def __call__(self, *args, **kwargs):
"""
Compose this layer with the inputs provided
"""
if 'one_step' in kwargs and kwargs['one_step']:
del kwargs['one_step']
args = [self.tensor_from_layer(arg, False) for arg in args]
kwargs = dict([(k, self.tensor_from_layer(v, False))
for k, v in kwargs.items()])
if hasattr(self, 'step_fprop'):
return self.step_fprop(*args, **kwargs)
else:
return self.fprop(*args, **kwargs)
new_obj = utils.copy(self)
args = [new_obj.tensor_from_layer(arg) for arg in args]
kwargs = dict([(k, new_obj.tensor_from_layer(v))
for k, v in kwargs.items()])
if 'do' in kwargs:
kind = kwargs['do']
del kwargs['do']
else:
kind = 'fprop'
if 'one_step' in kwargs:
del kwargs['one_step']
new_obj.prev_args = (args, kwargs)
if kind == 'fprop':
new_obj.fprop(*args, **kwargs)
elif kind == 'eval':
new_obj.get_cost(*args, **kwargs)
elif kind == 'train':
new_obj.get_grads(*args, **kwargs)
elif kind == 'run':
return new_obj.run(*args, **kwargs)
return new_obj
def __abs__(self, other):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
new_obj.out = abs(new_obj.out)
if hasattr(new_obj, 'grads'):
new_obj.grads = [TT.sgn(new_obj.out) * x for x in new_obj.grads]
return new_obj
def __abs__(self, other):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
new_obj.out = abs(new_obj.out)
if hasattr(new_obj, 'grads'):
new_obj.grads = [TT.sgn(new_obj.out) * x for x in new_obj.grads]
return new_obj
def __div__(self, other):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
other_var = new_obj.tensor_from_layer(other)
if hasattr(new_obj, 'grads') and hasattr(other, 'grads'):
new_obj.grads = [x * other_var for x in new_obj.grads]
for param, grad_param in zip(other.params, other.grads):
pos = new_obj.params.index(param)
new_obj.grads[pos] -= new_obj.out * grad_param
new_obj.grads = [x / (other_var**2) for x in new_obj.grads]
elif hasattr(new_obj, 'grads') and \
isinstance(other, theano.gof.Variable) and \
other.ndim == 0:
new_obj.grads = [x * other_var for x in new_obj.grads]
other_grads = TT.grad(other,
new_obj.params,
disconnected_inputs='ignore')
new_obj.grads = [(x - new_obj.cost * y) / (other_var**2)
for x, y in zip(new_obj.grads, other_grads)]
elif hasattr(new_obj, 'grads'):
raise ValueError('I do not know how to compute the gradients'
' of the subtracted term' + str(other) + '. Call'
' train on it if it is an output layer')
new_obj.out = new_obj.out / other_var
return new_obj
def __call__(self, *args, **kwargs):
"""
Compose this layer with the inputs provided
"""
if 'one_step' in kwargs and kwargs['one_step']:
del kwargs['one_step']
args = [self.tensor_from_layer(arg, False) for arg in args]
kwargs = dict([(k, self.tensor_from_layer(v, False))
for k,v in kwargs.items()])
if hasattr(self, 'step_fprop'):
return self.step_fprop(*args, **kwargs)
else:
return self.fprop(*args, **kwargs)
new_obj = utils.copy(self)
args = [new_obj.tensor_from_layer(arg) for arg in args]
kwargs = dict([(k, new_obj.tensor_from_layer(v)) for k,v in kwargs.items()])
if 'do' in kwargs:
kind = kwargs['do']
del kwargs['do']
else:
kind = 'fprop'
if 'one_step' in kwargs:
del kwargs['one_step']
new_obj.prev_args = (args, kwargs)
if kind == 'fprop':
new_obj.fprop(*args, **kwargs)
elif kind == 'eval':
new_obj.get_cost(*args, **kwargs)
elif kind == 'train':
new_obj.get_grads(*args, **kwargs)
elif kind == 'run':
return new_obj.run(*args, **kwargs)
return new_obj
def __div__(self, other):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
other_var = new_obj.tensor_from_layer(other)
if hasattr(new_obj, 'grads') and hasattr(other, 'grads'):
new_obj.grads = [ x * other_var for x in new_obj.grads]
for param, grad_param in zip(other.params, other.grads):
pos = new_obj.params.index(param)
new_obj.grads[pos] -= new_obj.out * grad_param
new_obj.grads = [ x / (other_var**2) for x in new_obj.grads]
elif hasattr(new_obj, 'grads') and \
isinstance(other, theano.gof.Variable) and \
other.ndim == 0:
new_obj.grads = [ x * other_var for x in new_obj.grads]
other_grads = TT.grad(other, new_obj.params,
disconnected_inputs='ignore')
new_obj.grads = [(x - new_obj.cost * y)/ (other_var**2)
for x,y in zip(new_obj.grads,
other_grads)]
elif hasattr(new_obj, 'grads'):
raise ValueError('I do not know how to compute the gradients'
' of the subtracted term' + str(other) + '. Call'
' train on it if it is an output layer')
