def build_model(self, train_set, test_set, validation_set):
"""
Building the model should be done prior to training. It will implement the training, testing and validation
functions.
This method should be called from any subsequent inheriting model.
:param loss: The loss funciton applied to training (cf. updates.py), e.g. mse.
:param update: The update function (optimization framework) used for training (cf. updates.py), e.g. sgd.
:param update_args: The args for the update function applied to training, e.g. (0.001,).
"""
print "### BUILDING MODEL ###"
self.train_args = {}
self.train_args['inputs'] = OrderedDict({})
self.train_args['outputs'] = OrderedDict({})
self.test_args = {}
self.test_args['inputs'] = OrderedDict({})
self.test_args['outputs'] = OrderedDict({})
self.validate_args = {}
self.validate_args['inputs'] = OrderedDict({})
self.validate_args['outputs'] = OrderedDict({})
self.sym_index = T.iscalar('index')
self.sym_batchsize = T.iscalar('batchsize')
self.sym_lr = T.scalar('learningrate')
self.batch_slice = slice(self.sym_index * self.sym_batchsize, (self.sym_index + 1) * self.sym_batchsize)
self.sh_train_x = theano.shared(np.asarray(train_set[0], dtype=theano.config.floatX), borrow=True)
self.sh_train_t = theano.shared(np.asarray(train_set[1], dtype=theano.config.floatX), borrow=True)
self.sh_test_x = theano.shared(np.asarray(test_set[0], dtype=theano.config.floatX), borrow=True)
self.sh_test_t = theano.shared(np.asarray(test_set[1], dtype=theano.config.floatX), borrow=True)
if validation_set is not None:
self.sh_valid_x = theano.shared(np.asarray(validation_set[0], dtype=theano.config.floatX), borrow=True)
self.sh_valid_t = theano.shared(np.asarray(validation_set[1], dtype=theano.config.floatX), borrow=True)
def getMinibatchTrainer(self, costFunction, variableToData, rms=True):
# define params
lr = T.fscalar('lr')
start = T.iscalar('start')
end = T.iscalar('end')
# Get the cost and its parameters.
params = costFunction[0]
cost = costFunction[1]
# Get the updates.
updates = self.getUpdates(cost, params, lr, rms)
# Store all state variables.
stateManager = StateManager([u[0] for u in updates])
# Slice the data
givens = dict()
for item in variableToData:
givens[item.variable] = item.slice(start,end)
# Define the training function.
train_model = theano.function(inputs=[theano.Param(start, borrow = True),
theano.Param(end, borrow=True),
theano.Param(lr, borrow=True)],
outputs=theano.Out(cost, borrow=True),
updates=updates,
givens=givens)
return train_model, stateManager
def f_train(self, t_x, t_corrupt = 0.2, t_rate = 0.1):
""" return training function of the following signiture:
input:
lower and upper indices on training data
alternative training data
return:
likelihood based cost
square distance between training data and prediction
"""
x = T.matrix('x') # pipe data through this symble
q = self.t_corrupt(x, t_corrupt)
h = self.t_encode(q)
z = self.t_decode(h)
L = - T.sum(x * T.log(z) + (1 - x) * T.log(1 - z), axis=1)
cost = T.mean(L) # to be returned
dist = T.mean(T.sqrt(T.sum((x - z) ** 2, axis = 1))) # to be returned
grad = T.grad(cost, self.parm)
diff = [(p, p - t_rate * g) for p, g in zip(self.parm, grad)]
t_fr = T.iscalar()
t_to = T.iscalar()
return theano.function(
[t_fr, t_to],
[cost, dist],
updates = diff,
givens = {x : t_x[t_fr:t_to]},
name = "DA_trainer")
def SimFnIdx(fnsim, embeddings, leftop, rightop):
"""
This function returns a Theano function to measure the similarity score
for a given triplet of entity indexes.
:param fnsim: similarity function (on Theano variables).
:param embeddings: an Embeddings instance.
:param leftop: class for the 'left' operator.
:param rightop: class for the 'right' operator.
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxo = T.iscalar('idxo')
idxr = T.iscalar('idxr')
idxl = T.iscalar('idxl')
# Graph
lhs = (embedding.E[:, idxl]).reshape((1, embedding.D))
rhs = (embedding.E[:, idxr]).reshape((1, embedding.D))
rell = (relationl.E[:, idxo]).reshape((1, relationl.D))
relr = (relationr.E[:, idxo]).reshape((1, relationr.D))
simi = fnsim(leftop(lhs, rell), rightop(rhs, relr))
"""
Theano function inputs.
:input idxl: index value of the 'left' member.
:input idxr: index value of the 'right' member.
:input idxo: index value of the relation member.
Theano function output.
:output simi: score value.
"""
return theano.function([idxl, idxr, idxo], [simi],
on_unused_input='ignore')
def RankRightFnIdx_filtered(fnsim, embeddings, leftop, rightop, subtensorspec=None):
"""
This function returns a Theano function to measure the similarity score of
all 'right' entities given couples of relation and 'left' entities (as
index values).
"""
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxl, idxo = T.iscalar('idxl'), T.iscalar('idxo')
rightparts = T.ivector('rightparts')
# Graph
lhs = (embedding.E[:, idxl]).reshape((1, embedding.D)) # lhs: 1xD vector containing the embedding of idxl
if subtensorspec is not None:
# We compute the score only for a subset of entities
rhs = (embedding.E[:, :subtensorspec]).T
else:
rhs = embedding.E.T # rhs: NxD embedding matrix
rhs = rhs[rightparts, :] # select the right parts not appearing
# in the train/valid/test sets
rell = (relationl.E[:, idxo]).reshape((1, relationl.D)) # rell: 1xD vector containing the embedding of idxo (relationl)
relr = (relationr.E[:, idxo]).reshape((1, relationr.D)) # relr: 1xD vector containing the embedding of idxo (relationr)
tmp = leftop(lhs, rell) # a = rell(lhs)
# b = relr(rhs)
simi = fnsim(tmp.reshape((1, tmp.shape[1])), rightop(rhs, relr)) # simi = fnsim(a, b)
return theano.function([idxl, idxo, rightparts], [simi], on_unused_input='ignore')
def RankRightFnIdx_Schema(fnsim, embeddings, prior, leftop, rightop, subtensorspec=None):
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxl, idxo = T.iscalar('idxl'), T.iscalar('idxo')
g = T.matrix('g')
# Graph
lhs = (embedding.E[:, idxl]).reshape((1, embedding.D)) # lhs: 1xD vector containing the embedding of idxl
if subtensorspec is not None:
# We compute the score only for a subset of entities
rhs = (embedding.E[:, :subtensorspec]).T
else:
rhs = embedding.E.T # rhs: NxD embedding matrix
rell = (relationl.E[:, idxo]).reshape((1, relationl.D)) # rell: 1xD vector containing the embedding of idxo (relationl)
relr = (relationr.E[:, idxo]).reshape((1, relationr.D)) # relr: 1xD vector containing the embedding of idxo (relationr)
tmp = leftop(lhs, rell) # a = rell(lhs)
# b = relr(rhs)
# Negative Energy
simi = fnsim(tmp.reshape((1, tmp.shape[1])), rightop(rhs, relr)) # simi = fnsim(a, b)
pen_simi = g[0, :].T * prior.P[idxo, 0].T + g[1, :].T * prior.P[idxo, 1].T
simi = simi - pen_simi
return theano.function([idxl, idxo, g], [simi], on_unused_input='ignore')
def build_model(shared_params, options, other_params):
"""
Build the complete neural network model and return the symbolic variables
"""
# symbolic variables
x = tensor.matrix(name="x", dtype=floatX)
y1 = tensor.iscalar(name="y1")
y2 = tensor.iscalar(name="y2")
# lstm cell
(ht, ct) = lstm_cell(x, shared_params, options, other_params) # gets the ht, ct
# softmax 1 i.e. frame type prediction
activation = tensor.dot(shared_params['softmax1_W'], ht).transpose() + shared_params['softmax1_b']
frame_pred = tensor.nnet.softmax(activation) # .transpose()
# softmax 2 i.e. gesture class prediction
#
# predicted probability for frame type
f_pred_prob = theano.function([x], frame_pred, name="f_pred_prob")
# predicted frame type
f_pred = theano.function([x], frame_pred.argmax(), name="f_pred")
# cost
cost = ifelse(tensor.eq(y1, 1), -tensor.log(frame_pred[0, 0] + options['log_offset'])
* other_params['begin_cost_factor'],
ifelse(tensor.eq(y1, 2), -tensor.log(frame_pred[0, 1] + options['log_offset'])
* other_params['end_cost_factor'],
ifelse(tensor.eq(y1, 3), -tensor.log(frame_pred[0, 2] + options['log_offset']),
tensor.abs_(tensor.log(y1)))), name='ifelse_cost')
# function for output of the currect lstm cell and softmax prediction
f_model_cell_output = theano.function([x], (ht, ct, frame_pred), name="f_model_cell_output")
# return the model symbolic variables and theano functions
return x, y1, y2, f_pred_prob, f_pred, cost, f_model_cell_output
def RankLeftFnIdx_Schema(fnsim, embeddings, prior, leftop, rightop, subtensorspec=None):
embedding, relationl, relationr = parse_embeddings(embeddings)
# Inputs
idxr, idxo = T.iscalar('idxr'), T.iscalar('idxo')
g = T.matrix('g')
# Graph
if subtensorspec is not None:
# We compute the score only for a subset of entities
lhs = (embedding.E[:, :subtensorspec]).T
else:
lhs = embedding.E.T
rhs = (embedding.E[:, idxr]).reshape((1, embedding.D))
rell = (relationl.E[:, idxo]).reshape((1, relationl.D))
relr = (relationr.E[:, idxo]).reshape((1, relationr.D))
tmp = rightop(rhs, relr)
simi = fnsim(leftop(lhs, rell), tmp.reshape((1, tmp.shape[1])))
pen_simi = g[0, :].T * prior.P[idxo, 0].T + g[1, :].T * prior.P[idxo, 1].T
simi = simi - pen_simi
return theano.function([idxr, idxo, g], [simi], on_unused_input='ignore')
def getTrainer(self,lossType="NLL"):
'''
return a function to do MBSGD on (trainX,trainY)
'''
trainY = T.ivector('y')
alpha = T.dscalar('a')
lowIdx = T.iscalar()
highIdx = T.iscalar()
trainX = T.matrix()
if lossType=="aNLL":
loss = self.aNLL(trainY)
elif lossType=='MSE':
loss = self.MSE(trainY)
else:
loss = self.NLL(trainY)
dW = T.grad(cost = loss, wrt = self.W)
db = T.grad(cost = loss, wrt = self.b)
updates = [(self.W,self.W - alpha * dW), (self.b,self.b - alpha * db)]
trainer = theano.function(
inputs = [trainX,trainY,alpha],
outputs = loss,
updates=updates,
givens = {
self.input : trainX,
},
allow_input_downcast=True
)
return trainer
def __init__(self, in_size, out_size, dim_y, dim_pos, hidden_size_encoder, hidden_size_decoder, cell = "gru", optimizer = "rmsprop", p = 0.5, num_sents = 1):
self.X = T.matrix("X")
self.Y_y = T.matrix("Y_y")
self.Y_pos = T.matrix("Y_pos")
self.in_size = in_size
self.out_size = out_size
self.dim_y = dim_y
self.dim_pos = dim_pos
self.hidden_size_encoder = hidden_size_encoder
self.hidden_size_decoder = hidden_size_decoder
self.cell = cell
self.drop_rate = p
self.num_sents = num_sents
self.is_train = T.iscalar('is_train') # for dropout
self.batch_size = T.iscalar('batch_size') # for mini-batch training
self.mask = T.matrix("mask")
self.mask_y = T.matrix("mask_y")
self.optimizer = optimizer
print "seq2seq out size ", self.out_size
if self.out_size == self.dim_y + self.dim_pos:
print "size right !"
self.define_layers()
self.define_train_test_funcs()
def multMatVect(v, A, m1, B, m2):
# TODO : need description for parameter and return
"""
Multiply the first half of v by A with a modulo of m1 and the second half
by B with a modulo of m2.
Notes
-----
The parameters of dot_modulo are passed implicitly because passing them
explicitly takes more time than running the function's C-code.
"""
if multMatVect.dot_modulo is None:
A_sym = tensor.lmatrix('A')
s_sym = tensor.ivector('s')
m_sym = tensor.iscalar('m')
A2_sym = tensor.lmatrix('A2')
s2_sym = tensor.ivector('s2')
m2_sym = tensor.iscalar('m2')
o = DotModulo()(A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym)
multMatVect.dot_modulo = function(
[A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym], o, profile=False)
# This way of calling the Theano fct is done to bypass Theano overhead.
f = multMatVect.dot_modulo
f.input_storage[0].storage[0] = A
f.input_storage[1].storage[0] = v[:3]
f.input_storage[2].storage[0] = m1
f.input_storage[3].storage[0] = B
f.input_storage[4].storage[0] = v[3:]
f.input_storage[5].storage[0] = m2
f.fn()
r = f.output_storage[0].storage[0]
return r
def test_compute_lnZ(self):
v = T.matrix('v')
z = T.iscalar('z')
V = cartesian([(0, 1)] * self.input_size, dtype=config.floatX)
#H = cartesian([(0, 1)] * self.hidden_size, dtype=config.floatX)
# We simulate having an infinite number of hidden units by adding lot of hidden units with parameters set to 0.
nb_hidden_units_to_add = 10000
model = iRBM(input_size=self.model.input_size,
hidden_size=self.model.hidden_size + nb_hidden_units_to_add,
beta=self.model.beta.get_value())
model.W.set_value(np.r_[self.model.W.get_value(), np.zeros((nb_hidden_units_to_add, model.input_size), dtype=theano.config.floatX)])
model.b.set_value(np.r_[self.model.b.get_value(), np.zeros((nb_hidden_units_to_add,), dtype=theano.config.floatX)])
model.c.set_value(self.model.c.get_value())
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
energies = []
for z in range(1, model.hidden_size+1):
energies.append(F_vz(V, z))
lnZ = logsumexp(-np.array(energies)).eval()
lnZ_using_free_energy = theano.function([v], logsumexp(-self.model.free_energy(v)))
assert_almost_equal(lnZ_using_free_energy(V), lnZ, decimal=5) # decimal=5 needed for float32
def compile_functions(self, opt, **args):
print '... compiling training functions'
gen_cost, gen_show_cost, dis_cost, cost_pfake, cost_ptrue = self.get_cost()
self.opt = opt
gen_updates = self.opt.get_updates(gen_cost, self.gen_params)
dis_updates = self.opt.get_updates(dis_cost, self.dis_params)
self.get_noise = theano.function([],
self.theano_rng.uniform(size=(self.batch_size, self.num_z),
low=-1, high=1)
)
start_index = T.iscalar('start_index')
end_index = T.iscalar('end_index')
if self.uint8_data:
given_train_x = T.cast(self.shared_train[start_index:end_index], dtype='float32')
else:
given_train_x = self.shared_train[start_index:end_index]
self.train_gen_model = theano.function(
[self.z],
gen_show_cost,
updates=gen_updates,
)
self.train_dis_model = theano.function(
[start_index, end_index, self.z],
[cost_pfake, cost_ptrue],
updates=dis_updates,
givens={self.x: given_train_x}
)
def compile(self, model):
assert isinstance(model, Model)
self.model = model
dataset = self.dataset
X, Y = dataset.preproc(dataset.X, dataset.Y)
self.X = theano.shared(X, "X")
self.Y = theano.shared(Y, "Y")
self.logger.info("compiling do_loglikelihood")
n_samples = T.iscalar("n_samples")
batch_idx = T.iscalar("batch_idx")
batch_size = T.iscalar("batch_size")
first = batch_idx * batch_size
last = first + batch_size
X_batch, Y_batch = dataset.late_preproc(self.X[first:last], self.Y[first:last])
log_PX, _, _, _, KL, Hp, Hq = model.log_likelihood(X_batch, n_samples=n_samples)
batch_L = T.sum(log_PX)
batch_L2 = T.sum(log_PX ** 2)
batch_KL = [T.sum(kl) for kl in KL]
batch_Hp = [T.sum(hp) for hp in Hp]
batch_Hq = [T.sum(hq) for hq in Hq]
self.do_loglikelihood = theano.function(
inputs=[batch_idx, batch_size, n_samples],
outputs=[batch_L, batch_L2] + batch_KL + batch_Hp + batch_Hq,
name="do_likelihood",
)
def compile_bn(data_set, model, make_updates):
"""
データをsharedにして、modelとoptimizerを使ってcomputational graphを作って、
コンパイルする。
Parameters
-----------
data_set : list of numpy.ndarray
feature_vec : ndarray
(n_pixels, D, n_tensors)
gt_vec : ndarray
(n_pixels, D)
test_feature_vec, test_gt_vec
model : models.Rcn1layerとか
optimizer : optimizers.SGDとか
"""
s_input, s_target, s_test_input, s_test_target = share_data_sets(*data_set)
nn, obj, train_mse, model_updates, model_param_l = model.make_graph_train()
test_mse, test_out = model.make_graph_test()
updates, opt_param_list = make_updates(loss=obj, param_list=nn.param_l)
i_batch = T.iscalar("i_batch")
index_list = T.ivector("index_list")
batch_size = T.iscalar("batch_size")
od = OrderedDict()
for k, e in updates.items() + model_updates.items():
od[k] = e
f_train = theano.function(
inputs=[i_batch, index_list, batch_size]+opt_param_list+model_param_l,
updates=od,
givens=[(nn.x_t3, s_input[index_list[i_batch*batch_size: i_batch*batch_size + batch_size]]),
(nn.t_mat, s_target[index_list[i_batch*batch_size: i_batch*batch_size + batch_size]])],
on_unused_input='warn')
f_training_error = theano.function(
inputs=[i_batch, index_list, batch_size]+model_param_l,
outputs=[train_mse],
givens=[(nn.x_t3, s_input[index_list[i_batch*batch_size: i_batch*batch_size + batch_size]]),
(nn.t_mat, s_target[index_list[i_batch*batch_size: i_batch*batch_size + batch_size]])],
on_unused_input='warn')
f_test_error = theano.function(
inputs=[i_batch, index_list, batch_size],
outputs=[test_mse],
givens=[(nn.x_t3, s_test_input[index_list[i_batch*batch_size: i_batch*batch_size + batch_size]]),
(nn.t_mat, s_test_target[index_list[i_batch*batch_size: i_batch*batch_size + batch_size]])])
f_output = theano.function(
inputs=[nn.x_t3],
outputs=[test_out])
result = [f_train, f_training_error, f_test_error, f_output, s_input,
s_target, s_test_input, s_test_target, nn.param_l]
return result
def multMatVect(v, A, m1, B, m2):
"""
multiply the first half of v by A with a modulo of m1
and the second half by B with a modulo of m2
Note: The parameters of dot_modulo are passed implicitly because passing
them explicitly takes more time then running the function's C-code.
