def addConnections(self, connections): global delta, Wmin, Wmax, awe self.connections = self.connections + connections i=0 for i, c in enumerate(connections): j = self.i + i # Weights self.weights.append( theano.shared( sp.csc_matrix( np.asarray( c.generateConnectionMatrix(self.o_shape, generate), dtype=self.input.dtype) ), name ='Wi_' + str(j))) self.Wmax.append( theano.shared( sp.csc_matrix( np.asarray( np.ones((sizeFromShape(c.i_shape),sizeFromShape(self.o_shape)))*Wmax, dtype=self.input.dtype) ), name ='WM_' + str(i))) self.Wmin.append( theano.shared( sp.csc_matrix( np.asarray( np.ones((sizeFromShape(c.i_shape),sizeFromShape(self.o_shape)))*Wmin, dtype=self.input.dtype) ), name ='WM_' + str(i))) # yw # out: nx1 # Wi: mxn # outT x WiT : 1xm self.yw.append( sparse.structured_dot( sparse.transpose(self.output), sparse.transpose(self.weights[j]))) # x_yw # in: nx1 self.x_yw.append( sparse.sub( sparse.transpose(c.input), self.yw[j])) print len(self.weights) print self.weights[i].type print self.weights[i].type.ndim print if self.weights: auxX=sparse.sub(self.Wmax[j], self.weights[i]) auxY=sparse.sub(self.weights[i], self.Wmin[j]) self.LR.append(delta*( sparse.sub( sparse.structured_pow( sparse.sub(self.Wmax[j], self.weights[i]), 1), sparse.structured_pow( sparse.sub(self.Wmin[j], self.weights[i]), 1)))) self.xy.append( self.LR[i]*sparse.structured_dot( c.input, sparse.transpose(self.output))) self.AWW.append( awe*delta*sparse.structured_pow( sparse.sub(self.Wmax[j], self.weights[i]), 1)*self.weights[i]) self.i +=i self.params[2] = self.weights
def propagate_thy_beliefs(self):
'''
Call this function to receive a string containing the path of the belief propagation algorithm.
We implement the algorithm listed in the paper mentioned in the comments above
Pseudocode:
-> Create an empty theano vector whose definitions will be iteratively changed.
-> Call compile_message_node_to_factor from the o node of the head predicate.
-> Let the functions recursively call each other
-> Collect their things somehow. @TODO: how. what format. Shall we use theano variables altogether or what
-> Return said stuff.
'''
# print "graph:bp: Starting belief propagation."
equation = self._comiple_message_node_(self.head_predicate.o,
"Fictional Label")
symbols = self._comiple_message_symbols_node_(self.head_predicate.o,
"Fictional Label")
#Define an empty dvector to be used as the 'y' label (which will later contain n hot information about desired entities)
y = sparse.csr_dmatrix('y')
# Do a softmax over the final BP Equation
equation = sparse.structured_exp(equation)
equation = sparse.row_scale(equation,
1.0 / sparse.sp_sum(equation, axis=1))
# Collect all the parameters (shared vars), found in the factors of this graph.
#parameters is a list of matrices (relation)
parameters = [x.M for x in symbols]
#Cross entropy loss
# loss = - y * T.log(equation) + (y - 1)*T.log(1-equation) # unregularized cross-entropy loss in theano
a = sparse.mul(y, sparse.structured_log(equation))
b = sparse.mul(
sparse.structured_add(y, -1.0),
sparse.structured_log(sparse.structured_add(equation, -1.0)))
loss = sparse.sub(b, a)
# Unregularied Loss
loss_dense = sparse.dense_from_sparse(loss)
cost = loss_dense.mean()
# cost = sparse.sp_sum(loss, axis = 1)/float(ne)
gradients = theano.grad(cost, parameters)
updated_matrices = [
sparse.sub(parameters[i], 0.1 * gradients[i])
for i in range(len(parameters))
]
# updated_matrices = [sparse.sub(parameters[i], sparse.row_scale(gradients[i], 0.1)) for i in range(len(parameters))]
# updated_matrices = [parameters[i] - 0.1 * gradients[i] for i in range(len(parameters))]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# DEBUG
