def keep_max(input, theta, k, sent_mask):
sig_input = T.nnet.sigmoid(T.dot(input, theta))
sent_mask = sent_mask.dimshuffle(0, 'x', 1, 'x')
sig_input = sig_input * sent_mask
#sig_input = T.dot(input, theta)
if k == 0:
result = input * T.addbroadcast(sig_input, 3)
return result, sig_input
# get the sorted idx
sort_idx = T.argsort(sig_input, axis=2)
k_max_ids = sort_idx[:,:,-k:,:]
dim0, dim1, dim2, dim3 = k_max_ids.shape
batchids = T.repeat(T.arange(dim0), dim1*dim2*dim3)
mapids = T.repeat(T.arange(dim1), dim2*dim3).reshape((1, dim2*dim3))
mapids = T.repeat(mapids, dim0, axis=0).flatten()
rowids = k_max_ids.flatten()
colids = T.arange(dim3).reshape((1, dim3))
colids = T.repeat(colids, dim0*dim1*dim2, axis=0).flatten()
sig_mask = T.zeros_like(sig_input)
choosed = sig_input[batchids, mapids, rowids, colids]
sig_mask = T.set_subtensor(sig_mask[batchids, mapids, rowids, colids], 1)
input_mask = sig_mask * sig_input
result = input * T.addbroadcast(input_mask, 3)
return result, sig_input
def fprop(self, x, mode='train'):
if mode == 'test':
# this is for use during test/validation time
x_avg = self.params.getParameter('x_avg')
elif mode == 'calculate':
x_avg = x.mean(self.norm_axis, keepdims=True)
elif mode == 'train':
# otherwise calculate the batch mean and std
x_avg = x.mean(self.norm_axis, keepdims=True)
# the following trick is learend from lasagne implementation
running_mean = theano.clone(self.params.getParameter('x_avg'), share_inputs=False)
running_mean_udpate = ((1 - self.alpha) * running_mean
+self.alpha * x_avg)
# set a default update for them
running_mean.default_update = running_mean_udpate
x_avg += 0 * running_mean
else:
raise "mode can only take ['train', 'test', 'calculate']"
self.x_avg = x_avg
x_avg = T.addbroadcast(x_avg, *self.norm_axis)
beta = T.addbroadcast(self.params.getParameter('beta'), *self.norm_axis)
bn_x = x / (x_avg + 1e-18) * beta
return bn_x if self.actFunc is None else self.actFunc(bn_x)
# End BatchExpNormLayer
#-------------------------------------------------------------------------------
def local_contrast_normalize(X, window, img_shape):
"""Return normalized X and the convolution transform
"""
batchsize, channels, R, C = img_shape
assert window.shape[0] == 1
assert window.shape[1] == channels
N = window.shape[2]
assert window.shape[3] == N
blur = tlinear.Conv2d(
filters=sharedX(window, 'LCN_window'),
img_shape=img_shape,
border_mode='full')
N2 = N//2
# remove global mean
X = X - X.mean(axis=[1, 2, 3]).dimshuffle(0, 'x', 'x', 'x')
#remove local mean
blurred_x = tensor.addbroadcast(blur.lmul(X), 1)
x2c = X - blurred_x[:, :, N2:R + N2, N2:C + N2]
# standardize contrast
blurred_x2c_sqr = tensor.addbroadcast(blur.lmul(x2c ** 2), 1)
x2c_lcn = x2c / tensor.sqrt((10 + blurred_x2c_sqr[:, :, N2:R + N2, N2:C + N2]))
return x2c_lcn, blur
def keep_max(input, theta, k):
"""
:type input: theano.tensor.tensor4
:param input: the input data
:type theta: theano.tensor.matrix
:param theta: the parameter for sigmoid function
:type k: int
:param k: the number k used to define top k sentence to remain
"""
sig_input = T.nnet.sigmoid(T.dot(input, theta))
if k == 0: # using all the sentences
result = input * T.addbroadcast(sig_input, 3)
return result, sig_input
# get the sorted idx
sort_idx = T.argsort(sig_input, axis=2)
k_max_ids = sort_idx[:,:,-k:,:]
dim0, dim1, dim2, dim3 = k_max_ids.shape
batchids = T.repeat(T.arange(dim0), dim1*dim2*dim3)
mapids = T.repeat(T.arange(dim1), dim2*dim3).reshape((1, dim2*dim3))
mapids = T.repeat(mapids, dim0, axis=0).flatten()
rowids = k_max_ids.flatten()
colids = T.arange(dim3).reshape((1, dim3))
colids = T.repeat(colids, dim0*dim1*dim2, axis=0).flatten()
# construct masked data
sig_mask = T.zeros_like(sig_input)
choosed = sig_input[batchids, mapids, rowids, colids]
sig_mask = T.set_subtensor(sig_mask[batchids, mapids, rowids, colids], 1)
input_mask = sig_mask * sig_input
result = input * T.addbroadcast(input_mask, 3)
return result, sig_input
def get_output_for(self, input, deterministic=False, **kwargs):
if deterministic:
# use stored mean and std
mean = self.mean
std = self.std
else:
# use this batch's mean and std
mean = input.mean(self.axes, keepdims=True)
std = input.std(self.axes, keepdims=True)
# and update the stored mean and std:
# we create (memory-aliased) clones of the stored mean and std
running_mean = theano.clone(self.mean, share_inputs=False)
running_std = theano.clone(self.std, share_inputs=False)
# set a default update for them
running_mean.default_update = (1 - self.alpha) * running_mean + self.alpha * mean
running_std.default_update = (1 - self.alpha) * running_std + self.alpha * std
# and include them in the graph so their default updates will be
# applied (although the expressions will be optimized away later)
mean += 0 * running_mean
std += 0 * running_std
std += self.epsilon
mean = T.addbroadcast(mean, *self.axes)
std = T.addbroadcast(std, *self.axes)
beta = T.addbroadcast(self.beta, *self.axes)
gamma = T.addbroadcast(self.gamma, *self.axes)
normalized = (input - mean) * (gamma / std) + beta
return self.nonlinearity(normalized)
def _ct(self, other):
''' Helper function to make tensors dimensions compatible'''
if (other.var_set == self.var_set):
return (self.pt_tensor, other.pt_tensor)
union_var_set = other.scope.union(self.scope)
vidx1 = frozenset(self.var_indices)
vidx2 = frozenset(other.var_indices)
union_indices = vidx1.union(vidx2)
shape1 = []
shape2 = []
b1 = []
b2 = []
u1 = []
u2 = []
for i,vidx in enumerate(sorted(union_indices)):
if (vidx in vidx1):
shape1.append(self.discrete_pgm.cardinalities[vidx])
u1.append(i)
else:
shape1.append(1)
b1.append(i)
if (vidx in vidx2):
shape2.append(self.discrete_pgm.cardinalities[vidx])
u2.append(i)
else:
shape2.append(1)
b2.append(i)
t1 = T.addbroadcast(T.unbroadcast(self.pt_tensor.reshape(shape1, len(shape1)), *u1), *b1)
t2 = T.addbroadcast(T.unbroadcast(other.pt_tensor.reshape(shape2, len(shape2)), *u2), *b2)
return (t1, t2)
def output(self, input):
if self.unflatten_input != None:
input = T.reshape(input, self.unflatten_input)
W_shuffled = self.W.val.dimshuffle(3, 0, 1, 2) # c01b to bc01
conv_out = dnn.dnn_conv(img=input,
kerns=W_shuffled,
subsample=(self.convstride, self.convstride),
border_mode=self.padsize)
conv_out = conv_out + self.b.val.dimshuffle('x', 0, 'x', 'x')
if self.batch_norm:
conv_out = (conv_out - T.mean(conv_out, axis = (0,2,3), keepdims = True)) / (1.0 + T.std(conv_out, axis=(0,2,3), keepdims = True))
conv_out = conv_out * T.addbroadcast(self.bn_std,0,2,3) + T.addbroadcast(self.bn_mean, 0,2,3)
self.out_store = conv_out
if self.activation == "relu":
self.out = T.maximum(0.0, conv_out)
elif self.activation == "tanh":
self.out = T.tanh(conv_out)
elif self.activation == None:
self.out = conv_out
#if self.residual:
# print "USING RESIDUAL"
# self.out += input
return self.out
def output(self, input_raw):
if self.flatten_input:
input = input_raw.flatten(2)
else:
input = input_raw
lin_output = T.dot(input, self.W) + self.b
if self.batch_norm:
lin_output = (lin_output - T.mean(lin_output, axis = 0, keepdims = True)) / (1.0 + T.std(lin_output, axis = 0, keepdims = True))
lin_output = (lin_output * T.addbroadcast(self.bn_std,0) + T.addbroadcast(self.bn_mean,0))
self.out_store = lin_output
if self.activation == None:
activation = lambda x: x
elif self.activation == "relu":
activation = lambda x: T.maximum(0.0, x)
elif self.activation == "exp":
activation = lambda x: T.exp(x)
elif self.activation == "tanh":
activation = lambda x: T.tanh(x)
elif self.activation == 'softplus':
activation = lambda x: T.nnet.softplus(x)
else:
raise Exception("Activation not found")
out = activation(lin_output)
#if self.residual:
# return out + input_raw
#else:
# return out
return out
def Softmax(x, temp = 1):
"""
Softmax Units.
