def onHostTest():
from PuzzleLib.Containers import Sequential
from PuzzleLib.Modules import Conv2D, MaxPool2D, Activation, relu, Flatten, Linear
from PuzzleLib.Cost.CrossEntropy import CrossEntropy
data = np.random.randn(50000, 3, 28, 28).astype(np.float32)
dataTarget = np.random.randint(low=0, high=10,
size=(50000, )).astype(np.int32)
seq = Sequential()
seq.append(Conv2D(3, 16, 9))
seq.append(MaxPool2D())
seq.append(Activation(relu))
seq.append(Conv2D(16, 32, 5))
seq.append(MaxPool2D())
seq.append(Activation(relu))
seq.append(Flatten())
seq.append(Linear(3 * 3 * 32, 10))
entr = CrossEntropy()
val = Validator(seq, entr)
val.validateFromHost(data, dataTarget)
print("Validation error on big data: %s" % val.error)
def buildNet():
from PuzzleLib.Containers import Sequential
from PuzzleLib.Modules import Conv2D, MaxPool2D, Activation, relu, Flatten, Linear
seq = Sequential()
seq.append(Conv2D(3, 32, 5, pad=2, wscale=0.0001, initscheme="gaussian"))
seq.append(MaxPool2D(3, 2))
seq.append(Activation(relu))
seq.append(Conv2D(32, 32, 5, pad=2, wscale=0.01, initscheme="gaussian"))
seq.append(MaxPool2D(3, 2))
seq.append(Activation(relu))
seq.append(Conv2D(32, 64, 5, pad=2, wscale=0.01, initscheme="gaussian"))
seq.append(MaxPool2D(3, 2))
seq.append(Activation(relu))
seq.append(Flatten())
seq.append(
Linear(seq.dataShapeFrom((1, 3, 32, 32))[1],
64,
wscale=0.1,
initscheme="gaussian"))
seq.append(Activation(relu))
seq.append(Linear(64, 10, wscale=0.1, initscheme="gaussian"))
return seq
def onDeviceTest():
from PuzzleLib.Containers import Sequential
from PuzzleLib.Modules import Conv2D, MaxPool2D, Activation, relu, Flatten, Linear
from PuzzleLib.Cost.CrossEntropy import CrossEntropy
from PuzzleLib.Optimizers.NesterovSGD import NesterovSGD
data = gpuarray.to_gpu(
np.random.randn(10000, 3, 28, 28).astype(np.float32))
dataTarget = gpuarray.to_gpu(
np.random.randint(low=0, high=10, size=(10000, )).astype(np.int32))
seq = Sequential()
seq.append(Conv2D(3, 16, 9))
seq.append(MaxPool2D())
seq.append(Activation(relu))
seq.append(Conv2D(16, 32, 5))
seq.append(MaxPool2D())
seq.append(Activation(relu))
seq.append(Flatten())
seq.append(Linear(3 * 3 * 32, 10))
entr = CrossEntropy()
opt = NesterovSGD()
opt.setupOn(seq)
def onBatchFinish(train):
print("Finished batch #%d, error=%s" %
(train.currBatch, train.cost.getError()))
trainer = Trainer(seq, entr, opt, onBatchFinish=onBatchFinish)
trainer.train(data, dataTarget)
def buildNet():
from PuzzleLib.Containers import Sequential
from PuzzleLib.Modules import Conv2D, MaxPool2D, Activation, relu, Flatten, Linear
seq = Sequential(name="lenet-5-like")
seq.append(Conv2D(1, 16, 3))
seq.append(MaxPool2D())
seq.append(Activation(relu))
seq.append(Conv2D(16, 32, 4))
seq.append(MaxPool2D())
seq.append(Activation(relu))
seq.append(Flatten())
seq.append(Linear(32 * 5 * 5, 1024))
seq.append(Activation(relu))
seq.append(Linear(1024, 10))
return seq
def onDeviceTest():
from PuzzleLib.Containers import Sequential
from PuzzleLib.Modules import Conv2D, MaxPool2D, Activation, relu, Flatten, Linear
data = gpuarray.to_gpu(
np.random.randn(10000, 3, 28, 28).astype(np.float32))
seq = Sequential()
seq.append(Conv2D(3, 16, 9))
seq.append(MaxPool2D())
seq.append(Activation(relu))
seq.append(Conv2D(16, 32, 5))
seq.append(MaxPool2D())
seq.append(Activation(relu))
seq.append(Flatten())
seq.append(Linear(3 * 3 * 32, 10))
calc = Calculator(seq)
calc.onBatchFinish = lambda calculator: print("Finished batch #%d" %
calculator.currBatch)
calc.calc(data)
def complexNetTest():
from PuzzleLib.Modules import Conv2D, MaxPool2D, Activation, relu, Flatten
data = gpuarray.to_gpu(
np.random.randn(128, 3, 150, 150).astype(np.float32))
seq = Sequential()
seq.append(Conv2D(3, 16, 11))
seq.append(MaxPool2D())
seq.append(Activation(relu))
seq.append(Conv2D(16, 16, 5))
seq.append(MaxPool2D())
seq.append(Activation(relu))
seq.append(Flatten())
seq(data)
grad = gpuarray.to_gpu(np.random.randn(*seq.data.shape).astype(np.float32))
seq.backward(grad)
seq.updateParams(1e-4)