"""
if multMatVect.dot_modulo is None:
A_sym = tensor.lmatrix("A")
s_sym = tensor.ivector("s")
m_sym = tensor.iscalar("m")
A2_sym = tensor.lmatrix("A2")
s2_sym = tensor.ivector("s2")
m2_sym = tensor.iscalar("m2")
o = DotModulo()(A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym)
multMatVect.dot_modulo = function([A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym], o)
# This way of calling the Theano fct is done to bypass Theano overhead.
f = multMatVect.dot_modulo
f.input_storage[0].storage[0] = A
f.input_storage[1].storage[0] = v[:3]
f.input_storage[2].storage[0] = m1
f.input_storage[3].storage[0] = B
f.input_storage[4].storage[0] = v[3:]
f.input_storage[5].storage[0] = m2
f.fn()
r = f.output_storage[0].storage[0]
return r
def compile(self, log_pxz, log_qpz, cost, a_pxz):
batch_idx = T.iscalar()
learning_rate = T.fscalar()
updates, norm_grad = self.hp.optimizer(cost, self.params.values(), lr=learning_rate)
self.outidx = {'cost':0, 'cost_p':1, 'cost_q':2, 'norm_grad':3}
outputs = [cost, log_pxz, log_qpz]
self.train = theano.function(inputs=[batch_idx, learning_rate],
givens={self.X:self.data['tr_X'][batch_idx * self.hp.batch_size :
(batch_idx+1) * self.hp.batch_size]},
outputs=outputs + [norm_grad], updates=updates)
self.validate = theano.function(inputs=[batch_idx],
givens={self.X:self.data['tr_X'][batch_idx * self.hp.test_batch_size :
(batch_idx+1) * self.hp.test_batch_size]},
outputs=outputs)
self.test = theano.function(inputs=[batch_idx],
givens={self.X:self.data['te_X'][batch_idx * self.hp.test_batch_size :
(batch_idx+1) * self.hp.test_batch_size]},
outputs=outputs)
n_samples = T.iscalar()
if self.resample_z:
self.data['ge_Z'] = srnd.normal((self.max_gen_samples, self.n_z), dtype=theano.config.floatX)
else:
self.data['ge_Z'] = shared(np.random.randn(self.max_gen_samples, self.n_z))
self.decode = theano.function(inputs=[n_samples],
givens={self.Z:self.data['ge_Z'][:n_samples]},
outputs=a_pxz)
def init_nnet(W, n_classes, vec_dim):
"""Initialize neural network.
Args:
W (theano.shared): embedding matrix
n_classes: number of classes to be predicted
vec_dim: dimensionality of the embeddings
"""
w_idx = TT.iscalar(name="w_idx")
y_gold = TT.iscalar(name="y_gold")
embs = W[w_idx]
Theta = theano.shared(value=ORTHOGONAL.sample((n_classes, vec_dim)),
name="Theta")
beta = theano.shared(value=HE_UNIFORM.sample((1, n_classes)), name="beta")
y_probs = TT.nnet.softmax(TT.dot(Theta, embs.T).flatten() + beta).flatten()
params = [Theta]
cost = -TT.mean(TT.log(y_probs[y_gold]))
updates = sgd_updates_adadelta(params, cost)
train = theano.function([w_idx, y_gold], cost, updates=updates)
y_pred = TT.argmax(y_probs)
y_score = y_probs[y_pred]
predict = theano.function([w_idx], (y_pred, y_score))
acc = TT.eq(y_gold, y_pred)
validate = theano.function([w_idx, y_gold], acc)
return (train, validate, predict, params)
def test_multMatVect():
A1 = tensor.lmatrix('A1')
s1 = tensor.ivector('s1')
m1 = tensor.iscalar('m1')
A2 = tensor.lmatrix('A2')
s2 = tensor.ivector('s2')
m2 = tensor.iscalar('m2')
g0 = rng_mrg.DotModulo()(A1, s1, m1, A2, s2, m2)
f0 = theano.function([A1, s1, m1, A2, s2, m2], g0)
i32max = numpy.iinfo(numpy.int32).max
A1 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
s1 = numpy.random.randint(0, i32max, 3).astype('int32')
m1 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
A2 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
s2 = numpy.random.randint(0, i32max, 3).astype('int32')
m2 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
f0.input_storage[0].storage[0] = A1
f0.input_storage[1].storage[0] = s1
f0.input_storage[2].storage[0] = m1
f0.input_storage[3].storage[0] = A2
f0.input_storage[4].storage[0] = s2
f0.input_storage[5].storage[0] = m2
r_a1 = rng_mrg.matVecModM(A1, s1, m1)
r_a2 = rng_mrg.matVecModM(A2, s2, m2)
f0.fn()
r_b = f0.output_storage[0].value
assert numpy.allclose(r_a1, r_b[:3])
assert numpy.allclose(r_a2, r_b[3:])
def test_clip_grad_int():
# test that integers don't crash clip gradient
x = tensor.iscalar()
y = tensor.iscalar()
z = tensor.iscalar()
c = tensor.clip(x, y, z)
tensor.grad(c, [x, y, z])
def __init__(self, rng, state):
Model.__init__(self)
self.state = state
# Compatibility towards older models
self.__dict__.update(state)
self.rng = rng
# Load dictionary
raw_dict = cPickle.load(open(self.dictionary, 'r'))
# Probabilities for each term in the corpus
self.str_to_idx = dict([(tok, tok_id) for tok, tok_id, _ in raw_dict])
self.idx_to_str = dict([(tok_id, tok) for tok, tok_id, freq in raw_dict])
# if '<s>' not in self.str_to_idx \
# or '</s>' not in self.str_to_idx:
# raise Exception("Error, malformed dictionary!")
# Number of words in the dictionary
self.idim = len(self.str_to_idx)
self.state['idim'] = self.idim
logger.debug("Initializing language model")
self.language_model = LanguageModel(self.state, self.rng, self)
# Init params
self.params = self.language_model.params
self.x_data = T.imatrix('x_data')
self.x_cost_mask = T.matrix('cost_mask')
self.x_max_length = T.iscalar('x_max_length')
# The training is done with a trick. We append a special </q> at the beginning of the session
# so that we can predict also the first query in the session starting from the session beginning token (</q>).
self.aug_x_data = T.concatenate([T.alloc(np.int32(self.eos_sym), 1, self.x_data.shape[1]), self.x_data])
self.training_x = self.aug_x_data[:self.x_max_length]
self.training_y = self.aug_x_data[1:self.x_max_length+1]
self.training_x_cost_mask = self.x_cost_mask[:self.x_max_length].flatten()
target_probs, self.eval_h = self.language_model.build_lm(self.training_x,
y=self.training_y,
mode=LanguageModel.EVALUATION)
# Prediction cost
self.prediction_cost = T.sum(-T.log(target_probs) * self.training_x_cost_mask)
# Sampling variables
self.n_samples = T.iscalar("n_samples")
self.n_steps = T.iscalar("n_steps")
(self.sample, self.sample_log_prob), self.sampling_updates \
= self.language_model.build_sampler(self.n_samples, self.n_steps)
# Beam-search variables
self.beam_source = T.lvector("beam_source")
self.beam_h = T.matrix("beam_h")
self.beam_step_num = T.lscalar("beam_step_num")
def test():
a = T.iscalar()
b = T.iscalar()
c = T.iscalar()
f1 = theano.function([a , b] , lt(a , b))
f2 = theano.function([a] , lt2(a) , on_unused_input='ignore')
print f1(1 , 2)
print f2(-2)
print f2(3)
def SimilarityFunction(fnsim, embeddings, leftop, rightop):
idxrel = T.iscalar("idxrel")
idxright = T.iscalar("idxright")
idxleft = T.iscalar("idxleft")
lhs = (embeddings.E[:, idxleft]).reshape((1, embeddings.D))
rhs = (embeddings.E[:, idxright]).reshape((1, embeddings.D))
rel = (embeddings.E[:, idxrel]).reshape((1, embeddings.D))
simi = fnsim(leftop(lhs, rel), rightop(rhs, rel))
return theano.function([idxleft, idxright, idxrel], [simi])
def EnergyFn(fnsim, embeddings, leftop, rightop):
embedding, relationl, relationr = parse_embeddings(embeddings)
idxl, idxo, idxr = T.iscalar('idxl'), T.iscalar('idxo'), T.iscalar('idxr')
lhs = (embedding.E[:, idxl]).reshape((1, embedding.D))
rhs = (embedding.E[:, idxr]).reshape((1, embedding.D))
rell = (relationl.E[:, idxo]).reshape((1, relationl.D))
relr = (relationr.E[:, idxo]).reshape((1, relationr.D))
energy = - fnsim(leftop(lhs, rell), rightop(rhs, relr))
return theano.function([idxl, idxr, idxo], [energy], on_unused_input='ignore')
def compile_functions(self, opt, **args):
print '... compiling training functions'
# propagte for training with batch normalization with upated std and mean for each batch
self.layer_outputs = self.network_fprop()
cost, show_cost = self.get_cost()
self.opt = opt
updates = self.opt.get_updates(cost, self.params)
# propagate again for validation with fixed mean and std for batch normalization
self.layer_outputs = self.network_fprop(isTest=True, noiseless=True)
self.final_output = self.layer_outputs[self.network_structure[-1]['name']]
errors = self.get_errors()
start_index = T.iscalar('start_index')
end_index = T.iscalar('end_index')
train_given = {}
print 'number of training inputs = ', self.ninputs
for i in xrange(self.ninputs):
if self.uint8_data:
train_given[self.xs[i]] = T.cast(self.shared_train[i][start_index:end_index], dtype='float32')
else:
train_given[self.xs[i]] = self.shared_train[i][start_index:end_index]
if self.batch_mean_subtraction:
assert self.train_mean is not None, 'train_mean cannot be None for batch mean subtraction'
assert len(self.train_mean) == self.ninputs, 'train_mean need to have the same number as number of inputs'
train_given[self.xs[i]] -= self.train_mean[i]
train_given[self.y] = self.shared_train_labels[start_index:end_index]
self.train_model = theano.function( inputs=[start_index, end_index],
outputs=[show_cost, errors], updates = updates,
givens = train_given
)
if hasattr(self, 'shared_valid'):
valid_given = {}
for i in xrange(self.ninputs):
if self.uint8_data:
valid_given[self.xs[i]] = T.cast(self.shared_valid[i][start_index:end_index], dtype='float32')
else:
valid_given[self.xs[i]] = self.shared_valid[i][start_index:end_index]
if self.batch_mean_subtraction:
assert self.train_mean is not None, 'train_mean cannot be None for batch mean subtraction'
assert len(self.train_mean) == self.ninputs, 'train_mean need to have the same number as number of inputs'
valid_given[self.xs[i]] -= self.train_mean[i]
valid_given[self.y] = self.shared_valid_labels[start_index:end_index]
self.validate_model = theano.function( inputs=[start_index,end_index],
outputs=errors,
givens = valid_given
)
def get_train(U_Ot, U_R, lenW, n_facts):
def phi_x1(x_t, L):
return T.concatenate([L[x_t].reshape((-1,)), zeros((2*lenW,)), zeros((3,))], axis=0)
def phi_x2(x_t, L):
return T.concatenate([zeros((lenW,)), L[x_t].reshape((-1,)), zeros((lenW,)), zeros((3,))], axis=0)
def phi_y(x_t, L):
return T.concatenate([zeros((2*lenW,)), L[x_t].reshape((-1,)), zeros((3,))], axis=0)
def phi_t(x_t, y_t, yp_t, L):
return T.concatenate([zeros(3*lenW,), T.stack(T.switch(T.lt(x_t,y_t), 1, 0), T.switch(T.lt(x_t,yp_t), 1, 0), T.switch(T.lt(y_t,yp_t), 1, 0))], axis=0)
def s_Ot(xs, y_t, yp_t, L):
result, updates = theano.scan(
lambda x_t, t: T.dot(T.dot(T.switch(T.eq(t, 0), phi_x1(x_t, L).reshape((1,-1)), phi_x2(x_t, L).reshape((1,-1))), U_Ot.T),
T.dot(U_Ot, (phi_y(y_t, L) - phi_y(yp_t, L) + phi_t(x_t, y_t, yp_t, L)))),
sequences=[xs, T.arange(T.shape(xs)[0])])
return result.sum()
def sR(xs, y_t, L, V):
result, updates = theano.scan(
lambda x_t, t: T.dot(T.dot(T.switch(T.eq(t, 0), phi_x1(x_t, L).reshape((1,-1)), phi_x2(x_t, L).reshape((1,-1))), U_R.T),
T.dot(U_R, phi_y(y_t, V))),
sequences=[xs, T.arange(T.shape(xs)[0])])
return result.sum()
x_t = T.iscalar('x_t')
m = [x_t] + [T.iscalar('m_o%d' % i) for i in xrange(n_facts)]
f = [T.iscalar('f%d_t' % i) for i in xrange(n_facts)]
r_t = T.iscalar('r_t')
gamma = T.scalar('gamma')
L = T.fmatrix('L') # list of messages
V = T.fmatrix('V') # vocab
r_args = T.stack(*m)
cost_arr = [0] * 2 * (len(m)-1)
updates_arr = [0] * 2 * (len(m)-1)
for i in xrange(len(m)-1):
cost_arr[2*i], updates_arr[2*i] = theano.scan(
lambda f_bar, t: T.switch(T.or_(T.eq(t, f[i]), T.eq(t, T.shape(L)-1)), 0, T.largest(gamma - s_Ot(T.stack(*m[:i+1]), f[i], t, L), 0)),
sequences=[L, T.arange(T.shape(L)[0])])
cost_arr[2*i+1], updates_arr[2*i+1] = theano.scan(
lambda f_bar, t: T.switch(T.or_(T.eq(t, f[i]), T.eq(t, T.shape(L)-1)), 0, T.largest(gamma + s_Ot(T.stack(*m[:i+1]), t, f[i], L), 0)),
sequences=[L, T.arange(T.shape(L)[0])])
cost1, u1 = theano.scan(
lambda r_bar, t: T.switch(T.eq(r_t, t), 0, T.largest(gamma - sR(r_args, r_t, L, V) + sR(r_args, t, L, V), 0)),
sequences=[V, T.arange(T.shape(V)[0])])
cost = cost1.sum()
for c in cost_arr:
cost += c.sum()
g_uo, g_ur = T.grad(cost, [U_Ot, U_R])
train = theano.function(
inputs=[r_t, gamma, L, V] + m + f,
outputs=[cost],
updates=[(U_Ot, U_Ot-alpha*g_uo), (U_R, U_R-alpha*g_ur)])
return train
def test14():
x = T.iscalar('x')
y = T.iscalar('y')
z = T.arange(x)
z = T.shape_padaxis(z, axis=1)
z2 = T.zeros((x,y))
z2 = z + z2
fn = theano.function(inputs=[x,y],outputs=[z2],allow_input_downcast=True)
res = fn(3,4)
print res, res[0].shape
def create_gradientfunctions(self, x_train):
"""This function takes as input the whole dataset and creates the entire model"""
x = T.matrix("x")
epoch = T.iscalar("epoch")
batch_size = x.shape[0]
alpha, beta = self.encoder(x)
z = self.sampler(alpha, beta)
reconstructed_x, logpxz = self.decoder(x,z)
# Expectation of (logpz - logqz_x) over logqz_x is equal to KLD (see appendix B):
# KLD = 0.5 * T.sum(1 + beta - alpha**2 - T.exp(beta), axis=1, keepdims=True)
#KLD = 0.5 * T.sum(1 + beta - (alpha**2 + T.exp(beta)) / (2*(self.prior_noise_level**2)) , axis=1, keepdims=True)
# KLD = cross-entroy of the sample distribution of sigmoid(z) from the beta distribution
alpha_prior = 1.0/self.prior_noise_level
beta_prior = 1.0/self.prior_noise_level
# sigmoidZ = T.nnet.sigmoid(z)
# KLD = 25*T.sum((alpha_prior-1)*sigmoidZ + (beta-1)*(1-sigmoidZ) - betaln(alpha_prior,beta), axis=1, keepdims=True)
# KLD = 0
KLD = -(betaln(alpha, beta) - betaln(alpha_prior, beta_prior) \
+ (alpha_prior - alpha)*T.psi(alpha_prior) + (beta_prior - beta)*T.psi(beta_prior) \
+ (alpha - alpha_prior + beta - beta_prior)*T.psi(alpha_prior+beta_prior))
# Average over batch dimension
logpx = T.mean(logpxz + KLD)
rmse_val = rmse_score(x, reconstructed_x)
# Compute all the gradients
gradients = T.grad(logpx, self.params.values())
# Adam implemented as updates
updates = self.get_adam_updates(gradients, epoch)
batch = T.iscalar('batch')
givens = {
x: x_train[batch*self.batch_size:(batch+1)*self.batch_size, :]
}
# Define a bunch of functions for convenience
update = theano.function([batch, epoch], logpx, updates=updates, givens=givens)
likelihood = theano.function([x], logpx)
eval_rmse = theano.function([x], rmse_val)
encode = theano.function([x], z)
decode = theano.function([z], reconstructed_x)
encode_alpha = theano.function([x], alpha)
encode_beta = theano.function([x], beta)
return update, likelihood, encode, decode, encode_alpha, encode_beta, eval_rmse
def make_theano_evaluator(use_log):
"""This returns a function(!) that calculates the gradient and cost. Heh."""