# print "Equation: ", equation
# print "Type of equation: ",type(equation)
# print "Symbols: ", symbols
# print "graph:bp: Belief propagation complete."
# print "Parameters are"
# for p in parameters:
# print p," and the type is :",type(p)
# print gradients
# print "Updated Matrices are :", type(updated_matrices[0])
# print colored(type(self.head_predicate.i.u),'red')
# print "Inputs: \n"
# print type(self.head_predicate.i.u)
# print type(y)
# print [ type(x) for x in parameters ]
# raw_input("Verify Symbols and Gradients ")
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
function = theano.function(
inputs=[self.head_predicate.i.u, y] +
parameters, #Inputs to this is the head predicates' symbolic var, and another dvector
# inputs = [self.head_predicate.i.u,parameters[0]], #Inputs to this is the head predicates' symbolic var, and another dvector
# outputs = updated_matrices #Output to this thing is the BP algorithm's output expression
outputs=[equation] + updated_matrices
# mode=theano.compile.MonitorMode(
# pre_func=self.inspect_inputs,
# post_func=self.inspect_outputs) #Output to this thing is the BP algorithm's output expression
# updates=tuple([(parameters[i], parameters[i] - 0.1 * gradients[i]) for i in range(len(parameters))]) #Updates are the gradients of cost wrt parameters
)
return function, symbols
def __init__(self, input, filter_shape, sigma,i_shape,o_shape, Wi = False, Wr = False):
global generate
# Mean neuron density ~80k/mm^3 in V2 (skoglund 1996)
# Synapse length follow a power law ()
# Synapse length for feedback interareal ~10-40mm, feedforward same, but less connections
# Synapse lengths is found by Sholl analysis.
# Ahould compare RF data with Van den Bergh 2010
# Initialize weights as a shared variable
#n_col=input.shape[1]
try:
if generate:
np.load('asd')
else:
Wi=np.load(i_file)
print '[info] Weights loaded from file!'
print 'Shape = ' + str(Wi.shape)
except IOError:
print "[info] Weights file wasn't found. Generating new connections"
kern1 = gkern2(filter_shape,sigma)
Wi = kernel2connection(kern1, i_shape, o_shape)
#Wi /= np.sum(Wi,1).reshape((Wi.shape[0],1))*15
print 'Shape = ' + str(Wi.shape)
np.save(i_file,Wi)
try:
if generate:
np.load('asd')
else:
Wr=np.load(r_file)
print 'Weights loaded from file!'
except IOError:
print "Weights file wasn't found. Generating new connections"
kern2 = gkern2(filter_shape,sigma)
Wr = kernel2connection(kern2, o_shape,o_shape)
#Wr /= np.sum(Wi,1)
np.save(r_file,Wr)
if np.sum(Wi,1)[0] != 1:
Wi /= np.sum(Wi,1).reshape((Wi.shape[0],1))*5
if np.sum(Wr,1)[0] != 1:
Wr /= np.sum(Wr,1).reshape((Wr.shape[0],1))
print np.sum(Wi,0)
print np.sum(Wi,1)
plt.plot(Wi[1,:])
plt.show()
self.Wi= theano.shared(
sp.csc_matrix(
np.asarray(
Wi,
dtype=input.dtype) ), name ='Wi')
self.Wr = theano.shared(
sp.csc_matrix(
np.asarray(
Wr,
dtype=input.dtype) ), name ='Wr')
# Output of the layer is the sigmoid of the convolved network
self.state = theano.shared(
sp.csc_matrix(
np.asarray(
np.zeros((o_shape[0]*o_shape[1],1)),
dtype=input.dtype) ), name ='St')
self.input = input
# I could do the same with biases if needed
#print self.input.get_value().shape
#print self.Wi.get_value().shape
self.output = theano.shared(
sp.csc_matrix(
np.asarray(
np.zeros((o_shape[0]*o_shape[1],1)),
dtype=input.dtype) ), name ='Out')
#sparse.structured_sigmoid(sparse.structured_dot(self.input, self.Wi)) #T.dot(self.input, self.Wi))
# input = external + recursive (from layer)
# self.input = T.dot(input, self.Wi) #+ T.sum(T.dot(self.state,self.Wr),1)
# out: nx1
# Wi: mxn
# outT x WiT : 1xm
self.yw = sparse.structured_dot(
sparse.transpose(self.output),
sparse.transpose(self.Wi))
# in: nx1
self.x_yw = sparse.sub(
sparse.transpose(self.input),
self.yw)
# optional: self.output = T.nnet.sigmoid(conv_out+self.output)
self.params = [self.Wi, self.Wr, self.state, self.output]
def sparse_mean_squared_error(y_true, y_pred):
_assert_sparse_module()
T.mean
return sparse_mean(th_sparse_module.sqr(
th_sparse_module.sub(y_true, y_pred)),
axis=-1)
