def test_devices():
cgt.set_precision("double")
cgt.update_config(backend="native")
N = 10
K = 3
compile_info = cgt.compilation.get_compile_info()
cuda_enabled = compile_info["CGT_ENABLE_CUDA"]
if not cuda_enabled:
raise SkipTest("cuda disabled")
Xval = np.random.randn(N, K).astype(cgt.floatX)
wval = np.random.randn(K).astype(cgt.floatX)
bval = np.asarray(np.random.randn()).astype(cgt.floatX)
yval = np.random.randn(N).astype(cgt.floatX)
with cgt.scoped_update_config(default_device=cgt.Device(devtype="gpu")):
X_nk = cgt.shared(Xval, "X", device=cgt.Device(devtype="gpu"))
y_n = cgt.shared(yval, "y")
w_k = cgt.shared(wval, "w")
b = cgt.shared(bval, name="b")
print "bval", bval
ypred = cgt.dot(cgt.square(X_nk), w_k) + b
err = cgt.sum(cgt.sin(ypred - y_n))
g = cgt.grad(err, [w_k, b])
outputs = [err] + g
f = cgt.function([], [err] + g)
results = f()
print results
assert np.allclose(results[0], np.sin(np.square(Xval).dot(wval) + bval - yval).sum())
args = parser.parse_args()
# Load data
# -----------------------
mnist = fetch_dataset("http://rll.berkeley.edu/cgt-data/mnist.npz")
Xdata = (mnist["X"] / 255.0).astype(cgt.floatX)
ydata = mnist["y"]
Ntrain = 1000 if args.unittest else 10000
Xtrain = Xdata[0:Ntrain]
ytrain = ydata[0:Ntrain]
sortinds = np.random.permutation(Ntrain)
Xtrain = Xtrain[sortinds]
ytrain = ytrain[sortinds]
batch_size = 128
cgt.update_config(backend="native")
# Make symbolic variables
# -----------------------
def build_fc_return_loss(X, y):
"""
Build fully connected network and return loss
"""
np.random.seed(0)
h1 = nn.rectify(nn.Affine(28 * 28, 256, weight_init=nn.IIDGaussian(std=0.1))(X))
h2 = nn.rectify(nn.Affine(256, 256, weight_init=nn.IIDGaussian(std=0.1))(h1))
logprobs = nn.logsoftmax(nn.Affine(256, 10, weight_init=nn.IIDGaussian(std=0.1))(h2))
neglogliks = -logprobs[cgt.arange(X.shape[0]), y]
loss = neglogliks.mean()
return loss
args = parser.parse_args()
# Load data
# -----------------------
mnist = fetch_dataset("http://rll.berkeley.edu/cgt-data/mnist.npz")
Xdata = (mnist["X"] / 255.).astype(cgt.floatX)
ydata = mnist["y"]
Ntrain = 1000 if args.unittest else 10000
Xtrain = Xdata[0:Ntrain]
ytrain = ydata[0:Ntrain]
sortinds = np.random.permutation(Ntrain)
Xtrain = Xtrain[sortinds]
ytrain = ytrain[sortinds]
batch_size = 128
cgt.update_config(backend="native")
# Make symbolic variables
# -----------------------
def build_fc_return_loss(X, y):
"""
Build fully connected network and return loss
"""
np.random.seed(0)
h1 = nn.rectify(
nn.Affine(28 * 28, 256, weight_init=nn.IIDGaussian(std=.1))(X))
h2 = nn.rectify(
nn.Affine(256, 256, weight_init=nn.IIDGaussian(std=.1))(h1))
logprobs = nn.logsoftmax(
def main():
import argparse
parser=argparse.ArgumentParser()
parser.add_argument("--epochs",type=int,default=10)