Applies row-wise softmax to the input supplied.
Args:
x: could be a ``theano.tensor`` or a ``theano.shared`` or ``numpy`` arrays or
``python lists``.
temp: temperature of type ``float``. Mainly used during distillation, normal
softmax prefer ``T=1``.
Notes:
Refer [3] for details.
.. [#] Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. "Distilling the knowledge in
a neural network." arXiv preprint arXiv:1503.02531 (2015).
Returns:
same as input: returns a row-wise softmax output of the same shape as the input.
"""
if temp != 1:
expo = T.exp(x / float(temp)) # at this moment this is mini_batch_size X num_classes.
normalizer = T.sum(expo,axis=1,keepdims=True) # at this moment this is mini_batch_size X 1.
T.addbroadcast(normalizer,1)
return expo / normalizer
else:
return T.nnet.softmax(x)
def output(self, input):
W_shuffled = self.W.dimshuffle(3, 0, 1, 2) # c01b to bc01
print "input ndim", input.ndim
conv_out = dnn.dnn_conv(img=input,
kerns=W_shuffled,
subsample=(self.stride, self.stride),
border_mode=self.padsize)
conv_out = conv_out + self.b.dimshuffle('x', 0, 'x', 'x')
if self.batch_norm:
conv_out = (conv_out - T.mean(conv_out, axis = (0,2,3), keepdims = True)) / (1.0 + T.std(conv_out, axis=(0,2,3), keepdims = True))
conv_out = conv_out * T.addbroadcast(self.bn_std,0,2,3) + T.addbroadcast(self.bn_mean, 0,2,3)
self.out_store = conv_out
if self.activation == "relu":
self.out = T.maximum(0.0, conv_out)
elif self.activation == "tanh":
self.out = T.tanh(conv_out)
elif self.activation == None:
self.out = conv_out
return T.specify_shape(self.out, (self.batch_size, self.out_channels, self.in_length / self.stride, self.in_length / self.stride))
def output(self, input):
if self.unflatten_input != None:
input = T.reshape(input, self.unflatten_input)
conv_out = deconv(input, self.W, subsample=(2, 2), border_mode=(2,2))
conv_out = conv_out + self.b.dimshuffle('x', 0, 'x', 'x')
if self.batch_norm:
conv_out = (conv_out - conv_out.mean(axis = (0,2,3), keepdims = True)) / (1.0 + conv_out.std(axis = (0,2,3), keepdims = True))
conv_out = conv_out * T.addbroadcast(self.bn_std,0,2,3) + T.addbroadcast(self.bn_mean,0,2,3)
if self.activation == "relu":
out = T.maximum(0.0, conv_out)
elif self.activation == "tanh":
out = T.tanh(conv_out)
elif self.activation == None:
out = conv_out
else:
raise Exception()
self.params = {'W' : self.W, 'b' : self.b}
if self.batch_norm:
self.params["mu"] = self.bn_mean
self.params["sigma"] = self.bn_std
return out
def resample_step(self): idx=self.theano_rng.multinomial(pvals=T.reshape(self.weights_now,(1,self.npcl))).T s_samp=T.sum(self.s_now*T.addbroadcast(idx,1),axis=0) h_samp=T.sum(self.h_now*T.addbroadcast(idx,1),axis=0) return T.cast(s_samp,'float32'), T.cast(h_samp,'float32')
def get_state(self):
st = super(LatentTypeWithTuningCurve, self).get_state()
# The filters are non-identifiable as we can negate both the
# temporal and the spatial filters and get the same net effect.
# By convention, choose the sign that results in the most
# positive temporal filter.
sign = T.sgn(T.sum(self.stim_resp_t, axis=0))
T.addbroadcast(sign, 0)
# Similarly, we can trade a constant between the spatial and temporal
# pieces. By convention, set the temporal filter to norm 1.
Z = T.sqrt(T.sum(self.stim_resp_t**2, axis=0))
T.addbroadcast(Z, 0)
# Compute the normalized temporal response
stim_resp_t = sign*(1.0/Z)*self.stim_resp_t
# Finally, reshape the spatial component as necessary
if self.spatial_ndim == 2:
stim_resp_x = sign*Z*self.stim_resp_x
stim_resp_x = T.reshape(stim_resp_x,
self.spatial_shape + (self.R,))
else:
stim_resp_x = sign*Z*self.stim_resp_x
st.update({'stim_response_x' : stim_resp_x,
'stim_response_t' : stim_resp_t})
return st
def log_p(self, L):
""" Compute log prob of the given value under this prior
Input: L ~ NxD
"""
assert L.ndim == 2, "L must be 2d!"
# Compute pairwise L2 norm
L1 = L.dimshuffle(0,'x',1) # Nx1xD
L2 = L.dimshuffle('x',0,1) # 1xNxD
T.addbroadcast(L1,1)
T.addbroadcast(L2,0)
# Compute pairwise distances
D = ((L1-L2)**2).sum(axis=2)
# Compute the kernel
K = T.exp(-D / self.sigma**2)
# Log prob is the log determinant of the pairwise distances
lp_det = T.log(self.d(K))
# Also multiply by a spherical Gaussian with standard deviation of 'bound'
# to prevent points from diverging to infinity
lp_gauss = self.gaussian.log_p(L)
return lp_det + lp_gauss
def embed(self,x, y, kth):
hidden = self.hidden_k(x,self.superw,self.dicw, kth)
size = y.ndim
y = T.addbroadcast(y,size - 1)
embedding = T.sum(hidden*y,0)/T.addbroadcast(T.cast(T.sum(y,0), 'int16'), size - 2)
return embedding
def __Theano_build__(self):
Td = T.tensor3('Td')
Ty = T.ivector('Ty')
Tlr = T.scalar('Tlr')
#Talpha = T.TensorType(dtype='float32', broadcastable=(0, 1, 1))('alpha')
A = theano.shared(np.ones((self.D.shape[0]))\
.astype('float32').reshape(-1, 1, 1), 'A')
Ttriple = [T.ivector('triple'+x) for x in ['i', 'j', 'l']]
Tneighbor = [T.ivector('neighbor'+x) for x in ['i', 'j']]
d = (Td * T.addbroadcast(A, 1, 2)).sum(0)
pull_error, _ = theano.scan(
fn = lambda i, j, d: d[i, j],
sequences=[Tneighbor[0], Tneighbor[1]],
outputs_info=None,
non_sequences=[d])
pull_error = pull_error.sum()
push_error, _ = theano.scan(
fn = lambda i, j, l, d: T.neq(Ty[i], Ty[l]) * T.maximum((d[i]-d[j]) - (d[i]-d[l]) +1, 0),
sequences=[Ttriple[0], Ttriple[1], Ttriple[2]],
outputs_info=None,
non_sequences=[d])
# zerocount = T.eq(linalg.diag(mask*T.maximum(lossij - lossil + 1, 0)), 0).sum()
error = pull_error.sum() + push_error.sum()
grad = T.grad(error, A)
newA = A - Tlr*grad #T.maximum(A - Tlr*grad, 0)
updates = [(A, newA/newA.sum())]
self.Ttrain = theano.function(Ttriple+Tneighbor+[Tlr],
Tlr*grad,
givens={Td: self.D,
Ty: self.y},
updates=updates,
allow_input_downcast=True,
on_unused_input='warn')
self.Tloss = theano.function(Ttriple+Tneighbor,
error,
givens={Td: self.D, Ty: self.y},
allow_input_downcast=True)
# eig, eigv = linalg.eig((d+d.T)/2.0)
# self.Tmineig = theano.function([],
# T.min(eig),
# givens={Td: self.D},
# allow_input_downcast=True)
self.Tmindist = theano.function([],
T.min(d),
givens={Td: self.D},
allow_input_downcast=True)
self.Ttransform = theano.function([Td], (Td*T.addbroadcast(A, 1, 2)).sum(0), allow_input_downcast=True)
self.TA = A
def __init__(self, model):
self.model = model
self.imp_model = model['impulse']
# Number of presynaptic neurons
self.N = model['N']
# Get parameters of the prior
self.alpha = self.imp_model['alpha']
# Create a basis for the impulse responses response
self.basis = create_basis(self.imp_model['basis'])
(_,self.B) = self.basis.shape
# The basis is interpolated once the data is specified
self.initialize_basis()
# Initialize memory for the filtered spike train
self.ir = theano.shared(name='ir',
value=np.zeros((1,self.N,self.B)))
# Define Dirichlet distributed weights by normalizing gammas
# The variables are log-gamma distributed
self.lng = T.dvector('w_lng')
self.g = T.exp(self.lng)
self.g2 = T.reshape(self.g, [self.N,self.B])