X = T.dmatrix('X')
triplets = T.imatrix('triplets')
alpha = T.dscalar('alpha')
lamb = T.dscalar('lambda')
no_dims = T.iscalar('no_dims')
N = T.iscalar('N')
triplets_A = triplets[:,0]
triplets_B = triplets[:,1]
triplets_C = triplets[:,2]
# Compute Student-t kernel. Look familiar?
sum_X = T.sum(X**2, axis=1)
a = -2 * (X.dot(X.T))
b = a + sum_X[np.newaxis,:] + sum_X[:,np.newaxis]
K = (1 + b / alpha) ** ((alpha+1)/-2)
# Compute value of cost function
P = K[triplets_A,triplets_B] / (
K[triplets_A,triplets_B] +
K[triplets_A,triplets_C])
if use_log:
C = -T.sum(T.log(P)) + lamb * T.sum(X**2)
else:
C = -T.sum(P) + lamb * T.sum(X**2)
# Compute gradient, for each dimension
const = (alpha+1) / alpha
dim = T.iscalar('dim')
def each_dim(dim):
if use_log:
A_to_B = (1 - P) * K[triplets_A,triplets_B] * (X[triplets_A][:,dim] - X[triplets_B][:,dim])
B_to_C = (1 - P) * K[triplets_A,triplets_C] * (X[triplets_A][:,dim] - X[triplets_C][:,dim])
else:
A_to_B = P*(1 - P) * K[triplets_A,triplets_B] * (X[triplets_A][:,dim] - X[triplets_B][:,dim])
B_to_C = P*(1 - P) * K[triplets_A,triplets_C] * (X[triplets_A][:,dim] - X[triplets_C][:,dim])
this_dim = (-const * T.stack(A_to_B - B_to_C, -A_to_B, B_to_C)).T
dC = T.extra_ops.bincount(triplets.ravel(),
weights=this_dim.ravel(),
# minlength=N
)
return -dC + 2*lamb*X[:,dim]
# loop across all dimensions... theano loops are weird, yes...
all_dims = (t.scan(each_dim,
# non_sequences=N,
sequences=T.arange(no_dims))
)[0].T
return t.function([X,N,no_dims,triplets,lamb,alpha],
[C, all_dims],
on_unused_input='ignore')
def compile_functions(self, opt, **args):
print '... compiling training functions'
# propagte for training with batch normalization with upated std and mean for each batch
self.layer_outputs = self.network_fprop(self.layers, self.x, self.y)
cost, show_cost = self.get_cost()
self.opt = opt
updates = self.opt.get_updates(cost, self.params)
# propagate again for validation with fixed mean and std for batch normalization
self.layer_outputs = self.network_fprop(self.layers, self.x, self.y, isTest=True, noiseless=True)
self.final_output = self.layer_outputs[self.network_structure[-1]['name']]
errors = self.get_errors()
start_index = T.iscalar('start_index')
end_index = T.iscalar('end_index')
train_given = {}
if self.uint8_data:
given_train_x = T.cast(self.shared_train[start_index:end_index], dtype='float32')
else:
given_train_x = self.shared_train[start_index:end_index]
if self.batch_mean_subtraction:
assert self.train_mean is not None, 'train_mean cannot be None for batch mean subtraction'
given_train_x -= self.train_mean
train_given[self.x] = given_train_x
train_given[self.y] = self.shared_train_labels[start_index:end_index]
self.train_model = theano.function( inputs=[start_index,end_index],
outputs=show_cost, updates = updates,
givens = train_given
)
if hasattr(self, 'shared_valid'):
valid_given = {}
if self.uint8_data:
given_valid_x = T.cast(self.shared_valid[start_index:end_index], dtype='float32')
else:
given_valid_x = self.shared_valid[start_index:end_index]
if self.batch_mean_subtraction:
assert self.train_mean is not None, 'train_mean cannot be None for batch mean subtraction'
given_valid_x -= self.train_mean
valid_given[self.x] = given_valid_x
valid_given[self.y] = self.shared_valid_labels[start_index:end_index]
self.validate_model = theano.function( inputs=[start_index, end_index],
outputs=errors,
givens = valid_given
)
def create_iter_functions(dataset,
output_layer,
X_tensor_type=T.matrix,
batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE,
momentum=MOMENTUM):
"""Create functions for training, validation and testing to iterate one
epoch.
"""
batch_index = T.iscalar('batch_index')
X_batch = X_tensor_type('x')
y_batch = T.ivector('y')
batch_slice = slice(batch_index * batch_size,
(batch_index + 1) * batch_size)
objective = lasagne.objectives.Objective(
output_layer,
loss_function=lasagne.objectives.categorical_crossentropy)
loss_train = objective.get_loss(X_batch, target=y_batch)
loss_eval = objective.get_loss(X_batch, target=y_batch, deterministic=True)
pred = T.argmax(output_layer.get_output(X_batch, deterministic=True),
axis=1)
accuracy = T.mean(T.eq(pred, y_batch), dtype=theano.config.floatX)
all_params = lasagne.layers.get_all_params(output_layer)
updates = lasagne.updates.nesterov_momentum(loss_train, all_params,
learning_rate, momentum)
iter_train = theano.function(
[batch_index],
loss_train,
updates=updates,
givens={
X_batch: dataset['X_train'][batch_slice],
y_batch: dataset['y_train'][batch_slice],
},
)
iter_valid = theano.function(
[batch_index],
[loss_eval, accuracy],
givens={
X_batch: dataset['X_valid'][batch_slice],
y_batch: dataset['y_valid'][batch_slice],
},
)
iter_test = theano.function(
[batch_index],
[loss_eval, accuracy],
givens={
X_batch: dataset['X_test'][batch_slice],
y_batch: dataset['y_test'][batch_slice],
},
)
return dict(
train=iter_train,
valid=iter_valid,
test=iter_test,
)
def test_tex_print():
tt_normalrv_noname_expr = tt.scalar("b") * NormalRV(
tt.scalar("\\mu"), tt.scalar("\\sigma"))
expected = textwrap.dedent(r"""
\begin{equation}
\begin{gathered}
b \in \mathbb{R}, \,\mu \in \mathbb{R}, \,\sigma \in \mathbb{R}
\\
a \sim \operatorname{N}\left(\mu, {\sigma}^{2}\right)\, \in \mathbb{R}
\end{gathered}
\\
(b \odot a)
\end{equation}
""")
assert tt_tprint(tt_normalrv_noname_expr) == expected.strip()
tt_normalrv_name_expr = tt.scalar("b") * NormalRV(
tt.scalar("\\mu"), tt.scalar("\\sigma"), size=[2, 1], name="X")
expected = textwrap.dedent(r"""
\begin{equation}
\begin{gathered}
b \in \mathbb{R}, \,\mu \in \mathbb{R}, \,\sigma \in \mathbb{R}
\\
X \sim \operatorname{N}\left(\mu, {\sigma}^{2}\right)\, \in \mathbb{R}^{2 \times 1}
\end{gathered}
\\
(b \odot X)
\end{equation}
""")
assert tt_tprint(tt_normalrv_name_expr) == expected.strip()
tt_2_normalrv_noname_expr = tt.matrix("M") * NormalRV(
tt.scalar("\\mu_2"), tt.scalar("\\sigma_2"))
tt_2_normalrv_noname_expr *= tt.scalar("b") * NormalRV(
tt_2_normalrv_noname_expr, tt.scalar("\\sigma")) + tt.scalar("c")
expected = textwrap.dedent(r"""
\begin{equation}
\begin{gathered}
M \in \mathbb{R}^{N^{M}_{0} \times N^{M}_{1}}
\\
\mu_2 \in \mathbb{R}, \,\sigma_2 \in \mathbb{R}
\\
b \in \mathbb{R}, \,\sigma \in \mathbb{R}, \,c \in \mathbb{R}
\\
a \sim \operatorname{N}\left(\mu_2, {\sigma_2}^{2}\right)\, \in \mathbb{R}
\\
d \sim \operatorname{N}\left((M \odot a), {\sigma}^{2}\right)\, \in \mathbb{R}^{N^{d}_{0} \times N^{d}_{1}}
\end{gathered}
\\
((M \odot a) \odot ((b \odot d) + c))
\end{equation}
""")
assert tt_tprint(tt_2_normalrv_noname_expr) == expected.strip()
expected = textwrap.dedent(r"""
\begin{equation}
\begin{gathered}
b \in \mathbb{Z}, \,c \in \mathbb{Z}, \,M \in \mathbb{R}^{N^{M}_{0} \times N^{M}_{1}}
\end{gathered}
\\
M\left[b, \,c\right]
\end{equation}
""")
# TODO: "c" should be "1".
assert (tt_tprint(
tt.matrix("M")[tt.iscalar("a"),
tt.constant(1, dtype="int")]) == expected.strip())
expected = textwrap.dedent(r"""
\begin{equation}
\begin{gathered}
M \in \mathbb{R}^{N^{M}_{0} \times N^{M}_{1}}
\end{gathered}
\\
M\left[1\right]
\end{equation}
""")
assert tt_tprint(tt.matrix("M")[1]) == expected.strip()
expected = textwrap.dedent(r"""
\begin{equation}
\begin{gathered}
M \in \mathbb{N}^{N^{M}_{0}}
\end{gathered}
\\
M\left[2:4:0\right]
\end{equation}
""")
assert tt_tprint(tt.vector("M", dtype="uint32")[0:4:2]) == expected.strip()
norm_rv = NormalRV(tt.scalar("\\mu"), tt.scalar("\\sigma"))
rv_obs = observed(tt.constant(1.0, dtype=norm_rv.dtype), norm_rv)
expected = textwrap.dedent(r"""
\begin{equation}
\begin{gathered}
\mu \in \mathbb{R}, \,\sigma \in \mathbb{R}
\\
a \sim \operatorname{N}\left(\mu, {\sigma}^{2}\right)\, \in \mathbb{R}
\end{gathered}
\\
a = 1.0
\end{equation}
""")
assert tt_tprint(rv_obs) == expected.strip()
from __future__ import absolute_import, print_function, division
import theano
import theano.tensor as tt
from six.moves import xrange
k = tt.iscalar("k")
A = tt.vector("A")
def inner_fct(prior_result, A):
return prior_result * A
# Symbolic description of the result
result, updates = theano.scan(fn=inner_fct,
outputs_info=tt.ones_like(A),
non_sequences=A,
n_steps=k)
# Scan has provided us with A**1 through A**k. Keep only the last
# value. Scan notices this and does not waste memory saving them.
final_result = result[-1]
power = theano.function(inputs=[A, k], outputs=final_result, updates=updates)
print(power(list(range(10)), 2))
#[ 0. 1. 4. 9. 16. 25. 36. 49. 64. 81.]
