Merge commits for debugging the gradient average error and commits for
documentation.
Conflicts:
examples/cifar10/alexnet.py
src/python/singa/layer.py
src/python/singa/optimizer.py
Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/5d20d353
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/5d20d353
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/5d20d353
Branch: refs/heads/master
Commit: 5d20d353bd09f2bd758f27f5e1851af7ae8d4123
Parents: 5db7eb6 6d4539e
Author: Wei Wang <[email protected]>
Authored: Mon Aug 15 20:31:21 2016 +0800
Committer: Wei Wang <[email protected]>
Committed: Mon Aug 15 20:31:21 2016 +0800
----------------------------------------------------------------------
examples/cifar10/alexnet.cc | 11 +++-----
examples/cifar10/alexnet.py | 53 +++++++-------------------------------
examples/cifar10/train.py | 19 +++++++-------
src/model/feed_forward_net.cc | 6 ++---
src/model/optimizer/sgd.cc | 4 +--
src/python/singa/layer.py | 30 +++++++++++++++++----
src/python/singa/net.py | 8 +++++-
src/python/singa/optimizer.py | 29 +++++++++++----------
src/python/singa/tensor.py | 8 +++---
9 files changed, 80 insertions(+), 88 deletions(-)
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http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5d20d353/examples/cifar10/alexnet.cc
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http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5d20d353/examples/cifar10/alexnet.py
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diff --cc examples/cifar10/alexnet.py
index 17b6a89,dae129f..02437b3
--- a/examples/cifar10/alexnet.py
+++ b/examples/cifar10/alexnet.py
@@@ -35,54 -36,20 +35,21 @@@ def create_net(use_cpu=False)
W0_specs = {'init': 'gaussian', 'mean': 0, 'std': 0.0001}
W1_specs = {'init': 'gaussian', 'mean': 0, 'std': 0.01}
W2_specs = {'init': 'gaussian', 'mean': 0, 'std': 0.01, 'decay_mult': 250}
- b_specs = {'init': 'constant', 'value': 0, 'lt_mult': 2}
- net.add(
- layer.Conv2D(
- 'conv1',
- 32,
- 5,
- 1,
- W_specs=W0_specs.copy(),
- b_specs=b_specs.copy(),
- pad=2,
- input_sample_shape=(
- 3,
- 32,
- 32,
- )))
++
+ b_specs = {'init': 'constant', 'value': 0, 'lr_mult': 2, 'decay_mult': 0}
+ net.add(layer.Conv2D('conv1', 32, 5, 1, W_specs=W0_specs.copy(),
b_specs=b_specs.copy(), pad=2, input_sample_shape=(3,32,32,)))
net.add(layer.MaxPooling2D('pool1', 3, 2, pad=1))
net.add(layer.Activation('relu1'))
- net.add(layer.LRN(name='lrn1'))
- net.add(
- layer.Conv2D(
- 'conv2',
- 32,
- 5,
- 1,
- W_specs=W1_specs.copy(),
- b_specs=b_specs.copy(),
- pad=2))
+ net.add(layer.LRN(name='lrn1', size=3, alpha=5e-5))
+ net.add(layer.Conv2D('conv2', 32, 5, 1, W_specs=W1_specs.copy(),
b_specs=b_specs.copy(), pad=2))
net.add(layer.Activation('relu2'))
- net.add(layer.MaxPooling2D('pool2', 3, 2, pad=1))
- net.add(layer.LRN('lrn2'))
- net.add(
- layer.Conv2D(
- 'conv3',
- 64,
- 5,
- 1,
- W_specs=W1_specs.copy(),
- b_specs=b_specs.copy(),
- pad=2))
+ net.add(layer.AvgPooling2D('pool2', 3, 2, pad=1))
+ net.add(layer.LRN('lrn2', size=3, alpha=5e-5))
+ net.add(layer.Conv2D('conv3', 64, 5, 1, W_specs=W1_specs.copy(),
b_specs=b_specs.copy(), pad=2))
net.add(layer.Activation('relu3'))
- net.add(layer.MaxPooling2D('pool3', 3, 2, pad=1))
+ net.add(layer.AvgPooling2D('pool3', 3, 2, pad=1))
net.add(layer.Flatten('flat'))
- net.add(
- layer.Dense(
- 'dense',
- 10,
- W_specs=W2_specs.copy(),
- b_specs=b_specs.copy()))
- net.add(layer.Dense('dense', 10, W_specs=W2_specs.copy(),
b_specs=b_specs.copy()))
++ net.add(layer.Dense( 'dense', 10, W_specs=W2_specs.copy(),
b_specs=b_specs.copy()))
for (p, specs) in zip(net.param_values(), net.param_specs()):
filler = specs.filler
if filler.type == 'gaussian':
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5d20d353/src/python/singa/layer.py
----------------------------------------------------------------------
diff --cc src/python/singa/layer.py
index b0fdb5e,1e9caeb..a9f3826
--- a/src/python/singa/layer.py
+++ b/src/python/singa/layer.py
@@@ -473,28 -388,29 +473,47 @@@ class LRN(Layer)
class Dense(Layer):
+ """Apply linear/affine transformation, also called inner-product or
+ fully connected layer.