self.g_sum = T.reshape(T.sum(self.g2, axis=1), [self.N,1])
# Normalize the gammas to get a Dirichlet draw
T.addbroadcast(self.g_sum, 1)
self.w_ir2 = self.g2 / self.g_sum
self.w_ir2.name = 'w_ir'
# Repeat them (in a differentiable manner) to create a 3-tensor
self.w_ir3 = T.reshape(self.w_ir2, [1,self.N,self.B])
# Make w_ir3 broadcastable in the 1st dim
T.addbroadcast(self.w_ir3,0)
# Take the elementwise product of the filtered stimulus and
# the repeated weights to get the weighted impulse current along each
# impulse basis dimension. Then sum over bases to get the
# total coupling current from each presynaptic neurons at
# all time points
self.I_imp = T.sum(self.ir*self.w_ir3, axis=2)
# Log probability of a set of independent log-gamma r.v.'s
# This is log p(log(g)) under the prior. Since we are taking the
# log, we multiply by a factor of g to ensure normalization and
# thus the \alpha-1 in the exponent becomes \alpha
self.log_p = -self.B*self.N*scipy.special.gammaln(self.alpha) \
+ T.sum(self.alpha*self.lng) \
- T.sum(self.g)
# Define a helper variable for the impulse response
# after projecting onto the basis
self.impulse = T.dot(self.w_ir2, T.transpose(self.ibasis))
def __init__(self, model, latent):
""" Initialize the stochastic block model for the adjacency matrix
"""
self.model = model
self.prms = model['network']['graph']
self.N = model['N']
self.N_dims = self.prms['N_dims']
# Get the latent location
self.location = latent[self.prms['locations']]
self.Lm = self.location.Lm
# self.location_prior = create_prior(self.prms['location_prior'])
#
# # Latent distance model has NxR matrix of locations L
# self.L = T.dvector('L')
# self.Lm = T.reshape(self.L, (self.N, self.N_dims))
# Compute the distance between each pair of locations
# Reshape L into a Nx1xD matrix and a 1xNxD matrix, then add the requisite
# broadcasting in order to subtract the two matrices
L1 = self.Lm.dimshuffle(0,'x',1) # Nx1xD
L2 = self.Lm.dimshuffle('x',0,1) # 1xNxD
T.addbroadcast(L1,1)
T.addbroadcast(L2,0)
#self.D = T.sqrt(T.sum((L1-L2)**2, axis=2))
#self.D = T.sum((L1-L2)**2, axis=2)
# It seems we need to use L1 norm for now because
# Theano doesn't properly compute the gradients of the L2
# norm. (It gives NaNs because it doesn't realize that some
# terms will cancel out)
# self.D = (L1-L2).norm(1, axis=2)
self.D = T.pow(L1-L2,2).sum(axis=2)
# There is a distance scale, \delta
self.delta = T.dscalar(name='delta')
# Define complete adjacency matrix
self.A = T.bmatrix('A')
# The probability of A is exponentially decreasing in delta
# self.pA = T.exp(-1.0*self.D/self.delta)
self.pA = T.exp(-0.5*self.D/self.delta**2)
if 'rho_refractory' in self.prms:
self.pA += T.eye(self.N) * (self.prms['rho_refractory']-self.pA)
# self.pA[np.diag_indices(self.N)] = self.prms['rho_refractory']
# Allow for scaling the log likelihood of the graph so that we can do
# Annealed importance sampling
self.lkhd_scale = theano.shared(value=1.0, name='lkhd_scale')
# Define log probability
self.lkhd = T.sum(self.A * T.log(self.pA) + (1 - self.A) * T.log(1 - self.pA))
# self.log_p = self.lkhd_scale * self.lkhd + self.location_prior.log_p(self.Lm)
self.log_p = self.lkhd_scale * self.lkhd
def get_t_weights(self, t):
"""
Generate vector of weights allowing selection of current timestep.
(if t is not an integer, the weights will linearly interpolate)
"""
n_seg = self.trajectory_length
t_compare = T.arange(n_seg, dtype=theano.config.floatX).reshape((1,n_seg))
diff = abs(T.addbroadcast(t,1) - T.addbroadcast(t_compare,0))
t_weights = T.max(T.join(1, (-diff+1).reshape((n_seg,1)), T.zeros((n_seg,1))), axis=1)
return t_weights.reshape((-1,1))
def __init__(self, input, n_filt, n_in, n_out, y, hist_len, y_len):
""" Initialize the parameters of the poisson regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
#self.W = theano.shared(value=numpy.identity(n_in, dtype=theano.config.floatX), name='W', borrow=True)
self.W = theano.shared(value=numpy.tile([-1, -1, -1, -1, 1, 1, 1, 1, 1]*numpy.ones((n_in,), dtype=theano.config.floatX)/n_filt, (1,n_filt,1) ).astype(theano.config.floatX), name='W', borrow=True)
#self.W = theano.shared(value=numpy.concatenate((-1*numpy.ones((4,)),numpy.ones((5,)),-1*numpy.ones((4,)),numpy.ones((5,))))*numpy.ones((n_in,), dtype=theano.config.floatX), name='W', borrow=True)
#self.W = theano.shared(value=.001*numpy.ones((n_in,), dtype=theano.config.floatX), name='W', borrow=True)
# initialize the baises b as a vector of n_out 0s
self.b = theano.shared(value=-1*numpy.ones((n_out,), dtype=theano.config.floatX), name='b', borrow=True)
self.h = theano.shared(value=numpy.zeros((hist_len,n_out), dtype=theano.config.floatX), name='h', borrow=True)
# helper variables for adagrad
self.b_helper = theano.shared(value=numpy.zeros((n_out,), \
dtype=theano.config.floatX), name='b_helper', borrow=True)
self.W_helper = theano.shared(value=numpy.tile(numpy.zeros((n_in,), \
dtype=theano.config.floatX), (1,n_filt,1) ), name='W_helper', borrow=True)
self.h_helper = theano.shared(value=numpy.zeros((hist_len,n_out), dtype=theano.config.floatX), name='h_helper', borrow=True)
# helper variables for L1
self.b_helper2 = theano.shared(value=numpy.zeros((n_out,), \
dtype=theano.config.floatX), name='b_helper2', borrow=True)
self.W_helper2 = theano.shared(value=numpy.tile(numpy.zeros((n_in,), \
dtype=theano.config.floatX), (1,n_filt,1) ), name='W_helper2', borrow=True)
self.h_helper2 = theano.shared(value=numpy.zeros((hist_len,n_out), dtype=theano.config.floatX), name='h_helper', borrow=True)
# parameters of the model
self.params = [self.W, self.b, self.h]
self.params_helper = [self.W_helper, self.b_helper, self.h_helper]
self.params_helper2 = [self.W_helper2, self.b_helper2, self.h_helper2]
#history dependent input
self.h_in = theano.shared(value=numpy.zeros((y_len,n_out), dtype=theano.config.floatX), borrow=True)
for hi in xrange(hist_len):
self.h_in = T.set_subtensor(self.h_in[(1+hi):y_len], self.h_in[(1+hi):y_len] + T.addbroadcast(T.shape_padleft(self.h[hi,:],n_ones=1),0)*y[0:(y_len-(hi+1))])
# compute vector of expected values (for each output) in symbolic form
self.E_y_given_x = T.log(1+T.exp(T.sum(input*T.addbroadcast(self.W,0), axis=1) + self.b + self.h_in)) #sums over multiple filters
self.input_responses = T.sum(input*T.addbroadcast(self.W,0), axis=1) + self.b #sums over multiple filters
def mulclassloss(self,kth,x,y,label):
#mutiple label classification loss using wikidata for pretrain
hidden = self.hidden_k(x,self.w,self.dicw,kth)
print "hidden type : "+str(hidden.type)
size = y.ndim
y = T.addbroadcast(y,size - 1)
embedding = T.sum(hidden*y,0)/T.addbroadcast(T.cast(T.sum(y,0), 'int16'), size - 2)
#embedding = T.sum(hidden*y,0)/ T.addbroadcast(T.sum(y,0), size-2)
print "embedding type : "+str(embedding.type)
logloss = (0. - T.sum(T.log(1. / (1. + T.exp(0. - (T.dot(embedding, self.w["mulw"])+self.w["mulb"])*label)))))/embedding.shape[0]
return logloss
def node_update(self, s_, h_, m_) :
"""
Update params in Attention.
