def trainHardTest(optCls, dtype, *args, **kwargs):
from PuzzleLib.Containers.Sequential import Sequential
from PuzzleLib.Modules.Conv2D import Conv2D
from PuzzleLib.Modules.BatchNorm2D import BatchNorm2D
from PuzzleLib.Modules.Activation import Activation, relu
from PuzzleLib.Modules.Cast import Cast
from PuzzleLib.Cost.MSE import MSE
seq = Sequential()
seq.append(Conv2D(4, 8, 5, pad=1))
seq.append(BatchNorm2D(8))
seq.append(Activation(relu))
seq.append(Conv2D(8, 16, 5, pad=1))
seq.calcMode(dtype)
seq.append(Cast(intype=dtype, outtype=np.float32))
optimizer = optCls(*args, **kwargs)
optimizer.setupOn(seq, useGlobalState=True)
mse = MSE()
data = gpuarray.to_gpu(np.random.randn(4, 4, 5, 5).astype(dtype))
target = gpuarray.to_gpu(np.random.randn(4, 16, 1, 1).astype(np.float32))
for i in range(200):
error, grad = mse(seq(data), target)
optimizer.zeroGradParams()
seq.backward(grad)
optimizer.update()
if (i + 1) % 5 == 0:
print("Iteration #%d error: %s" % (i + 1, error))
def trainSimpleTest(optCls, dtype, *args, **kwargs):
from PuzzleLib.Containers.Sequential import Sequential
from PuzzleLib.Modules.Linear import Linear
from PuzzleLib.Modules.Activation import Activation, relu
from PuzzleLib.Modules.Cast import Cast
from PuzzleLib.Cost.MSE import MSE
seq = Sequential()
seq.append(Linear(128, 64, useBias=False))
seq.append(Activation(relu))
seq.append(Linear(64, 32, useBias=False))
seq.append(Activation(relu))
seq.append(Linear(32, 16))
seq.calcMode(dtype)
seq.append(Cast(intype=dtype, outtype=np.float32))
optimizer = optCls(*args, **kwargs)
optimizer.setupOn(seq, useGlobalState=True)
mse = MSE()
data = gpuarray.to_gpu(np.random.randn(16, 128).astype(dtype))
target = gpuarray.to_gpu(np.random.randn(16, 16).astype(np.float32))
for i in range(200):
error, grad = mse(seq(data), target)
optimizer.zeroGradParams()
seq.backward(grad)
optimizer.update()
if (i + 1) % 5 == 0:
print("Iteration #%d error: %s" % (i + 1, error))