+ Args:
+ num_output (int): output feature length.
+ use_bias (bool): add a bias vector or not to the transformed feature
+ W_specs (dict): specs for the weight matrix
+ 'name' for parameter name
+ 'lr_mult' for learning rate multiplier
+ 'decay_mult' for weight decay multiplier
+ 'init' for init method, which could be 'gaussian', 'uniform',
+ 'xavier' and ''
+ 'std', 'mean', 'high', 'low' for corresponding init methods
+ 'clamp' for gradient constraint, value is scalar
+ 'regularizer' for regularization, currently support 'l2'
+ b_specs (dict): specs for the bias vector, same fields as W_specs.
+ W_transpose (bool): if true, output=x*W.T+b;
+ input_sample_shape (tuple): input feature length
+ """
def __init__(self, name, num_output, use_bias=True,
W_specs=None, b_specs=None,
- W_transpose=True, input_sample_shape=None):
+ W_transpose=False, input_sample_shape=None):
+ """Apply linear/affine transformation, also called inner-product or
+ fully connected layer.
+
+ Args:
+ num_output (int): output feature length.
+ use_bias (bool): add a bias vector or not to the transformed
feature
+ W_specs (dict): specs for the weight matrix
+ 'name' for parameter name
+ 'lr_mult' for learning rate multiplier
+ 'decay_mult' for weight decay multiplier
+ 'init' for init method, which could be 'gaussian', 'uniform',
+ 'xavier' and ''
+ 'std', 'mean', 'high', 'low' for corresponding init methods
+ 'clamp' for gradient constraint, value is scalar
+ 'regularizer' for regularization, currently support 'l2'
+ b_specs (dict): specs for the bias vector, same fields as W_specs.
+ W_transpose (bool): if true, output=x*W.T+b;
+ input_sample_shape (tuple): input feature length
+ """
super(Dense, self).__init__(name)
conf = self.conf.dense_conf
conf.num_output = num_output
@@@ -508,15 -424,12 +527,15 @@@
W_specs['name'] = name + '_weight'
if 'name' not in b_specs:
b_specs['name'] = name + '_bias'
- self.conf.param.extend([_construct_param_specs_from_dict(W_specs)])
- self.param_specs.append(_construct_param_specs_from_dict(W_specs))
- self.conf.param.extend([_construct_param_specs_from_dict(b_specs)])
- self.param_specs.append(_construct_param_specs_from_dict(b_specs))
+ wspecs = _construct_param_specs_from_dict(W_specs)
+ bspecs = _construct_param_specs_from_dict(b_specs)
+ self.conf.param.extend([wspecs, bspecs])
+ self.param_specs.extend([wspecs, bspecs])
# dense layer is transparent to engine.
- self.layer = _create_layer('singa', 'Dense')
+ if engine == 'cudnn':
+ self.layer = _create_layer('singacuda', 'Dense')
+ else:
+ self.layer = _create_layer(engine, 'Dense')
if input_sample_shape is not None:
self.setup(input_sample_shape)
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5d20d353/src/python/singa/optimizer.py
----------------------------------------------------------------------
diff --cc src/python/singa/optimizer.py
index 14cf3c0,32f03d4..74e6ade
--- a/src/python/singa/optimizer.py
+++ b/src/python/singa/optimizer.py
@@@ -107,23 -100,24 +107,25 @@@ class Optimizer(object)
Args:
name (str): parameter name
- specs (ParamSpec): protobuf obj
- """
- assert type(specs) == model_pb2.ParamSpec, \
+ specs (ParamSpec): protobuf obj, including regularizer and
+ constraint, multipliers for learning rate and weight decay.
