def loop (row):
one_indices = T.nonzero(row)[0]
zero_indices = T.eq(row, 0).nonzero()[0]
random = shared_randomstreams.RandomStreams(5)
ind1=random.random_integers(size=(1,), low=0, high=one_indices.shape[0]-1, ndim=None)
ind2=random.random_integers(size=(50,), low=0, high=zero_indices.shape[0]-1, ndim=None)
return one_indices[ind1], zero_indices[ind2]
def __init__(self,
incoming,
assume_normalized=False,
seed=1234,
action_dtype='int32',
name='ProbabilisticResolver'):
"""
:param incoming: a lasagne layer that outputs action probability vectors
WARNING! We assume that incoming probabilities are all nonnegative even if assume_normalized=False.
:type incoming: lasagne.layers.Layer
:param assume_normalized: if set to True, the incoming layer is assumed to
return outputs that add up to 1 (e.g. softmax output) along last axis
:type assume_normalized: bool
:param seed: - random seed
:type seed: int
:action_dtype: type of action (usually (u)int 32 or 64)
:type action_dtype: string or dtype
:param name: layer name (using lasagne conventions)
:type name: string
"""
# probas float[2] - probability of random and optimal action respectively
self.assume_normalized = assume_normalized
self.action_dtype = action_dtype
self.rng = random_streams.RandomStreams(seed)
super(ProbabilisticResolver, self).__init__(incoming, name=name)
def __init__(self, init='glorot_uniform', activation='linear',
input_dim=None, vocab_size=None, n_noise = 25, Pn=[0.5, 0.5],
weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, **kwargs):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.input_dim = input_dim
self.vocab_size = vocab_size
self.n_noise = n_noise
self.Pn = theano.shared(numpy.array(Pn).astype(config.floatX))
self.rng = RS.RandomStreams(seed=SEED)
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.initial_weights = weights
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(NCE, self).__init__(**kwargs)
def dropout_layer(layer, p_dropout):
# 获取999999之间的一个随机数
srng = shared_randomstreams.RandomStreams(
np.random.RandomState(0).randint(999999))
# 获取掩模
mask = srng.binomial(n=1, p=1 - p_dropout, size=layer.shape)
return layer * T.cast(mask, theano.config.floatX)
def dropout_layer(layer, p_dropout):
'''
Drop out functionality using theano
'''
srng = shared_randomstreams.RandomStreams(
np.random.RandomState(0).randint(999999))
mask = srng.binomial(n=1, p=1 - p_dropout, size=layer.shape)
return layer * Tensor.cast(mask, theano.config.floatX)
def __init__(self, inpt, in_dim, out_dim, param_names=["W", "b"]):
self.inpt = inpt
self.out_dim = out_dim
self.in_dim = in_dim
self.param_names = param_names
self.eps = t_random.RandomStreams().normal((
inpt.shape[0],
out_dim,
))
self.initialise()
def __init__(
self,
init='glorot_uniform',
activation='linear',
input_dim=None,
vocab_size=None,
n_noise=25,
Pn=[0.5, 0.5],
weights=None,
W_regularizer=None,
b_regularizer=None,
activity_regularizer=None,
W_constraint=None,
b_constraint=None,
bias=True,
**kwargs
): #kwargs allow passing keyworded variable length of arguments to function
self.init = initializations.get(
init
) #use Glorot uniform distribution between +- sqrt(6 / (fan_in + fan_out))
self.activation = activations.get(
activation) #use linear activation function
self.input_dim = input_dim
self.vocab_size = vocab_size
self.n_noise = n_noise
self.Pn = theano.shared(
numpy.array(Pn).astype(config.floatX)
) #self.Pn is a shared variable now (used by different functions)
self.rng = RS.RandomStreams(
seed=SEED
) #create a stream of random variables with various distributions
self.W_regularizer = regularizers.get(
W_regularizer) #apply penalties on weight during optimization
self.b_regularizer = regularizers.get(
b_regularizer) #apply penalties on bias during optimization
self.activity_regularizer = regularizers.get(
activity_regularizer
) #apply penalties on activity during optimization
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.initial_weights = weights
if self.input_dim:
kwargs['input_shape'] = (
self.input_dim,
) #add another argument named input_shape with a vlue of self.input_dim
super(NCE, self).__init__(**kwargs)
def _get_train_output(self, layer_input):
"""Return layer's output used for training.
When training, p_dropout weights are dropped out.
:param layer_input: Layer input.
:return: layer output.
