def use(self,dataset):
outputs = []
for example in dataset:
input,dummy = example
if len(input.shape) == 1:
input = input.reshape((1,-1))
data_vis_bias = np.dot(input,self.V)+self.b
if self.n_inference_iterations > 0:
if self.approximate_inference == 'mean_field':
target_singles = 1./(1+np.exp(-data_vis_bias))
for k in range(self.n_inference_iterations):
if self.damping_factor > 0:
target_singles *= self.damping_factor
target_singles += (1.-self.damping_factor)/(1+np.exp(-data_vis_bias-np.dot(target_singles,self.U)))
else:
target_singles = 1./(1+np.exp(-data_vis_bias-np.dot(target_singles,self.U)))
elif self.approximate_inference == 'loopy_belief_propagation':
target_singles = np.zeros((input.shape[0],self.target_size))
log_messages = np.zeros((self.target_size,self.target_size))
softplus_out1 = np.zeros((self.target_size,self.target_size))
softplus_out2 = np.zeros((self.target_size,self.target_size))
for i in range(input.shape[0]):
# Loopy BP
data_bias = data_vis_bias[i]
log_messages[:] = 0
for k in range(self.n_inference_iterations):
# Update messages
acc_log_messages = data_bias + log_messages.sum(axis=1) - log_messages
if self.damping_factor > 0:
log_messages *= self.damping_factor
mlnonlin.softplus(acc_log_messages,softplus_out2)
acc_log_messages += self.U
mlnonlin.softplus(acc_log_messages,softplus_out1)
log_messages += (1-self.damping_factor) * (softplus_out1-softplus_out2)
else:
mlnonlin.softplus(acc_log_messages,softplus_out2)
acc_log_messages += self.U
mlnonlin.softplus(acc_log_messages,softplus_out1)
log_messages = softplus_out1-softplus_out2
# Single marginals
target_singles[i] = 1./(1+np.exp(-data_bias-log_messages.sum(axis=1)))
else:
raise ValueError('approximate_inference \'%\' unknown' % self.approximate_inference)
else:
target_singles = 1./(1+np.exp(-data_vis_bias))
if self.binary_outputs:
outputs += [ target_singles > 0.5 ]
else:
outputs += [ target_singles ]
return outputs
def test_softplus():
"""
Testing nonlinear softplus.
"""
input = np.random.randn(20)
output = np.zeros((20))
nonlinear.softplus(input,output)
print 'NumPy vs mathutils.nonlinear diff. output:',np.sum(np.abs(output-np.log(1+np.exp(input))))
print 'Testing nonlinear reclin'
input = np.random.randn(30,20)
output = np.zeros((30,20))
nonlinear.reclin(input,output)
print 'NumPy vs mathutils.nonlinear diff. output:',np.sum(np.abs(output-(input>0)*input))
print 'Testing nonlinear reclin deriv.'
dinput = np.zeros((30,20))
doutput = np.random.randn(30,20)
nonlinear.dreclin(output,doutput,dinput)
print 'NumPy vs mathutils.nonlinear diff. output:',np.sum(np.abs(dinput-(input>0)*doutput))
def test_softplus():
"""
Testing nonlinear softplus.
"""
input = np.random.randn(20)
output = np.zeros((20))
nonlinear.softplus(input, output)
print 'NumPy vs mathutils.nonlinear diff. output:', np.sum(
np.abs(output - np.log(1 + np.exp(input))))
print 'Testing nonlinear reclin'
input = np.random.randn(30, 20)
output = np.zeros((30, 20))
nonlinear.reclin(input, output)
print 'NumPy vs mathutils.nonlinear diff. output:', np.sum(
np.abs(output - (input > 0) * input))
print 'Testing nonlinear reclin deriv.'