"""
preact = tensor.dot(h_, self.params[self._p(self.prefix, 'U')])
preact += tensor.addbroadcast(tensor.dot(s_, self.params[self._p(self.prefix, 'W')]).dimshuffle('x', 0, 1), 0)
preact = tensor.dot(tensor.tanh(preact), self.params[self._p(self.prefix, 'va')]) * m_
alpha = tensor.nnet.softmax(preact.dimshuffle(1, 0)).dimshuffle(1, 0, 'x')
c = (h_ * tensor.addbroadcast(alpha, 2)).sum(axis=0) # c is (samples,2*hidden)
return c, alpha
def test_dnn_batchnorm_train():
if not dnn.dnn_available(test_ctx_name):
raise SkipTest(dnn.dnn_available.msg)
if dnn.version(raises=False) < 5000:
raise SkipTest("batch normalization requires cudnn v5+")
utt.seed_rng()
for mode in ('per-activation', 'spatial'):
for vartype in (T.ftensor4, T.ftensor3, T.fmatrix, T.fvector):
x, scale, bias = (vartype(n) for n in ('x', 'scale', 'bias'))
ndim = x.ndim
eps = 5e-3 # some non-standard value to test if it's used
# forward pass
out, x_mean, x_invstd = dnn.dnn_batch_normalization_train(
x, scale, bias, mode, eps)
# reference forward pass
if mode == 'per-activation':
axes = (0,)
elif mode == 'spatial':
axes = (0,) + tuple(range(2, ndim))
x_mean2 = x.mean(axis=axes, keepdims=True)
x_invstd2 = T.inv(T.sqrt(x.var(axis=axes, keepdims=True) + eps))
scale2 = T.addbroadcast(scale, *axes)
bias2 = T.addbroadcast(bias, *axes)
out2 = (x - x_mean2) * (scale2 * x_invstd2) + bias2
# backward pass
dy = vartype('dy')
grads = T.grad(None, wrt=[x, scale, bias], known_grads={out: dy})
# reference backward pass
grads2 = T.grad(None, wrt=[x, scale, bias], known_grads={out2: dy})
# compile
f = theano.function([x, scale, bias, dy],
[out, x_mean, x_invstd, out2, x_mean2, x_invstd2] +
grads + grads2, mode=mode_with_gpu)
# run
for data_shape in ((10, 20, 30, 40), (4, 3, 1, 1), (1, 1, 5, 5)):
data_shape = data_shape[:ndim]
param_shape = tuple(1 if d in axes else s
for d, s in enumerate(data_shape))
X = 4 + 3 * numpy.random.randn(*data_shape).astype('float32')
Dy = -1 + 2 * numpy.random.randn(*data_shape).astype('float32')
Scale = numpy.random.randn(*param_shape).astype('float32')
Bias = numpy.random.randn(*param_shape).astype('float32')
outputs = f(X, Scale, Bias, Dy)
# compare outputs
utt.assert_allclose(outputs[0], outputs[0 + 3]) # out
utt.assert_allclose(outputs[1], outputs[1 + 3]) # mean
utt.assert_allclose(outputs[2], outputs[2 + 3]) # invstd
# compare gradients
utt.assert_allclose(outputs[6], outputs[6 + 3]) # dx
utt.assert_allclose(outputs[7], outputs[7 + 3], rtol=3e-3) # dscale
utt.assert_allclose(outputs[8], outputs[8 + 3]) # dbias
def multi_dim_softmax(X): """ compute a softmax for a filter_map at each point X : a 4d tensor (batch, feature_map, x, y) returns a 4d tensor (batch, softmax, x, y) """ maxs = X.max(axis=1, keepdims=True) maxs = tensor.addbroadcast(maxs, 1) exps = tensor.exp(X-maxs) sums = exps.sum(axis=1, keepdims=True) sums = tensor.addbroadcast(sums, 1) return exps/sums
def objective(self, x):
# first, reshape x into a set of parameters we need
i, W, b = 0, [], []
for shape in self.layer_shapes:
l = np.prod(shape)
W.append(x[:, i:i+l].reshape((n_batch,)+shape))
i += l
l = shape[1]
b.append(x[:, i:i+l].reshape((n_batch, 1, l)))
# calculate the cost
z = T.tile(self.mini_batch.reshape((1, 50, 784)), (20, 1, 1))
for wi, bi in zip(W, b):
z = T.nnet.sigmoid(T.batched_dot(z, wi) + T.addbroadcast(bi,1))
return T.mean((z-T.addbroadcast(T.extra_ops.to_one_hot(self.classes, 10).reshape((1, 50, 10)),0))**2, axis=2)
def get_output_for(self, input, deterministic=False, collect=False,
**kwargs):
if collect:
# use this batch's mean and var
if self.stat_indices is None:
mean = input.mean(self.axes, keepdims=True)
var = input.var(self.axes, keepdims=True)
else:
mean = input[self.stat_indices].mean(self.axes, keepdims=True)
var = input[self.stat_indices].var(self.axes, keepdims=True)
# and update the stored mean and var:
# we create (memory-aliased) clones of the stored mean and var
running_mean = theano.clone(self.mean, share_inputs=False)
running_var = theano.clone(self.var, share_inputs=False)
# set a default update for them
if self.alpha is not 'single_pass':
running_mean.default_update = (
(1 - self.alpha) * running_mean + self.alpha * mean)
running_var.default_update = (
(1 - self.alpha) * running_var + self.alpha * var)
else:
print "Collecting using single pass..."
# this is ugly figure out what can be safely removed...
running_mean.default_update = (0 * running_mean + 1.0 * mean)
running_var.default_update = (0 * running_var + 1.0 * var)
# and include them in the graph so their default updates will be
# applied (although the expressions will be optimized away later)
mean += 0 * running_mean
var += 0 * running_var
elif deterministic:
# use stored mean and var
mean = self.mean
var = self.var
else:
# use this batch's mean and var
mean = input.mean(self.axes, keepdims=True)
var = input.var(self.axes, keepdims=True)
mean = T.addbroadcast(mean, *self.axes)
var = T.addbroadcast(var, *self.axes)
normalized = (input - mean) / T.sqrt(var + self.epsilon)
if self.return_stats:
return [normalized, mean, var]
else:
return normalized
def get_weights(self, h_t, w_tm1, M_t, **kwargs):
batch_size = self.heads[0].input_shape[0] # QKFIX: Get the size of the batches from the 1st head
num_heads = len(self.heads)
k_t = self.nonlinearity_key(T.dot(h_t, self.W_hid_to_key) + self.b_hid_to_key)
beta_t = self.nonlinearity_beta(T.dot(h_t, self.W_hid_to_beta) + self.b_hid_to_beta)
g_t = self.nonlinearity_gate(T.dot(h_t, self.W_hid_to_gate) + self.b_hid_to_gate)
# QKFIX: If the nonlinearity is softmax (which is usually the case), then the activations
# need to be reshaped (T.nnet.softmax only accepts 2D inputs)
try:
s_t = self.nonlinearity_shift(T.dot(h_t, self.W_hid_to_shift) + self.b_hid_to_shift)
except ValueError:
shift_activation_t = T.dot(h_t, self.W_hid_to_shift) + self.b_hid_to_shift
s_t = self.nonlinearity_shift(shift_activation_t.reshape((h_t.shape[0] * num_heads, self.num_shifts)))
s_t = s_t.reshape(shift_activation_t.shape)
gamma_t = self.nonlinearity_gamma(T.dot(h_t, self.W_hid_to_gamma) + self.b_hid_to_gamma)
# Content Addressing (3.3.1)
beta_t = T.addbroadcast(beta_t, 2)
betaK = beta_t * similarities.cosine_similarity(k_t, M_t)
w_c = lasagne.nonlinearities.softmax(betaK.flatten(ndim=2))
w_c = w_c.reshape(betaK.shape)
# Interpolation (3.3.2)
g_t = T.addbroadcast(g_t, 2)