def test_value_h(self):
"tests that the value of the kl divergence decreases with each update to h_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
init_H = e_step.init_H_hat(V=X)
init_Mu1 = e_step.init_S_hat(V=X)
prev_setting = config.compute_test_value
config.compute_test_value = 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0., 1., H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(
-5., 5., Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
newH = e_step.infer_H_hat(V=X, H_hat=H_var, S_hat=Mu1_var)
h_idx = newH[:, idx]
h_i_func = function([H_var, Mu1_var, idx], h_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0, var_s1_hat = Sigma1)
trunc_kl_func = function([H_var, Mu1_var], trunc_kl)
for i in xrange(self.N):
prev_kl = trunc_kl_func(H, Mu1)
H[:, i] = h_i_func(H, Mu1, i)
#we don't update mu, the whole point of the split e step is we don't have to
new_kl = trunc_kl_func(H, Mu1)
increase = new_kl - prev_kl
print 'failures after iteration ', i, ': ', (increase >
self.tol).sum()
mx = increase.max()
if mx > 1e-4:
print 'increase amounts of failing examples:'
print increase[increase > self.tol]
print 'failing H:'
print H[increase > self.tol, :]
print 'failing Mu1:'
print Mu1[increase > self.tol, :]
print 'failing V:'
print X[increase > self.tol, :]
raise Exception(
'after mean field step in h, kl divergence should decrease, but some elements increased by as much as '
+ str(mx) + ' after updating h_' + str(i))
def build_finetune_functions(self, datasets, batch_size, learning_rate):
'''Generates a function `train` that implements one step of
finetuning, a function `validate` that computes the error on a
batch from the validation set, and a function `test` that
computes the error on a batch from the testing set
:type datasets: list of pairs of theano.tensor.TensorType
:param datasets: It is a list that contain all the datasets;
the has to contain three pairs, `train`,
`valid`, `test` in this order, where each pair
is formed of two Theano variables, one for the
datapoints, the other for the labels
:type batch_size: int
:param batch_size: size of a minibatch
:type learning_rate: float
:param learning_rate: learning rate used during finetune stage
'''
(train_set_x, train_set_y) = datasets[0]
(valid_set_x, valid_set_y) = datasets[1]
(test_set_x, test_set_y) = datasets[2]
# compute number of minibatches for training, validation and testing
# n_train_size = train_set_y.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_y.get_value(borrow=True).shape[0]
n_valid_batches /= batch_size
n_test_batches = test_set_y.get_value(borrow=True).shape[0]
n_test_batches /= batch_size
index = T.iscalar('index') # index to a [mini]batch
# compute the gradients with respect to the model parameters
gparams = T.grad(self.finetune_cost, self.params)
# compute list of fine-tuning updates
updates = []
for param, gparam in zip(self.params, gparams):
updates.append((param, param - gparam * learning_rate))
#shift the indeces in Y for each mini batch
offset = theano.shared(value=numpy.asarray(
[[1, 1, 0] for i in range(batch_size)], dtype='int32'),
name='offset')
train_fn = theano.function(
inputs=[index],
on_unused_input='ignore',
outputs=self.finetune_cost,
updates=updates,
givens={
self.x:
train_set_x[train_set_y[index * batch_size][0]:train_set_y[
(index + 1) * batch_size][0]],
#index * batch_size:
#(index + 1) * batch_size],
self.y:
train_set_y[index * batch_size:(index + 1) * batch_size] -
offset * train_set_y[index * batch_size][0]
})
test_score_i = theano.function(
[index],
self.errors,
on_unused_input='ignore',
givens={
self.x:
test_set_x[test_set_y[index * batch_size][0]:test_set_y[
(index + 1) * batch_size][0]],
#index * batch_size:
#(index + 1) * batch_size],
self.y:
test_set_y[index * batch_size:(index + 1) * batch_size] -
offset * test_set_y[index * batch_size][0]
})
valid_score_i = theano.function(
[index],
self.errors,
on_unused_input='ignore',
givens={
self.x:
valid_set_x[valid_set_y[index * batch_size][0]:valid_set_y[
(index + 1) * batch_size][0]],
#index * batch_size:
#(index + 1) * batch_size],
self.y:
valid_set_y[index * batch_size:(index + 1) * batch_size] -
offset * valid_set_y[index * batch_size][0]
})
# Create a function that scans the entire validation set
def valid_score():
return [valid_score_i(i) for i in xrange(n_valid_batches - 1)]
# Create a function that scans the entire test set
def test_score():
return [test_score_i(i) for i in xrange(n_test_batches - 1)]
return train_fn, valid_score, test_score
initialization='he',
weightnorm=WEIGHT_NORM)
out = T.nnet.relu(out)
# Output
# We apply the softmax later
out = lib.ops.Linear('SampleLevel.Output',
DIM,
Q_LEVELS,
out,
weightnorm=WEIGHT_NORM)
return out
sequences = T.imatrix('sequences')
h0 = T.tensor3('h0')
reset = T.iscalar('reset')
mask = T.matrix('mask')
if args.debug:
# Solely for debugging purposes.
# Maybe I should set the compute_test_value=warn from here.
sequences.tag.test_value = numpy.zeros((BATCH_SIZE, SEQ_LEN+OVERLAP), dtype='int32')
h0.tag.test_value = numpy.zeros((BATCH_SIZE, N_RNN, H0_MULT*DIM), dtype='float32')
reset.tag.test_value = numpy.array(1, dtype='int32')
mask.tag.test_value = numpy.ones((BATCH_SIZE, SEQ_LEN+OVERLAP), dtype='float32')
input_sequences = sequences[:, :-FRAME_SIZE]
target_sequences = sequences[:, FRAME_SIZE:]
target_mask = mask[:, FRAME_SIZE:]
def __init__(self,
Nlayers = 1, # number of layers
Ndirs = 1, # unidirectional or bidirectional
Nx = 100, # input size
Nh = 100, # hidden layer size
Ny = 100, # output size
Ah = "relu", # hidden unit activation (e.g. relu, tanh, lstm)
Ay = "linear", # output unit activation (e.g. linear, sigmoid, softmax)
predictPer = "frame", # frame or sequence
loss = None, # loss function (e.g. mse, ce, ce_group, hinge, squared_hinge)
L1reg = 0.0, # L1 regularization
L2reg = 0.0, # L2 regularization
multiReg = 0.0, # regularization of agreement of predictions on data of different conditions
momentum = 0.0, # SGD momentum
seed = 15213, # random seed for initializing the weights
frontEnd = None, # a lambda function for transforming the input
filename = None, # initialize from file
initParams = None, # initialize from given dict
):
if filename is not None: # load parameters from file
with smart_open(filename, "rb") as f:
initParams = dill.load(f)
if initParams is not None: # load parameters from given dict
self.paramNames = []
self.params = []
for k, v in initParams.iteritems():
if type(v) is numpy.ndarray:
self.addParam(k, v)
else:
setattr(self, k, v)
self.paramNames.append(k)
# F*ck, locals()[k] = v doesn't work; I have to do this statically
Nlayers, Ndirs, Nx, Nh, Ny, Ah, Ay, predictPer, loss, L1reg, L2reg, momentum, frontEnd \
= self.Nlayers, self.Ndirs, self.Nx, self.Nh, self.Ny, self.Ah, self.Ay, self.predictPer, self.loss, self.L1reg, self.L2reg, self.momentum, self.frontEnd
else: # Initialize parameters randomly
# Names of parameters to save to file
self.paramNames = ["Nlayers", "Ndirs", "Nx", "Nh", "Ny", "Ah", "Ay", "predictPer", "loss", "L1reg", "L2reg", "momentum", "frontEnd"]
for name in self.paramNames:
value = locals()[name]
setattr(self, name, value)
# Values of parameters for building the computational graph
self.params = []
# Initialize random number generators
global rng
rng = numpy.random.RandomState(seed)
# Construct parameter matrices
Nlstm = 4 if Ah == 'lstm' else 1
self.addParam("Win", rand_init((Nx, Nh * Ndirs * Nlstm), Ah))
self.addParam("Wrec", rand_init((Nlayers, Ndirs, Nh, Nh * Nlstm), Ah))
self.addParam("Wup", rand_init((Nlayers - 1, Nh * Ndirs, Nh * Ndirs * Nlstm), Ah))
self.addParam("Wout", rand_init((Nh * Ndirs, Ny), Ay))
if Ah != "lstm":
self.addParam("Bhid", zeros((Nlayers, Nh * Ndirs)))
else:
self.addParam("Bhid", numpy.tile(numpy.hstack([full((Nlayers, Nh), 1.0), zeros((Nlayers, Nh * 3))]), (1, Ndirs)))
self.addParam("Bout", zeros(Ny))
self.addParam("h0", zeros((Nlayers, Ndirs, Nh)))
if Ah == "lstm":
self.addParam("c0", zeros((Nlayers, Ndirs, Nh)))
# Compute total number of parameters
self.nParams = sum(x.get_value().size for x in self.params)
# Initialize gradient tensors when using momentum
if momentum > 0:
self.dparams = [theano.shared(zeros(x.get_value().shape)) for x in self.params]
# Build computation graph
input = T.ftensor4() # stream * time * feature
mask = T.imatrix()
mask_int = [(mask % 2).nonzero(), (mask >= 2).nonzero()]
mask_float = [T.cast((mask % 2).dimshuffle((1, 0)).reshape((mask.shape[1], mask.shape[0], 1)), theano.config.floatX),
T.cast((mask >= 2).dimshuffle((1, 0)).reshape((mask.shape[1], mask.shape[0], 1)), theano.config.floatX)]
# mask_int = [(mask & 1).nonzero(), (mask & 2).nonzero()]
# mask_float = [T.cast((mask & 1).dimshuffle((1, 0)).reshape((mask.shape[1], mask.shape[0], 1)), theano.config.floatX),
# T.cast(((mask & 2) / 2).dimshuffle((1, 0)).reshape((mask.shape[1], mask.shape[0], 1)), theano.config.floatX)]
def step_rnn(x_t, mask, h_tm1, W, h0):
h_tm1 = T.switch(mask, h0, h_tm1)
return [ACTIVATION[Ah](x_t + h_tm1.dot(W))]
def step_lstm(x_t, mask, c_tm1, h_tm1, W, c0, h0):
c_tm1 = T.switch(mask, c0, c_tm1)
h_tm1 = T.switch(mask, h0, h_tm1)
a = x_t + h_tm1.dot(W)
f_t = T.nnet.sigmoid(a[:, :, :Nh])
i_t = T.nnet.sigmoid(a[:, :, Nh : Nh * 2])
o_t = T.nnet.sigmoid(a[:, :, Nh * 2 : Nh * 3])
c_t = T.tanh(a[:, :, Nh * 3:]) * i_t + c_tm1 * f_t
h_t = T.tanh(c_t) * o_t
return [c_t, h_t]
x = input if frontEnd is None else frontEnd(input)
for i in range(Nlayers):
h = (x.dimshuffle((2, 1, 0, 3)).dot(self.Win) if i == 0 else h.dot(self.Wup[i-1])) + self.Bhid[i]
# (2, 1, 0, 3): condition * stream * time * feature => time * stream * condition * feature
rep = lambda x: T.extra_ops.repeat(T.extra_ops.repeat(x.reshape((1, 1, -1)), h.shape[2], axis = 1), h.shape[1], axis = 0)
if Ah != "lstm":
h = T.concatenate([theano.scan(
fn = step_rnn,
sequences = [h[:, :, :, Nh * d : Nh * (d+1)], mask_float[d]],
outputs_info = [rep(self.h0[i, d])],
non_sequences = [self.Wrec[i, d], rep(self.h0[i, d])],
go_backwards = (d == 1),
)[0][::(1 if d == 0 else -1)] for d in range(Ndirs)], axis = 3)
else:
h = T.concatenate([theano.scan(
fn = step_lstm,
sequences = [h[:, :, :, Nh * 4 * d : Nh * 4 * (d+1)], mask_float[d]],
outputs_info = [rep(self.c0[i, d]), rep(self.h0[i, d])],
non_sequences = [self.Wrec[i, d], rep(self.c0[i, d]), rep(self.h0[i, d])],
go_backwards = (d == 1),
)[0][1][::(1 if d == 0 else -1)] for d in range(Ndirs)], axis = 3)
if predictPer == "sequence":
h = h.dimshuffle((1, 0, 2, 3)) # time * stream * condition * feature => stream * time * condition * feature
h = T.concatenate([h[mask_int[1 - d]][:, :, Nh * d : Nh * (d+1)] for d in range(Ndirs)], axis = 2) # sequence * condition * feature
h = h.dimshuffle((1, 0, 2)) # sequence * condition * feature => condition * sequence * feature
else:
h = h.dimshuffle((2, 1, 0, 3)) # time * stream * condition * feature => condition * stream * time * feature
output = ACTIVATION[Ay](h.dot(self.Wout) + self.Bout)
output_mean = output.mean(axis = 0)
output_var = output.var(axis = 0)
# Compute loss function
if loss is None:
loss = {"linear": "mse", "sigmoid": "ce", "softmax": "ce_group"}[self.Ay]
if loss == "ctc":
label = T.imatrix()
label_time = T.imatrix()
tol = T.iscalar()
cost = theano.scan(fn = lambda prob: ctc_cost(prob, mask, label, label_time, tol), \
sequences = [output])[0].mean()
else:
if predictPer == "sequence":
label = T.fmatrix()
y = output_mean
t = label
elif predictPer == "frame":
label = T.ftensor3()
indices = (mask >= 0).nonzero()
y = output_mean[indices]
t = label[indices]
cost = T.mean({
"ce": -T.mean(T.log(y) * t + T.log(1 - y) * (1 - t), axis = 1),
"ce_group": -T.log((y * t).sum(axis = 1)),
"mse": T.mean((y - t) ** 2, axis = 1),
"hinge": T.mean(relu(1 - y * (t * 2 - 1)), axis = 1),
"squared_hinge": T.mean(relu(1 - y * (t * 2 - 1)) ** 2, axis = 1),
}[loss])
# Add regularization
cost += sum(abs(x).sum() for x in self.params) / self.nParams * L1reg
cost += sum(T.sqr(x).sum() for x in self.params) / self.nParams * L2reg
if predictPer == "sequence":
cost += output_var.mean() * multiReg
else:
indices = (mask >= 0).nonzero()
cost += output_var[indices].mean() * multiReg
# Compute updates for network parameters
updates = []
lrate = T.fscalar()
clip = T.fscalar()
grad = T.grad(cost, self.params)
grad_clipped = [T.maximum(T.minimum(g, clip), -clip) for g in grad]
if momentum > 0:
for w, d, g in zip(self.params, self.dparams, grad_clipped):
updates.append((w, w + momentum * momentum * d - (1 + momentum) * lrate * g))
updates.append((d, momentum * d - lrate * g))
else:
for w, g in zip(self.params, grad_clipped):
updates.append((w, w - lrate * g))