parser.add_argument("--profile",action="store_true")
parser.add_argument("--dropout",action="store_true")
parser.add_argument("--stepsize",type=float, default=.001)
parser.add_argument("--model",choices=["dense","conv"],default="dense")
parser.add_argument("--unittest",action="store_true")
parser.add_argument("--grad_check",action="store_true")
parser.add_argument("--devtype",choices=["cpu","gpu"],default="cpu")
args = parser.parse_args()
if args.grad_check: cgt.set_precision("quad")
# from mldata.org http://mldata.org/repository/data/viewslug/mnist-original/
# converted to npz
mnist = fetch_dataset("http://rll.berkeley.edu/cgt-data/mnist.npz")
Xdata = (mnist["X"]/255.).astype(cgt.floatX)
ydata = mnist["y"]
np.random.seed(0)
cgt.update_config(default_device=cgt.core.Device(devtype=args.devtype), backend="native")
if args.model=="conv":
Xdata = Xdata.reshape(-1, 1, 28, 28)
Xtrain = Xdata[0:60000]
ytrain = ydata[0:60000]
Xtest = Xdata[60000:70000]
ytest = ydata[60000:70000]
sortinds = np.random.permutation(60000)
Xtrain = Xtrain[sortinds]
ytrain = ytrain[sortinds]
X = cgt.tensor4("X",fixed_shape=(None,1,28,28)) if args.model=="conv" else cgt.matrix("X", fixed_shape=(None,28*28))
y = cgt.vector("y",dtype='i8')
if args.model == "dense":
p_drop_input,p_drop_hidden = (0.2, 0.5) if args.dropout else (0,0)
w_h = init_weights(784, 256)
w_h2 = init_weights(256, 256)
w_o = init_weights(256, 10)
pofy_drop = dense_model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden)
pofy_nodrop = dense_model(X, w_h, w_h2, w_o, 0., 0.)
params = [w_h, w_h2, w_o]
elif args.model == "conv":
p_drop_conv,p_drop_hidden = (0.2, 0.5) if args.dropout else (0,0)
w = init_weights(32, 1, 3, 3)
w2 = init_weights(64, 32, 3, 3)
w3 = init_weights(128, 64, 3, 3)
w4 = init_weights(128 * 2 * 2, 625)
w_o = init_weights(625, 10)
pofy_drop = convnet_model(X, w, w2, w3, w4, w_o, p_drop_conv, p_drop_hidden)
pofy_nodrop = convnet_model(X, w, w2, w3, w4, w_o, 0., 0.)
params = [w, w2, w3, w4, w_o]
else:
raise RuntimeError("Unreachable")
cost_drop = -cgt.mean(categorical.loglik(y, pofy_drop))
updates = rmsprop_updates(cost_drop, params, stepsize=args.stepsize)
y_nodrop = cgt.argmax(pofy_nodrop, axis=1)
cost_nodrop = -cgt.mean(categorical.loglik(y, pofy_nodrop))
err_nodrop = cgt.cast(cgt.not_equal(y_nodrop, y), cgt.floatX).mean()
train = cgt.function(inputs=[X, y], outputs=[], updates=updates)
computeloss = cgt.function(inputs=[X, y], outputs=[err_nodrop,cost_nodrop])
batch_size=128
from cgt.tests import gradcheck_model
if args.grad_check:
cost_nodrop = cgt.core.clone(cost_nodrop, {X:Xtrain[:1],y:ytrain[:1]})
print "doing gradient check..."
print "------------------------------------"
gradcheck_model(cost_nodrop, params[0:1])
print "success!"