w_g = g_t * w_c + (1. - g_t) * w_tm1
# Convolutional Shift (3.3.2)
# NOTE: This library is using a flat (zero-padded) convolution instead of the circular
# convolution from the original paper. In practice, this change has a minimal impact.
w_g_padded = w_g.reshape((h_t.shape[0] * num_heads, self.memory_shape[0])).dimshuffle(0, 'x', 'x', 1)
conv_filter = s_t.reshape((h_t.shape[0] * num_heads, self.num_shifts)).dimshuffle(0, 'x', 'x', 1)
pad = (self.num_shifts // 2, (self.num_shifts - 1) // 2)
w_g_padded = padding.pad(w_g_padded, [pad], batch_ndim=3)
convolution = T.nnet.conv2d(w_g_padded, conv_filter,
input_shape=(None if batch_size is None else \
batch_size * num_heads, 1, 1, self.memory_shape[0] + pad[0] + pad[1]),
filter_shape=(None if batch_size is None else \
batch_size * num_heads, 1, 1, self.num_shifts),
subsample=(1, 1),
border_mode='valid')
w_tilde = convolution[T.arange(h_t.shape[0] * num_heads), T.arange(h_t.shape[0] * num_heads), 0, :]
w_tilde = w_tilde.reshape((h_t.shape[0], num_heads, self.memory_shape[0]))
# Sharpening (3.3.2)
gamma_t = T.addbroadcast(gamma_t, 2)
w = T.pow(w_tilde + 1e-6, gamma_t)
w /= T.sum(w, axis=2).dimshuffle(0, 1, 'x')
return w
def output(self, input):
conv_out = conv.conv2d(
input, self.conv_w,
filter_shape=self.filter_shape, image_shape=self.image_shape
)
conv_b = T.addbroadcast(self.conv_b, 1, 2)
conv_out = conv_out + conv_b # add bias
pool_out = downsample.max_pool_2d(
conv_out, (self.poolsize, self.poolsize), ignore_border=True
)
pool_w = T.addbroadcast(self.pool_w, 1, 2)
pool_b = T.addbroadcast(self.pool_b, 1, 2)
pool_out = pool_out * pool_w + pool_b
return 1.7159*T.tanh(2/3 * pool_out)
def sample_joint(self, sp): t2_samp=self.theano_rng.multinomial(pvals=T.reshape(self.weights_now,(1,self.npcl))).T s2_samp=T.cast(T.sum(self.s_now*T.addbroadcast(t2_samp,1),axis=0),'float32') diffs=(s2_samp-sp) abs_term=T.sum(T.abs_(diffs)/self.b,axis=1) alpha=T.exp(-abs_term) probs_unnorm=self.weights_past*alpha probs=probs_unnorm/T.sum(probs_unnorm) t1_samp=self.theano_rng.multinomial(pvals=T.reshape(probs,(1,self.npcl))).T s1_samp=T.cast(T.sum(self.s_past*T.addbroadcast(t1_samp,1),axis=0),'float32') return [s1_samp, s2_samp]
def get_uhs_operator(uhs, depth, n_hidden, rhos):
"""
:param uhs:
:param depth:
:param n_hidden:
:param rhos: can be shared variable or constant of shape (depth, )!!
:return:
"""
# Will use a Fourier matrix (will be O(n^2)...)
# Doesn't seem to slow things down much though!
exp_phases = [T.cos(uhs), T.sin(uhs)]
neg_exp_phases = [T.cos(uhs[:, ::-1]), -T.sin(uhs[:, ::-1])]
ones_ = [T.ones((depth, 1), dtype=theano.config.floatX), T.zeros((depth, 1), dtype=theano.config.floatX)]
rhos_reshaped = T.reshape(rhos, (depth, 1), ndim=2)
rhos_reshaped = T.addbroadcast(rhos_reshaped, 1)
eigvals_re = rhos_reshaped * T.concatenate((ones_[0], exp_phases[0], -ones_[0], neg_exp_phases[0]), axis=1)
eigvals_im = rhos_reshaped * T.concatenate((ones_[1], exp_phases[1], -ones_[1], neg_exp_phases[1]), axis=1)
phase_array = -2 * np.pi * np.outer(np.arange(n_hidden), np.arange(n_hidden)) / n_hidden
f_array_re_val = np.cos(phase_array) / n_hidden
f_array_im_val = np.sin(phase_array) / n_hidden
f_array_re = theano.shared(f_array_re_val.astype(theano.config.floatX), name="f_arr_re")
f_array_im = theano.shared(f_array_im_val.astype(theano.config.floatX), name="f_arr_im")
a_k = T.dot(eigvals_re, f_array_re) + T.dot(eigvals_im, f_array_im)
uhs_op = rep_vec(a_k, n_hidden, n_hidden) # shape (depth, 2 * n_hidden - 1)
return uhs_op
def create_gradients(self, loss, deterministic=False):
# load networks
l_px_mu, l_px_logsigma, l_pa_mu, l_pa_logsigma, \
l_qa_mu, l_qa_logsigma, l_qz_mu, l_qz_logsigma, l_qa, l_qz, l_cv, c, v = self.network
# load params
p_params = lasagne.layers.get_all_params(
[l_px_mu, l_pa_mu, l_pa_logsigma], trainable=True)
qa_params = lasagne.layers.get_all_params(l_qa_mu, trainable=True)
qz_params = lasagne.layers.get_all_params(l_qz, trainable=True)
cv_params = lasagne.layers.get_all_params(l_cv, trainable=True)
# load neural net outputs (probabilities have been precomputed)
log_pxz, log_px_given_z, log_pz = self.log_pxz, self.log_px_given_z, self.log_pz
log_qza_given_x = self.log_qza_given_x
log_qz_given_x = self.log_qz_given_x
log_qz_given_x_dgz = self.log_qz_given_x_dgz
cv = T.addbroadcast(lasagne.layers.get_output(l_cv),1)
# compute learning signals
l0 = log_px_given_z + log_pz - log_qz_given_x - cv # NOTE: this disn't have q(a)
l_avg, l_var = l0.mean(), l0.var()
c_new = 0.8*c + 0.2*l_avg
v_new = 0.8*v + 0.2*l_var
l = (l0 - c_new) / T.maximum(1, T.sqrt(v_new))
l_target = (l0 - c_new) / T.maximum(1, T.sqrt(v_new))
# compute grad wrt p
p_grads = T.grad(-log_pxz.mean(), p_params)
# compute grad wrt q_a
elbo = T.mean(log_pxz - log_qza_given_x)
qa_grads = T.grad(-elbo, qa_params)
# compute grad wrt q_z
qz_target = T.mean(dg(l_target) * log_qz_given_x_dgz)
qz_grads = T.grad(-0.2*qz_target, qz_params) # 5x slower rate for q
# compute grad of cv net
cv_target = T.mean(l0**2)
cv_grads = [0.2*g for g in T.grad(cv_target, cv_params)]
# combine and clip gradients
clip_grad = 1
max_norm = 5
grads = p_grads + qa_grads + qz_grads + cv_grads
mgrads = lasagne.updates.total_norm_constraint(grads, max_norm=max_norm)
cgrads = [T.clip(g, -clip_grad, clip_grad) for g in mgrads]
return cgrads
def t_forward_step(self, mask, cur_w_in_sig, pre_out_sig, w_hidden_hidden, b_act, ln_s1, ln_b1, ln_s2, ln_b2):
pre_w_sig = T.dot(pre_out_sig, w_hidden_hidden)
inner_act = self.activation
pre_w_sig_ln = self.ln(pre_w_sig, ln_b1, ln_s1)
cur_w_in_sig_ln = self.ln(cur_w_in_sig, ln_b2, ln_s2)
out_sig = inner_act(T.add(cur_w_in_sig_ln, pre_w_sig_ln, b_act))
mask = T.addbroadcast(mask, 1)
out_sig_m = mask * out_sig + (1. - mask) * pre_out_sig
return [out_sig_m]
def sample_joint(self, sp):
t2_samp = self.theano_rng.multinomial(
pvals=T.reshape(self.weights_now, (1, self.npcl))).T
s2_samp = T.cast(
T.sum(self.s_now * T.addbroadcast(t2_samp, 1), axis=0), 'float32')
h2_samp = T.cast(
T.sum(self.h_now * T.addbroadcast(t2_samp, 1), axis=0), 'float32')
diffs = self.b * (s2_samp - sp)
sqr_term = T.sum(diffs**2, axis=1)
alpha = T.exp(-sqr_term)
probs_unnorm = self.weights_past * alpha
probs = probs_unnorm / T.sum(probs_unnorm)
t1_samp = self.theano_rng.multinomial(
pvals=T.reshape(probs, (1, self.npcl))).T
s1_samp = T.cast(
T.sum(self.s_past * T.addbroadcast(t1_samp, 1), axis=0), 'float32')
h1_samp = T.cast(
T.sum(self.h_past * T.addbroadcast(t1_samp, 1), axis=0), 'float32')
return [s1_samp, h1_samp, s2_samp, h2_samp]
def setup(self, bottom, top):
attention = T.tensor4("attention")
input = T.tensor4("input")
v = T.matrix("v")
attention_bc = T.addbroadcast(attention, 1)
attended = T.mul(input, attention_bc)
result = T.sum(attended, axis=(2, 3))
result_g_attention, result_g_input = T.Lop(result, [attention, input],
v)
self.f = theano.function([attention, input], result)
self.b_attention = theano.function([attention, input, v],
result_g_attention)
self.b_input = theano.function([attention, input, v],
result_g_attention)
def keep_max(input, theta, k):
sig_input = T.nnet.sigmoid(T.dot(input, theta))
#sig_input = T.dot(input, theta)
if k == 0:
result = input * T.addbroadcast(sig_input, 3)
return result, sig_input
# get the sorted idx
sort_idx = T.argsort(sig_input, axis=2)
k_max_ids = sort_idx[:,:,-k:,:]
dim0, dim1, dim2, dim3 = k_max_ids.shape
batchids = T.repeat(T.arange(dim0), dim1*dim2*dim3)
mapids = T.repeat(T.arange(dim1), dim2*dim3).reshape((1, dim2*dim3))
mapids = T.repeat(mapids, dim0, axis=0).flatten()
rowids = k_max_ids.flatten()
colids = T.arange(dim3).reshape((1, dim3))
colids = T.repeat(colids, dim0*dim1*dim2, axis=0).flatten()
sig_mask = T.zeros_like(sig_input)
choosed = sig_input[batchids, mapids, rowids, colids]
sig_mask = T.set_subtensor(sig_mask[batchids, mapids, rowids, colids], 1)
input_mask = sig_mask * sig_input
result = input * T.addbroadcast(input_mask, 3)
return result, sig_input
def __call__(self, input, input_lm=None, h0=None, c0=None):
batch_size = input_lm.shape[0]
if h0 == None:
h0 = T.alloc(np.asarray(0., dtype=theano.config.floatX), batch_size, self.n_hidden)
if c0 == None:
c0 = T.alloc(np.asarray(0., dtype=theano.config.floatX), batch_size, self.n_hidden)
if input_lm == None:
def step(x_t, h_tm_prev, c_tm_prev):
x_i = T.dot(x_t, self.W_i) + self.b_i
x_f = T.dot(x_t, self.W_f) + self.b_f
x_c = T.dot(x_t, self.W_c) + self.b_c
x_o = T.dot(x_t, self.W_o) + self.b_o
i_t = self.inner_activation(x_i + T.dot(h_tm_prev, self.U_i))
f_t = self.inner_activation(x_f + T.dot(h_tm_prev, self.U_f))
c_t = f_t * c_tm_prev + i_t * self.activation(x_c + T.dot(h_tm_prev, self.U_c)) # internal memory
o_t = self.inner_activation(x_o + T.dot(h_tm_prev, self.U_o))
h_t = o_t * self.activation(c_t) # actual hidden state
return [h_t, c_t]
self.h_1, _ = theano.scan(step,
sequences=input.dimshuffle(1, 0, 2),
outputs_info=[h0, c0]
)
else:
def step(x_t, mask, h_tm_prev, c_tm_prev):
x_i = T.dot(x_t, self.W_i) + self.b_i
x_f = T.dot(x_t, self.W_f) + self.b_f
x_c = T.dot(x_t, self.W_c) + self.b_c
x_o = T.dot(x_t, self.W_o) + self.b_o
i_t = self.inner_activation(x_i + T.dot(h_tm_prev, self.U_i))
f_t = self.inner_activation(x_f + T.dot(h_tm_prev, self.U_f))
c_t = f_t * c_tm_prev + i_t * self.activation(x_c + T.dot(h_tm_prev, self.U_c)) # internal memory
o_t = self.inner_activation(x_o + T.dot(h_tm_prev, self.U_o))
h_t = o_t * self.activation(c_t) # actual hidden state
h_t = mask * h_t + (1 - mask) * h_tm_prev
c_t = mask * c_t + (1 - mask) * c_tm_prev
return [h_t, c_t]
self.h_1, _ = theano.scan(step,
sequences=[input.dimshuffle(1, 0, 2),
T.addbroadcast(input_lm.dimshuffle(1, 0, 'x'), -1)],
outputs_info=[h0, c0])
self.h_1 = self.h_1[0].dimshuffle(1, 0, 2)
return self.h_1[:, -1, :]
def get_padded_shuffled_mask(self, mask, X, pad=0):
# mask is (nb_samples, time)
if mask is None:
mask = T.ones((X.shape[0], X.shape[1]))
mask = T.shape_padright(mask) # (nb_samples, time, 1)
mask = T.addbroadcast(mask, -1) # (time, nb_samples, 1) matrix.
mask = mask.dimshuffle(1, 0, 2) # (time, nb_samples, 1)
if pad > 0:
# left-pad in time with 0
padding = alloc_zeros_matrix(pad, mask.shape[1], 1)
mask = T.concatenate([padding, mask], axis=0)
return mask.astype('int8')
def __init__(self, attended_layer, attended_layer_mask,
condition_layer, gate_covariance=False, covariance_decay=None,
name=None):
MergeLayer.__init__(self, [attended_layer, attended_layer_mask,
condition_layer], name=name)
self.gate_covariance = gate_covariance
self.covariance_decay = covariance_decay
if gate_covariance:
n_units = attended_layer.output_shape[-1]
self.w_gate = self.add_param(init.Constant(0.0),
(n_units,), name="gate")
self.b_gate = self.add_param(init.Constant(1.0),
(1,), name="gate")
self.b_gate = T.addbroadcast(self.b_gate, 0)
def sequence_iteration(self,
output,
mask,
use_dropout=0,
dropout_value=0.5):
dot_product = T.dot(output, self.t_w_out)
linear_o = T.add(dot_product, self.t_b_out)
mask = T.addbroadcast(mask, 2) # to do nesseccary?
output = T.mul(mask, linear_o) + T.mul((1. - mask), 1e-6)
return output # result
def apply(self, char_seq, sample_matrix, char_aux):
# Time as first dimension
embeddings = self.lookup.apply(char_seq)
gru_out = self.dgru.apply(**merge(
self.gru_fork.apply(embeddings, as_dict=True), {'mask': char_aux}))
wgru_out = tensor.exp(
self.wl.apply(self.bidir_w.apply(embeddings, char_aux)))
if self.dgru_depth > 1:
gru_out = gru_out[-1]
gru_out = tensor.addbroadcast(wgru_out, 2) * gru_out
sampled_representation = tensor.tanh(
tensor.batched_dot(sample_matrix, gru_out.dimshuffle([1, 0, 2])))
return sampled_representation.dimshuffle([1, 0, 2]), wgru_out
def get_padded_shuffled_mask(self, train, X, pad=0):
mask = self.get_input_mask(train)
if mask is None:
mask = T.ones_like(X.sum(axis=-1)) # is there a better way to do this without a sum?
# mask is (nb_samples, time)
mask = T.shape_padright(mask) # (nb_samples, time, 1)
mask = T.addbroadcast(mask, -1) # (time, nb_samples, 1) matrix.
mask = mask.dimshuffle(1, 0, 2) # (time, nb_samples, 1)
if pad > 0:
# left-pad in time with 0
padding = alloc_zeros_matrix(pad, mask.shape[1], 1)
mask = T.concatenate([padding, mask], axis=0)
return mask.astype('int8')
def output(self, input_scalars):
"""
Computes the n_output output scalars
@param input_scalars: the layer's input
@return: n_output scalars
"""
z = T.dot(input_scalars, self.W) + T.addbroadcast(self.b, 0)
if self.activation == 'linear':
return z
elif self.activation == 'rectified':
return T.maximum(z, 0)
elif self.activation == 'tanh':
return T.tanh(z)
else:
raise "Invalid activation %s" % self.activation
def get_output_for(self, inputs, deterministic=False, **kwargs):
event = inputs[0] #(None, 1000, embed)
feature_idx = inputs[1] #(None, 1000, feature_num, embed)
feature_b = inputs[2] #(None, 1000, feature_num, 1)
feature_trans = inputs[3] #(None, 1000, feature_num, 1)
feature_value = inputs[4] #(None, 1000, feature_num)
value_up = T.shape_padright(feature_value,
1) #(None, 1000, feature_num, 1)
bias_value = feature_trans * (value_up + feature_b)
bias_value_broad = T.addbroadcast(bias_value,
3) #make the last axis broadcastable
v_idx = T.sum(feature_idx *
lasagne.nonlinearities.tanh(bias_value_broad),
axis=2) #(None, 1000, embed)
return v_idx + event
def RBM_Free_Energy(x, y):
# make input data binary
data = makeBinary(x)
# determine initial params
(W_init, b_v_init, b_h_init) = initParams(x.shape[0], NUM_HID)
W = theano.shared(W_init, name='W')
b_v = theano.shared(b_v_init.reshape(b_v_init.shape[0], 1), name='b_v')
b_h = theano.shared(b_h_init.reshape(b_h_init.shape[0], 1), name='b_h')
# compute free energy
v = T.matrix('v')
F = -T.dot(T.flatten(b_v, 1), v)\
- T.sum(T.log(1.0 + T.exp(T.addbroadcast(b_h, 1) + T.dot(W, v))), axis=0)
free_energy = theano.function([v], F.sum())
value = free_energy(data)
print ' Total Free Energy =', value
# approximate expected free energy
# using k=1 constrastive divergence
rng = RandomStreams(RANDOM_SEED)
h_0_mean = 1.0 / (1.0 + T.exp(-T.addbroadcast(b_h, 1) - T.dot(W, v)))
h_0 = rng.binomial(size=h_0_mean.shape, n=1, p=h_0_mean)
v_0_mean = 1.0 / (1.0 + T.exp(-T.addbroadcast(b_v, 1) - T.dot(W.T, h_0)))
v_0 = rng.binomial(size=v_0_mean.shape, n=1, p=v_0_mean)
F_exp = -T.dot(T.flatten(b_v, 1), v_0)\
- T.sum(T.log(1.0 + T.exp(T.addbroadcast(b_h, 1) + T.dot(W, v_0))), axis=0)
exp_free_energy = theano.function([v], F_exp.sum())
value = exp_free_energy(data)
print 'Estimated Expected Free Energy =', value
# compute param deltas
dParams = T.grad(F.sum() - F_exp.sum(), [W, b_v, b_h],
consider_constant=[v_0])
dParams_func = theano.function([v], dParams)
value = dParams_func(data)
def __init__(self,
incoming,
n_codewords=24,
V=lasagne.init.Normal(0.1),
gamma=lasagne.init.Constant(0.1),
eps=0.00001,
input_var=None,
initializers=None,
spatial_level=1,
**kwargs):
"""
Creates a BoF layer
:param incoming:
:param n_codewords: number of codewords
:param V: initializer used for the codebook
:param gamma: initializer used for the scaling factors
:param eps: epsilon used to ensure numerical stability
:param input_var: input_var of the model (used to compile a function that extract the features fed to layer)
:param initializers:
:param spatial_level: 0 (no spatial segmentation), 1 (first spatial level)
:param pooling_type: either 'mean' or 'max'
:param kwargs:
"""
super(CBoF_Layer, self).__init__(incoming, **kwargs)
self.n_codewords = n_codewords
self.spatial_level = spatial_level
n_filters = self.input_shape[1]
self.eps = eps
# Create parameters
self.V = self.add_param(V, (n_codewords, n_filters, 1, 1), name='V')
self.gamma = self.add_param(gamma, (1, n_codewords, 1, 1),
name='gamma')
# Make gammas broadcastable
self.gamma = T.addbroadcast(self.gamma, 0, 2, 3)
# Compile function used for feature extraction
if input_var is not None:
self.features_fn = theano.function([input_var],
lasagne.layers.get_output(
incoming,
deterministic=True))
if initializers is not None:
initializers.append(self.initialize_layer)
def get_padded_shuffled_mask(self, train, X, pad=0):
mask = self.get_input_mask(train)
if mask is None:
mask = T.ones_like(X.sum(axis=-1)) # is there a better way to do this without a sum?
