName)
network.forwardProp(in_train)
predicted = network.predict(network.nnLayers[numLayers-1].output)
accuracy = util.getAccuracy(np.argmax(label_train,axis=1),np.argmax(predicted,axis=1))
f1_macro,f1_micro = util.fi_macro_micro(np.argmax(label_train,axis=1),np.argmax(predicted,axis=1), numClass)
print('PERFORMANCE ON TRAINING DATA:')
print(accuracy, f1_macro, f1_micro)
network.forwardProp(in_test)
predicted = network.predict(network.nnLayers[numLayers-1].output)
accuracy = util.getAccuracy(np.argmax(label_test,axis=1),np.argmax(predicted,axis=1))
f1_macro,f1_micro = util.fi_macro_micro(np.argmax(label_test,axis=1),np.argmax(predicted,axis=1), numClass)
print('PERFORMANCE ON VALIDATION DATA')
print(accuracy, f1_macro, f1_micro)
NoImages = len(os.listdir(args.test))
inputs = np.zeros((NoImages,inSize))
itrNo = 0
for imgName in os.listdir(args.test):
imgName = args.test+ '/' + imgName
if(args.dataset == 'Cat-Dog'):
img = util.rgb2gray(mpimg.imread(imgName))
else:
img = mpimg.imread(imgName)
inputs[itrNo,:] = img.reshape(inSize)
itrNo += 1
network.forwardProp(inputs)
predict = network.predict(network.nnLayers[numLayers-1].output)
predicted = np.argmax(predict,axis=1)
print(predicted)