new_obj.out = new_obj.out / other_var
return new_obj
def __pow__(self, power):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
power = self.tensor_from_layer(power)
new_obj.out = new_obj.out**power
if hasattr(new_obj, 'grads'):
raise NotImplemented
return new_obj
def __ge__(self, other):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
other = self.tensor_from_layer(other)
new_obj.out = new_obj.out.__ge__(other)
if hasattr(new_obj, 'grads'):
raise NotImplemented
return new_obj
def __getitem__(self, pos):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
pos = self.tensor_from_layer(pos)
new_obj.out = new_obj.out.__getitem__(pos)
if hasattr(new_obj, 'grads'):
raise NotImplemented
return new_obj
def __getitem__(self, pos):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
pos = self.tensor_from_layer(pos)
new_obj.out = new_obj.out.__getitem__(pos)
if hasattr(new_obj, 'grads'):
raise NotImplemented
return new_obj
def __ge__(self, other):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
other = self.tensor_from_layer(other)
new_obj.out = new_obj.out.__ge__(other)
if hasattr(new_obj, 'grads'):
raise NotImplemented
return new_obj
def __pow__(self, power):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
power = self.tensor_from_layer(power)
new_obj.out = new_obj.out**power
if hasattr(new_obj, 'grads'):
raise NotImplemented
return new_obj
def clone(**new_inputs):
new_obj = utils.copy(self)
# Reorder inputs
assert len(new_obj.inputs) == len(new_inputs.items())
pairs = [(x, new_inputs[x.name]) for x in inputs]
new_obj.inputs = new_inputs.values()
new_obj.out = theano.clone(new_obj.out, replace=pairs)
if hasattr(new_obj, 'cost'):
new_obj.cost = theano.clone(new_obj.cost, replace=pairs)
if hasattr(new_obj, 'grads'):
new_obj.grads = theano.clone(new_obj.grads, replace=pairs)
if hasattr(new_obj, 'sample'):
new_obj.sample = theano.clone(new_obj.sample, replace=pairs)
return new_obj
def clone(**new_inputs):
new_obj = utils.copy(self)
# Reorder inputs
assert len(new_obj.inputs) == len(new_inputs.items())
pairs=[(x, new_inputs[x.name]) for x in inputs]
new_obj.inputs = new_inputs.values()
new_obj.out = theano.clone(new_obj.out, replace=pairs)
if hasattr(new_obj, 'cost'):
new_obj.cost = theano.clone(new_obj.cost, replace=pairs)
if hasattr(new_obj, 'grads'):
new_obj.grads = theano.clone(new_obj.grads, replace=pairs)
if hasattr(new_obj, 'sample'):
new_obj.sample = theano.clone(new_obj.sample, replace=pairs)
return new_obj
def clone(self, **new_inputs):
new_obj = utils.copy(self)
# Reorder inputs
assert len(new_obj.inputs) == len(new_inputs.items())
# TODO: error with inputs arg here. corrected missing self argument, this method must not be used
pairs = [(x, new_inputs[x.name]) for x in inputs]
new_obj.inputs = new_inputs.values()
new_obj.out = theano.clone(new_obj.out, replace=pairs)
if hasattr(new_obj, 'cost'):
new_obj.cost = theano.clone(new_obj.cost, replace=pairs)
if hasattr(new_obj, 'grads'):
new_obj.grads = theano.clone(new_obj.grads, replace=pairs)
if hasattr(new_obj, 'sample'):
new_obj.sample = theano.clone(new_obj.sample, replace=pairs)
return new_obj
def get_sample(self, **kwargs):
"""
Get a sample from the curren model.
! Only works for output layers
"""
if not hasattr(self, 'get_cost'):
raise TypeError('Non-output layer does not support this method')
new_obj = utils.copy(self)
try:
o_args, o_kwargs = new_obj.prev_args
except:
o_args, o_kwargs = ([], {})
kwargs = dict([(k, new_obj.tensor_from_layer(v)) for k,v in kwargs.items()])
for (k,v) in kwargs.items():
o_kwargs[k] = v
new_obj.prev_args = (o_args, o_kwargs)
sample = new_obj.compute_sample(*o_args, **o_kwargs)
return sample
def train(self, **kwargs):
"""
Compute the cost and gradients of the current layer with respect to
its parameters.
! Only works for output layers
"""
if not hasattr(self, 'get_grads'):
raise TypeError('Non-output layer does not support this method')
new_obj = utils.copy(self)
try:
o_args, o_kwargs = new_obj.prev_args
except:
o_args, o_kwargs = ([], {})
kwargs = dict([(k, new_obj.tensor_from_layer(v)) for k,v in kwargs.items()])
for (k,v) in kwargs.items():
o_kwargs[k] = v
new_obj.prev_args = (o_args, o_kwargs)
new_obj.get_grads(*o_args, **o_kwargs)
return new_obj
def get_sample(self, **kwargs):
"""
Get a sample from the curren model.
