def pickling_variable(v, target=None):
""" This function only apply for trainable parameters
Warning
-------
This pickling method won't save "auxiliary_variables" and "updates"
tag of variables
"""
# load variable
if isinstance(v, str):
try:
if os.path.exists(v):
v = open(v, 'r')
except:
pass
name, value, dtype, roles = cPickle.loads(v)
v = variable(value, dtype=dtype, name=name, target=target)
for i in roles:
add_role(v, i)
return v
elif is_trainable_variable(v):
name = v.name if ':' not in v.name else v.name.split(':')[0]
value = get_value(v)
dtype = v.dtype.as_numpy_dtype if hasattr(
v.dtype, 'as_numpy_dtype') else v.dtype
# ====== shape and roles ====== #
roles = getattr(v.tag, 'roles', [])
return cPickle.dumps([name, value, dtype, roles],
protocol=cPickle.HIGHEST_PROTOCOL)
else:
raise Exception('Variable must be in string form or trainable variable'
' (i.e. SharedVariable in theano)')
def get_mean_logsigma(self, x):
b_mean = 0. if not hasattr(self, 'b_mean') else self.b_mean
b_logsigma = 0. if not hasattr(self, 'b_logsigma') else self.b_logsigma
mean = self.activation(K.dot(x, self.W_mean) + b_mean)
logsigma = self.activation(K.dot(x, self.W_logsigma) + b_logsigma)
mean.name = 'variational_mean'
logsigma.name = 'variational_logsigma'
add_role(mean, VARIATIONAL_MEAN)
add_role(logsigma, VARIATIONAL_LOGSIGMA)
return mean, logsigma
def rnn(input_dim,
hidden_dim,
W_init=glorot_uniform,
b_init=constant(0.),
bidirectional=False,
one_vector=False,
return_variable=True,
name=None):
""" Fast initalize all Standard RNN weights
Parameters
----------
one_vector: bool
if True, all the weights are flatten and concatenated into 1 big vector
return_variable: bool
if False, only return the numpy array
bidirectional: bool
if True, return parameters for both forward and backward RNN
Return
------
[W_i, b_wi, R_h, b_wh]
"""
if name is None: name = uuid()
def init():
W_i = W_init((input_dim, hidden_dim))
b_wi = b_init((hidden_dim))
R_h = W_init((hidden_dim, hidden_dim))
b_wh = b_init((hidden_dim))
return [W_i, b_wi, R_h, b_wh]
params = init() + init() if bidirectional else init()
roles = [WEIGHT, BIAS]
if one_vector:
params = [np.concatenate([p.flatten() for p in params])]
roles = [PARAMETER]
# names
if one_vector:
names = [name + '_rnn']
else:
names = ["_W_i", "_b_wi", "_R_h", "_b_wh"]
if bidirectional:
names = [i + '_fw' for i in names] + [i + '_bw' for i in names]
names = [name + i for i in names]
# create variable or not
if return_variable:
params = [variable(p, name=n) for p, n in zip(params, names)]
for i, p in enumerate(params):
add_role(p, roles[i % 2])
return params if len(params) > 1 else params[0]
def set_inputs(self, *inputs):
self._input_info = []
self._inputs = []
for i in inputs:
if not K.is_placeholder(i):
raise ValueError('Only accept input which is placeholder.')
name, dtype, shape = i.name, i.dtype, K.get_shape(i)
self._input_info.append([name, dtype, shape])
self._inputs.append(i)
# ====== Try to check if the inputs match the Ops ====== #
try:
# call this to initialize the parameters and get
# estimated output shape (we assume training and deploying
# mode get the same shape).
for i in self._inputs:
add_role(i, TRAINING)
self._y_train = self._seq_ops(*self._inputs)
for i in self._inputs:
add_role(i, DEPLOYING)
self._y_pred = self._seq_ops(*self._inputs)
# create default output
if len(self._output_info) == 0:
shape = K.get_shape(self._y_train)
self._outputs = [
K.placeholder(shape=shape,
dtype=self._y_train.dtype,
name='output1')
]
self._output_info = [('output1', self._y_train.dtype, shape)]
# reset all functions
for i, j in self._functions.items():
del self._functions[i]
del j
self._functions = {}
except Exception, e:
warnings.warn('Inputs do not match the Ops requirements, '
'error: ' + str(e))
self._input_info = []
self._inputs = []
def test_computational_graph2(self):
np.random.seed(1208)
X = K.variable(np.zeros((8, 12)), name='X')
Y = K.variable(np.random.rand(12, 8), name='Y')
Z = K.placeholder(shape=(8, 8), name='Z')
a = K.dot(X, Y)
add_role(a, AUXILIARY)
add_updates(a, X, X + 12)
a = a + Z
g1 = K.ComputationGraph(a)
self.assertEqual(len(g1.trainable_variables), 2)
self.assertEqual(len(g1.placeholders), 1)
self.assertEqual(len(g1.updates), 1)
self.assertEqual(len(g1.auxiliary_variables), 1)
f = K.function(Z, [a] + g1.auxiliary_variables)
output = f(np.random.rand(8, 8))
self.assertEqual(repr(np.sum(output[0]))[:5], "32.20")
self.assertEqual(np.sum(output[1]), 0)
self.assertEqual(np.unique(K.eval(X)).tolist(), [12.])
def lstm(input_dim,
hidden_dim,
W_init=glorot_uniform,
b_init=constant(0.),
bidirectional=False,
one_vector=False,
return_variable=True,
name=None):
""" Fast initalize all Standard LSTM weights (without peephole connection)
Parameters
----------
one_vector: bool
if True, all the weights are flatten and concatenated into 1 big vector
return_variable: bool
if False, only return the numpy array
bidirectional: bool
if True, return parameters for both forward and backward RNN
Return
------
[W_i, b_wi, W_f, b_wf, W_c, b_wc, W_o, b_wo,
R_i, b_ri, R_f, b_rf, R_c, b_rc, R_o, b_ro]
"""
if name is None: name = uuid()
def init():
# input to hidden
W_i = W_init((input_dim, hidden_dim))
b_wi = b_init((hidden_dim))
W_f = W_init((input_dim, hidden_dim))
b_wf = b_init((hidden_dim))
W_c = W_init((input_dim, hidden_dim))
b_wc = b_init((hidden_dim))
W_o = W_init((input_dim, hidden_dim))
b_wo = b_init((hidden_dim))
# hidden to hidden
R_i = W_init((hidden_dim, hidden_dim))
b_ri = b_init((hidden_dim))
R_f = W_init((hidden_dim, hidden_dim))
b_rf = b_init((hidden_dim))
R_c = W_init((hidden_dim, hidden_dim))
b_rc = b_init((hidden_dim))
R_o = W_init((hidden_dim, hidden_dim))
b_ro = b_init((hidden_dim))
return [
W_i, b_wi, W_f, b_wf, W_c, b_wc, W_o, b_wo, R_i, b_ri, R_f, b_rf,
R_c, b_rc, R_o, b_ro
]
params = init() + init() if bidirectional else init()
roles = [WEIGHT, BIAS]
if one_vector:
params = [np.concatenate([p.flatten() for p in params])]
roles = [PARAMETER]
# names
if one_vector:
names = [name + '_lstm']
else:
names = [
"_W_i", "_b_wi", "_W_f", "_b_wf", "_W_c", "_b_wc", "_W_o", "_b_wo",
"_R_i", "_b_ri", "_R_f", "_b_rf", "_R_c", "_b_rc", "_R_o", "_b_ro"
]
if bidirectional:
names = [i + '_fw' for i in names] + [i + '_bw' for i in names]
names = [name + i for i in names]
# create variable or not
if return_variable:
params = [variable(p, name=n) for p, n in zip(params, names)]
for i, p in enumerate(params):
add_role(p, roles[i % 2])
return params if len(params) > 1 else params[0]
def create_params(self, spec, shape, name, nnops, roles=[], nb_params=1):
"""
Parameters
----------
spec: variable, numpy.ndarray, function
specification for initializing the weights
shape: tuple, list
expected shape for given variable
name: str
name for the variable
nnops: NNOps
parent operator of this parameters
roles: odin.basic.VariableRole
categories of this variable
nb_params: int
number of parameters that horizontally stacked into
given `shape (e.g. nb_params=2, create 2 parameters with
given `shape and horizontally stack them into 1 parameters)
* do NOT support when `spec` is variable.