# Create functions to be called from outside
if loss == "ctc":
inputs = [input, mask, label, label_time, tol, lrate, clip]
else:
inputs = [input, mask, label, lrate, clip]
self.train = theano.function(
inputs = inputs,
outputs = cost,
updates = updates,
)
self.predict = theano.function(inputs = [input, mask], outputs = output)
train_freq_print = 20
valid_freq_print = 500
sample_strings = ['i am alien lamp and i love the neural nets'] * 50#['Sous le pont Mirabeau coule la Seine.']*50
algo = 'adam' # adam, sgd
#model_file_load = "/u/lambalex/models/handwriting/handwriting/71535347/saved_model.pkl"
#model_file_load = "/u/lambalex/models/handwriting/handwriting/81356894/saved_model.pkl"
#model_file_load = "/u/lambalex/models/handwriting/handwriting/10406114/saved_model.pkl"
#model_file_load = "saved_model.pkl"
#model_file_load = "/u/lambalex/models/handwriting/handwriting/33757048/saved_model.pkl"
model_file_load = None
#model_file_load = "/u/lambalex/models/handwriting/handwriting/90207341/saved_model.pkl"
#model_file_load = "/u/lambalex/models/handwriting/handwriting/11151138/saved_model.pkl"
#model_file_load = "/u/lambalex/models/handwriting_pf/handwriting/52780486/saved_model.pkl"
num_steps_sample = T.iscalar('num_steps_sample')
exp_id = np.random.randint(0, 100000000, 1)[0]
dump_path = os.path.join(os.environ.get('TMP_PATH'), 'handwriting',str(exp_id))
os.umask(055)
os.makedirs(dump_path, 0777)
os.makedirs(dump_path + "/src", 0777)
os.chmod(dump_path, 0o777)
fh = open(dump_path + "/derpy_file.txt", "w")
fh.write("DERP DERP DERP DERP")
fh.close()
def __init__(self, We_initial, params):
#self.textfile = open(params.outfile, 'w')
We = theano.shared(We_initial)
embsize = We_initial.shape[1]
hidden = params.hidden
input_init = np.random.uniform(-0.1, 0.1, (10, MAX_lENGTH, params.num_labels)).astype('float32')
self.input_init = theano.shared(input_init)
input_var = T.imatrix(name='inputs')
target_var = T.imatrix(name='targets')
mask_var = T.fmatrix(name='masks')
mask_var1 = T.fmatrix(name='masks1')
length = T.iscalar()
Wyy0 = np.random.uniform(-0.02, 0.02, (params.num_labels + 1, params.num_labels)).astype('float32')
Wyy = theano.shared(Wyy0)
l_in_word = lasagne.layers.InputLayer((None, None))
l_mask_word = lasagne.layers.InputLayer(shape=(None, None))
if params.emb ==1:
l_emb_word = lasagne.layers.EmbeddingLayer(l_in_word, input_size= We_initial.shape[0] , output_size = embsize, W =We)
else:
l_emb_word = lasagne_embedding_layer_2(l_in_word, embsize, We)
l_lstm_wordf = lasagne.layers.LSTMLayer(l_emb_word, hidden, mask_input=l_mask_word)
l_lstm_wordb = lasagne.layers.LSTMLayer(l_emb_word, hidden, mask_input=l_mask_word, backwards = True)
concat = lasagne.layers.concat([l_lstm_wordf, l_lstm_wordb], axis=2)
l_reshape_concat = lasagne.layers.ReshapeLayer(concat,(-1,2*hidden))
l_local = lasagne.layers.DenseLayer(l_reshape_concat, num_units= params.num_labels, nonlinearity=lasagne.nonlinearities.linear)
network_params = lasagne.layers.get_all_params(l_local, trainable=True)
network_params.append(Wyy)
#print len(network_params)
f = open('ccctag_CRF_Bilstm_Viterbi_.Batchsize_10_dropout_0_LearningRate_0.01_0.0512_tagversoin_2.pickle','r')
data = pickle.load(f)
f.close()
for idx, p in enumerate(network_params):
p.set_value(data[idx])
def inner_function( targets_one_step, mask_one_step, prev_label, tg_energy):
"""
:param targets_one_step: [batch_size, t]
:param prev_label: [batch_size, t]
:param tg_energy: [batch_size]
:return:
"""
new_ta_energy = T.dot(prev_label, Wyy[:-1,:-1])
new_ta_energy_t = tg_energy + T.sum(new_ta_energy*targets_one_step, axis =1)
tg_energy_t = T.switch(mask_one_step, new_ta_energy_t, tg_energy)
return [targets_one_step, tg_energy_t]
local_energy = lasagne.layers.get_output(l_local, {l_in_word: input_var, l_mask_word: mask_var})
local_energy = local_energy.reshape((-1, length, params.num_labels))
local_energy = local_energy*mask_var[:,:,None]
#####################
# for the end symbole of a sequence
####################
end_term = Wyy[:-1,-1]
local_energy = local_energy + end_term.dimshuffle('x', 'x', 0)*mask_var1[:,:, None]
predy_init = self.input_init[:,:length,:]
a_params = [self.input_init]
predy = T.nnet.softmax(predy_init.reshape((-1, params.num_labels)))
predy = predy.reshape((-1, length, params.num_labels))
prediction = T.argmax(predy_init, axis=2)
predy = predy*mask_var[:,:,None]
targets_shuffled = predy.dimshuffle(1, 0, 2)
target_time0 = targets_shuffled[0]
masks_shuffled = mask_var.dimshuffle(1, 0)
initial_energy0 = T.dot(target_time0, Wyy[-1,:-1])
initials = [target_time0, initial_energy0]
[ _, target_energies], _ = theano.scan(fn=inner_function, outputs_info=initials, sequences=[targets_shuffled[1:], masks_shuffled[1:]])
cost11 = target_energies[-1] + T.sum(T.sum(local_energy*predy, axis=2)*mask_var, axis=1)
predy_f = predy.reshape((-1, params.num_labels))
y_f = target_var.flatten()
if (params.annealing ==0):
lamb = params.L3
elif (params.annealing ==1):
lamb = params.L3* (1 - 0.01*t_t)
cost = T.mean(-cost11)
#from adam import adam
#updates_a = adam(cost, a_params, params.eta)
updates_a = lasagne.updates.sgd(cost, a_params, params.eta)
updates_a = lasagne.updates.apply_momentum(updates_a, a_params, momentum=0.9)
self.inf_fn = theano.function([input_var, mask_var, mask_var1, length], cost, updates = updates_a)
self.eval_fn = theano.function([input_var, mask_var, mask_var1, length], [prediction, -cost11], on_unused_input='ignore')
config.lr_decay = 1.02
config.weight_decay = 1e-7
config.max_grad_norm = 10
config.num_steps = 35
config.max_epoch = 20 # number of epochs after which learning decay starts
config.drop_x = 0.25 # variational dropout rate over input word embeddings
config.drop_i = 0.75 # variational dropout rate over inputs of RHN layers(s), applied seperately in each RHN layer
config.drop_s = 0.25 # variational dropout rate over recurrent state
config.drop_o = 0.75 # variational dropout rate over outputs of RHN layer(s), applied before classification layer
config.vocab_size = 10000
print("Data loading")
train_data, valid_data, test_data, _ = ptb_raw_data(config.data_path)
print('Compiling model')
_is_training = T.iscalar('is_training')
_lr = theano.shared(cast_floatX(config.learning_rate), 'lr')
_input_data = T.imatrix('input_data') # (batch_size, num_steps)
_noise_x = T.matrix('noise_x') # (batch_size, num_steps)
# model
_theano_rng = RandomStreams(config.seed // 2 +
321) # generates random numbers directly on GPU
flat_probs, params, rhn_updates, hidden_states = stacked.model(
_input_data, _noise_x, _lr, _is_training, config, _theano_rng)
# loss
_targets = T.imatrix('targets') # (batch_size, num_steps)
flat_targets = _targets.T.flatten()
xentropies = T.nnet.categorical_crossentropy(
flat_probs, flat_targets) # (batch_size * num_steps,)
# We apply the softmax later
out = lib.ops.Linear('SampleLevel.Output',
DIM,
Q_LEVELS,
out,
weightnorm=WEIGHT_NORM)
return out
sequences_8k = T.imatrix('sequences_8k') #batch size*samplenum
sequences_up = T.imatrix('sequences_up')
condition = T.matrix('con')
con_h0 = T.tensor3('con_h0')
h0 = T.tensor3('h0') #(batch size, N_RNN, DIM)
big_h0 = T.tensor3('big_h0') #(batch size, N_BIG_RNN, BIG_DIM)
reset = T.iscalar('reset')
mask = T.matrix('mask') #batch size*samplenum
batch_size = T.iscalar('batch_size')
lr = T.scalar('lr')
con_input_sequences = condition
big_input_sequences = sequences_8k #The last BIG_FRAME_SIZE frames do not need (tier3)
big_input_sequences = big_input_sequences.reshape((1, batch_size, 1, -1))
big_input_sequences = T.nnet.neighbours.images2neibs(big_input_sequences,
(1, 2 * OVERLAP),
neib_step=(1, OVERLAP),
mode='valid')
big_input_sequences = big_input_sequences.reshape((batch_size, -1))
input_sequences = sequences_8k[:, 0:-(OVERLAP - FRAME_SIZE)] #(tier2)
def create_model(s1_ae,
s2_ae,
s3_ae,
s1_shape,
s1_var,
s2_shape,
s2_var,
s3_shape,
s3_var,
mask_shape,
mask_var,
lstm_size=250,
win=T.iscalar('theta)'),
output_classes=26,
fusiontype='concat',
w_init_fn=las.init.Orthogonal(),
use_peepholes=True):
s1_bn_weights, s1_bn_biases, s1_bn_shapes, s1_bn_nonlinearities = s1_ae
s2_weights, s2_biases, s2_shapes, s2_nonlinearities = s2_ae
s3_weights, s3_biases, s3_shapes, s3_nonlinearities = s3_ae
gate_parameters = Gate(W_in=w_init_fn,
W_hid=w_init_fn,
b=las.init.Constant(0.))
cell_parameters = Gate(
W_in=w_init_fn,
W_hid=w_init_fn,
# Setting W_cell to None denotes that no cell connection will be used.
W_cell=None,
b=las.init.Constant(0.),
# By convention, the cell nonlinearity is tanh in an LSTM.
nonlinearity=tanh)
l_s1 = InputLayer(s1_shape, s1_var, 's1_im')
l_mask = InputLayer(mask_shape, mask_var, 'mask')
l_s2 = InputLayer(s2_shape, s2_var, 's2_im')
l_s3 = InputLayer(s3_shape, s3_var, 's3_im')
symbolic_batchsize_s1 = l_s1.input_var.shape[0]
symbolic_seqlen_s1 = l_s1.input_var.shape[1]
symbolic_batchsize_s2 = l_s2.input_var.shape[0]
symbolic_seqlen_s2 = l_s2.input_var.shape[1]
symbolic_batchsize_s3 = l_s3.input_var.shape[0]
symbolic_seqlen_s3 = l_s3.input_var.shape[1]
l_reshape1_s1 = ReshapeLayer(l_s1, (-1, s1_shape[-1]), name='reshape1_s1')
l_encoder_s1 = create_pretrained_encoder(
l_reshape1_s1, s1_bn_weights, s1_bn_biases, s1_bn_shapes,
s1_bn_nonlinearities, ['fc1_s1', 'fc2_s1', 'fc3_s1', 'bottleneck_s1'])
s1_len = las.layers.get_output_shape(l_encoder_s1)[-1]
l_reshape2_s1 = ReshapeLayer(
l_encoder_s1, (symbolic_batchsize_s1, symbolic_seqlen_s1, s1_len),
name='reshape2_s1')
l_delta_s1 = DeltaLayer(l_reshape2_s1, win, name='delta_s1')
l_delta_s1_dropout = DropoutLayer(l_delta_s1, name='dropout_s1')
# s2 images
l_reshape1_s2 = ReshapeLayer(l_s2, (-1, s2_shape[-1]), name='reshape1_s2')
l_encoder_s2 = create_pretrained_encoder(
l_reshape1_s2, s2_weights, s2_biases, s2_shapes, s2_nonlinearities,
['fc1_s2', 'fc2_s2', 'fc3_s2', 'bottleneck_s2'])
s2_len = las.layers.get_output_shape(l_encoder_s2)[-1]
l_reshape2_s2 = ReshapeLayer(
l_encoder_s2, (symbolic_batchsize_s2, symbolic_seqlen_s2, s2_len),
name='reshape2_s2')
l_delta_s2 = DeltaLayer(l_reshape2_s2, win, name='delta_s2')
l_delta_s2_dropout = DropoutLayer(l_delta_s2, name='dropout_s2')
# s3 images
l_reshape1_s3 = ReshapeLayer(l_s3, (-1, s3_shape[-1]), name='reshape1_s3')
l_encoder_s3 = create_pretrained_encoder(
l_reshape1_s3, s3_weights, s3_biases, s3_shapes, s3_nonlinearities,
['fc1_s3', 'fc2_s3', 'fc3_s3', 'bottleneck_s3'])
s3_len = las.layers.get_output_shape(l_encoder_s3)[-1]
l_reshape2_s3 = ReshapeLayer(
l_encoder_s3, (symbolic_batchsize_s3, symbolic_seqlen_s3, s3_len),
name='reshape2_s3')
l_delta_s3 = DeltaLayer(l_reshape2_s3, win, name='delta_s3')
l_delta_s3_dropout = DropoutLayer(l_delta_s3, name='dropout_s3')
l_lstm_s1 = LSTMLayer(
l_delta_s1_dropout,
lstm_size * 2,
peepholes=use_peepholes,
# We need to specify a separate input for masks
mask_input=l_mask,
# Here, we supply the gate parameters for each gate
ingate=gate_parameters,
forgetgate=gate_parameters,
cell=cell_parameters,
outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True,
grad_clipping=5.,
name='lstm_s1')
l_lstm_s2 = LSTMLayer(
l_delta_s2_dropout,
lstm_size * 2,
peepholes=use_peepholes,
# We need to specify a separate input for masks
mask_input=l_mask,
# Here, we supply the gate parameters for each gate
ingate=gate_parameters,
forgetgate=gate_parameters,
cell=cell_parameters,
outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True,
grad_clipping=5.,
name='lstm_s2')
l_lstm_s3 = LSTMLayer(
l_delta_s3_dropout,
lstm_size * 2,
peepholes=use_peepholes,
# We need to specify a separate input for masks
mask_input=l_mask,
# Here, we supply the gate parameters for each gate
ingate=gate_parameters,
forgetgate=gate_parameters,
cell=cell_parameters,
outgate=gate_parameters,
# We'll learn the initialization and use gradient clipping
learn_init=True,
grad_clipping=5.,
name='lstm_s3')
# We'll combine the forward and backward layer output by summing.
# Merge layers take in lists of layers to merge as input.
if fusiontype == 'adasum':
l_fuse = AdaptiveElemwiseSumLayer([l_lstm_s1, l_lstm_s2, l_lstm_s3],
name='adasum1')
elif fusiontype == 'sum':
l_fuse = ElemwiseSumLayer([l_lstm_s1, l_lstm_s2, l_lstm_s3],
name='sum1')
elif fusiontype == 'concat':
l_fuse = ConcatLayer([l_lstm_s1, l_lstm_s2, l_lstm_s3],
axis=-1,
name='concat')
l_fuse_dropout = DropoutLayer(l_fuse, name='concat_dropout')
f_lstm_agg, b_lstm_agg = create_blstm(l_fuse_dropout, l_mask,
lstm_size * 2, cell_parameters,
gate_parameters, 'lstm_agg')
l_sum2 = ElemwiseSumLayer([f_lstm_agg, b_lstm_agg], name='sum2')
# reshape to (num_examples * seq_len, lstm_size)
l_reshape3 = ReshapeLayer(l_sum2, (-1, lstm_size * 2), name='reshape3')
