def test_path_layer_train():
init_rng()
class_num = 3
trans_mat = np.zeros((class_num, class_num))
trans_mat[0, 0] = 0.8
trans_mat[0, 1] = 0.1
trans_mat[0, 2] = 0.1
trans_mat[1, 0] = 0.1
trans_mat[1, 1] = 0.1
trans_mat[1, 2] = 0.8
trans_mat[2, 0] = 0.1
trans_mat[2, 1] = 0.1
trans_mat[2, 2] = 0.8
def gen_train(trans_mat, sample_num, class_num):
X = []
Y = []
x = [1.0 / class_num for i in xrange(class_num)]
pre_y = 0
for _i in xrange(sample_num):
X.append(x)
trans_prob = trans_mat[pre_y]
y = np.random.choice(class_num, 1, p=trans_prob)[0]
Y.append(y)
pre_y = y
return np.asarray(X, dtype=np.float32), np.asarray(Y, dtype=np.int32)
X, Y = gen_train(trans_mat, 100, class_num)
print Counter(Y)
trans_mat_prior = np.zeros((class_num + 1, class_num))
trans_mat_prior[0] = X[0]
trans_mat_prior[1:] = trans_mat
print "init trains mat"
print trans_mat_prior
# layer = PathTransitionLayer(class_num,trans_mat_prior)
layer = PathTransitionLayer(class_num)
train_x = T.fmatrix("x")
train_y = T.ivector("y")
cost = layer.cost(train_x, train_y)
params = layer.params()
gparams = T.grad(cost, params)
updates = []
learning_rate = 0.01
for param, gparam in zip(params, gparams):
updates.append((param, param - learning_rate * gparam))
iternum = 10
for i in xrange(iternum):
X, Y = gen_train(trans_mat, 100, class_num)
foo = theano.function(inputs=[train_x, train_y], outputs=[cost], updates=updates)
print "%d,cost=%f" % (i, foo(X, Y)[0])
trained_trans_mat = layer.params()[0].eval()
print trained_trans_mat
print "training done."
def test_path_transition_layer():
init_rng()
sample_num = 5
class_num = 10
X = theano.tensor.nnet.softmax(np.random.random((sample_num, class_num)))
y = (9, 9, 9, 9, 9)
layer = PathTransitionLayer(class_num)
cost = layer.cost(X, y).eval()
y_pred = layer.predict(X).eval()
y_pred_cost = layer.cost(X, y_pred).eval()
print "optimized cost = ", cost
print "y_pred = ", y_pred
print "y_pred_cost", y_pred_cost
assert y_pred_cost < cost
y_score = 1000
logadd_score = 0.0
trans_mat = theano.tensor.nnet.softmax(layer.tag_trans_matrix).eval()
X = X.eval()
for path in itertools.product(range(class_num), repeat=sample_num):
score = trans_mat[0, path[0]] + X[0, path[0]]
for idx in range(1, sample_num):
score += trans_mat[path[idx - 1] + 1, path[idx]] + X[idx, path[idx]]
score = score # .eval()
logadd_score += math.exp(score)
if path == y:
y_score = score
logadd_score = math.log(logadd_score)
bruteforce_cost = logadd_score - y_score
print "bruteforce cost = {0} with logadd = {1} and selected_path_score = {2}".format(
bruteforce_cost, logadd_score, y_score
)
bruteforce_y_pred = np.argmax(X, axis=1) # because trans_mat is const matrix
print "brueforce y_pred = ", bruteforce_y_pred
assert math.fabs(bruteforce_cost - cost) < 1e-6
assert not np.any(y_pred - bruteforce_y_pred)
def test_path_transition_layer2():
init_rng()
sample_num = 5
class_num = 10
y1 = (9, 9, 9, 9, 9)
X1 = np.zeros((sample_num, class_num))
X1[range(sample_num), y1] = 1
y2 = (0, 1, 2, 3, 4)
X2 = np.zeros((sample_num, class_num))
X2[range(sample_num), y2] = 1
layer = PathTransitionLayer(class_num)
cost1 = layer.cost(X1, y1).eval()
cost2 = layer.cost(X2, y2).eval()
cost1_2 = layer.cost(X1, y2).eval()
cost2_1 = layer.cost(X2, y1).eval()
y_pred1 = layer.predict(X1).eval()
y_pred2 = layer.predict(X2).eval()
print "X1 = ", X1
print "X2 = ", X2
print "y1 = ", y1
print "y2 = ", y2
print "cost1 = ", cost1
print "cost2 = ", cost2
print "cost1_2 = ", cost1_2
print "cost2_1 = ", cost2_1
print "y_pred1 = ", y_pred1
print "y_pred2 = ", y_pred2
def __init__(self,rng,x,y,sent_length,masks,
model_params):
# x shpae: (batch_size,max_term_per_sent+3,max_sentence_length)
# ,where max_sentence_length = max_term_per_sent + window_size - 1
self.L1_reg = model_params['L1_reg']
self.L2_reg = model_params['L2_reg']
self.x = x
self.y = y
self.sent_length = sent_length
self.masks = masks
self.max_sentence_length = model_params['max_sentence_length']
self.window_size = model_params['window_size']
self.max_term_per_sent = self.max_sentence_length - self.window_size + 1
self.word_num = model_params['word_num']
self.POS_num = model_params['POS_num']
self.verbpos_num = model_params['verbpos_num']
self.wordpos_num = model_params['wordpos_num']