"""
random = shared_randomstreams.RandomStreams()
mask = random.binomial(n=1,
p=1. - self.p_dropout,
size=layer_input.shape)
return layer_input * T.cast(mask, theano.config.floatX)
def __init__(self,
incoming,
assume_normalized=False,
seed=1234,
output_dtype='int32',
name='MultiProbabilisticResolver'):
self.assume_normalized = assume_normalized
self.rng = random_streams.RandomStreams(seed)
super(MultiProbabilisticResolver,
self).__init__(incoming, name=name, output_dtype=output_dtype)
def dropout_layer(layer, p_dropout):
"""Return layer has been dropout.
Args:
layer: The layer.
p_dropout: The probability of dropout.
Returns:
The layer has been dropout.
"""
srs = shared_randomstreams.RandomStreams(np.random.RandomState(
0).randint(999999))
mask = srs.binomial(n=1, p=1 - p_dropout, size=layer.shape)
return layer * T.cast(mask, theano.config.floatX) / (1 - p_dropout)
def Dropout_Func(rng, value, p):
'''
>>>type rng: numpy.random.RandomState
>>>para rng: initalize weight randomly
>>>type value: theano.tensor.TensorType
>>>para value: input data
>>>type p: float
>>>para p: dropout rate
'''
srng = shared_randomstreams.RandomStreams(rng.randint(2011010539))
mask = srng.binomial(n=1, p=1 - p, size=value.shape)
return value * T.cast(mask, theano.config.floatX)
def dropout_layer(layer, p_dropout):
"""
Dropout some components of a layer according to p_dropout
:param layer: given layer
:param p_dropout: percent dropout
:return: pruned layer
"""
random_generator = randomgen.RandomGenerator()
shared_random_number_generator = shared_randomstreams.RandomStreams(
random_generator.randint(0, high=999999))
mask = shared_random_number_generator.binomial(n=1,
p=1 - p_dropout,
size=layer.shape)
return layer * tensor.cast(mask, theano.config.floatX)
def __init__(self, incoming, epsilon=None, seed=1234, name='EpsilonGreedyResolver',**kwargs):
"""
epsilon float scalar: probability of random choice instead of optimal one
seed constant: - random seed
"""
if epsilon is None:
epsilon = theano.shared(np.float32(0.1), "e-greedy.epsilon")
self.epsilon = epsilon
# probas float[2] - probability of random and optimal action respectively
self.probas = T.stack([epsilon, 1 - epsilon])
self.rng = random_streams.RandomStreams(seed)
super(EpsilonGreedyResolver, self).__init__(incoming, name=name,**kwargs)
def __init__(self, sizes,p_dropout=0):
self.num_layers = len(sizes)
self.sizes = sizes
self.weights = [theano.shared(np.asarray(np.random.normal(loc=0.0,scale=np.sqrt(1.0/y),size=(y, x)),dtype=theano.config.floatX),
name='w',
borrow=True)
for x, y in zip(self.sizes[:-1], self.sizes[1:])]
self.biases = [theano.shared(np.asarray(np.random.normal(loc=0.0,scale=2.0,size=(1,y)),dtype = theano.config.floatX),
name='b',
broadcastable = (True,False))
for y in self.sizes[1:]]
self.params = []
for weight,bias in zip (self.weights,self.biases):
self.params.append(weight)
self.params.append(bias)
x = T.matrix('x')
y = T.matrix('y')
a = x
a_dropout = x
for i in xrange(len(self.weights)-1):
srgn = shared_randomstreams.RandomStreams(np.random.RandomState(0).randint(99999))
mask = srgn.binomial(n=1,p = 1-p_dropout,size=self.weights[i].shape)
z_dropout = T.dot(a_dropout,self.weights[i].transpose()*T.cast(mask.transpose(),theano.config.floatX))+self.biases[i]
z = (1-p_dropout)*T.dot(a,self.weights[i].transpose())+self.biases[i]
a = ReLU(z)
a_dropout = ReLU(z_dropout)
z = (1-p_dropout)*T.dot(a,self.weights[-1].transpose())+self.biases[-1]
mask = srgn.binomial(n=1,p = 1-p_dropout,size=self.weights[-1].shape)
z_dropout = T.dot(a_dropout,self.weights[-1].transpose()*T.cast(mask.transpose(),theano.config.floatX))+self.biases[-1]
a = softmax(z)
epsilon = 10e-16
a_dropout = softmax(z_dropout)
self.cost = T.mean(-T.log(a_dropout[T.arange(a.shape[0]),T.argmax(y,axis=1)]+ epsilon))
lmbda = T.scalar('lmbda')
for w in self.weights:
self.cost += 0.5*(lmbda)*T.mean(T.log(w**2+ epsilon))
self.grads = [T.grad(self.cost,param) for param in self.params]
eta = T.scalar('eta')
