def get_pseudo_likelihood_cost(self, updates):
"""Stochastic approximation to the pseudo-likelihood"""
# index of bit i in expression p(x_i | x_{\i})
bit_i_idx = theano.shared(value=0, name='bit_i_idx')
# binarize the input image by rounding to nearest integer
xi = tensor.round(self.input)
# calculate free energy for the given bit configuration
fe_xi = self.free_energy(xi)
# flip bit x_i of matrix xi and preserve all other bits x_{\i}
# Equivalent to xi[:,bit_i_idx] = 1-xi[:, bit_i_idx], but assigns
# the result to xi_flip, instead of working in place on xi.
xi_flip = tensor.set_subtensor(xi[:, bit_i_idx], 1 - xi[:, bit_i_idx])
# calculate free energy with bit flipped
fe_xi_flip = self.free_energy(xi_flip)
# equivalent to e^(-FE(x_i)) / (e^(-FE(x_i)) + e^(-FE(x_{\i})))
cost = - tensor.mean(self.n_visible * nnet.softplus(fe_xi - fe_xi_flip))
# increment bit_i_idx % number as part of updates
updates[bit_i_idx] = (bit_i_idx + 1) % self.n_visible
return cost
def free_energy_given_s_h(self, s, h, With_fast=False):
alpha = self.get_alpha(With_fast)
mu = self.get_mu(With_fast)
W = self.get_filters(With_fast)
h_bias = self.get_h_bias(With_fast)
conv_v_bias = self.get_conv_v_bias(With_fast)
out_softplus = 0.5*alpha*(s**2) - alpha*mu*s*h + 0.5*alpha*(mu**2)*h - h_bias*h
rval = tensor.sum(out_softplus,axis=[1,2,3]) - tensor.sum(nnet.softplus(self.convdot(s*h, W)+conv_v_bias),axis=[1,2,3])
assert rval.ndim==1
return rval
def free_energy_given_v(self, v):
# This is accurate up to a multiplicative constant
# because I dropped some terms involving 2pi
def pre_sigmoid(x):
assert x.owner and x.owner.op == nnet.sigmoid
return x.owner.inputs[0]
pre_convhs_h = pre_sigmoid(self.mean_convhs_h_given_v(v))
rval = tensor.add(
-tensor.sum(nnet.softplus(pre_convhs_h),axis=[1,2,3,4]), #the shape of pre_convhs_h: 64 x 11 x 32 x 8 x 8
(0.5/self.sigma) * tensor.sum((v-self.bias_v)**2, axis=[1,2,3]), #shape: 64 x 1 x 98 x 98
)
assert rval.ndim==1
return rval
def free_energy(self, sample, type='vis'):
''' Function to compute the free energy '''
assert type in ('vis','hid')
if type is 'vis':
wx_b = T.dot(sample, self.W) + self.hbias
bias_term = T.dot(sample, self.vbias)
else:
wx_b = T.dot(sample, self.W.T) + self.vbias
bias_term = T.dot(sample, self.hbias)
hidden_term = T.sum(nnet.softplus(wx_b),axis = 1)
#hidden_term = T.sum(T.log(1 + T.exp(wx_b)),axis = 1)
return -hidden_term - bias_term
def free_energy_given_v(self, v, With_fast=False):
# This is accurate up to a multiplicative constant
# because I dropped some terms involving 2pi
def pre_sigmoid(x):
assert x.owner and x.owner.op == nnet.sigmoid
return x.owner.inputs[0]
pre_convhs_h = pre_sigmoid(self.mean_convhs_h_given_v(v,With_fast))
rval = tensor.add(
-tensor.sum(nnet.softplus(pre_convhs_h),axis=[1,2,3,4]), #the shape of pre_convhs_h: 64 x 11 x 32 x 8 x 8
0.5 * tensor.sum(self.get_v_prec(With_fast) * (v**2), axis=[1,2,3]), #shape: 64 x 1 x 98 x 98
)
assert rval.ndim==1
return rval
def free_energy_given_v(self, v, With_fast=False):
# This is accurate up to a multiplicative constant
# because I dropped some terms involving 2pi
def pre_sigmoid(x):
assert x.owner and x.owner.op == nnet.sigmoid
return x.owner.inputs[0]
pre_convhs_h = pre_sigmoid(self.mean_convhs_h_given_v(v, With_fast))
rval = tensor.add(
-tensor.sum(nnet.softplus(pre_convhs_h), axis=[
1, 2, 3, 4
]), #the shape of pre_convhs_h: 64 x 11 x 32 x 8 x 8
0.5 *
tensor.sum(self.get_v_prec(With_fast) *
(v**2), axis=[1, 2, 3]), #shape: 64 x 1 x 98 x 98
)
assert rval.ndim == 1
return rval
def free_energy_given_v(self, v, With_fast=False):