# Now, we can apply feed-forward layers as usual.
# We want the network to predict a classification for the sequence,
# so we'll use a the number of classes.
l_softmax = DenseLayer(l_reshape3,
num_units=output_classes,
nonlinearity=las.nonlinearities.softmax,
name='softmax')
l_out = ReshapeLayer(l_softmax, (-1, symbolic_seqlen_s1, output_classes),
name='output')
return l_out, l_fuse
def test_grad_s(self):
"tests that the gradients with respect to s_i are 0 after doing a mean field update of s_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
model.test_batch_size = X.shape[0]
init_H = e_step.init_H_hat(V=X)
init_Mu1 = e_step.init_S_hat(V=X)
prev_setting = config.compute_test_value
config.compute_test_value = 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0., 1., H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(
-5., 5., Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
S = e_step.infer_S_hat(V=X, H_hat=H_var, S_hat=Mu1_var)
s_idx = S[:, idx]
s_i_func = function([H_var, Mu1_var, idx], s_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0, var_s1_hat = Sigma1)
grad_Mu1 = T.grad(trunc_kl.sum(), Mu1_var)
grad_Mu1_idx = grad_Mu1[:, idx]
grad_func = function([H_var, Mu1_var, idx], grad_Mu1_idx)
for i in xrange(self.N):
Mu1[:, i] = s_i_func(H, Mu1, i)
g = grad_func(H, Mu1, i)
assert not contains_nan(g)
g_abs_max = np.abs(g).max()
if g_abs_max > self.tol:
raise Exception(
'after mean field step, gradient of kl divergence wrt mean field parameter should be 0, but here the max magnitude of a gradient element is '
+ str(g_abs_max) + ' after updating s_' + str(i))
def jobman(state, channel):
# load dataset
state['null_sym_source'] = 15000
state['null_sym_target'] = 15000
state['n_sym_source'] = state['null_sym_source'] + 1
state['n_sym_target'] = state['null_sym_target'] + 1
state['nouts'] = state['n_sym_target']
state['nins'] = state['n_sym_source']
rng = numpy.random.RandomState(state['seed'])
if state['loopIters'] > 0:
train_data, valid_data, test_data = get_data(state)
else:
train_data = None
valid_data = None
test_data = None
########### Training graph #####################
## 1. Inputs
if state['bs'] == 1:
x = TT.lvector('x')
x_mask = TT.vector('x_mask')
y = TT.lvector('y')
y0 = y
y_mask = TT.vector('y_mask')
else:
x = TT.lmatrix('x')
x_mask = TT.matrix('x_mask')
y = TT.lmatrix('y')
y0 = y
y_mask = TT.matrix('y_mask')
# 2. Layers and Operators
bs = state['bs']
embdim = state['dim_mlp']
# Source Sentence
emb = MultiLayer(rng,
n_in=state['nins'],
n_hids=[state['rank_n_approx']],
activation=[state['rank_n_activ']],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='emb')
emb_words = []
if state['rec_gating']:
gater_words = []
if state['rec_reseting']:
reseter_words = []
for si in xrange(state['encoder_stack']):
emb_words.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[embdim],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='emb_words_%d' % si))
if state['rec_gating']:
gater_words.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='gater_words_%d' % si))
if state['rec_reseting']:
reseter_words.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='reseter_words_%d' % si))
add_rec_step = []
rec_proj = []
if state['rec_gating']:
rec_proj_gater = []
if state['rec_reseting']:
rec_proj_reseter = []
for si in xrange(state['encoder_stack']):
if si > 0:
rec_proj.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[embdim],
activation=['lambda x:x'],
init_fn=state['rec_weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['rec_weight_scale'],
name='rec_proj_%d' % si))
if state['rec_gating']:
rec_proj_gater.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='rec_proj_gater_%d' % si))
if state['rec_reseting']:
rec_proj_reseter.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='rec_proj_reseter_%d' % si))
add_rec_step.append(
eval(state['rec_layer'])(rng,
n_hids=state['dim'],
activation=state['activ'],
bias_scale=state['bias'],
scale=state['rec_weight_scale'],
init_fn=state['rec_weight_init_fn'],
weight_noise=state['weight_noise_rec'],
dropout=state['dropout_rec'],
gating=state['rec_gating'],
gater_activation=state['rec_gater'],
reseting=state['rec_reseting'],
reseter_activation=state['rec_reseter'],
name='add_h_%d' % si))
def _add_op(words_embeddings,
words_mask=None,
prev_val=None,
si=0,
state_below=None,
gater_below=None,
reseter_below=None,
one_step=False,
bs=1,
init_state=None,
use_noise=True):
seqlen = words_embeddings.out.shape[0] // bs
rval = words_embeddings
gater = None
reseter = None
if state['rec_gating']:
gater = gater_below
if state['rec_reseting']:
reseter = reseter_below
if si > 0:
rval += rec_proj[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if state['rec_gating']:
projg = rec_proj_gater[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if gater: gater += projg
else: gater = projg
if state['rec_reseting']:
projg = rec_proj_reseter[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if reseter: reseter += projg
else: reseter = projg
if not one_step:
rval = add_rec_step[si](rval,
nsteps=seqlen,
batch_size=bs,
mask=words_mask,
gater_below=gater,
reseter_below=reseter,
one_step=one_step,
init_state=init_state,
use_noise=use_noise)
else:
rval = add_rec_step[si](rval,
mask=words_mask,
state_before=prev_val,
gater_below=gater,
reseter_below=reseter,
one_step=one_step,
init_state=init_state,
use_noise=use_noise)
return rval
add_op = Operator(_add_op)
# Target Sentence
emb_t = MultiLayer(rng,
n_in=state['nouts'],
n_hids=[state['rank_n_approx']],
activation=[state['rank_n_activ']],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='emb_t')
emb_words_t = []
if state['rec_gating']:
gater_words_t = []
if state['rec_reseting']:
reseter_words_t = []
for si in xrange(state['decoder_stack']):
emb_words_t.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[embdim],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='emb_words_t_%d' % si))
if state['rec_gating']:
gater_words_t.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='gater_words_t_%d' % si))
if state['rec_reseting']:
reseter_words_t.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='reseter_words_t_%d' % si))
proj_everything_t = []
if state['rec_gating']:
gater_everything_t = []
if state['rec_reseting']:
reseter_everything_t = []
for si in xrange(state['decoder_stack']):
proj_everything_t.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[embdim],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='proj_everything_t_%d' % si,
learn_bias=False))
if state['rec_gating']:
gater_everything_t.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='gater_everything_t_%d' % si,
learn_bias=False))
if state['rec_reseting']:
reseter_everything_t.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='reseter_everything_t_%d' % si,
learn_bias=False))
add_rec_step_t = []
rec_proj_t = []
if state['rec_gating']:
rec_proj_t_gater = []
if state['rec_reseting']:
rec_proj_t_reseter = []
for si in xrange(state['decoder_stack']):
if si > 0:
rec_proj_t.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[embdim],
activation=['lambda x:x'],
init_fn=state['rec_weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['rec_weight_scale'],
name='rec_proj_%d' % si))
if state['rec_gating']:
rec_proj_t_gater.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='rec_proj_t_gater_%d' % si))
if state['rec_reseting']:
rec_proj_t_reseter.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='rec_proj_t_reseter_%d' % si))
add_rec_step_t.append(
eval(state['rec_layer'])(rng,
n_hids=state['dim'],
activation=state['activ'],
bias_scale=state['bias'],
scale=state['rec_weight_scale'],
init_fn=state['rec_weight_init_fn'],
weight_noise=state['weight_noise_rec'],
dropout=state['dropout_rec'],
gating=state['rec_gating'],
gater_activation=state['rec_gater'],
reseting=state['rec_reseting'],
reseter_activation=state['rec_reseter'],
name='add_h_t_%d' % si))
if state['encoder_stack'] > 1:
encoder_proj = []
for si in xrange(state['encoder_stack']):
encoder_proj.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim'] * state['maxout_part']],
activation=['lambda x: x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='encoder_proj_%d' % si,
learn_bias=(si == 0)))
encoder_act_layer = UnaryOp(activation=eval(state['unary_activ']),
indim=indim,
pieces=pieces,
rng=rng)
def _add_t_op(words_embeddings,
everything=None,
words_mask=None,
prev_val=None,
one_step=False,
bs=1,
init_state=None,
use_noise=True,
gater_below=None,
reseter_below=None,
si=0,
state_below=None):
seqlen = words_embeddings.out.shape[0] // bs
rval = words_embeddings
gater = None
if state['rec_gating']:
gater = gater_below
reseter = None
if state['rec_reseting']:
reseter = reseter_below
if si > 0:
if isinstance(state_below, list):
state_below = state_below[-1]
rval += rec_proj_t[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if state['rec_gating']:
projg = rec_proj_t_gater[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if gater: gater += projg
else: gater = projg
if state['rec_reseting']:
projg = rec_proj_t_reseter[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if reseter: reseter += projg
else: reseter = projg
if everything:
rval = rval + proj_everything_t[si](everything)
if state['rec_gating']:
everyg = gater_everything_t[si](everything,
one_step=one_step,
use_noise=use_noise)
if gater: gater += everyg
else: gater = everyg
if state['rec_reseting']:
everyg = reseter_everything_t[si](everything,
one_step=one_step,
use_noise=use_noise)
if reseter: reseter += everyg
else: reseter = everyg
if not one_step:
rval = add_rec_step_t[si](rval,
nsteps=seqlen,
batch_size=bs,
mask=words_mask,
one_step=one_step,
init_state=init_state,
gater_below=gater,
reseter_below=reseter,
use_noise=use_noise)
else:
rval = add_rec_step_t[si](rval,
mask=words_mask,
state_before=prev_val,
one_step=one_step,
gater_below=gater,
reseter_below=reseter,
use_noise=use_noise)
return rval
add_t_op = Operator(_add_t_op)
outdim = state['dim_mlp']
if not state['deep_out']:
outdim = state['rank_n_approx']
if state['bias_code']:
bias_code = []
for si in xrange(state['decoder_stack']):
bias_code.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=[state['activ']],
bias_scale=[state['bias']],
scale=state['weight_scale'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
name='bias_code_%d' % si))
if state['avg_word']:
word_code_nin = state['rank_n_approx']
word_code = MultiLayer(rng,
n_in=word_code_nin,
n_hids=[outdim],
activation='lambda x:x',
bias_scale=[state['bias_mlp'] / 3],
scale=state['weight_scale'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
learn_bias=False,
name='word_code')
proj_code = MultiLayer(rng,
n_in=state['dim'],
n_hids=[outdim],
activation='lambda x: x',
bias_scale=[state['bias_mlp'] / 3],
scale=state['weight_scale'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
learn_bias=False,
name='proj_code')
proj_h = []
for si in xrange(state['decoder_stack']):
proj_h.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[outdim],
activation='lambda x: x',
bias_scale=[state['bias_mlp'] / 3],
scale=state['weight_scale'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
name='proj_h_%d' % si))
if state['bigram']:
proj_word = MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[outdim],
activation=['lambda x:x'],
bias_scale=[state['bias_mlp'] / 3],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='emb_words_lm')
if state['deep_out']:
indim = 0
pieces = 0
act_layer = UnaryOp(activation=eval(state['unary_activ']))
drop_layer = DropOp(rng=rng, dropout=state['dropout'])
if state['deep_out']:
indim = state['dim_mlp'] / state['maxout_part']
rank_n_approx = state['rank_n_approx']
rank_n_activ = state['rank_n_activ']
else:
indim = state['rank_n_approx']
rank_n_approx = 0
rank_n_activ = None
output_layer = SoftmaxLayer(rng,
indim,
state['nouts'],
state['weight_scale'],
-1,
rank_n_approx=rank_n_approx,
rank_n_activ=rank_n_activ,
weight_noise=state['weight_noise'],
init_fn=state['weight_init_fn'],
name='out')
def _pop_op(everything,
accum,
everything_max=None,
everything_min=None,
word=None,
aword=None,
one_step=False,
use_noise=True):
rval = proj_h[0](accum[0], one_step=one_step, use_noise=use_noise)
for si in xrange(1, state['decoder_stack']):
rval += proj_h[si](accum[si],
one_step=one_step,
use_noise=use_noise)
if state['mult_out']:
rval = rval * everything
else:
rval = rval + everything
if aword and state['avg_word']:
wcode = aword
if one_step:
if state['mult_out']:
rval = rval * wcode
else:
rval = rval + wcode
else:
if not isinstance(wcode, TT.TensorVariable):
wcode = wcode.out
shape = wcode.shape
rshape = rval.shape
rval = rval.reshape(
[rshape[0] / shape[0], shape[0], rshape[1]])
wcode = wcode.dimshuffle('x', 0, 1)
if state['mult_out']:
rval = rval * wcode
else:
rval = rval + wcode
rval = rval.reshape(rshape)
if word and state['bigram']:
if one_step:
if state['mult_out']:
rval *= proj_word(emb_t(word, use_noise=use_noise),
one_step=one_step,
use_noise=use_noise)
else:
rval += proj_word(emb_t(word, use_noise=use_noise),
one_step=one_step,
use_noise=use_noise)
else:
if isinstance(word, TT.TensorVariable):
shape = word.shape
ndim = word.ndim
else:
shape = word.shape
ndim = word.out.ndim
pword = proj_word(emb_t(word, use_noise=use_noise),
one_step=one_step,
use_noise=use_noise)
shape_pword = pword.shape
if ndim == 1:
pword = Shift()(pword.reshape([shape[0], 1, outdim]))
else:
pword = Shift()(pword.reshape([shape[0], shape[1],
outdim]))
if state['mult_out']:
rval *= pword.reshape(shape_pword)
else:
rval += pword.reshape(shape_pword)
if state['deep_out']:
rval = drop_layer(act_layer(rval), use_noise=use_noise)
return rval
pop_op = Operator(_pop_op)
# 3. Constructing the model
gater_below = None
if state['rec_gating']:
gater_below = gater_words[0](emb(x))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words[0](emb(x))
encoder_acts = [
add_op(emb_words[0](emb(x)),
x_mask,
bs=x_mask.shape[1],
si=0,
gater_below=gater_below,
reseter_below=reseter_below)
]
if state['encoder_stack'] > 1:
everything = encoder_proj[0](last(encoder_acts[-1]))
for si in xrange(1, state['encoder_stack']):
gater_below = None
if state['rec_gating']:
gater_below = gater_words[si](emb(x))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words[si](emb(x))
encoder_acts.append(
add_op(emb_words[si](emb(x)),
x_mask,
bs=x_mask.shape[1],
si=si,
state_below=encoder_acts[-1],
gater_below=gater_below,
reseter_below=reseter_below))
if state['encoder_stack'] > 1:
everything += encoder_proj[si](last(encoder_acts[-1]))
if state['encoder_stack'] <= 1:
encoder = encoder_acts[-1]
everything = LastState(ntimes=True, n=y.shape[0])(encoder)
else:
everything = encoder_act_layer(everything)
everything = everything.reshape(
[1, everything.shape[0], everything.shape[1]])
everything = LastState(ntimes=True, n=y.shape[0])(everything)
if state['bias_code']:
init_state = [bc(everything[-1]) for bc in bias_code]
else:
init_state = [None for bc in bias_code]
if state['avg_word']:
shape = x.shape
pword = emb(x).out.reshape(
[shape[0], shape[1], state['rank_n_approx']])
pword = pword * x_mask.dimshuffle(0, 1, 'x')
aword = pword.sum(0) / TT.maximum(1., x_mask.sum(0).dimshuffle(0, 'x'))
aword = word_code(aword, use_noise=False)
else:
aword = None
gater_below = None
if state['rec_gating']:
gater_below = gater_words_t[0](emb_t(y0))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words_t[0](emb_t(y0))
has_said = [
add_t_op(emb_words_t[0](emb_t(y0)),
everything,
y_mask,
bs=y_mask.shape[1],
gater_below=gater_below,
reseter_below=reseter_below,
init_state=init_state[0],
si=0)
]
for si in xrange(1, state['decoder_stack']):
gater_below = None
if state['rec_gating']:
gater_below = gater_words_t[si](emb_t(y0))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words_t[si](emb_t(y0))
has_said.append(
add_t_op(emb_words_t[si](emb_t(y0)),
everything,
y_mask,
bs=y_mask.shape[1],
state_below=has_said[-1],
gater_below=gater_below,
reseter_below=reseter_below,
init_state=init_state[si],
si=si))
if has_said[0].out.ndim < 3:
for si in xrange(state['decoder_stack']):
shape_hs = has_said[si].shape
if y0.ndim == 1:
shape = y0.shape
has_said[si] = Shift()(has_said[si].reshape(
[shape[0], 1, state['dim_mlp']]))
else:
shape = y0.shape
has_said[si] = Shift()(has_said[si].reshape(
[shape[0], shape[1], state['dim_mlp']]))
has_said[si].out = TT.set_subtensor(has_said[si].out[0, :, :],
init_state[si])
has_said[si] = has_said[si].reshape(shape_hs)
else:
for si in xrange(state['decoder_stack']):
has_said[si] = Shift()(has_said[si])
has_said[si].out = TT.set_subtensor(has_said[si].out[0, :, :],
init_state[si])
model = pop_op(proj_code(everything), has_said, word=y0, aword=aword)
nll = output_layer.train(
state_below=model, target=y0, mask=y_mask, reg=None) / TT.cast(
y.shape[0] * y.shape[1], 'float32')
valid_fn = None
noise_fn = None
x = TT.lvector(name='x')
n_steps = TT.iscalar('nsteps')
temp = TT.scalar('temp')
gater_below = None
if state['rec_gating']:
gater_below = gater_words[0](emb(x))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words[0](emb(x))
encoder_acts = [
add_op(emb_words[0](emb(x), use_noise=False),
si=0,
use_noise=False,
gater_below=gater_below,
reseter_below=reseter_below)
]
if state['encoder_stack'] > 1:
everything = encoder_proj[0](last(encoder_acts[-1]), use_noise=False)
for si in xrange(1, state['encoder_stack']):
gater_below = None
if state['rec_gating']:
gater_below = gater_words[si](emb(x))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words[si](emb(x))
encoder_acts.append(
add_op(emb_words[si](emb(x), use_noise=False),
si=si,
state_below=encoder_acts[-1],
use_noise=False,
gater_below=gater_below,
reseter_below=reseter_below))
if state['encoder_stack'] > 1:
everything += encoder_proj[si](last(encoder_acts[-1]),
use_noise=False)
if state['encoder_stack'] <= 1:
encoder = encoder_acts[-1]
everything = last(encoder)
else:
everything = encoder_act_layer(everything)
init_state = []
for si in xrange(state['decoder_stack']):
if state['bias_code']:
init_state.append(
TT.reshape(bias_code[si](everything, use_noise=False),
[1, state['dim']]))
else:
init_state.append(TT.alloc(numpy.float32(0), 1, state['dim']))
if state['avg_word']:
aword = emb(x, use_noise=False).out.mean(0)
aword = word_code(aword, use_noise=False)
else:
aword = None
def sample_fn(*args):
aidx = 0
word_tm1 = args[aidx]
aidx += 1
prob_tm1 = args[aidx]
has_said_tm1 = []
for si in xrange(state['decoder_stack']):
aidx += 1
has_said_tm1.append(args[aidx])
aidx += 1
ctx = args[aidx]
if state['avg_word']:
aidx += 1
awrd = args[aidx]
val = pop_op(proj_code(ctx),
has_said_tm1,
word=word_tm1,
aword=awrd,
one_step=True,
use_noise=False)
sample = output_layer.get_sample(state_below=val, temp=temp)
logp = output_layer.get_cost(state_below=val.out.reshape(
[1, TT.cast(output_layer.n_in, 'int64')]),
temp=temp,
target=sample.reshape([1, 1]),
use_noise=False)
gater_below = None
if state['rec_gating']:
gater_below = gater_words_t[0](emb_t(sample))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words_t[0](emb_t(sample))
has_said_t = [
add_t_op(emb_words_t[0](emb_t(sample)),
ctx,
prev_val=has_said_tm1[0],
gater_below=gater_below,
reseter_below=reseter_below,
one_step=True,
use_noise=True,
si=0)
]
for si in xrange(1, state['decoder_stack']):
gater_below = None
if state['rec_gating']:
gater_below = gater_words_t[si](emb_t(sample))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words_t[si](emb_t(sample))
has_said_t.append(
add_t_op(emb_words_t[si](emb_t(sample)),
ctx,
prev_val=has_said_tm1[si],
gater_below=gater_below,
reseter_below=reseter_below,
one_step=True,
use_noise=True,
si=si,
state_below=has_said_t[-1]))
for si in xrange(state['decoder_stack']):
if isinstance(has_said_t[si], list):
has_said_t[si] = has_said_t[si][-1]
rval = [sample, TT.cast(logp, 'float32')] + has_said_t
return rval
sampler_params = [everything]
if state['avg_word']:
sampler_params.append(aword)
states = [TT.alloc(numpy.int64(0), n_steps)]
states.append(TT.alloc(numpy.float32(0), n_steps))
states += init_state
outputs, updates = scan(sample_fn,
states=states,
params=sampler_params,
n_steps=n_steps,
name='sampler_scan')
samples = outputs[0]
probs = outputs[1]
sample_fn = theano.function([n_steps, temp, x],
[samples, probs.sum()],
updates=updates,
profile=False,
name='sample_fn')
model = LM_Model(cost_layer=nll,
weight_noise_amount=state['weight_noise_amount'],
valid_fn=valid_fn,
sample_fn=sample_fn,
clean_before_noise_fn=False,
noise_fn=noise_fn,
indx_word=state['indx_word_target'],
indx_word_src=state['indx_word'],
character_level=False,
rng=rng)
if state['loopIters'] > 0: algo = SGD(model, state, train_data)
else: algo = None
def hook_fn():
if not hasattr(model, 'word_indxs'): model.load_dict()
if not hasattr(model, 'word_indxs_src'):
model.word_indxs_src = model.word_indxs
old_offset = train_data.offset
if state['sample_reset']: train_data.reset()
ns = 0
for sidx in xrange(state['sample_n']):
while True:
batch = train_data.next()
if batch:
break
x = batch['x']
y = batch['y']
#xbow = batch['x_bow']
masks = batch['x_mask']
if x.ndim > 1:
for idx in xrange(x.shape[1]):
ns += 1
if ns > state['sample_max']:
break
print 'Input: ',
for k in xrange(x[:, idx].shape[0]):
print model.word_indxs_src[x[:, idx][k]],
if model.word_indxs_src[x[:, idx][k]] == '<eol>':
break
print ''
print 'Target: ',
for k in xrange(y[:, idx].shape[0]):
print model.word_indxs[y[:, idx][k]],
if model.word_indxs[y[:, idx][k]] == '<eol>':
break
print ''
senlen = len(x[:, idx])
if len(numpy.where(masks[:, idx] == 0)[0]) > 0:
senlen = numpy.where(masks[:, idx] == 0)[0][0]
if senlen < 1:
continue
xx = x[:senlen, idx]
#xx = xx.reshape([xx.shape[0], 1])
model.get_samples(state['seqlen'] + 1, 1, xx)
else:
ns += 1
model.get_samples(state['seqlen'] + 1, 1, x)
if ns > state['sample_max']:
break
train_data.offset = old_offset
return
main = MainLoop(train_data,
valid_data,
None,
model,
algo,
state,
channel,
reset=state['reset'],
hooks=hook_fn)
if state['reload']: main.load()
if state['loopIters'] > 0: main.main()
if state['sampler_test']:
# This is a test script: we only sample
if not hasattr(model, 'word_indxs'): model.load_dict()
if not hasattr(model, 'word_indxs_src'):
model.word_indxs_src = model.word_indxs
indx_word = pkl.load(open(state['word_indx'], 'rb'))
try:
while True:
try:
seqin = raw_input('Input Sequence: ')
n_samples = int(raw_input('How many samples? '))
alpha = float(raw_input('Inverse Temperature? '))
seqin = seqin.lower()
seqin = seqin.split()
seqlen = len(seqin)
seq = numpy.zeros(seqlen + 1, dtype='int64')
for idx, sx in enumerate(seqin):
try:
seq[idx] = indx_word[sx]
except:
seq[idx] = indx_word[state['oov']]
seq[-1] = state['null_sym_source']
except Exception:
print 'Something wrong with your input! Try again!'