self.updates = [(param,param-eta*grad) for param,grad in zip(self.params,self.grads)]
self.feedforward = theano.function([x],a)
self.train = theano.function([x,y,eta,lmbda],self.cost,updates = self.updates)
def __init__(self, optimizers=sgd):
''' model hyperparameters '''
self.random_seed = 999
self.batch_size = 100
self.split = 0.7
self.theano_rng = rs.RandomStreams(self.random_seed)
self.optimizer = optimizers
''' Theano Tensor variables '''
self.index = T.lscalar() # minibatch index tensor
self.visibles = [] # list of tensor variables to visible units
self.W_params = [] # list of weight params tensors
self.vbias = [] # list of vbias params tensors
self.hbias = [] # list of hbias params tensors
self.masks = [] # list of tensor gradient masks
self.params = [] # list of params for gradient calculation
''' Theano Shared variables '''
self.train_visibles = [] # list of shared variables to inputs
self.valid_visibles = [] # list of shared variables to inputs
self.num_samples = 0 # total number of samples used
self.num_hidden = 0 # number of hidden units
self.shapes = [] # list of number of features
self.types = [] # list of type for each feature
def __init__(self, mu, logvar, samples=10, **kwargs):
self.rng = shared_randomstreams.RandomStreams(1692)
self.samples = samples
super(GaussianSamplerLayer, self).__init__([mu, logvar], **kwargs)
def dropout_layer(layer, p_dropout):
srng = shared_randomstreams.RandomStreams(
np.random.RandomState(0).randint(999999))
mask = srng.binomial(n=1, p=1 - p_dropout, size=layer.shape)
return layer * T.cast(mask, 'float32')
def dropoutLayer(layer, pDropout):
srng = shared_randomstreams.RandomStreams(np.random.RandomState(0).randint(999999))
mask = srng.binomial(n=1, p=1-pDropout, size=layer.shape)
return layer*T.cast(mask, theano.config.floatX)
def dropout(X, p):
srng = t_random.RandomStreams()
retain_prob = 1 - p
X *= srng.binomial(X.shape, p=retain_prob, dtype=theano.config.floatX)
X /= retain_prob
return X.astype(theano.config.floatX)
np.random.seed(0)
print(np.random.permutation(10))
np.random.seed(1)
print(np.random.permutation(10))
np.random.seed(2)
print(np.random.permutation(10))
print("---------")
np.random.seed(42)
print(np.random.randn(4))
print(np.random.randn(4))
print(np.random.randn(4))
np.random.seed(
42
) #np.random.RandomState maintains the number just as before that have exacly the same seed
print(np.random.randn(4))
print("--------------")
import theano
import theano.tensor as T
from theano.tensor import shared_randomstreams
srng = shared_randomstreams.RandomStreams(
np.random.RandomState(0).randint(999999))
print(srng)
def __dropout(self, layer):
srng = shared_randomstreams.RandomStreams(
np.random.RandomState(0).randint(999999))
mask = srng.binomial(n=1, p=1.0 - Layer.dropout, size=layer.shape)
return layer * T.cast(mask, theano.config.floatX)
def dropout_layer(layer, p_dropout): #用来生成随机的（0或1）的权重w，来模拟弃权
srng = shared_randomstreams.RandomStreams(
np.random.RandomState(0).randint(999999))
mask = srng.binomial(n=1, p=1 - p_dropout, size=layer.shape)
return layer * T.cast(mask, theano.config.floatX)
#!/usr/bin/env python
from theano import function
import theano.tensor as T
from theano.tensor import shared_randomstreams
import numpy as np
import numpy.random
from scipy.special import gammaincinv
from numpy.linalg import norm
# tensor stand-in for np.random.RandomState
rngT = shared_randomstreams.RandomStreams()
rng = numpy.random.RandomState()
# {{{ Fastfood Params }}}
n, d = T.dscalars('n', 'd')
# transform dimensions to be a power of 2
d0, n0 = d, n
l = T.ceil(T.log2(d)) # TODO cast to int
d = 2**l
k = T.ceil(n / d) # TODO cast to int
n = d * k
# generate parameter 'matrices'
B = rng.choice([-1, 1], size=(k, d))
G = rng.normal(size=(k, d), dtype=np.float64)
PI = np.array([rng.permutation(d) for _ in xrange(k)]).T
S = np.empty((k * d, 1), dtype=np.float64)
# generate scaling matrix, S
for i in xrange(k):
for j in xrange(d):
p1 = rng.uniform(size=d)
p2 = d / 2