# This is accurate up to a multiplicative constant
# because I dropped some terms involving 2pi
alpha = self.get_conv_alpha(With_fast)
W = self.get_filters_hs(With_fast)
vW = self.convdot(v, W)
vW_broadcastable = vW.dimshuffle(0,3,4,1,2)
#change 64 x 11 x 32 x 8 x 8 to 64 x 8 x 8 x 11 x 32 for broadcasting
pre_convhs_h_parts = self.get_conv_mu(With_fast)*vW_broadcastable + self.get_conv_bias_hs(With_fast) + 0.5*(vW_broadcastable**2)/alpha
pre_convhs_h = tensor.add(
pre_convhs_h_parts.dimshuffle(0,3,4,1,2),
-0.5*self.conv_problem_term(v,With_fast))
rval = tensor.add(
-tensor.sum(nnet.softplus(pre_convhs_h),axis=[1,2,3,4]), #the shape of pre_convhs_h: 64 x 11 x 32 x 8 x 8
0.5 * tensor.sum(self.get_v_prec(With_fast) * (v**2), axis=[1,2,3]), #shape: 64 x 1 x 98 x 98
)
assert rval.ndim==1
return rval
def free_energy_given_h(self, h):
"""
Calculate the free energy of a hidden unit configuration by
marginalizing over the visible units.
Parameters
----------
h : tensor_like
Theano symbolic representing the hidden unit states, with the
first dimension indexing training examples and the second
indexing data dimensions.
Returns
-------
f : tensor_like
1-dimensional tensor (vector) representing the free energy
associated with each row of v.
"""
sigmoid_arg = self.input_to_v_from_h(h)
return -tensor.dot(h, self.bias_hid) - nnet.softplus(sigmoid_arg).sum(axis=1)
def free_energy_given_h(self, h):
"""
Calculate the free energy of a hidden unit configuration by
marginalizing over the visible units.
Parameters
----------
h : tensor_like
Theano symbolic representing the hidden unit states, with the
first dimension indexing training examples and the second
indexing data dimensions.
Returns
-------
f : tensor_like
1-dimensional tensor (vector) representing the free energy
associated with each row of v.
"""
sigmoid_arg = self.input_to_v_from_h(h)
return (-tensor.dot(h, self.hidbias) -
nnet.softplus(sigmoid_arg).sum(axis=1))
def free_energy_given_v(self, v):
"""
Calculate the free energy of a visible unit configuration by
marginalizing over the hidden units.
Parameters
----------
v : tensor_like
Theano symbolic representing the hidden unit states for a batch of
training examples, with the first dimension indexing training
examples and the second indexing data dimensions.
Returns
-------
f : tensor_like
0-dimensional tensor (i.e. effectively a scalar) representing the
free energy of the visible unit configuration.
"""
hid_inp = self.input_to_h_from_v(v)
squared_term = (self.visbias - v) ** 2 / self.sigma
return squared_term.sum(axis=1) - nnet.softplus(hid_inp).sum(axis=1)
def free_energy_given_v(self, v):
sigmoid_arg = self.input_to_h_from_v(v)
return (-T.dot(v, self.get_bv()) - nnet.softplus(sigmoid_arg).sum())
def theano_softplus(self, x):
return nets.softplus(x)
def __init__(self, rng, input, filter_shape, poolsize=(2,2), stride=None, if_pool=False, act=None, share_with=None,
tied=None, border_mode='valid'):
self.input = input
if share_with:
self.W = share_with.W
self.b = share_with.b
self.W_delta = share_with.W_delta
self.b_delta = share_with.b_delta
elif tied:
self.W = tied.W.dimshuffle(1,0,2,3)
self.b = tied.b
self.W_delta = tied.W_delta.dimshuffle(1,0,2,3)
self.b_delta = tied.b_delta
else:
fan_in = np.prod(filter_shape[1:])
poolsize_size = np.prod(poolsize) if poolsize else 1
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) / poolsize_size)
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(
np.asarray(
rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX
),
borrow=True
)
b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