continue
sentences = []
all_probs = []
for sidx in xrange(n_samples):
#import ipdb; ipdb.set_trace()
[values, probs] = model.sample_fn(seqlen * 3, alpha, seq)
sen = []
for k in xrange(values.shape[0]):
if model.word_indxs[values[k]] == '<eol>':
break
sen.append(model.word_indxs[values[k]])
sentences.append(" ".join(sen))
all_probs.append(-probs)
sprobs = numpy.argsort(all_probs)
for pidx in sprobs:
print pidx, "(%f):" % (-all_probs[pidx]), sentences[pidx]
print
except KeyboardInterrupt:
print 'Interrupted'
pass
def __init__(self,
n_in,
hidden_layer_size,
n_out,
L1_reg,
L2_reg,
hidden_layer_type,
output_type='LINEAR',
network_type='DNN',
dropout_rate=0.0):
""" This function initialises a neural network
:param n_in: Dimensionality of input features
:type in: Integer
:param hidden_layer_size: The layer size for each hidden layer
:type hidden_layer_size: A list of integers
:param n_out: Dimensionality of output features
:type n_out: Integrer
:param hidden_layer_type: the activation types of each hidden layers, e.g., TANH, LSTM, GRU, BLSTM
:param L1_reg: the L1 regulasation weight
:param L2_reg: the L2 regulasation weight
:param output_type: the activation type of the output layer, by default is 'LINEAR', linear regression.
:param dropout_rate: probability of dropout, a float number between 0 and 1.
"""
logger = logging.getLogger("DNN initialization")
self.n_in = int(n_in)
self.n_out = int(n_out)
self.n_layers = len(hidden_layer_size)
self.dropout_rate = dropout_rate
self.is_train = T.iscalar('is_train')
assert len(hidden_layer_size) == len(hidden_layer_type)
self.x = T.matrix('x')
self.y = T.matrix('y')
if network_type == "S2S":
self.d = T.ivector('d')
self.f = T.matrix('f')
self.L1_reg = L1_reg
self.L2_reg = L2_reg
self.rnn_layers = []
self.params = []
self.delta_params = []
rng = np.random.RandomState(123)
BLSTM_variants = ['BLSTM', 'BSLSTM', 'BLSTME']
Encoder_variants = ['RNNE', 'LSTME', 'BLSTME', 'SLSTME']
for i in range(self.n_layers):
if i == 0:
input_size = n_in
else:
input_size = hidden_layer_size[i - 1]
if hidden_layer_type[i - 1] in BLSTM_variants:
input_size = hidden_layer_size[i - 1] * 2
if i == 0:
layer_input = self.x
else:
layer_input = self.rnn_layers[i - 1].output
### sequence-to-sequence mapping ###
if hidden_layer_type[i - 1] in Encoder_variants:
dur_input = self.d
frame_feat_input = self.f
if network_type == "S2S":
seq2seq_model = DistributedSequenceEncoder(
rng, layer_input, dur_input)
layer_input = T.concatenate(
(seq2seq_model.encoded_output, frame_feat_input),
axis=1)
input_size = input_size + 4
else:
logger.critical(
"This network type: %s is not supported right now! \n Please use one of the following: DNN, RNN, S2S\n"
% (network_type))
sys.exit(1)
if hidden_layer_type[i] == 'SLSTM':
hidden_layer = SimplifiedLstm(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'SGRU':
hidden_layer = SimplifiedGRU(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'GRU':
hidden_layer = GatedRecurrentUnit(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'LSTM_NFG':
hidden_layer = LstmNFG(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'LSTM_NOG':
hidden_layer = LstmNOG(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'LSTM_NIG':
hidden_layer = LstmNIG(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'LSTM_NPH':
hidden_layer = LstmNoPeepholes(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'LSTM':
hidden_layer = VanillaLstm(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'LSTME':
hidden_layer = VanillaLstm(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'CLSTM':
hidden_layer = ContextLstm(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'LSTMD':
hidden_layer = VanillaLstmDecoder(rng,
layer_input,
input_size,
hidden_layer_size[i],
self.n_out,
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'BSLSTM':
hidden_layer = BidirectionSLstm(rng,
layer_input,
input_size,
hidden_layer_size[i],
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'BLSTM':
hidden_layer = BidirectionLstm(rng,
layer_input,
input_size,
hidden_layer_size[i],
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'BLSTME':
hidden_layer = BidirectionLstm(rng,
layer_input,
input_size,
hidden_layer_size[i],
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'RNN':
hidden_layer = VanillaRNN(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'RNNE':
hidden_layer = VanillaRNN(rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'RNND':
hidden_layer = VanillaRNNDecoder(rng,
layer_input,
input_size,
hidden_layer_size[i],
self.n_out,
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'TANH':
hidden_layer = SigmoidLayer(rng,
layer_input,
input_size,
hidden_layer_size[i],
activation=T.tanh,
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'SIGMOID':
hidden_layer = SigmoidLayer(rng,
layer_input,
input_size,
hidden_layer_size[i],
activation=T.nnet.sigmoid,
p=self.dropout_rate,
training=self.is_train)
else:
logger.critical(
"This hidden layer type: %s is not supported right now! \n Please use one of the following: SLSTM, BSLSTM, TANH, SIGMOID\n"
% (hidden_layer_type[i]))
sys.exit(1)
self.rnn_layers.append(hidden_layer)
self.params.extend(hidden_layer.params)
input_size = hidden_layer_size[-1]
if hidden_layer_type[-1] == 'BSLSTM' or hidden_layer_type[
-1] == 'BLSTM':
input_size = hidden_layer_size[-1] * 2
if hidden_layer_type[-1] == "RNND" or hidden_layer_type[-1] == "LSTMD":
self.final_layer = self.rnn_layers[-1]
else:
if output_type == 'LINEAR':
self.final_layer = LinearLayer(rng, self.rnn_layers[-1].output,
input_size, self.n_out)
elif output_type == 'SIGMOID':
self.final_layer = SigmoidLayer(rng,
self.rnn_layers[-1].output,
input_size,
self.n_out,
activation=T.nnet.sigmoid)
else:
logger.critical(
"This output layer type: %s is not supported right now! \n Please use one of the following: LINEAR, SIGMOID\n"
% (output_type))
sys.exit(1)
self.params.extend(self.final_layer.params)
self.updates = {}
for param in self.params:
self.updates[param] = theano.shared(
value=np.zeros(param.get_value(borrow=True).shape,
dtype=theano.config.floatX),
name='updates')
self.finetune_cost = T.mean(
T.sum((self.final_layer.output - self.y)**2, axis=1))
self.errors = T.mean(
T.sum((self.final_layer.output - self.y)**2, axis=1))
import theano
import theano.tensor as T
import numpy as np
theano.config.warn.subtensor_merge_bug = False
i = T.iscalar("i")
x = T.iscalar("x")
y = T.iscalar("y")
A = T.imatrix("A")
def inner_sum(prior_x, B):
return prior_x + B
def inner_sum2D(x_t, y_t, u):
return x_t + y_t + u
row_count = 3
column_count = 4
# Symbolic description of the result
result, updates = theano.scan(
fn=inner_sum2D,
sequences=dict(input=T.flatten(A), taps=[column_count]),
outputs_info=dict(initial=T.flatten(A), taps=[-1, -column_count]),
#non_sequences=
n_steps=x * y)
# Scan has provided us with A ** 1 through A ** k. Keep only the last
def __init__(self, We_initial, char_embedd_table_initial, params):
self.textfile = open(params.outfile, 'w')
We = theano.shared(We_initial)
We_inf = theano.shared(We_initial)
embsize = We_initial.shape[1]
hidden = params.hidden
self.en_hidden_size = params.hidden_inf
self.num_labels = params.num_labels
self.de_hidden_size = params.de_hidden_size
self.lstm_layers_num = 1
char_embedd_dim = params.char_embedd_dim
char_dic_size = len(params.char_dic)
char_embedd_table = theano.shared(char_embedd_table_initial)
char_embedd_table_inf = theano.shared(char_embedd_table_initial)
input_var = T.imatrix(name='inputs')
target_var = T.imatrix(name='targets')
target_var_in = T.imatrix(name='in_targets')
mask_var = T.fmatrix(name='masks')
mask_var1 = T.fmatrix(name='masks1')
length = T.iscalar()
length0 = T.iscalar()
t_t = T.fscalar()
t_t0 = T.fscalar()
char_input_var = T.itensor3(name='char-inputs')
use_dropout = T.fscalar()
use_dropout0 = T.fscalar()
Wyy0 = np.random.uniform(
-0.02, 0.02,
(self.num_labels + 1, self.num_labels + 1)).astype('float32')
Wyy = theano.shared(Wyy0)
l_in_word = lasagne.layers.InputLayer((None, None))
l_mask_word = lasagne.layers.InputLayer(shape=(None, None))
if params.emb == 1:
l_emb_word = lasagne.layers.EmbeddingLayer(
l_in_word,
input_size=We_initial.shape[0],
output_size=embsize,
W=We)
else:
l_emb_word = lasagne_embedding_layer_2(l_in_word, embsize, We)
layer_char_input = lasagne.layers.InputLayer(shape=(None, None,
Max_Char_Length),
input_var=char_input_var,
name='char-input')
layer_char = lasagne.layers.reshape(layer_char_input, (-1, [2]))
layer_char_embedding = lasagne.layers.EmbeddingLayer(
layer_char,
input_size=char_dic_size,
output_size=char_embedd_dim,
W=char_embedd_table,
name='char_embedding')
layer_char = lasagne.layers.DimshuffleLayer(layer_char_embedding,
pattern=(0, 2, 1))
# first get some necessary dimensions or parameters
conv_window = 3
num_filters = params.num_filters
# construct convolution layer
cnn_layer = lasagne.layers.Conv1DLayer(
layer_char,
num_filters=num_filters,
filter_size=conv_window,
pad='full',
nonlinearity=lasagne.nonlinearities.tanh,
name='cnn')
# infer the pool size for pooling (pool size should go through all time step of cnn)
_, _, pool_size = cnn_layer.output_shape
# construct max pool layer
pool_layer = lasagne.layers.MaxPool1DLayer(cnn_layer,
pool_size=pool_size)
# reshape the layer to match lstm incoming layer [batch * sent_length, num_filters, 1] --> [batch, sent_length, num_filters]
output_cnn_layer = lasagne.layers.reshape(pool_layer,
(-1, length, [1]))