! Only works for output layers
"""
if not hasattr(self, 'get_cost'):
raise TypeError('Non-output layer does not support this method')
new_obj = utils.copy(self)
try:
o_args, o_kwargs = new_obj.prev_args
except:
o_args, o_kwargs = ([], {})
kwargs = dict([(k, new_obj.tensor_from_layer(v))
for k, v in kwargs.items()])
for (k, v) in kwargs.items():
o_kwargs[k] = v
new_obj.prev_args = (o_args, o_kwargs)
sample = new_obj.compute_sample(*o_args, **o_kwargs)
return sample
def validate(self, **kwargs):
"""
Recompute the cost error (without the gradients)
It only works for output layers !
"""
if not hasattr(self, 'get_cost'):
raise TypeError('Non-output layer does not support this method')
new_obj = utils.copy(self)
try:
o_args, o_kwargs = new_obj.prev_args
except:
o_args, o_kwargs = ([], {})
kwargs = dict([(k, new_obj.tensor_from_layer(v)) for k,v in kwargs.items()])
for (k,v) in kwargs.items():
o_kwargs[k] = v
new_obj.prev_args = (o_args, o_kwargs)
new_obj.get_cost(*o_args, **o_kwargs)
return new_obj
def validate(self, **kwargs):
"""
Recompute the cost error (without the gradients)
It only works for output layers !
"""
if not hasattr(self, 'get_cost'):
raise TypeError('Non-output layer does not support this method')
new_obj = utils.copy(self)
try:
o_args, o_kwargs = new_obj.prev_args
except:
o_args, o_kwargs = ([], {})
kwargs = dict([(k, new_obj.tensor_from_layer(v))
for k, v in kwargs.items()])
for (k, v) in kwargs.items():
o_kwargs[k] = v
new_obj.prev_args = (o_args, o_kwargs)
new_obj.get_cost(*o_args, **o_kwargs)
return new_obj
def train(self, **kwargs):
"""
Compute the cost and gradients of the current layer with respect to
its parameters.
! Only works for output layers
"""
if not hasattr(self, 'get_grads'):
raise TypeError('Non-output layer does not support this method')
new_obj = utils.copy(self)
try:
o_args, o_kwargs = new_obj.prev_args
except:
o_args, o_kwargs = ([], {})
kwargs = dict([(k, new_obj.tensor_from_layer(v))
for k, v in kwargs.items()])
for (k, v) in kwargs.items():
o_kwargs[k] = v
new_obj.prev_args = (o_args, o_kwargs)
new_obj.get_grads(*o_args, **o_kwargs)
return new_obj
def __add__(self, other):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
other_var = new_obj.tensor_from_layer(other)
new_obj.out = new_obj.out + other_var
# Summing cost layers:
if hasattr(new_obj, 'grads') and hasattr(other, 'grads'):
for param, grad_param in zip(other.params, other.grads):
pos = new_obj.params.index(param)
new_obj.grads[pos] += grad_param
elif hasattr(new_obj, 'grads') and \
isinstance(other, theano.gof.Variable) and \
other.ndim == 0:
other_grads = TT.grad(other,
new_obj.params,
disconnected_inputs='warn')
new_obj.grads = [x + y for x, y in zip(new_obj.grads, other_grads)]
elif hasattr(new_obj, 'grads'):
raise ValueError('I do not know how to compute the gradients'
' of the added term' + str(other) + '. Call'
' train on it if it is an output layer')
return new_obj
def __add__(self, other):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
other_var = new_obj.tensor_from_layer(other)
new_obj.out = new_obj.out + other_var
# Summing cost layers:
if hasattr(new_obj, 'grads') and hasattr(other, 'grads'):
for param, grad_param in zip(other.params, other.grads):
pos = new_obj.params.index(param)
new_obj.grads[pos] += grad_param
elif hasattr(new_obj, 'grads') and \
isinstance(other, theano.gof.Variable) and \
other.ndim == 0:
other_grads = TT.grad(other, new_obj.params,
disconnected_inputs='ignore')
new_obj.grads = [x + y for x,y in zip(new_obj.grads,
other_grads)]
elif hasattr(new_obj, 'grads'):
raise ValueError('I do not know how to compute the gradients'
' of the added term' + str(other) + '. Call'
' train on it if it is an output layer')
return new_obj
def reshape(self, shape):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
new_obj.out = new_obj.out.reshape(shape)
return new_obj
def reshape(self, shape):
assert hasattr(self, 'out'), 'all layers need a default output'
new_obj = utils.copy(self)
new_obj.out = new_obj.out.reshape(shape)
return new_obj
