def main(depth=2, width=512, nb_epoch=30):
prefer_gpu()
# Configuration here isn't especially good. But, for demo..
with Model.define_operators({"**": clone, ">>": chain}):
model = ReLu(width) >> ReLu(width) >> Softmax()
train_data, dev_data, _ = datasets.mnist()
train_X, train_y = model.ops.unzip(train_data)
dev_X, dev_y = model.ops.unzip(dev_data)
dev_y = to_categorical(dev_y)
with model.begin_training(train_X, train_y, L2=1e-6) as (trainer, optimizer):
epoch_loss = [0.0]
def report_progress():
with model.use_params(optimizer.averages):
print(epoch_loss[-1], model.evaluate(dev_X, dev_y), trainer.dropout)
epoch_loss.append(0.0)
trainer.each_epoch.append(report_progress)
trainer.nb_epoch = nb_epoch
trainer.dropout = 0.3
trainer.batch_size = 128
trainer.dropout_decay = 0.0
train_X = model.ops.asarray(train_X, dtype="float32")
y_onehot = to_categorical(train_y)
for X, y in trainer.iterate(train_X, y_onehot):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
loss = ((yh - y) ** 2.0).sum() / y.shape[0]
backprop(yh - y, optimizer)
epoch_loss[-1] += loss
with model.use_params(optimizer.averages):
print("Avg dev.: %.3f" % model.evaluate(dev_X, dev_y))
with open("out.pickle", "wb") as file_:
pickle.dump(model, file_, -1)
def mnist():
train_data, dev_data, _ = datasets.mnist()
train_X, train_y = NumpyOps().unzip(train_data)
dev_X, dev_y = NumpyOps().unzip(dev_data)
dev_y = to_categorical(dev_y, nb_classes=10)
train_y = to_categorical(dev_y, nb_classes=10)
return (train_X[:1000], train_y[:1000]), (dev_X, dev_y)
def main(depth=2, width=512, nb_epoch=30):
prefer_gpu()
torch.set_num_threads(1)
train_data, dev_data, _ = datasets.mnist()
train_X, train_y = Model.ops.unzip(train_data)
dev_X, dev_y = Model.ops.unzip(dev_data)
dev_y = to_categorical(dev_y)
model = PyTorchWrapper(
PyTorchFeedForward(
depth=depth,
width=width,
input_size=train_X.shape[1],
output_size=dev_y.shape[1],
))
with model.begin_training(train_X, train_y,
L2=1e-6) as (trainer, optimizer):
epoch_loss = [0.0]
def report_progress():
# with model.use_params(optimizer.averages):
print(epoch_loss[-1], model.evaluate(dev_X, dev_y),
trainer.dropout)
epoch_loss.append(0.0)
trainer.each_epoch.append(report_progress)
trainer.nb_epoch = nb_epoch
trainer.dropout = 0.3
trainer.batch_size = 128
trainer.dropout_decay = 0.0
train_X = model.ops.asarray(train_X, dtype="float32")
y_onehot = to_categorical(train_y)
for X, y in trainer.iterate(train_X, y_onehot):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
loss = ((yh - y)**2.0).sum() / y.shape[0]
backprop(yh - y, optimizer)
epoch_loss[-1] += loss
with model.use_params(optimizer.averages):
print("Avg dev.: %.3f" % model.evaluate(dev_X, dev_y))
with open("out.pickle", "wb") as file_:
pickle.dump(model, file_, -1)
def main(depth=2, width=512, nb_epoch=30):
prefer_gpu()
torch.set_num_threads(1)
train_data, dev_data, _ = datasets.mnist()
train_X, train_y = Model.ops.unzip(train_data)
dev_X, dev_y = Model.ops.unzip(dev_data)
dev_y = to_categorical(dev_y)
model = PyTorchWrapper(
PyTorchFeedForward(
depth=depth,
width=width,
input_size=train_X.shape[1],
output_size=dev_y.shape[1],
)
)
with model.begin_training(train_X, train_y, L2=1e-6) as (trainer, optimizer):
epoch_loss = [0.0]
def report_progress():
# with model.use_params(optimizer.averages):
print(epoch_loss[-1], model.evaluate(dev_X, dev_y), trainer.dropout)
epoch_loss.append(0.0)
trainer.each_epoch.append(report_progress)
trainer.nb_epoch = nb_epoch
trainer.dropout = 0.3
trainer.batch_size = 128
trainer.dropout_decay = 0.0
train_X = model.ops.asarray(train_X, dtype="float32")
y_onehot = to_categorical(train_y)
for X, y in trainer.iterate(train_X, y_onehot):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
loss = ((yh - y) ** 2.0).sum() / y.shape[0]
backprop(yh - y, optimizer)
epoch_loss[-1] += loss
with model.use_params(optimizer.averages):
print("Avg dev.: %.3f" % model.evaluate(dev_X, dev_y))
with open("out.pickle", "wb") as file_:
pickle.dump(model, file_, -1)
def main(depth=2, width=512, nb_epoch=30):
if CupyOps.xp != None:
Model.ops = CupyOps()
Model.Ops = CupyOps
# Configuration here isn't especially good. But, for demo..
with Model.define_operators({'**': clone, '>>': chain}):
model = ReLu(width) >> ReLu(width) >> Softmax()
train_data, dev_data, _ = datasets.mnist()
train_X, train_y = model.ops.unzip(train_data)
dev_X, dev_y = model.ops.unzip(dev_data)
dev_y = to_categorical(dev_y)
with model.begin_training(train_X, train_y,
L2=1e-6) as (trainer, optimizer):
epoch_loss = [0.]
def report_progress():
with model.use_params(optimizer.averages):
print(epoch_loss[-1], model.evaluate(dev_X, dev_y),
trainer.dropout)
epoch_loss.append(0.)
trainer.each_epoch.append(report_progress)
trainer.nb_epoch = nb_epoch
trainer.dropout = 0.3
trainer.batch_size = 128
trainer.dropout_decay = 0.0
train_X = model.ops.asarray(train_X, dtype='float32')
y_onehot = to_categorical(train_y)
for X, y in trainer.iterate(train_X, y_onehot):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
loss = ((yh - y)**2.).sum() / y.shape[0]
backprop(yh - y, optimizer)
epoch_loss[-1] += loss
with model.use_params(optimizer.averages):
print('Avg dev.: %.3f' % model.evaluate(dev_X, dev_y))
with open('out.pickle', 'wb') as file_:
pickle.dump(model, file_, -1)