def update_learner(self, example):
self.input[self.input_order] = example
# fprop
np.multiply(self.input, self.W, self.input_times_W)
np.add.accumulate(self.input_times_W[:, :-1],
axis=1,
out=self.acc_input_times_W[:, 1:])
self.acc_input_times_W[:, 0] = 0
self.acc_input_times_W += self.c[:, np.newaxis]
mlnonlin.sigmoid(self.acc_input_times_W, self.hid)
if self.untied_weights:
np.multiply(self.hid, self.V, self.Whid)
else:
np.multiply(self.hid, self.W, self.Whid)
mllin.sum_columns(self.Whid, self.recact)
self.recact += self.b
mlnonlin.sigmoid(self.recact, self.rec)
# bprop
np.subtract(self.rec, self.input, self.drec)
self.drec *= self.alpha
self.db[:] = self.drec
if self.untied_weights:
np.multiply(self.drec, self.hid, self.dV)
np.multiply(self.drec, self.V, self.dhid)
self.dW[:] = 0
else:
np.multiply(self.drec, self.hid, self.dW)
np.multiply(self.drec, self.W, self.dhid)
mlnonlin.dsigmoid(self.hid, self.dhid, self.dacc_input_times_W)
mllin.sum_rows(self.dacc_input_times_W, self.dc)
np.add.accumulate(self.dacc_input_times_W[:, :0:-1],
axis=1,
out=self.dWenc[:, -2::-1])
self.dWenc[:, -1] = 0
self.dWenc *= self.input
self.dW += self.dWenc
self.dW *= self.learning_rate / (
1. + self.decrease_constant * self.n_updates)
self.db *= self.learning_rate / (
1. + self.decrease_constant * self.n_updates)
self.dc *= self.learning_rate / (
1. + self.decrease_constant * self.n_updates)
self.W -= self.dW
self.b -= self.db
self.c -= self.dc
if self.untied_weights:
self.dV *= self.learning_rate / (
1. + self.decrease_constant * self.n_updates)
self.V -= self.dV
self.n_updates += 1
def update_learner(self,example):
self.input[self.input_order] = example
# fprop
np.multiply(self.input,self.W,self.input_times_W)
np.add.accumulate(self.input_times_W[:,:-1],axis=1,out=self.acc_input_times_W[:,1:])
self.acc_input_times_W[:,0] = 0
self.acc_input_times_W += self.c[:,np.newaxis]
mlnonlin.sigmoid(self.acc_input_times_W,self.hid)
if self.untied_weights:
np.multiply(self.hid,self.V,self.Whid)
else:
np.multiply(self.hid,self.W,self.Whid)
mllin.sum_columns(self.Whid,self.recact)
self.recact += self.b
mlnonlin.sigmoid(self.recact,self.rec)
# bprop
np.subtract(self.rec,self.input,self.drec)
self.drec *= self.alpha
self.db[:] = self.drec
if self.untied_weights:
np.multiply(self.drec,self.hid,self.dV)
np.multiply(self.drec,self.V,self.dhid)
self.dW[:] = 0
else:
np.multiply(self.drec,self.hid,self.dW)
np.multiply(self.drec,self.W,self.dhid)
mlnonlin.dsigmoid(self.hid,self.dhid,self.dacc_input_times_W)
mllin.sum_rows(self.dacc_input_times_W,self.dc)
np.add.accumulate(self.dacc_input_times_W[:,:0:-1],axis=1,out=self.dWenc[:,-2::-1])
self.dWenc[:,-1] = 0
self.dWenc *= self.input
self.dW += self.dWenc
self.dW *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.db *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.dc *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.W -= self.dW
self.b -= self.db
self.c -= self.dc
if self.untied_weights:
self.dV *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.V -= self.dV
self.n_updates += 1
def update_learner(self, vec_input):
self.vec_input[self.input_order] = vec_input
#fprop
self.fprop()
#bprob, computing gradient of -log p(vec_input)
np.subtract(self.vec_recProb,self.vec_input,self.vec_grad_bias_inp)
np.multiply(self.vec_grad_bias_inp,self.mat_h,self.mat_grad_V)
np.multiply(self.vec_grad_bias_inp,self.mat_V,self.mat_grad_h)
mlnonlin.dsigmoid(self.mat_h,self.mat_grad_h,self.mat_grad_temp)
mllin.sum_rows(self.mat_grad_temp,self.vec_grad_bias_h)
np.add.accumulate(self.mat_grad_temp[:,:0:-1],axis=1,out=self.mat_grad_W[:,-2::-1])
self.mat_grad_W[:,-1] = 0
self.mat_grad_W *= self.vec_input
#update
self.vec_bias_inp -= self.learning_rate*self.vec_grad_bias_inp
self.vec_bias_h -= self.learning_rate*self.vec_grad_bias_h
self.mat_W -= self.learning_rate*self.mat_grad_W
self.mat_V -= self.learning_rate*self.mat_grad_V
def update_learner(self, vec_input):
self.vec_input[self.input_order] = vec_input
#fprop
self.fprop()
#bprob, computing gradient of -log p(vec_input)
np.subtract(self.vec_recProb, self.vec_input, self.vec_grad_bias_inp)
np.multiply(self.vec_grad_bias_inp, self.mat_h, self.mat_grad_V)
np.multiply(self.vec_grad_bias_inp, self.mat_V, self.mat_grad_h)
mlnonlin.dsigmoid(self.mat_h, self.mat_grad_h, self.mat_grad_temp)
mllin.sum_rows(self.mat_grad_temp, self.vec_grad_bias_h)
np.add.accumulate(self.mat_grad_temp[:, :0:-1],
axis=1,
out=self.mat_grad_W[:, -2::-1])
self.mat_grad_W[:, -1] = 0
self.mat_grad_W *= self.vec_input
#update
self.vec_bias_inp -= self.learning_rate * self.vec_grad_bias_inp
self.vec_bias_h -= self.learning_rate * self.vec_grad_bias_h
self.mat_W -= self.learning_rate * self.mat_grad_W
self.mat_V -= self.learning_rate * self.mat_grad_V