dinput = np.zeros((30, 20))
doutput = np.random.randn(30, 20)
nonlinear.dreclin(output, doutput, dinput)
print 'NumPy vs mathutils.nonlinear diff. output:', np.sum(
np.abs(dinput - (input > 0) * doutput))
def train(self,trainset):
if self.stage == 0:
self.initialize(trainset)
for it in range(self.stage,self.n_stages):
for example in trainset:
input,target = example
if len(input.shape) == 1:
input = input.reshape((1,-1))
target = target.reshape((1,-1))
data_vis_bias = np.dot(input,self.V)+self.b
if self.n_inference_iterations > 0:
if self.approximate_inference == 'mean_field':
target_singles = 1./(1+np.exp(-data_vis_bias))
for k in range(self.n_inference_iterations):
if self.damping_factor > 0:
target_singles *= self.damping_factor
target_singles += (1.-self.damping_factor)/(1+np.exp(-data_vis_bias-np.dot(target_singles,self.U)))
else:
target_singles = 1./(1+np.exp(-data_vis_bias-np.dot(target_singles,self.U)))
sum_target_pairs = np.dot(target_singles.T,target_singles)
elif self.approximate_inference == 'loopy_belief_propagation':
target_singles = np.zeros((input.shape[0],self.target_size))
sum_target_pairs = np.zeros((self.target_size,self.target_size))
log_messages = np.zeros((self.target_size,self.target_size))
softplus_out1 = np.zeros((self.target_size,self.target_size))
softplus_out2 = np.zeros((self.target_size,self.target_size))
for i in range(input.shape[0]):
# Loopy BP
data_bias = data_vis_bias[i]
log_messages[:] = 0
for k in range(self.n_inference_iterations):
# Update messages
acc_log_messages = data_bias + log_messages.sum(axis=1) - log_messages
if self.damping_factor > 0:
log_messages *= self.damping_factor
mlnonlin.softplus(acc_log_messages,softplus_out2)
acc_log_messages += self.U
mlnonlin.softplus(acc_log_messages,softplus_out1)
log_messages += (1-self.damping_factor) * (softplus_out1-softplus_out2)
else:
mlnonlin.softplus(acc_log_messages,softplus_out2)
acc_log_messages += self.U
mlnonlin.softplus(acc_log_messages,softplus_out1)
log_messages = softplus_out1-softplus_out2
# Single marginals
target_singles[i] = 1./(1+np.exp(-data_bias-log_messages.sum(axis=1)))
# Pair-wise marginals
acc_log_messages = data_bias + log_messages.sum(axis=1) - log_messages
p11 = np.exp(self.U+acc_log_messages.T+acc_log_messages)
p10 = np.exp(acc_log_messages)
p01 = np.exp(acc_log_messages.T)
sum_p = 1 + p11 + p10 + p01
sum_target_pairs += p11 / sum_p
else:
raise ValueError('approximate_inference \'%\' unknown' % self.approximate_inference)
else:
target_singles = 1./(1+np.exp(-data_vis_bias))
# apply CRF gradient update
db = np.sum(target_singles,axis=0) - np.sum(target,axis=0)
dV = np.dot(input.T,target_singles-target)
self.b -= self.lr/input.shape[0] * db
self.V -= self.lr/input.shape[0] * dV
if self.n_inference_iterations > 0:
dU = sum_target_pairs - np.dot(target.T,target)
self.U -= self.lr/input.shape[0] * dU
# Ensuring symmetry and 0 diagonal
self.U = 0.5*(self.U+self.U.T)
self.U -= np.diag(np.diag(self.U))
self.stage = self.n_stages
doutput = np.random.randn(30, 20)
nonlinear.dsigmoid(output, doutput, dinput)
print 'NumPy vs mathutils.nonlinear diff. output:', np.sum(
np.abs(dinput - doutput * output * (1 - output)))
print 'Testing nonlinear softmax'
input = np.random.randn(20)
output = np.zeros((20))
nonlinear.softmax(input, output)
print 'NumPy vs mathutils.nonlinear diff. output:', np.sum(
np.abs(output - np.exp(input) / np.sum(np.exp(input))))
print 'Testing nonlinear softplus'
input = np.random.randn(20)
output = np.zeros((20))
nonlinear.softplus(input, output)
print 'NumPy vs mathutils.nonlinear diff. output:', np.sum(
np.abs(output - np.log(1 + np.exp(input))))
print 'Testing nonlinear reclin'
input = np.random.randn(30, 20)
output = np.zeros((30, 20))
nonlinear.reclin(input, output)
print 'NumPy vs mathutils.nonlinear diff. output:', np.sum(
np.abs(output - (input > 0) * input))
print 'Testing nonlinear reclin deriv.'
dinput = np.zeros((30, 20))
doutput = np.random.randn(30, 20)
nonlinear.dreclin(output, doutput, dinput)
print 'NumPy vs mathutils.nonlinear diff. output:', np.sum(
print 'Testing nonlinear sigmoid deriv.' dinput = np.zeros((30,20)) doutput = np.random.randn(30,20) nonlinear.dsigmoid(output,doutput,dinput) print 'NumPy vs mathutils.nonlinear diff. output:',np.sum(np.abs(dinput-doutput*output*(1-output))) print 'Testing nonlinear softmax' input = np.random.randn(20) output = np.zeros((20)) nonlinear.softmax(input,output) print 'NumPy vs mathutils.nonlinear diff. output:',np.sum(np.abs(output-np.exp(input)/np.sum(np.exp(input)))) print 'Testing nonlinear softplus' input = np.random.randn(20) output = np.zeros((20)) nonlinear.softplus(input,output) print 'NumPy vs mathutils.nonlinear diff. output:',np.sum(np.abs(output-np.log(1+np.exp(input)))) print 'Testing nonlinear reclin' input = np.random.randn(30,20) output = np.zeros((30,20)) nonlinear.reclin(input,output) print 'NumPy vs mathutils.nonlinear diff. output:',np.sum(np.abs(output-(input>0)*input)) print 'Testing nonlinear reclin deriv.' dinput = np.zeros((30,20)) doutput = np.random.randn(30,20) nonlinear.dreclin(output,doutput,dinput) print 'NumPy vs mathutils.nonlinear diff. output:',np.sum(np.abs(dinput-(input>0)*doutput))