def test_autoencoder():
shapes = [[8,3,8],[10,5,10],[30,10,30]]
for nnshape in shapes:
parD = nn.initialize(nnshape)
alpha = 25
x = np.identity(nnshape[0])
y = x
#iterations parameter must be high enough for bigger shapes
parD = nn.trainNN(parD,alpha,x,y,iterations=5000)
autoout = nn.testNN(x,parD,outreturn=True)
assert np.array_equal(np.around(autoout),x)
def test_autoencoder():
shapes = [[8, 3, 8], [10, 5, 10], [30, 10, 30]]
for nnshape in shapes:
parD = nn.initialize(nnshape)
alpha = 25
x = np.identity(nnshape[0])
y = x
#iterations parameter must be high enough for bigger shapes
parD = nn.trainNN(parD, alpha, x, y, iterations=5000)
autoout = nn.testNN(x, parD, outreturn=True)
assert np.array_equal(np.around(autoout), x)
def test(testFile,modelFile,classifier):
if(classifier == 'nearest'):
print("Testing k-Nearest Neighbour Classifier")
correctAngle = getCorrectValues(testFile) #Returns a dictionary of test image_names -> correct_angles
import nearest as near
K = 25
predictedAngle = near.testNearest(testFile,modelFile,K,correctAngle)
print("Accuracy for k-Nearest Neighbour is: ", end=" ")
print(near.getAccuracy(predictedAngle, correctAngle), end="%")
print()
elif(classifier == 'adaboost'):
testAdaBoost(testFile)
elif(classifier == 'nnet'):
print("Testing Neural Network Classifier")
import neuralnet as nn
model = getModel(modelFile)
nn.testNN(model,testFile)
elif(classifier == 'best'):
import best as b
model = getModel(modelFile)
b.testBest(model,testFile)
def testBest(model,testFile):
print("Testing Best Classifier")
import neuralnet as nn
nn.testNN(model, testFile)
import neuralnet as nn
import numpy as np
shapes = [[8, 3, 8], [10, 5, 10], [30, 10, 30], [8, 40, 8]]
shape = -1
for nnshape in shapes:
shape += 1
datacollect = list()
for alpha in [1] + list(range(5, 101, 5)):
for iterations in range(100, 5001, 100):
parD = nn.initialize(nnshape)
x = np.identity(nnshape[0])
y = x
#iterations parameter must be high enough for bigger shapes
parD = nn.trainNN(parD, alpha, x, y, iterations=iterations)
autoout = nn.testNN(x, parD, outreturn=True)
avgerr = np.average(abs(autoout - x))
datacollect.append([shape, alpha, iterations, avgerr])
np.save("%d_data" % shape, np.array(datacollect))
parD = initialize(nnshape)
batches = list(range(10,len(outtrain),10)) # train in batches of 10
for iterations in tqdm(range(3000)):
for index, batch in enumerate(batches):
if index == 0:
batch_intrain = intrain[:batch,:]
batch_outtrain = np.matrix(outtrain[:batch]).T
elif batch == batches[-1]:
batch_intrain = intrain[batch:,:]
batch_outtrain = np.matrix(outtrain[batch:]).T
else:
batch_intrain = intrain[batches[index-1]:batches[index],:]
batch_outtrain = np.matrix(outtrain[batches[index-1]:batches[index]]).T
parD = forback(parD, alpha,
batch_intrain,
batch_outtrain,
nnshape)
AUC = testNN(intest,parD,AUC=True,y=outtest)
count += 1
allseqs = seqio.getseqs("rap1-lieb-test.txt")[0]
binarr = seqio.onehot(allseqs)
with open("predictions.txt","w") as f:
for i, seq in enumerate(allseqs):
output = float(testNN(binarr[i],parD,outreturn=True))
f.write(seq + "\t" + "{:5.4f}".format(output) + "\n")
import neuralnet as nn
import numpy as np
shapes = [[8,3,8],[10,5,10],[30,10,30],[8,40,8]]
shape = -1
for nnshape in shapes:
shape += 1
datacollect = list()
for alpha in [1] + list(range(5,101,5)):
for iterations in range(100,5001,100):
parD = nn.initialize(nnshape)
x = np.identity(nnshape[0])
y = x
#iterations parameter must be high enough for bigger shapes
parD = nn.trainNN(parD,alpha,x,y,iterations=iterations)
autoout = nn.testNN(x,parD,outreturn=True)
avgerr = np.average(abs(autoout - x))
datacollect.append([shape,alpha,iterations,avgerr])
np.save("%d_data" % shape,np.array(datacollect))
intest = inputarray[test]
outtest = outputarray[test]
parD = initialize(nnshape)
batches = list(range(10, len(outtrain), 10)) # train in batches of 10
for iterations in tqdm(range(3000)):
for index, batch in enumerate(batches):
if index == 0:
batch_intrain = intrain[:batch, :]
batch_outtrain = np.matrix(outtrain[:batch]).T
elif batch == batches[-1]:
batch_intrain = intrain[batch:, :]
batch_outtrain = np.matrix(outtrain[batch:]).T
else:
batch_intrain = intrain[batches[index - 1]:batches[index], :]
batch_outtrain = np.matrix(
outtrain[batches[index - 1]:batches[index]]).T
parD = forback(parD, alpha, batch_intrain, batch_outtrain, nnshape)
AUC = testNN(intest, parD, AUC=True, y=outtest)
count += 1
allseqs = seqio.getseqs("rap1-lieb-test.txt")[0]
binarr = seqio.onehot(allseqs)
with open("predictions.txt", "w") as f:
for i, seq in enumerate(allseqs):
output = float(testNN(binarr[i], parD, outreturn=True))
f.write(seq + "\t" + "{:5.4f}".format(output) + "\n")