def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
ns = maxlen
# make matrix here of corresponding h(t)
# hs[-1] = initial hidden state (zeros)
hs = zeros((ns+1, self.hdim))
# predicted probas
ps = zeros((ns, self.vdim))
#### YOUR CODE HERE ####
H = self.params.H
U = self.params.U
L = self.sparams.L
bptt = self.bptt
##
# Forward propagation
for t in xrange(ns):
hs[t + 1] = sigmoid(H.dot(hs[t]) + L[ys[t]])
ps[t] = softmax(U.dot(hs[t + 1]))
ys = ys + [multinomial_sample(ps[t])]
#ys.append(multinomial_sample(ps[t]))
J -= log(ps[t][ys[t]])
if ys[t + 1] == end:
break
if t == ns - 1:
ys = ys + [end]
#### YOUR CODE HERE ####
return ys, J
def fill_unknowns(words, vocab):
ret = words
p = np.array([freq for word, freq in dict(vocab["freq"]).items()])
#print "summize all probability -> ",np.sum(p)
for i in range(len(words)):
if words[i] == "UUUNKKK":
ret[i] = vocab.index[multinomial_sample(p)]
else:
ret[i] = words[i]
return ret
def fill_unknowns(words):
#### YOUR CODE HERE ####
ret = words
for i in xrange(len(ret)):
if ret[i] == 'UUUNKKK':
index = multinomial_sample(vocab.freq)
ret[i] = list(vocab.index)[index]
#### END YOUR CODE ####
return ret
def fill_unknowns(words,vocab):
ret = words
p = np.array([freq for word, freq in dict(vocab["freq"]).items()])
#print "summize all probability -> ",np.sum(p)
for i in range(len(words)):
if words[i] == "UUUNKKK":
ret[i] = vocab.index[multinomial_sample(p)]
else:
ret[i] = words[i]
return ret
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
ns = maxlen
# make matrix here of corresponding h(t)
# hs[-1] = initial hidden state (zeros)
hs = zeros((ns + 1, self.hdim))
# predicted probas
ps = zeros((ns, self.vdim))
for t in xrange(ns):
hs[t] = sigmoid(
self.params.H.dot(hs[t - 1]) + self.sparams.L[ys[t]])
ps[t] = softmax(self.params.U.dot(hs[t]))
ys = ys + [multinomial_sample(ps[t])]
J -= log(ps[t][ys[t]])
if ys[t + 1] == end:
break
if t == ns - 1:
ys = ys + [end]
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
h_ant = zeros((1, self.hdim))
for step in xrange(maxlen):
a1 = self.params.H.dot(h_ant.T).T + self.sparams.L[ys[step]]
h = sigmoid( a1 )
a2 = self.params.U.dot(h.T).T
# print "h.shape %s" % (h.shape,)
# print "a2.shape %s" % (a2.shape,)
# print "self.params.U.shape %s" % (self.params.U.shape,)
y_hat = softmax( a2 )
h_ant = h
ys.append( multinomial_sample(y_hat) )
J -= log( y_hat[:,ys[step]] )
ys.append(end)
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
ps = zeros((maxlen, self.vdim))
hs = zeros((maxlen, self.hdim))
H = self.params.H
L = self.sparams.L
U = self.params.U
start = init
for i in xrange(maxlen):
hs[i+1] = sigmoid(H.dot(hs[i]) + L[start])
ps[i] = softmax(U.dot(hs[i+1]))
start = multinomial_sample(ps[i])
J -= log(ps[i][start])
ys.append(start)
if start == end:
break
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
ns = len(ys)
t = 0
nextIdx = init
hs = zeros((maxlen+1, self.hdim))
ps = zeros((maxlen, self.vdim))
while ns <= maxlen and nextIdx != end:
hs[t] = sigmoid(self.params.H.dot(hs[t-1]) + self.sparams.L[ys[t]])
ps[t] = softmax(self.params.U.dot(hs[t]))
J -= log(ps[t,ys[t]])
nextIdx = multinomial_sample(ps[t])
ys.append(nextIdx)
ns = len(ys)
t += 1
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
h_ant = zeros((1, self.hdim))
for step in xrange(maxlen):