return
if args.profile: cgt.profiler.start()
print fmt_row(10, ["Epoch","Train NLL","Train Err","Test NLL","Test Err","Epoch Time"])
for i_epoch in xrange(args.epochs):
tstart = time.time()
for start in xrange(0, Xtrain.shape[0], batch_size):
end = start+batch_size
train(Xtrain[start:end], ytrain[start:end])
if args.unittest: return
elapsed = time.time() - tstart
trainerr, trainloss = computeloss(Xtrain[:len(Xtest)], ytrain[:len(Xtest)])
testerr, testloss = computeloss(Xtest, ytest)
print fmt_row(10, [i_epoch, trainloss, trainerr, testloss, testerr, elapsed])
if args.profile: cgt.execution.profiler.print_stats()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--profile",action="store_true")
parser.add_argument("--unittest",action="store_true")
parser.add_argument("--epochs",type=int,default=10)
parser.add_argument("--devtype",choices=["cpu","gpu"],default="cpu")
args = parser.parse_args()
cgt.update_config(default_device=cgt.core.Device(devtype=args.devtype), backend="native")
batchsize = 64
Xshape = (batchsize, 3, 32, 32)
X = cgt.tensor4("X", fixed_shape = Xshape)
y = cgt.vector("y", fixed_shape = (batchsize,), dtype='i4')
conv1 = nn.SpatialConvolution(3, 32, kernelshape=(5,5), pad=(2,2),
weight_init=nn.IIDGaussian(std=1e-4))(X)
relu1 = nn.rectify(conv1)
pool1 = nn.max_pool_2d(relu1, kernelshape=(3,3), stride=(2,2))
conv2 = nn.SpatialConvolution(32, 32, kernelshape=(5,5), pad=(2,2),
weight_init=nn.IIDGaussian(std=0.01))(pool1)
relu2 = nn.rectify(conv2)
pool2 = nn.max_pool_2d(relu2, kernelshape=(3,3), stride=(2,2))
conv3 = nn.SpatialConvolution(32, 64, kernelshape=(5,5), pad=(2,2),
weight_init=nn.IIDGaussian(std=0.01))(pool2)
pool3 = nn.max_pool_2d(conv3, kernelshape=(3,3), stride=(2,2))
relu3 = nn.rectify(pool3)
d0,d1,d2,d3 = relu3.shape
flatlayer = relu3.reshape([d0,d1*d2*d3])
nfeats = cgt.infer_shape(flatlayer)[1]
ip1 = nn.Affine(nfeats, 10)(flatlayer)
logprobs = nn.logsoftmax(ip1)
loss = -logprobs[cgt.arange(batchsize), y].mean()
params = nn.get_parameters(loss)
updates = rmsprop_updates(loss, params, stepsize=1e-3)
train = cgt.function(inputs=[X, y], outputs=[loss], updates=updates)
if args.profile: cgt.profiler.start()
data = fetch_dataset("http://rll.berkeley.edu/cgt-data/cifar10.npz")
Xtrain = data["X_train"]
ytrain = data["y_train"]
print fmt_row(10, ["Epoch","Train NLL","Train Err","Test NLL","Test Err","Epoch Time"])
for i_epoch in xrange(args.epochs):
for start in xrange(0, Xtrain.shape[0], batchsize):
tstart = time.time()
end = start+batchsize
print train(Xtrain[start:end], ytrain[start:end]), time.time()-tstart
if start > batchsize*5: break
# elapsed = time.time() - tstart
# trainerr, trainloss = computeloss(Xtrain[:len(Xtest)], ytrain[:len(Xtest)])
# testerr, testloss = computeloss(Xtest, ytest)
# print fmt_row(10, [i_epoch, trainloss, trainerr, testloss, testerr, elapsed])
if args.profile:
cgt.profiler.print_stats()
return
if args.unittest:
break
def __init__(self,
model="dense",
im_size=[28, 28],
dropout=True,
devtype="cpu",
grad_check=True,
reg=0):
if grad_check: cgt.set_precision("quad")
self.model = model
self.reg = reg
np.random.seed(0)
cgt.update_config(default_device=cgt.core.Device(devtype=devtype),
backend="native")
print(model)
# MLP with 1 hidden layer
if model == "dense1":
self.Xsize = 2 * im_size[0] * im_size[1] + im_size[0] + im_size[1]
self.X = cgt.matrix("X", fixed_shape=(None, self.Xsize))
self.y = cgt.vector("y", dtype='i8')
self.p_drop_input, self.p_drop_hidden = (0.2,
0.5) if dropout else (0,
0)
self.w_h = init_weights(self.Xsize, 256)
self.w_o = init_weights(256, 8)
self.pofy_drop = dense_model1(self.X, self.w_h, self.w_o,
self.p_drop_input,
self.p_drop_hidden)
self.pofy_nodrop = dense_model1(self.X, self.w_h, self.w_o, 0., 0.)