self.word_feature_num = model_params['word_feature_num']
self.POS_feature_num = model_params['POS_feature_num']
self.wordpos_feature_num = model_params['wordpos_feature_num']
self.verbpos_feature_num = model_params['verbpos_feature_num']
self.conv_window = model_params['conv_window']
self.conv_hidden_feature_num = model_params['conv_hidden_feature_num']
self.hidden_layer_size = model_params['hidden_layer_size']
self.tags_num = model_params['tags_num']
# we have 4 lookup tables here:
# 1,word vector
# output shape: (batch size,1,max_sentence_length * word_feature_num)
# 2,POS tag vector
# output shape: (batch size,1,max_sentence_length * POS_feature_num)
# 3,verb position vector
# output shape: (batch size,1,max_sentence_length * verbpos_feature_num)
# 4,word position vector
# output shape: (batch size,max_term_per_sent,1,max_sentence_length * wordpos_feature_num)
self.wordvec = LookupTableLayer(inputs = x[:,0:1,:], table_size = self.word_num,
window_size = self.max_sentence_length, feature_num = self.word_feature_num,
reshp = (x.shape[0],1,1,x.shape[2] * self.word_feature_num))
self.POSvec = LookupTableLayer(inputs = x[:,1:2,:], table_size = self.POS_num,
window_size = self.max_sentence_length, feature_num = self.POS_feature_num,
reshp = (x.shape[0],1,1,x.shape[2] * self.POS_feature_num))
self.verbpos_vec = LookupTableLayer(inputs = x[:,2:3,:], table_size = self.verbpos_num,
window_size = self.max_sentence_length, feature_num = self.verbpos_feature_num,
reshp = (x.shape[0],1,1,x.shape[2] * self.verbpos_feature_num))
self.wordpos_vec = LookupTableLayer(inputs = x[:,3:,:], table_size = self.wordpos_num,
window_size = self.max_sentence_length, feature_num = self.wordpos_feature_num,
reshp = (x.shape[0],self.max_term_per_sent,1,x.shape[2] * self.wordpos_feature_num))
# conv_word.out.shape = (batch_size,1,conv_hidden_feature_num,max_sentence_length-conv_window+1)
# conv_POS.out.shape = (batch_size,1,conv_hidden_feature_num,max_sentence_length-conv_window+1)
# conv_verbpos.out.shape = (batch_size,1,conv_hidden_feature_num,max_sentence_length-conv_window+1)
# conv_wordpos.out.shape = (batch_size,max_sentence_length,conv_hidden_feature_num,max_sentence_length-conv_window+1)
# note. all output above have been seted 'dimshuffle'
self.conv_word = Conv1DLayer('conv_word',rng,self.wordvec.output,\
self.conv_hidden_feature_num,1,self.conv_window,self.word_feature_num)
self.conv_POS = Conv1DLayer('conv_POS',rng,self.POSvec.output,\
self.conv_hidden_feature_num,1,self.conv_window,self.POS_feature_num)
self.conv_verbpos = Conv1DLayer('conv_verbpos',rng,self.verbpos_vec.output,\
self.conv_hidden_feature_num,1,self.conv_window,self.verbpos_feature_num)
self.conv_wordpos = Conv1DLayer('conv_wordpos',rng,self.wordpos_vec.output,\
self.conv_hidden_feature_num,self.max_term_per_sent,self.conv_window,self.wordpos_feature_num)
# the first max_sentence_length means each element of it is one prediction for that word
# the second max_sentence_length means each element of it is one output of conv
# conv_out shape: (batch_size,max_term_per_sent,conv_hidden_feature_num,max_term_per_sent)
self.conv_out = self.conv_word.output + self.conv_POS.output + self.conv_verbpos.output + self.conv_wordpos.output
self.conv_out = self.conv_out.dimshuffle(1,0,2,3,4).reshape((x.shape[0],self.max_term_per_sent,self.conv_hidden_feature_num,-1))
# max_out shape: (batch_size,max_term_per_sent,conv_hidden_feature_num)
self.max_out = T.max(self.conv_out,axis=3).reshape((self.conv_out.shape[0],self.max_term_per_sent,-1))
# hidden layer
# hidden layer perform one linear map and one nolinear transform
# ,then in likelihood, it performs another linear map. This is
# what senna do (P.7, figure 1).
# hidden_layer OUTPUT SHAPE: (batch_size, max_term_per_sent, hidden_layer_size)
self.hidden_layer = HiddenLayer(rng=rng, input=self.max_out,
n_in = self.conv_hidden_feature_num,
n_out = self.hidden_layer_size,
activation=T.tanh)
# TODO we use poitwise likelihood here
self.sentce_loglikelihood = PathTransitionLayer(rng,self.hidden_layer.output,
self.y,self.masks,
self.max_term_per_sent,
self.hidden_layer_size,
self.tags_num)
self._likelihood = self.sentce_loglikelihood.negative_log_likelihood_pointwise()
self._errors = self.sentce_loglikelihood.errors()