a1 = self.params.H.dot(h_ant.T).T + self.sparams.L[ys[step]]
h = sigmoid(a1)
a2 = self.params.U.dot(h.T).T
# print "h.shape %s" % (h.shape,)
# print "a2.shape %s" % (a2.shape,)
# print "self.params.U.shape %s" % (self.params.U.shape,)
y_hat = softmax(a2)
h_ant = h
ys.append(multinomial_sample(y_hat))
J -= log(y_hat[:, ys[step]])
ys.append(end)
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
ns = maxlen
hs = np.zeros((ns+1,self.hdim))
#### YOUR CODE HERE ####
for i in range(ns):
hs[i+1] = sigmoid(self.params.H.dot(hs[i])+self.params.W.dot(self.sparams.L[ys[i]]))
p = self.hierarchicalU.getDistribution(hs[i+1])
y = multinomial_sample(p)
ys.append(y)
if y == end:
break
p = p*make_onehot(y,self.vdim)
J += -np.log(np.sum(p))
##
#x only compute the node which gradient is updated
x = self.hierarchicalU.getSumSquareU(self.hierarchicalU.root)
Jreg = 0.5*self.lreg*(np.sum(self.params.H**2)+np.sum(self.params.W**2)+ x)
#### YOUR CODE HERE ####
return ys, J+Jreg
def generate_sequence(self, init, end, h0, maxlen=100, words = []):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
if len(words) > 0:
ys.extend(words)
hs = zeros((maxlen + 1, self.hdim))
hs[-1] = h0
ps = zeros((maxlen, self.vdim))
t = 0
while ys[t] != end and t < maxlen:
# print ys[-1]
hs[t, :] = sigmoid((self.params.H.dot(hs[t - 1, :].T)).T + self.sparams.L[ys[t], :])
ps[t, :] = softmax(self.params.U.dot(hs[t, :].T)).T
# y = argmax(ps[t, :])
y = multinomial_sample(ps[t, :])
if t >= len(words):
ys.append(y)
J += - log(ps[t, y])
t += 1
return ys, J
def generate_sequence(self, d, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
hs = zeros((maxlen+1, self.hdim))
curr = init
t = 0
d_vec = self.sparams.D[d]
while curr != end and len(ys) < maxlen:
x_t = curr
zs_t = self.params.H.dot(hs[t-1]) + self.sparams.L[x_t] + d_vec
hs[t] = sigmoid(zs_t)
ps_t = softmax(self.params.U.dot(hs[t]) + self.params.G.dot(d_vec))
y = multinomial_sample(ps_t)
ys.append(y)
curr = y
J += -1*log(ps_t[y])
t += 1
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
h = zeros((self.hdim,))
for i in xrange(maxlen):
h = sigmoid(dot(self.params.H, h) + self.sparams.L[ys[i]])
probs = softmax(dot(self.sparams.U, h))
sampled_word = multinomial_sample(probs**0.75) # 0.75 turns the temperature up -> more fun
ys.append(sampled_word)
J -= log(probs[sampled_word])
if sampled_word == end:
break
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = list of index of start words (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = init # emitted sequence
#### YOUR CODE HERE ####
h = np.zeros(self.hdim)
for x in ys:
z = self.params.H.dot(h) + self.sparams.L[x]
h = sigmoid(z)
while ys[-1] != end:
x = ys[-1]
z = self.params.H.dot(h) + self.sparams.L[x]
h = sigmoid(z)
y_hat = softmax(self.params.U.dot(h))
y = multinomial_sample(y_hat)
J -= np.log(y_hat[y])
ys.append(y)
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
h = zeros(self.hdim)
t = 1
while t < maxlen:
# shape Dh
h = sigmoid(self.params.H.dot(h) + self.sparams.L[ys[t-1]])
# shape V
p = softmax(self.params.U.dot(h))
ys += [multinomial_sample(p)]
J += -log(p[ys[t]])
if ys[t] == end:
break
t += 1
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#generate one word and let it be next input
#### YOUR CODE HERE ####
t = 0
hs = zeros(self.hdim)
while True:
if (len(ys) > maxlen) or (ys[-1] == end):
break