self.W_delta = theano.shared(
np.zeros(filter_shape, dtype=theano.config.floatX),
borrow=True
)
# b_update_values = np.zeros((5,filter_shape[0]), dtype=theano.config.floatX)
self.b_delta = theano.shared(value=b_values, borrow=True)
#EHA: define update history for momentum gradient
# self.W_update = theano.shared(
# np.zeros(filter_shape, dtype=theano.config.floatX),
# borrow=True
# )
#
# # b_update_values = np.zeros((5,filter_shape[0]), dtype=theano.config.floatX)
# self.b_update = theano.shared(value=b_values, borrow=True)
#ipdb.set_trace()
conv_out = nnet.conv2d(
input=input,
filters=self.W,
filter_shape=filter_shape,
border_mode=border_mode)
#if poolsize:
if if_pool:
pooled_out = downsample.max_pool_2d(
input=conv_out,
ds=poolsize,
st=stride,
ignore_border=True)
tmp = pooled_out + self.b.dimshuffle('x', 0, 'x', 'x')
else:
tmp = conv_out + self.b.dimshuffle('x', 0, 'x', 'x')
# if act == ConvolutionLayer.ACT_TANH:
# self.output = T.tanh(tmp)
# elif act == ConvolutionLayer.ACT_SIGMOID:
# self.output = nnet.sigmoid(tmp)
# elif act == ConvolutionLayer.ACT_ReLu:
# self.output = tmp * (tmp>0)
# elif act == ConvolutionLayer.ACT_SoftPlus:
# self.output = T.log2(1+T.exp(tmp))
# else:
# self.output = tmp
if act == 'tanh':
self.output = T.tanh(tmp)
elif act == 'sigmoid':
self.output = nnet.sigmoid(tmp)
elif act == 'relu':
# self.output = tmp * (tmp>0)
# self.output = nnet.relu(tmp)
self.output = 0.5 * (tmp + abs(tmp)) + 1e-9
elif act == 'softplus':
# self.output = T.log2(1+T.exp(tmp))
self.output = nnet.softplus(tmp)
elif act == 'linear':
self.output = tmp
# store parameters of this layer
self.params = [self.W, self.b]
#EHA: parameter update- list of 5 previous updates
# self.params_update = [5*[self.W_update], 5*[self.b_update]]
self.deltas = [self.W_delta, self.b_delta]
def __init__(self, rng, input, signal_shape, filter_shape, poolsize=(2, 2, 2), stride=None, if_pool=False, if_hidden_pool=False,
act=None,
share_with=None,
tied=None,
border_mode='valid'):
self.input = input
if share_with:
self.W = share_with.W
self.b = share_with.b
self.W_delta = share_with.W_delta
self.b_delta = share_with.b_delta
elif tied:
self.W = tied.W.dimshuffle(1,0,2,3)
self.b = tied.b
self.W_delta = tied.W_delta.dimshuffle(1,0,2,3)
self.b_delta = tied.b_delta
else:
fan_in = np.prod(filter_shape[1:])
poolsize_size = np.prod(poolsize) if poolsize else 1
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) / poolsize_size)
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(
np.asarray(
rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX
),
borrow=True
)
b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
self.W_delta = theano.shared(
np.zeros(filter_shape, dtype=theano.config.floatX),
borrow=True
)
self.b_delta = theano.shared(value=b_values, borrow=True)
# convolution
conv_out = nnet.conv3d(
input,
filters=self.W,
input_shape=signal_shape,
filter_shape=filter_shape,
border_mode=border_mode)
#if poolsize:
if if_pool:
conv_out = conv_out.dimshuffle(0,2,1,3,4) #maxpool3d works on last 3 dimesnions
pooled_out = pools.pool_3d(
input=conv_out,
ds=poolsize,
ignore_border=True)
tmp_out = pooled_out.dimshuffle(0,2,1,3,4)
tmp = tmp_out + self.b.dimshuffle('x', 'x', 0, 'x', 'x')
elif if_hidden_pool:
pooled_out = pools.pool_2d(
input=conv_out,
ds=poolsize[:2],
st=stride,
ignore_border=True)
tmp = pooled_out + self.b.dimshuffle('x', 'x', 0, 'x', 'x')
else:
tmp = conv_out + self.b.dimshuffle('x', 'x', 0, 'x', 'x')
if act == 'tanh':
self.output = T.tanh(tmp)
elif act == 'sigmoid':
self.output = nnet.sigmoid(tmp)
elif act == 'relu':