self.params = [self.w_h, self.w_o]
self.l1 = cgt.abs(self.w_h).sum() + cgt.abs(self.w_o).sum()
self.cost_drop = -cgt.mean(
categorical.loglik(self.y,
self.pofy_drop)) + self.reg * self.l1
# MLP with 2 hidden layers
elif model == "dense2":
self.Xsize = 2 * im_size[0] * im_size[1] + im_size[0] + im_size[1]
self.X = cgt.matrix("X", fixed_shape=(None, self.Xsize))
self.y = cgt.vector("y", dtype='i8')
self.p_drop_input, self.p_drop_hidden = (0.2,
0.5) if dropout else (0,
0)
self.w_h = init_weights(self.Xsize, 256)
self.w_h2 = init_weights(256, 256)
self.w_o = init_weights(256, 8)
self.pofy_drop = dense_model2(self.X, self.w_h, self.w_h2,
self.w_o, self.p_drop_input,
self.p_drop_hidden)
self.pofy_nodrop = dense_model2(self.X, self.w_h, self.w_h2,
self.w_o, 0., 0.)
self.params = [self.w_h, self.w_h2, self.w_o]
self.l1 = cgt.abs(self.w_h).sum() + cgt.abs(
self.w_h2).sum() + cgt.abs(self.w_o).sum()
self.cost_drop = -cgt.mean(
categorical.loglik(self.y,
self.pofy_drop)) + self.reg * self.l1
# MLP with 3 hidden layers
elif model == "dense3":
self.Xsize = 2 * im_size[0] * im_size[1] + im_size[0] + im_size[1]
self.X = cgt.matrix("X", fixed_shape=(None, self.Xsize))
self.y = cgt.vector("y", dtype='i8')
self.p_drop_input, self.p_drop_hidden = (
0.0, [0.5, 0.5, 0.5]) if dropout else (0, [0, 0, 0])
self.w_h = init_weights(self.Xsize, 256)
self.w_h2 = init_weights(256, 256)
self.w_h3 = init_weights(256, 256)
self.w_o = init_weights(256, 8)
self.pofy_drop = dense_model3(self.X, self.w_h, self.w_h2,
self.w_h3, self.w_o,
self.p_drop_input,
self.p_drop_hidden)
self.pofy_nodrop = dense_model3(self.X, self.w_h, self.w_h2,
self.w_h3, self.w_o, 0.,
[0., 0., 0.])
self.params = [self.w_h, self.w_h2, self.w_h3, self.w_o]
self.l1 = cgt.abs(self.w_h).sum() + cgt.abs(self.w_h2).sum() + cgt.abs(self.w_h3).sum() + \
cgt.abs(self.w_o).sum()
self.cost_drop = -cgt.mean(
categorical.loglik(self.y,
self.pofy_drop)) + self.reg * self.l1
else:
raise RuntimeError("Unknown Model")
self.y_nodrop = cgt.argmax(self.pofy_nodrop, axis=1)
self.cost_nodrop = -cgt.mean(
categorical.loglik(self.y, self.pofy_nodrop))
self.err_nodrop = cgt.cast(cgt.not_equal(self.y_nodrop, self.y),
cgt.floatX).mean()
self.computeloss = cgt.function(
inputs=[self.X, self.y],
outputs=[self.err_nodrop, self.cost_nodrop])
self.y_out = cgt.function(inputs=[self.X], outputs=[self.y_nodrop])
self.updates = rmsprop_updates(self.cost_drop, self.params)
self.train = cgt.function(inputs=[self.X, self.y],
outputs=[],
updates=self.updates)