-
+ '''
+ assert isinstance(specs, model_pb2.ParamSpec), \
'specs should be model_pb2.ParamSpec instance'
if specs.HasField('regularizer'):
self.regularizers[name] = CppRegularizer(specs.regularizer)
+ elif specs.decay_mult != 1:
+ self.regularizers[name] = L2Regularizer(
+ specs.decay_mult * self.regularizer.coefficient)
+
if specs.HasField('constraint'):
self.constraints[name] = CppConstraint(specs.constraint)
+
if specs.lr_mult != 1:
self.learning_rate_multiplier[name] = specs.lr_mult
- if specs.decay_mult != 1:
- self.decay_multiplier[name] = specs.decay_mult
- def apply_regularizer_constraint(self, value, grad, name=None, step=None):
- """Apply regularization and constraint if available.
+ def apply_regularizer_constraint(self, epoch, value, grad, name=None):
+ '''Apply regularization and constraint if available.
If there are both global regularizer (constraint) and param specific
regularizer (constraint), it would use the param specific one.
@@@ -189,32 -184,24 +191,27 @@@
class SGD(Optimizer):
+ '''The vallina Stochasitc Gradient Descent algorithm with momentum.
- def __init__(self, lr=None, momentum=None, decay=None):
- """The vallina Stochasitc Gradient Descent algorithm.
+ See the base Optimizer for all arguments.
+ '''
- See the base Optimizer for all arguments.
- """
- super(SGD, self).__init__(lr, momentum, decay)
+ def __init__(self, lr=None, momentum=None, weight_decay=None, lr_gen=None,
+ regularizer=None, constraint=None):
- super(
- SGD,
- self).__init__(
- lr,
- momentum,
- weight_decay,
- lr_gen,
- regularizer,
- constraint)
++ super(SGD, self).__init__(lr, momentum, weight_decay, lr_gen,
++ regularizer, constraint)
conf = model_pb2.OptimizerConf()
- if momentum is not None:
- conf.momentum = momentum
+ if self.momentum is not None:
+ conf.momentum = self.momentum
+ conf.type = 'sgd'
self.opt = singa.CreateOptimizer('SGD')
self.opt.Setup(conf.SerializeToString())
- def apply_with_lr(self, step, lr, grad, value, name):
- self.apply_regularizer_constraint(value, grad, name, step)
+ def apply_with_lr(self, epoch, lr, grad, value, name):
+ self.apply_regularizer_constraint(epoch, value, grad, name)
+ if name is not None and name in self.learning_rate_multiplier:
+ lr = lr * self.learning_rate_multiplier[name]
- self.opt.Apply(step, lr, name, grad.singa_tensor, value.singa_tensor)
+ self.opt.Apply(epoch, lr, name, grad.singa_tensor, value.singa_tensor)
return value
@@@ -260,9 -240,11 +257,11 @@@ class AdaGrad(Optimizer)
self.opt = singa.CreateOptimizer('AdaGrad')
self.opt.Setup(conf.SerializeToString())
- def apply_with_lr(self, step, lr, grad, value, name):
- grad = self.apply_regularizer_constraint(step, value, grad, name)
+ def apply_with_lr(self, epoch, lr, grad, value, name):
+ grad = self.apply_regularizer_constraint(epoch, value, grad, name)
+ if name is not None and name in self.learning_rate_multiplier:
+ lr = lr * self.learning_rate_multiplier[name]
- self.opt.Apply(step, lr, name, grad.singa_tensor, value.singa_tensor)
+ self.opt.Apply(epoch, lr, name, grad.singa_tensor,
value.singa_tensor)
return value
@@@ -286,9 -265,11 +285,11 @@@ class RMSProp(Optimizer)
self.opt = singa.CreateOptimizer('RMSProp')
self.opt.Setup(conf.SerializeToString())
- def apply_with_lr(self, step, lr, grad, value, name):
- grad = self.apply_regularizer_constraint(step, value, grad, name)
+ def apply_with_lr(self, epoch, lr, grad, value, name):
+ grad = self.apply_regularizer_constraint(epoch, value, grad, name)
+ if name is not None and name in self.learning_rate_multiplier:
+ lr = lr * self.learning_rate_multiplier[name]
- self.opt.Apply(step, lr, name, grad.singa_tensor, value.singa_tensor)
+ self.opt.Apply(epoch, lr, name, grad.singa_tensor,
value.singa_tensor)
return value
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5d20d353/src/python/singa/tensor.py
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