# mask is (nb_samples, time)
mask = T.shape_padright(mask) # (nb_samples, time, 1)
mask = T.addbroadcast(mask, -1) # the new dimension (the '1') is made broadcastable
# see http://deeplearning.net/software/theano/library/tensor/basic.html#broadcasting-in-theano-vs-numpy
mask = mask.dimshuffle(1, 0, 2) # (time, nb_samples, 1)
if pad > 0:
# left-pad in time with 0
padding = alloc_zeros_matrix(pad, mask.shape[1], 1)
mask = T.concatenate([padding, mask], axis=0)
return mask.astype('int8')
def permute_dimensions(x, pattern):
'''Transpose dimensions.
pattern should be a tuple or list of
dimension indices, e.g. [0, 2, 1].
'''
if len(
pattern
) < x.ndim: # [DV] handle the case that one dimension is to be dropped
bcaxis = []
for i in range(x.ndim):
if i not in pattern:
bcaxis.append(i)
x = T.addbroadcast(x, *bcaxis)
pattern = tuple(pattern)
return x.dimshuffle(pattern)
def __call__(self, input,input_lm=None, return_list = False):
# activation function
if input_lm == None:
self.h_l, _ = theano.scan(self.step2,
sequences=input.dimshuffle(1,0,2),
outputs_info=theano.shared(value=np.zeros((self.batch_size,self.n_hidden),
dtype=theano.config.floatX),borrow=True))
else:
self.h_l, _ = theano.scan(self.step,
sequences=[input.dimshuffle(1,0,2),T.addbroadcast(input_lm.dimshuffle(1,0,'x'), -1)],
outputs_info=theano.shared(value=np.zeros((self.batch_size,self.n_hidden),
dtype=theano.config.floatX),borrow=True))
self.h_l = self.h_l.dimshuffle(1,0,2)
if return_list == True:
return self.h_l
return self.h_l[:,-1,:]
def get_attention(Wg, bg, M, w):
g_t = T.nnet.sigmoid(T.dot(x_t, Wg) + bg) # [instances, mem]
# eqn 11
k = T.dot(h_tm1, self.Wk) + self.bk # [instances, memory_size]
# eqn 13
beta = T.dot(h_tm1, self.Wb) + self.bb
beta = T.log(1 + T.exp(beta))
beta = T.addbroadcast(beta, 1) # [instances, 1]
# eqn 12
w_hat = T.nnet.softmax(beta * cosine_dist(M, k))
# eqn 14
return (1 - g_t) * w + g_t * w_hat # [instances, mem]
def get_output_for(self, inputs, **kwargs):
inputs = autocrop(inputs, self.cropping)
# modify broadcasting pattern.
if self.broadcastable is not None:
for n, broadcasting_dim in enumerate(self.broadcastable):
for dim, broadcasting in enumerate(broadcasting_dim):
if broadcasting:
inputs[n] = T.addbroadcast(inputs[n], dim)
output = None
for input in inputs:
if output is not None:
output = self.merge_function(output, input)
else:
output = input
return output
def __call__(self, input, input_lm=None, h0=None):
batch_size = input.shape[0]
if h0 == None:
h0 = T.alloc(np.asarray(0., dtype=theano.config.floatX),
batch_size, self.n_hidden)
if input_lm == None:
def step(x_t, h_tm_prev):
x_z = T.dot(x_t, self.W_z) + self.b_z
x_r = T.dot(x_t, self.W_r) + self.b_r
x_h = T.dot(x_t, self.W_h) + self.b_h
z_t = self.inner_activation(x_z + T.dot(h_tm_prev, self.U_z))
r_t = self.inner_activation(x_r + T.dot(h_tm_prev, self.U_r))
hh_t = self.activation(x_h + T.dot(r_t * h_tm_prev, self.U_h))
h_t = (1 - z_t) * hh_t + z_t * h_tm_prev
h_t = T.cast(h_t, dtype=theano.config.floatX)
return h_t
self.output, _ = theano.scan(step,
sequences=input.dimshuffle(1, 0, 2),
outputs_info=h0)
else:
def step(x_t, mask, h_tm_prev):
x_z = T.dot(x_t, self.W_z) + self.b_z
x_r = T.dot(x_t, self.W_r) + self.b_r
x_h = T.dot(x_t, self.W_h) + self.b_h
z_t = self.inner_activation(x_z + T.dot(h_tm_prev, self.U_z))
r_t = self.inner_activation(x_r + T.dot(h_tm_prev, self.U_r))
hh = self.activation(x_h + T.dot(r_t * h_tm_prev, self.U_h))
h_t = z_t * h_tm_prev + (1 - z_t) * hh
h_t = mask * h_t + (1 - mask) * h_tm_prev
h_t = T.cast(h_t, dtype=theano.config.floatX)
return h_t
self.output, _ = theano.scan(
step,
sequences=[
input.dimshuffle(1, 0, 2),
T.addbroadcast(input_lm.dimshuffle(1, 0, 'x'), -1)
],
outputs_info=h0)
h = self.output # [max_length, batch_size, hidden_size]
h = h.dimshuffle(1, 0, 2)
return h, h[:, -1, :]
def output(self, input_vectors, input_scalars):
"""
Calculate the n_output transformed vectors for this layer
@param input_scalars: n_input x n_output scalar vector
@param input_vectors: n_input vectors (actual shape should be (n_batch, n_input, n_dimension)
"""
mat = input_scalars.reshape((n_batch, self.n_input, self.n_output))
z = T.batched_tensordot(input_vectors, mat, [[1], [1]]).swapaxes(
1, 2) + T.addbroadcast(self.b, 0, 2)
if self.activation == 'linear':
return z
elif self.activation == 'rectified':
return T.maximum(z, 0)
elif self.activation == 'tanh':
return T.tanh(z)
else:
raise "Unknown activation, %s" % self.activation
def make_network_multiscale(
network_type, loss_function, lr, n_scales, net_options,
do_clip=True, make_pr_func=False):
target_var = T.matrix('targets')
lr_var = theano.shared(np.array(lr, dtype=floatX))
print("Building model and compiling functions...")