hs = sigmoid(self.params.H.dot(hs) + self.sparams.L[ys[t]])
ps = softmax(self.params.U.dot(hs))
y = multinomial_sample(ps)
J += -log(ps[y])
ys.append(y)
t += 1
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
h = zeros(self.hdim)
for t in xrange(1, maxlen + 1):
x = ys[t - 1]
h = sigmoid(dot(self.params.H, h) + self.sparams.L[x])
y_hat = softmax(dot(self.params.U, h))
y = multinomial_sample(y_hat)
print "selected %d of %d with p=%f" % (y, y_hat.shape[0], y_hat[y])
J += -log(y_hat[y])
ys.append(y)
ys.append(end)
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
ns = maxlen
hs = zeros((ns+1, self.hdim)) # hidden layers, hs[-1] is the initial, which is zeros here
ps = zeros((ns, self.vdim)) # predicted probas
for t in xrange(maxlen):
hs[t] = sigmoid(self.params.H.dot(hs[t - 1]) + self.sparams.L[ys[t]])
probs = softmax(self.params.U.dot(hs[t]))
y = multinomial_sample(probs)
ys.append(y)
J += - log(probs[y])
if y == end:
break
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
hs = zeros((maxlen+1, self.hdim))
ps = zeros((maxlen, self.vdim))
for w in range(maxlen):
z1 = self.params.H.dot(hs[w-1]) + self.sparams.L[ys[w]]
hs[w] = sigmoid(z1)
z2 = self.params.U.dot(hs[w])
ps = softmax(z2)
y = multinomial_sample(ps)
ys.append(y)
J += -log(ps[y])
if y == end:
break
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
hs = zeros((maxlen + 1, self.hdim))
ps = zeros((maxlen, self.vdim))
for w in range(maxlen):
z1 = self.params.H.dot(hs[w - 1]) + self.sparams.L[ys[w]]
hs[w] = sigmoid(z1)
z2 = self.params.U.dot(hs[w])
ps = softmax(z2)
y = multinomial_sample(ps)
ys.append(y)
J += -log(ps[y])
if y == end:
break
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
hs = zeros((maxlen+1, self.hdim))
for t in xrange(maxlen):
hs[t] = sigmoid(dot(self.params.H, hs[t - 1]) + self.sparams.L[ys[t]])
y_hat = softmax(dot(self.params.U, hs[t]))
y_index = multinomial_sample(y_hat)
ys.append(y_index)
J -= log(y_hat[y_index])
if y_index == end:
break
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
#### YOUR CODE HERE ####
t = 0
hs = zeros(self.hdim)
while True:
if (len(ys) > maxlen) or (ys[-1] == end):
break
hs = sigmoid(self.params.H.dot(hs) + self.sparams.L[ys[t], :])
ps = softmax(self.params.U.dot(hs))
y = multinomial_sample(ps)
J += -log(ps[y])
ys.append(y)
t += 1
#### YOUR CODE HERE ####
return ys, J
def generate_sequence(self, init, end, maxlen=100):
"""
Generate a sequence from the language model,
by running the RNN forward and selecting,
at each timestep, a random word from the
a word from the emitted probability distribution.
The MultinomialSampler class (in nn.math) may be helpful
here for sampling a word. Use as:
y = multinomial_sample(p)
to sample an index y from the vector of probabilities p.
Arguments:
init = index of start word (word_to_num['<s>'])
end = index of end word (word_to_num['</s>'])
maxlen = maximum length to generate
Returns:
ys = sequence of indices
J = total cross-entropy loss of generated sequence
"""
J = 0 # total loss
ys = [init] # emitted sequence
ht = zeros(self.hdim)
#### YOUR CODE HERE ####
for t in range(maxlen):
if ys[-1] == end:
break
ht = sigmoid(dot(self.params.H, ht) + self.sparams.L[ys[t], :])
pt = softmax(dot(self.params.U, ht))
yt = multinomial_sample(pt)
ys.append(yt)
J -= log(pt[yt])
#### YOUR CODE HERE ####
return ys, J
