from cmatrix import ConvolutionMatrix
from nnet import NNet
from verbosePrint import vprint
import verbosePrint
np.random.seed(1)
batch_size = 128
iterations = 300
cm = ConvolutionMatrix(rows=9, cols=16, shapes=((28, 28), (3, 3)))
hLen = cm.outputLength()
nn = NNet(sizes=[784, hLen, 10], batch_size=batch_size)
nn.replaceLayer(0, cm)
nn.setActivations(['tanh', 'softmax'])
nn.setMaskPr({1: 2})
nn.setAlpha(2)
nn.scale(0.01, 0)
nn.scale(0.1, 1)
# vprint(0, nn, quit=True)
# params = (test_images, test_labels)
# nn.checkup(*params)
for j in range(0, iterations + 1):
correct_cnt = 0
for i in range(int(len(images) / batch_size)):
batch_start, batch_end = ((i * batch_size), ((i + 1) * batch_size))
prediction = nn.learn(images[batch_start:batch_end],
labels[batch_start:batch_end])
# vprint(i, nn, suffix='a', quit=True)
[0, 0],
]
inputTraining = inputData
targetTraining = targetData
# nn = NNet(sizes=[5, 3], bias=True)
nn = NNet(sizes=[6, 12, 2], bias=True)
# nn = NNet([[[-0.829638, 0.164111, 0.398885],
# [-0.603684, -0.603331, -0.819179],
# [-0.080592, -0.386044, -0.931615],
# [0.762514, -0.142887, -0.737862],
# [0.175430, 0.790112, -0.267367],
# [-0.732674, -0.825474, 0.232357]]], bias=True)
# ]])
nn.setActivations(['relu', 'linear'])
nn.setVerbose([])
nn.checkup(inputData, targetData)
verbosePrint.vIteration = -1
verbosePrint.stage = ''
cycles = 1000
report = cycles / 10
for iteration in range(cycles + 1):
vprint(iteration, '~~~~~~~~~~~ Iteration %d ~~~~~~~~~~~' % iteration)
combinedError = 0
for row_index in range(len(targetTraining)):
datain = inputTraining[row_index:row_index + 1]
from netExamples.lecture.p3of5 import inputData
from netExamples.lecture.p3of5 import inputTraining
# from lecture.p3of5 import inputData as inputTraining
from netExamples.lecture.p3of5 import atLeast as targetData
from netExamples.lecture.p3of5 import atLeastTraining as targetTraining
# from lecture.p3of5 import atLeast as targetTraining
# nn = NNet(sizes=[5, 3], bias=True)
nn = NNet(
[[[-0.829638, 0.164111, 0.398885], [-0.603684, -0.603331, -0.819179],
[-0.080592, -0.386044, -0.931615], [0.762514, -0.142887, -0.737862],
[0.175430, 0.790112, -0.267367], [-0.732674, -0.825474, 0.232357]]],
bias=True)
# ]])
nn.setActivations(['linear'])
nn.setVerbose([])
nn.checkup(inputData, targetData)
verbosePrint.vIteration = -1
verbosePrint.stage = ''
cycles = 80
report = cycles / 10
for iteration in range(cycles + 1):
vprint(iteration, '~~~~~~~~~~~ Iteration %d ~~~~~~~~~~~' % iteration)
combinedError = 0
for row_index in range(len(targetTraining)):
datain = inputTraining[row_index:row_index + 1]
[0, 1, 0],
[0, 1, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 1],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
]
nn = NNet(sizes=[5, 3], bias=True)
nn.setActivations(['sigmoid'])
verbosePrint.vIteration = -1
verbosePrint.stage = ''
nn.checkup(inputData, targetData)
cycles = 40
report = cycles/10
for iteration in range(cycles + 1):
vprint(iteration, '~~~~~~~~~~~ Iteration %d ~~~~~~~~~~~' % iteration)
combinedError = 0
for row_index in range(len(targetData)):
datain = inputData[row_index:row_index + 1]
goal_prediction = targetData[row_index:row_index + 1]
from netExamples.lecture.sosb \
import inputData, inputTraining, targetData, targetTraining
from nnet import NNet
from verbosePrint import vprint
import verbosePrint
nn = NNet(sizes=[12, 22, 4], bias=True)
nn.setActivations(['tanh', 'sigmoid'])
verbosePrint.vIteration = -1
verbosePrint.stage = ''
cycles = 20
report = max(1, cycles / 10)
checkupParams = (inputData, targetData, inputTraining, 25)
if cycles > 0:
nn.checkup(*checkupParams)
for iteration in range(cycles + 1):
vprint(iteration, '~~~~~~~~~~~ Iteration %d ~~~~~~~~~~~' % iteration)
combinedError = 0
for row_index in range(len(targetTraining)):
datain = inputTraining[row_index:row_index + 1]
goal_prediction = targetTraining[row_index:row_index + 1]
prediction = nn.fire(datain)
# print('Prediction:' + str(prediction))
vprint(iteration, nn)
error = (goal_prediction - prediction)**2
combinedError += error
from netExamples.lecture.sosb \
import inputData, inputTraining, targetData, targetTraining
from cmatrix import ConvolutionMatrix
from nnet import NNet
from verbosePrint import vprint
import verbosePrint
nn = NNet(sizes=[12, 40, 4], bias=True)
cm = ConvolutionMatrix(rows=3, cols=4, bias=True, shapes=((1, 12), (1, 3)))
nn.replaceLayer(0, cm)
nn.setActivations(['softMax', 'sigmoid'])
verbosePrint.vIteration = -1
verbosePrint.stage = ''
cycles = 100
report = max(1, cycles / 10)
checkupParams = (inputData, targetData, inputTraining, 25)
if cycles > 0:
nn.checkup(*checkupParams)
for iteration in range(cycles + 1):
vprint(iteration, '~~~~~~~~~~~ Iteration %d ~~~~~~~~~~~' % iteration)
combinedError = 0
for row_index in range(len(targetTraining)):
datain = inputTraining[row_index:row_index + 1]
goal_prediction = targetTraining[row_index:row_index + 1]
prediction = nn.fire(datain)
# print('Prediction:' + str(prediction))
vprint(iteration, nn)
error += np.sum((test_labels[i:i + 1] - layer_2) ** 2)
correct_cnt += int(np.argmax(layer_2) == \
np.argmax(test_labels[i:i + 1]))
sys.stdout.write(
" Test-Err:" + str(error / float(len(test_images)))[0:5]
+ " Test-Acc:" + str(correct_cnt / float(len(test_images)))
)
print()
if demo == 82:
np.random.seed(1)
iterations = 351
nn = NNet(sizes=[784, 40, 10])
nn.setActivations(['brelu', 'linear'])
nn.setAlpha(0.005)
nn.scale(0.1)
nn.fire(images[0:1])
vprint(0, nn)
for j in range(iterations):
error, correct_cnt = (0.0, 0)
for i in range(len(images)):
vprint(i, nn, quit=True)
prediction = nn.learn(images[i:i+1], labels[i:i+1])
vprint(i, nn, suffix='b', quit=True)
error += np.sum((labels[i:i+1] - prediction) ** 2)
correct_cnt += int(np.argmax(prediction) == \