if n_scales >= 1:
input_var_list = [T.tensor4('inputs{}'.format(i))
for i in range(n_scales)]
network = getattr(network_design, network_type)(input_var_list,
**net_options)
else:
# if the network requires input_var not a list, set n_sources=-1
input_var_list = [T.addbroadcast(T.tensor4('inputs{}'.format(i)))
for i in range(1)]
network = getattr(network_design, network_type)(input_var_list[0],
**net_options)
# Compute loss
prediction = lasagne.layers.get_output(network)
if do_clip:
prediction = T.clip(prediction, epsilon, one-epsilon)
loss = loss_function(prediction, target_var)
loss = loss.mean()
params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.adagrad(
loss, params, learning_rate=lr_var)
test_prediction = lasagne.layers.get_output(network, deterministic=True)
if do_clip:
test_prediction = T.clip(test_prediction, epsilon, one-epsilon)
test_loss = loss_function(test_prediction, target_var)
test_loss = test_loss.mean()
train_func = theano.function(input_var_list+[target_var],
loss, updates=updates)
val_func = theano.function(input_var_list+[target_var],
[test_prediction, test_loss])
if make_pr_func:
pr_func = theano.function(input_var_list, test_prediction)
return network, input_var_list, lr_var, train_func, val_func, pr_func
else:
return network, input_var_list, lr_var, train_func, val_func
def do_apply(self, input_):
X = input_
naxes = self.naxes
broadcast_n = T.addbroadcast(self.n, 0)
if naxes == 4: # CNN
if self.use_population:
u = self.u / broadcast_n
else:
u = T.mean(X, axis=[0, 2, 3])
b_u = u.dimshuffle('x', 0, 'x', 'x')
if self.use_population:
s = self.s / broadcast_n
else:
s = T.mean(T.sqr(X - b_u), axis=[0, 2, 3])
X = (X - b_u) / T.sqrt(s.dimshuffle('x', 0, 'x', 'x') + self.e)
X = self.g.dimshuffle('x', 0, 'x', 'x')*X +\
self.b.dimshuffle('x', 0, 'x', 'x')
elif naxes == 3: # RNN
if self.use_population:
u = self.u / broadcast_n
else:
u = T.mean(X, axis=[0, 1])
b_u = u.dimshuffle('x', 'x', 0)
if self.use_population:
s = self.s / broadcast_n
else:
s = T.mean(T.sqr(X - b_u), axis=[0, 1])
X = (X - b_u) / T.sqrt(s.dimshuffle('x', 'x', 0) + self.e)
X = self.g.dimshuffle('x', 'x', 0)*X +\
self.b.dimshuffle('x', 'x', 0)
elif naxes == 2: # FC
if self.use_population:
u = self.u / broadcast_n
else:
u = T.mean(X, axis=0)
if self.use_population:
s = self.s / broadcast_n
else:
s = T.mean(T.sqr(X - u), axis=0)
X = (X - u) / T.sqrt(s + self.e)
X = self.g * X + self.b
else:
raise NotImplementedError
return X, u, s
def output(self, input_raw):
input = input_raw
lin_output = T.dot(input, self.W) + self.b
if self.batch_norm:
lin_output = (lin_output -
T.mean(lin_output, axis=0, keepdims=True)) / (
1.0 + T.std(lin_output, axis=0, keepdims=True))
lin_output = (lin_output * T.addbroadcast(self.bn_std, 0) +
T.addbroadcast(self.bn_mean, 0))
if self.layer_norm:
lin_output = (lin_output -
T.mean(lin_output, axis=1, keepdims=True)) / (
1.0 + T.std(lin_output, axis=1, keepdims=True))
lin_output = (lin_output * T.addbroadcast(self.bn_std, 0) +
T.addbroadcast(self.bn_mean, 0))
if self.norm_prop:
lin_output = lin_output / T.sqrt(T.mean(T.sqr(lin_output), axis=0))
lin_output = (lin_output * T.addbroadcast(self.bn_std, 0) +
T.addbroadcast(self.bn_mean, 0))
clip_preactive = True
if clip_preactive:
lin_output = theano.tensor.clip(lin_output, -10, 10)
self.out_store = lin_output
if self.activation == None:
activation = lambda x: x
elif self.activation == "relu":
activation = lambda x: T.maximum(0.0, x)
elif self.activation == "lrelu":
activation = lambda x: T.nnet.relu(x, alpha=0.02)
elif self.activation == "exp":
activation = lambda x: T.exp(x)
elif self.activation == "tanh":
activation = lambda x: T.tanh(x)
elif self.activation == 'softplus':
activation = lambda x: T.nnet.softplus(x)
elif self.activation == 'sigmoid':
activation = lambda x: T.nnet.sigmoid(x)
else:
raise Exception("Activation not found")
out = activation(lin_output)
return out
def sequence_iteration(self,
output,
mask,
use_dropout=0,
dropout_value=0.5):
dot_product = T.dot(output, self.t_w_out)
net_o = T.add(dot_product, self.t_b_out)
ex_net = T.exp(net_o)
sum_net = T.sum(ex_net, axis=2, keepdims=True)
softmax_o = ex_net / sum_net
mask = T.addbroadcast(mask, 2) # to do nesseccary?
output = T.mul(mask, softmax_o) + T.mul((1. - mask), 1e-6)
return output #result
def get_inverse_for(self, input, **kwargs):
C_fft = T.addbroadcast(theano.tensor.fft.rfft(self.C_pad), 0)
C_fft_norm2 = C_fft[:, :, 0]**2 + C_fft[:, :, 1]**2
C_fft_inv = C_fft
C_fft_inv = T.set_subtensor(C_fft_inv[:, :, 0],
C_fft[:, :, 0] / C_fft_norm2)
C_fft_inv = T.set_subtensor(C_fft_inv[:, :, 1],
-C_fft[:, :, 1] / C_fft_norm2)
z_fft = theano.tensor.fft.rfft(input)
Cz_fft = z_fft
Cz_fft = T.set_subtensor(
Cz_fft[:, :, 0], z_fft[:, :, 0] * C_fft_inv[:, :, 0] -
z_fft[:, :, 1] * C_fft_inv[:, :, 1])
Cz_fft = T.set_subtensor(
Cz_fft[:, :, 1], z_fft[:, :, 0] * C_fft_inv[:, :, 1] +
z_fft[:, :, 1] * C_fft_inv[:, :, 0])
rlt = theano.tensor.fft.irfft(Cz_fft)
return rlt
def g_deconv(self, z_ver, in_dims, out_dims, weight_name, fspec):
""" Inverse operation for each type of f used in convnets """
f_type, f_dims = fspec
assert z_ver is not None
num_channels = in_dims[0] if in_dims is not None else None
num_filters, width, height = out_dims[:3]
if f_type in ['globalmeanpool']:
u = T.addbroadcast(z_ver, 2, 3)
assert in_dims[1] == 1 and in_dims[2] == 1, \
"global pooling needs in_dims (1,1): %s" % str(in_dims)
elif f_type in ['maxpool']:
sh, str, size = z_ver.shape, f_dims[0], f_dims[1]
assert str == size, "depooling requires stride == size"
u = T.zeros((sh[0], sh[1], sh[2] * str, sh[3] * str),
dtype=z_ver.dtype)
for x in xrange(str):
for y in xrange(str):
u = T.set_subtensor(u[:, :, x::str, y::str], z_ver)
u = u[:, :, :width, :height]
elif f_type in ['convv', 'convf']:
filter_size, str = (f_dims[1], f_dims[1]), f_dims[2]
W_shape = (num_filters, num_channels) + filter_size
W = self.weight(self.rand_init_conv(W_shape), weight_name)
if str > 1:
# upsample if strided version
sh = z_ver.shape
u = T.zeros((sh[0], sh[1], sh[2] * str, sh[3] * str),
dtype=z_ver.dtype)
u = T.set_subtensor(u[:, :, ::str, ::str], z_ver)
else:
u = z_ver # no strides, only deconv
u = conv2d(u,
W,
filter_shape=W_shape,
border_mode='valid' if 'convf' in f_type else 'full')
u = u[:, :, :width, :height]
else:
raise NotImplementedError('Layer %s has no convolutional decoder' %
f_type)
return u
def outputs(self, groundtruth, groundtruth_mask, **inputs):
# Copy-pasted from all_outputs, because Theano does not support ellipsis
outputs = self.merge(**dict_subset(inputs, self.merge_names))
if self.value_softmax:
logger.debug('Applying value softmax')
outputs = (tensor.addbroadcast(outputs[:, :1], 1) +
self.softmax.apply(outputs[:, 1:]))
if self.same_value_for_wrong:
logger.debug('Same value for apriori wrong actions')
wrong_output = outputs[:, 0]
outputs = outputs[:, 1:]
indices = tensor.repeat(tensor.arange(groundtruth.shape[1]),
groundtruth.shape[0])
wrong_mask = tensor.ones_like(outputs)
wrong_mask = tensor.set_subtensor(
wrong_mask[indices, groundtruth.T.flatten()], 0)
outputs = (outputs * (1 - wrong_mask) +
wrong_output[:, None] * wrong_mask)
return outputs
def __init__(self,
incoming,
kernel_shape=[5, 5],
input_shape=[50, 50],
C=lasagne.init.Normal(0.01),
**kwargs):
super(CircMatLayerSparse2D, self).__init__(incoming, **kwargs)
num_inputs = self.input_shape[1]
self.kernel_shape = kernel_shape
self.input_shape = input_shape
self.C = self.add_param(C, (1, kernel_shape[0] * kernel_shape[1]),
name='C')
self.C = T.addbroadcast(self.C, 0)
#self.C_pad = self.C.reshape(kernel_shape, ndim=2)
self.C_pad = T.zeros(input_shape)
self.C_pad = T.set_subtensor(
self.C_pad[:kernel_shape[0], :kernel_shape[1]],
self.C.reshape(kernel_shape, ndim=2))
self.C_pad = self.C_pad.reshape([1, input_shape[0] * input_shape[1]])