# self.output = tmp * (tmp>0)
self.output = 0.5 * (tmp + abs(tmp)) + 1e-9
elif act == 'softplus':
# self.output = T.log2(1+T.exp(tmp))
self.output = nnet.softplus(tmp)
else:
self.output = tmp
self.get_activation = theano.function(
[self.input],
self.output,
updates=None,
name='get hidden activation')
# store parameters of this layer
self.params = [self.W, self.b]
self.deltas = [self.W_delta, self.b_delta]
def free_energy_given_h(self, h):
sigmoid_arg = self.input_to_v_from_h(h)
return (-T.dot(h, self.get_bh()) -
nnet.softplus(sigmoid_arg).sum())
def free_energy(self, v_sample):
''' Function to compute the free energy '''
wx_b = tensor.dot(v_sample, self.W) + self.hbias
vbias_term = tensor.dot(v_sample, self.vbias)
hidden_term = tensor.sum(nnet.softplus(wx_b), axis=1)
return -hidden_term - vbias_term
def free_energy_given_v(self, v):
sigmoid_arg = self.input_to_h_from_v(v)
return (-T.dot(v, self.get_bv()) -
nnet.softplus(sigmoid_arg).sum())
def __init__(self, rng, input, signal_shape, filter_shape, poolsize=(2, 2, 2), stride=None, if_pool=False, if_hidden_pool=False,
act=None,
share_with=None,
tied=None,
border_mode='valid'):
self.input = input
if share_with:
self.W = share_with.W
self.b = share_with.b
self.W_delta = share_with.W_delta
self.b_delta = share_with.b_delta
elif tied:
self.W = tied.W.dimshuffle(1,0,2,3)
self.b = tied.b
self.W_delta = tied.W_delta.dimshuffle(1,0,2,3)
self.b_delta = tied.b_delta
else:
fan_in = np.prod(filter_shape[1:])
poolsize_size = np.prod(poolsize) if poolsize else 1
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) / poolsize_size)
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(
np.asarray(
rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX
),
borrow=True
)
b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
self.W_delta = theano.shared(
np.zeros(filter_shape, dtype=theano.config.floatX),
borrow=True
)
self.b_delta = theano.shared(value=b_values, borrow=True)
# convolution
conv_out = conv3d2d.conv3d(
signals=input,
filters=self.W,
signals_shape=signal_shape,
filters_shape=filter_shape,
border_mode=border_mode)
#if poolsize:
if if_pool:
conv_out = conv_out.dimshuffle(0,2,1,3,4) #maxpool3d works on last 3 dimesnions
pooled_out = maxpool3d.max_pool_3d(
input=conv_out,
ds=poolsize,
ignore_border=True)
tmp_out = pooled_out.dimshuffle(0,2,1,3,4)
tmp = tmp_out + self.b.dimshuffle('x', 'x', 0, 'x', 'x')
elif if_hidden_pool:
pooled_out = downsample.max_pool_2d(
input=conv_out,
ds=poolsize[:2],
st=stride,
ignore_border=True)
tmp = pooled_out + self.b.dimshuffle('x', 'x', 0, 'x', 'x')
else:
tmp = conv_out + self.b.dimshuffle('x', 'x', 0, 'x', 'x')
if act == 'tanh':
self.output = T.tanh(tmp)
elif act == 'sigmoid':
self.output = nnet.sigmoid(tmp)
elif act == 'relu':
# self.output = tmp * (tmp>0)
self.output = 0.5 * (tmp + abs(tmp)) + 1e-9
elif act == 'softplus':
# self.output = T.log2(1+T.exp(tmp))
self.output = nnet.softplus(tmp)
else:
self.output = tmp
self.get_activation = theano.function(
[self.input],
self.output,
updates=None,
name='get hidden activation')
# store parameters of this layer
self.params = [self.W, self.b]
self.deltas = [self.W_delta, self.b_delta]
def theano_softplus(self, x):
return nets.softplus(x)
def free_energy(self, v_sample):
wx_b = tensor.dot(v_sample, self.W) + self.hbias
vbias_term = 0.5*tensor.sqr(v_sample - self.vbias).sum(axis=1)
hidden_term = nnet.softplus(wx_b).sum(axis=1)
return -hidden_term + vbias_term
def __init__(self, rng, input, filter_shape, image_shape, poolsize):
"""
Allocate a LeNetConvPoolLayer with shared variable internal parameters.
