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Updates for chainer 5.1.0, ProgressBar 2.5 #2

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Updates for chainer 5.1.0, ProgressBar 2.5 #2

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816c1e4
changes for ProgressBar
sometimescasey Dec 14, 2018
526713a
zero_grads() update for chainer 5.1.0
sometimescasey Dec 17, 2018
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7 changes: 4 additions & 3 deletions gconv_experiments/MNIST_ROT/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ def preprocess_mnist_data(train_data, test_data, train_labels, test_labels):
def train_epoch(train_data, train_labels, model, optimizer, batchsize, transformations, silent, gpu=0, finetune=False):

N = train_data.shape[0]
pbar = ProgressBar(0, N)
pbar = ProgressBar(maxval=N).start()
perm = np.random.permutation(N)
sum_accuracy = 0
sum_loss = 0
Expand All @@ -100,7 +100,7 @@ def train_epoch(train_data, train_labels, model, optimizer, batchsize, transform
x_batch = cuda.to_gpu(x_batch.astype(np.float32))
y_batch = cuda.to_gpu(y_batch.astype(np.int32))

optimizer.zero_grads()
model.cleargrads()
x = Variable(x_batch)
t = Variable(y_batch)

Expand All @@ -114,12 +114,13 @@ def train_epoch(train_data, train_labels, model, optimizer, batchsize, transform
if not silent:
pbar.update(i + y_batch.size)

pbar.finish()
return sum_loss, sum_accuracy


def validate(test_data, test_labels, model, batchsize, silent, gpu):
N_test = test_data.shape[0]
pbar = ProgressBar(0, N_test)
pbar = ProgressBar(maxval=N_test).start()
sum_accuracy = 0
sum_loss = 0

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5 changes: 3 additions & 2 deletions gconv_experiments/conv_bn_act.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@

from chainer import Chain
import chainer.functions as F
import chainer.links as L


class ConvBNAct(Chain):
Expand All @@ -13,7 +14,7 @@ def __init__(self,

if bn:
out_channels = self.conv.W.data.shape[0]
self.add_link('bn', F.BatchNormalization(out_channels))
self.add_link('bn', L.BatchNormalization(out_channels))
else:
self.bn = None

Expand All @@ -24,7 +25,7 @@ def __call__(self, x, train, finetune):
y = self.conv(x)

if self.bn:
y = self.bn(y, test=not train, finetune=finetune)
y = self.bn(y, finetune=finetune)
if self.act:
y = self.act(y)

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