"""
if not isinstance(roles, (tuple, list)):
roles = [roles]
if not isinstance(nnops, NNOps):
raise Exception('nnops must be instance of odin.nnet.base.NNOps')
shape = tuple(shape) # convert to tuple if needed
if any(d <= 0 for d in shape):
raise ValueError(
("Cannot create param with a non-positive shape dimension. "
"Tried to create param with shape=%r, name=%r") %
(shape, name))
# ====== create parameters ====== #
spec = as_tuple(spec, nb_params)
spec = [_initialize_param(name, s, shape) for s in spec]
# check shape returned
shape = list(set([i[-1] for i in spec]))
if len(shape) > 1:
raise Exception(
'shape are inconsitent among all given "spec", the '
'created shape is: %s' % str(shape))
shape = shape[0]
# check spec returned
spec = [i[0] for i in spec]
if isinstance(spec[0], np.ndarray):
with K.variable_scope(nnops.name):
spec = np.concatenate(spec, axis=-1)
shape = spec.shape
spec = K.variable(spec, name=name)
elif K.is_trainable_variable(spec[0]):
if nb_params > 1:
with K.variable_scope(nnops.name):
spec = np.concatenate([K.get_value(i) for i in spec],
axis=-1)
shape = spec.shape
spec = K.variable(spec, name=name)
else:
spec = spec[0]
elif K.is_variable(spec[0]):
shape = (shape[0] * nb_params,) if len(shape) == 1 \
else shape[:-1] + (shape[-1] * nb_params,)
spec = K.concatenate(spec, axis=-1)
# ====== assign annotations ====== #
# only add role for trainable variables
for i in roles:
if isinstance(i, VariableRole) and K.is_trainable_variable(spec):
add_role(spec, i)
# return actual variable or expression
# override other parameters with same name
self._variables[name] = spec
# set parameter attribute for NNOps
setattr(nnops, name, spec)
return spec
def randrectify(x, lower=0.3, upper=0.8, shared_axes='auto'):
""" This function is adpated from Lasagne
Original work Copyright (c) 2014-2015 lasagne contributors
All rights reserved.
LICENSE: https://github.com/Lasagne/Lasagne/blob/master/LICENSE
Applies a randomized leaky rectify activation to x.
The randomized leaky rectifier was first proposed and used in the Kaggle
NDSB Competition, and later evaluated in [1]_. Compared to the standard
leaky rectifier :func:`leaky_rectify`, it has a randomly sampled slope
for negative input during training, and a fixed slope during evaluation.
Equation for the randomized rectifier linear unit during training:
:math:`\\varphi(x) = \\max((\\sim U(lower, upper)) \\cdot x, x)`
During evaluation, the factor is fixed to the arithmetic mean of `lower`
and `upper`.
Parameters
----------
lower : Theano shared variable, expression, or constant
The lower bound for the randomly chosen slopes.
upper : Theano shared variable, expression, or constant
The upper bound for the randomly chosen slopes.
shared_axes : 'auto', 'all', int or tuple of int
The axes along which the random slopes of the rectifier units are
going to be shared. If ``'auto'`` (the default), share over all axes
except for the second - this will share the random slope over the
minibatch dimension for dense layers, and additionally over all
spatial dimensions for convolutional layers. If ``'all'``, share over
all axes, thus using a single random slope.
References
----------
.. [1] Bing Xu, Naiyan Wang et al. (2015):
Empirical Evaluation of Rectified Activations in Convolutional Network,
http://arxiv.org/abs/1505.00853
"""
input_shape = get_shape(x)
# ====== check lower and upper ====== #
if is_trainable_variable(lower):
add_role(lower, ACTIVATION_PARAMETER)
lower.name = 'lower'
if is_trainable_variable(upper):
add_role(upper, ACTIVATION_PARAMETER)
upper.name = 'upper'
if not is_variable(lower > upper) and lower > upper:
raise ValueError("Upper bound for Randomized Rectifier needs "
"to be higher than lower bound.")
# ====== check shared_axes ====== #
if shared_axes == 'auto':
shared_axes = (0, ) + tuple(range(2, len(input_shape)))
elif shared_axes == 'all':
shared_axes = tuple(range(len(input_shape)))
elif isinstance(shared_axes, int):
shared_axes = (shared_axes, )
else:
shared_axes = shared_axes
# ====== main logic ====== #
if not is_training() or upper == lower:
x = relu(x, (upper + lower) / 2.0)
else: # Training mode
shape = list(input_shape)
if builtins.any(s is None for s in shape):
shape = list(x.shape)
for ax in shared_axes:
shape[ax] = 1
rnd = random_uniform(tuple(shape), low=lower, high=upper, dtype=FLOATX)
rnd = addbroadcast(rnd, *shared_axes)
x = relu(x, rnd)
add_shape(x, input_shape)
return x