:type rng: np.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.dtensor4
:param input: symbolic image tensor, of shape image_shape
:type filter_shape: tuple or list of length 4
:param filter_shape: (number of filters, num input feature maps,
filter height, filter width)
:type image_shape: tuple or list of length 4
:param image_shape: (batch size, num input feature maps,
image height, image width)
:type poolsize: tuple or list of length 2
:param poolsize: the downsampling (pooling) factor (#rows, #cols)
"""
assert image_shape[1] == filter_shape[1]
self.input = input
self.filter_shape = filter_shape
# there are "num input feature maps * filter height * filter width"
# inputs to each hidden unit
fan_in = np.prod(filter_shape[1:])
# each unit in the lower layer receives a gradient from:
# "num output feature maps * filter height * filter width" /
# pooling size
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) //
np.prod(poolsize))
# initialize weights with random weights
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(
np.asarray(
rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX
),
borrow=True
)
# the bias is a 1D tensor -- one bias per output feature map
b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
# convolve input feature maps with filters
conv_out = conv2d(
input=input,
filters=self.W,
filter_shape=filter_shape,
input_shape=image_shape
)
# downsample each feature map individually, using maxpooling
pooled_out = downsample.max_pool_2d(
input=conv_out,
ds=poolsize,
ignore_border=True
)
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
self.output = softplus(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
# store parameters of this layer
self.params = [self.W, self.b]
# keep track of model input
self.input = input
def free_energy_given_h(self, h):
sigmoid_arg = self.input_to_v_from_h(h)
return (-T.dot(h, self.get_bh()) - nnet.softplus(sigmoid_arg).sum())
def __init__(self,
rng,
input,
filter_shape,
poolsize=(2, 2),
stride=None,
if_pool=False,
act=None,
share_with=None,
tied=None,
border_mode='valid'):
self.input = input
if share_with:
self.W = share_with.W
self.b = share_with.b
self.W_delta = share_with.W_delta
self.b_delta = share_with.b_delta
elif tied:
self.W = tied.W.dimshuffle(1, 0, 2, 3)
self.b = tied.b
self.W_delta = tied.W_delta.dimshuffle(1, 0, 2, 3)
self.b_delta = tied.b_delta
else:
fan_in = np.prod(filter_shape[1:])
poolsize_size = np.prod(poolsize) if poolsize else 1
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) /
poolsize_size)
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(np.asarray(rng.uniform(low=-W_bound,
high=W_bound,
size=filter_shape),
dtype=theano.config.floatX),
borrow=True)
b_values = np.zeros((filter_shape[0], ),
dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
self.W_delta = theano.shared(np.zeros(filter_shape,
dtype=theano.config.floatX),
borrow=True)
# b_update_values = np.zeros((5,filter_shape[0]), dtype=theano.config.floatX)
self.b_delta = theano.shared(value=b_values, borrow=True)
#EHA: define update history for momentum gradient
# self.W_update = theano.shared(
# np.zeros(filter_shape, dtype=theano.config.floatX),
# borrow=True
# )
#
# # b_update_values = np.zeros((5,filter_shape[0]), dtype=theano.config.floatX)
# self.b_update = theano.shared(value=b_values, borrow=True)
#ipdb.set_trace()
conv_out = nnet.conv2d(input=input,
filters=self.W,
filter_shape=filter_shape,
border_mode=border_mode)
#if poolsize:
if if_pool:
pooled_out = downsample.max_pool_2d(input=conv_out,
ds=poolsize,
st=stride,
ignore_border=True)
tmp = pooled_out + self.b.dimshuffle('x', 0, 'x', 'x')
else:
tmp = conv_out + self.b.dimshuffle('x', 0, 'x', 'x')
# if act == ConvolutionLayer.ACT_TANH:
# self.output = T.tanh(tmp)
# elif act == ConvolutionLayer.ACT_SIGMOID:
# self.output = nnet.sigmoid(tmp)
# elif act == ConvolutionLayer.ACT_ReLu:
# self.output = tmp * (tmp>0)
# elif act == ConvolutionLayer.ACT_SoftPlus:
# self.output = T.log2(1+T.exp(tmp))
# else:
# self.output = tmp
if act == 'tanh':
self.output = T.tanh(tmp)
elif act == 'sigmoid':
self.output = nnet.sigmoid(tmp)
elif act == 'relu':
# self.output = tmp * (tmp>0)
# self.output = nnet.relu(tmp)
self.output = 0.5 * (tmp + abs(tmp)) + 1e-9
elif act == 'softplus':
# self.output = T.log2(1+T.exp(tmp))
self.output = nnet.softplus(tmp)
elif act == 'linear':
self.output = tmp
# store parameters of this layer
self.params = [self.W, self.b]
#EHA: parameter update- list of 5 previous updates
# self.params_update = [5*[self.W_update], 5*[self.b_update]]
self.deltas = [self.W_delta, self.b_delta]