# finally, concatenate the two incoming layers together.
l_emb_word = lasagne.layers.concat([output_cnn_layer, l_emb_word],
axis=2)
l_lstm_wordf = lasagne.layers.LSTMLayer(l_emb_word,
hidden,
mask_input=l_mask_word)
l_lstm_wordb = lasagne.layers.LSTMLayer(l_emb_word,
hidden,
mask_input=l_mask_word,
backwards=True)
concat = lasagne.layers.concat([l_lstm_wordf, l_lstm_wordb], axis=2)
l_reshape_concat = lasagne.layers.ReshapeLayer(concat,
(-1, 2 * hidden))
l_local = lasagne.layers.DenseLayer(
l_reshape_concat,
num_units=self.num_labels,
nonlinearity=lasagne.nonlinearities.linear)
network_params = lasagne.layers.get_all_params(l_local, trainable=True)
network_params.append(Wyy)
print len(network_params)
f = open(
'ccctag_BiLSTM_CNN_CRF_num_filters_30_dropout_1_LearningRate_0.01_0.0_400_emb_1_tagversoin_2.pickle',
'r')
data = pickle.load(f)
f.close()
for idx, p in enumerate(network_params):
p.set_value(data[idx])
self.params = []
self.hos = []
self.Cos = []
self.encoder_lstm_layers = []
self.decoder_lstm_layers = []
ei, di, dt = T.imatrices(3) #place holders
decoderInputs0, em, em1, dm, tf, di0 = T.fmatrices(6)
ci = T.itensor3()
#### the last one is for the stary symbole
self.de_lookuptable = theano.shared(name="Decoder LookUpTable",
value=init_xavier_uniform(
self.num_labels + 1,
self.de_hidden_size),
borrow=True)
self.linear = theano.shared(
name="Linear",
value=init_xavier_uniform(
self.de_hidden_size + 2 * self.en_hidden_size,
self.num_labels),
borrow=True)
self.linear_bias = theano.shared(
name="Hidden to Bias",
value=np.asarray(np.random.randn(self.num_labels, ) * 0.,
dtype=theano.config.floatX),
borrow=True)
#self.hidden_decode = theano.shared(name="Hidden to Decode", value= init_xavier_uniform(2*hidden, self.de_hidden_size), borrow = True)
#self.hidden_bias = theano.shared(
# name="Hidden to Bias",
# value=np.asarray(np.random.randn(self.de_hidden_size, )*0., dtype=theano.config.floatX) ,
# borrow=True
# )
input_var_shuffle = input_var.dimshuffle(1, 0)
mask_var_shuffle = mask_var.dimshuffle(1, 0)
target_var_in_shuffle = target_var_in.dimshuffle(1, 0)
target_var_shuffle = target_var.dimshuffle(1, 0)
self.params += [
We_inf, self.linear, self.linear_bias, self.de_lookuptable
] #concatenate
state_below = We_inf[input_var_shuffle.flatten()].reshape(
(input_var_shuffle.shape[0], input_var_shuffle.shape[1], embsize))
###### character word embedding
layer_char_input_inf = lasagne.layers.InputLayer(
shape=(None, None, Max_Char_Length),
input_var=char_input_var,
name='char-input')
layer_char_inf = lasagne.layers.reshape(layer_char_input_inf,
(-1, [2]))
layer_char_embedding_inf = lasagne.layers.EmbeddingLayer(
layer_char_inf,
input_size=char_dic_size,
output_size=char_embedd_dim,
W=char_embedd_table_inf,
name='char_embedding_inf')
layer_char_inf = lasagne.layers.DimshuffleLayer(
layer_char_embedding_inf, pattern=(0, 2, 1))
#layer_char_inf = lasagne.layers.DropoutLayer(layer_char_inf, p=0.5)
cnn_layer_inf = lasagne.layers.Conv1DLayer(
layer_char_inf,
num_filters=num_filters,
filter_size=conv_window,
pad='full',
nonlinearity=lasagne.nonlinearities.tanh,
name='cnn_inf')
pool_layer_inf = lasagne.layers.MaxPool1DLayer(cnn_layer_inf,
pool_size=pool_size)
output_cnn_layer_inf = lasagne.layers.reshape(pool_layer_inf,
(-1, length, [1]))
char_params = lasagne.layers.get_all_params(output_cnn_layer_inf,
trainable=True)
self.params += char_params
###### [batch, sent_length, num_filters]
#char_state_below = lasagne.layers.get_output(output_cnn_layer_inf, {layer_char_input_inf:char_input_var})
char_state_below = lasagne.layers.get_output(output_cnn_layer_inf)
char_state_below = dropout_layer(char_state_below, use_dropout, trng)
char_state_shuff = char_state_below.dimshuffle(1, 0, 2)
state_below = T.concatenate([state_below, char_state_shuff], axis=2)
state_below = dropout_layer(state_below, use_dropout, trng)
enclstm_f = LSTM(embsize + num_filters, self.en_hidden_size)
enclstm_b = LSTM(embsize + num_filters, self.en_hidden_size, True)
self.encoder_lstm_layers.append(enclstm_f) #append
self.encoder_lstm_layers.append(enclstm_b) #append
self.params += enclstm_f.params + enclstm_b.params #concatenate
hs_f, Cs_f = enclstm_f.forward(state_below, mask_var_shuffle)
hs_b, Cs_b = enclstm_b.forward(state_below, mask_var_shuffle)
hs = T.concatenate([hs_f, hs_b], axis=2)
Cs = T.concatenate([Cs_f, Cs_b], axis=2)
#hs0 = T.concatenate([hs_f[-1], hs_b[0]], axis=1)
#Cs0 = T.concatenate([Cs_f[-1], Cs_b[0]], axis=1)
#self.hos += T.tanh(tensor.dot(hs0, self.hidden_decode) + self.hidden_bias),
#self.Cos += T.tanh(tensor.dot(Cs0, self.hidden_decode) + self.hidden_bias),
self.hos += T.alloc(np.asarray(0., dtype=theano.config.floatX),
input_var_shuffle.shape[1], self.de_hidden_size),
self.Cos += T.alloc(np.asarray(0., dtype=theano.config.floatX),
input_var_shuffle.shape[1], self.de_hidden_size),
Encoder = hs
state_below = self.de_lookuptable[
target_var_in_shuffle.flatten()].reshape(
(target_var_in_shuffle.shape[0],
target_var_in_shuffle.shape[1], self.de_hidden_size))
for i in range(self.lstm_layers_num):
declstm = LSTM(self.de_hidden_size, self.de_hidden_size)
self.decoder_lstm_layers += declstm, #append
self.params += declstm.params #concatenate
ho, Co = self.hos[i], self.Cos[i]
state_below, Cs = declstm.forward(state_below, mask_var_shuffle,
ho, Co)
decoder_lstm_outputs = T.concatenate([state_below, Encoder], axis=2)
linear_outputs = T.dot(decoder_lstm_outputs,
self.linear) + self.linear_bias[None, None, :]
softmax_outputs, updates = theano.scan(
fn=lambda x: T.nnet.softmax(x),
sequences=[linear_outputs],
)
def _NLL(pred, y, m):
return -m * T.log(pred[T.arange(input_var.shape[0]), y])
def _step2(ctx_, state_, hs_, Cs_):
hs, Cs = [], []
token_idxs = T.cast(state_.argmax(axis=-1), "int32")
msk_ = T.fill((T.zeros_like(token_idxs, dtype="float32")), 1.)
msk_ = msk_.dimshuffle('x', 0)
state_below0 = self.de_lookuptable[token_idxs].reshape(
(1, input_var_shuffle.shape[1], self.de_hidden_size))
for i, lstm in enumerate(self.decoder_lstm_layers):
h, C = lstm.forward(state_below0, msk_, hs_[i],
Cs_[i]) #mind msk
hs += h[-1],
Cs += C[-1],
state_below0 = h
hs, Cs = T.as_tensor_variable(hs), T.as_tensor_variable(Cs)
state_below0 = state_below0.reshape(
(input_var.shape[0], self.de_hidden_size))
state_below0 = T.concatenate([ctx_, state_below0], axis=1)
newpred = T.dot(state_below0, self.linear).reshape(
(input_var_shuffle.shape[1],
self.num_labels)) + self.linear_bias[None, :]
state_below = T.nnet.softmax(newpred)
extra_p = T.zeros_like(hs[:, :, 0])
state_below = T.concatenate([state_below, extra_p.T], axis=1)
return state_below, hs, Cs
hs0, Cs0 = T.as_tensor_variable(
self.hos, name="hs0"), T.as_tensor_variable(self.Cos, name="Cs0")
train_outputs, _ = theano.scan(fn=_step2,
sequences=[Encoder],
outputs_info=[decoderInputs0, hs0, Cs0],
n_steps=input_var_shuffle.shape[0])
predy = train_outputs[0].dimshuffle(1, 0, 2)
predy = predy[:, :, :-1] * mask_var[:, :, None]
predy0 = predy.reshape((-1, self.num_labels))
def inner_function(targets_one_step, mask_one_step, prev_label,
tg_energy):
"""
:param targets_one_step: [batch_size, t]
:param prev_label: [batch_size, t]
:param tg_energy: [batch_size]
:return:
"""
new_ta_energy = T.dot(prev_label, Wyy[:-1, :-1])
new_ta_energy_t = tg_energy + T.sum(
new_ta_energy * targets_one_step, axis=1)
tg_energy_t = T.switch(mask_one_step, new_ta_energy_t, tg_energy)
return [targets_one_step, tg_energy_t]
local_energy = lasagne.layers.get_output(
l_local, {
l_in_word: input_var,
l_mask_word: mask_var,
layer_char_input: char_input_var
})
local_energy = local_energy.reshape((-1, length, self.num_labels))
local_energy = local_energy * mask_var[:, :, None]
#####################
# for the end symbole of a sequence
####################
end_term = Wyy[:-1, -1]
local_energy = local_energy + end_term.dimshuffle(
'x', 'x', 0) * mask_var1[:, :, None]
#predy0 = lasagne.layers.get_output(l_local_a, {l_in_word_a:input_var, l_mask_word_a:mask_var})
predy_in = T.argmax(predy0, axis=1)
A = T.extra_ops.to_one_hot(predy_in, self.num_labels)
A = A.reshape((-1, length, self.num_labels))
#predy = predy0.reshape((-1, length, 25))
#predy = predy*mask_var[:,:,None]
targets_shuffled = predy.dimshuffle(1, 0, 2)
target_time0 = targets_shuffled[0]
masks_shuffled = mask_var.dimshuffle(1, 0)
initial_energy0 = T.dot(target_time0, Wyy[-1, :-1])
initials = [target_time0, initial_energy0]
[_, target_energies], _ = theano.scan(
fn=inner_function,
outputs_info=initials,
sequences=[targets_shuffled[1:], masks_shuffled[1:]])
cost11 = target_energies[-1] + T.sum(
T.sum(local_energy * predy, axis=2) * mask_var, axis=1)
# compute the ground-truth energy
targets_shuffled0 = A.dimshuffle(1, 0, 2)
target_time00 = targets_shuffled0[0]
initial_energy00 = T.dot(target_time00, Wyy[-1, :-1])
initials0 = [target_time00, initial_energy00]
[_, target_energies0], _ = theano.scan(
fn=inner_function,
outputs_info=initials0,
sequences=[targets_shuffled0[1:], masks_shuffled[1:]])
cost110 = target_energies0[-1] + T.sum(
T.sum(local_energy * A, axis=2) * mask_var, axis=1)
#predy_f = predy.reshape((-1, 25))
y_f = target_var.flatten()
if (params.annealing == 0):
lamb = params.L3
elif (params.annealing == 1):
lamb = params.L3 * (1 - 0.01 * t_t)
if (params.regutype == 0):
ce_hinge = lasagne.objectives.categorical_crossentropy(
predy0 + eps, y_f)
ce_hinge = ce_hinge.reshape((-1, length))
ce_hinge = T.sum(ce_hinge * mask_var, axis=1)
cost = T.mean(-cost11) + lamb * T.mean(ce_hinge)
else:
entropy_term = -T.sum(predy0 * T.log(predy0 + eps), axis=1)
entropy_term = entropy_term.reshape((-1, length))
entropy_term = T.sum(entropy_term * mask_var, axis=1)
cost = T.mean(-cost11) - lamb * T.mean(entropy_term)
"""
f = open('F0_simple.pickle')
PARA = pickle.load(f)
f.close()
l2_term = sum(lasagne.regularization.l2(x-PARA[index]) for index, x in enumerate(a_params))
cost = T.mean(-cost11) + params.L2*l2_term
"""
##from adam import adam
##updates_a = adam(cost, self.params, params.eta)
#updates_a = lasagne.updates.sgd(cost, self.params, params.eta)
#updates_a = lasagne.updates.apply_momentum(updates_a, self.params, momentum=0.9)
from momentum import momentum
updates_a = momentum(cost, self.params, params.eta, momentum=0.9)
if (params.regutype == 0):
self.train_fn = theano.function(
inputs=[ei, ci, dt, em, em1, length0, t_t0, di0, use_dropout0],
outputs=[cost, ce_hinge],
updates=updates_a,
on_unused_input='ignore',
givens={
input_var: ei,
char_input_var: ci,
target_var: dt,
mask_var: em,
mask_var1: em1,
length: length0,
t_t: t_t0,
decoderInputs0: di0,
use_dropout: use_dropout0
})
#self.train_fn = theano.function([input_var, target_var, mask_var, mask_var1, length, t_t], [cost, ce_hinge], updates = updates_a, on_unused_input='ignore')
else:
self.train_fn = theano.function(
inputs=[ei, dt, em, em1, length0, t_t0, di0, use_dropout0],
outputs=[cost, entropy_term],
updates=updates_a,
on_unused_input='ignore',
givens={
input_var: ei,
char_input_var: ci,
target_var: dt,
mask_var: em,
mask_var1: em1,
length: length0,
t_t: t_t0,
decoderInputs0: di0,
use_dropout: use_dropout0
})
#self.train_fn = theano.function([input_var, target_var, mask_var, mask_var1, length, t_t], [cost, entropy_term], updates = updates_a, on_unused_input='ignore')
prediction = T.argmax(predy, axis=2)
corr = T.eq(prediction, target_var)
corr_train = (corr * mask_var).sum(dtype=theano.config.floatX)
num_tokens = mask_var.sum(dtype=theano.config.floatX)
self.eval_fn = theano.function(
inputs=[ei, ci, dt, em, em1, length0, di0, use_dropout0],
outputs=[cost11, cost110, corr_train, num_tokens, prediction],
on_unused_input='ignore',
givens={
input_var: ei,
char_input_var: ci,
target_var: dt,
mask_var: em,
mask_var1: em1,
length: length0,
decoderInputs0: di0,
use_dropout: use_dropout0
})
# at work in In(y, value=1). In the case of In(w, value=2, name='w_by_name').
# We override the symbolic variable's name attribute with a name to be used
# for this function.
# 4. Using Shared Variables
# It is also possible to make a function with an internal state. For
# example, let's say we want to make an accumulator: at the beginning,
# the initialized to zero. Then, on each fucntion call, the state is
# incremented by the function's arguments.
# First let's define teh accumulator function. It adds its argument to the
# interanl state, and returns teh old state value.
from theano import shared
state = shared(0)
inc = T.iscalar('inc')
accumulator = function([inc], state, updates=[(state, state + inc)])
# This code introduces a few concepts. The shared function constructs so-
# called shared variables. These are hybrid symbolic and non-symbolic
# variables whose value may be shared between multiple functions. Shared
# variables can be used in symbolic expressions just like the objects
# returned by dmatrices(...) but they also have an internal value that
# defines the value taken by this symbolic variable in all the functions
# that use it. It is called a shared variable because its value is shared
# between many .set_value() methods. We will com back to this soon.
# The other new thing in this code is the updates parameter of function.
# updates must be supplied with a list of pairs of the form (shared-
# variable, new expression). It can also be a dictionary whose keys are
# shared-variables and values are the new expression. Either way, it means
# "whenever this function runs, it will replace the .value of each shared
def __init__(self, nh, nc, ne, de, cs, em, init, featdim):
'''
nh :: dimension of the hidden layer
nc :: number of classes
ne :: number of word embeddings in the vocabulary
de :: dimension of the word embeddings
cs :: word window context size
'''
# parameters of the model
self.featdim = featdim
tmp_emb = 0.2 * numpy.random.uniform(-1.0, 1.0, (ne + 1, de))
if init:
for row in xrange(ne + 1):
if em[row] is not None:
tmp_emb[row] = em[row]
self.emb = theano.shared(tmp_emb.astype(
theano.config.floatX)) # add one for PADDING at the end
# weights for LSTM
n_in = de * cs
print "de,cs", de, cs
# print "n_i",n_i
n_hidden = n_i = n_c = n_o = n_f = nh
n_y = nc
print "n_y", n_y
print "n_hidden, n_i, n_c, n_o,nh", n_hidden, n_i, n_c, n_o, nh
self.W_xi = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_in, n_i)).astype(dtype))
self.W_hi = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_hidden, n_i)).astype(dtype))
self.W_ci = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_c, n_i)).astype(dtype))
self.b_i = theano.shared(numpy.cast[dtype](uniform(-0.5, .5,
size=n_i)))
self.W_xf = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_in, n_f)).astype(dtype))
self.W_hf = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_hidden, n_f)).astype(dtype))
self.W_cf = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_c, n_f)).astype(dtype))
self.b_f = theano.shared(numpy.cast[dtype](uniform(0, 1., size=n_f)))
self.W_xc = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_in, n_c)).astype(dtype))
self.W_hc = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_hidden, n_c)).astype(dtype))
self.b_c = theano.shared(numpy.zeros(n_c, dtype=dtype))
self.W_xo = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_in, n_o)).astype(dtype))
self.W_ho = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_hidden, n_o)).astype(dtype))
self.W_co = theano.shared(0.2 * uniform(-1.0, 1.0,
(n_c, n_o)).astype(dtype))
self.b_o = theano.shared(numpy.cast[dtype](uniform(-0.5, .5,
size=n_o)))
self.W_hy = theano.shared(
0.2 * uniform(-1.0, 1.0, (n_hidden + featdim, n_y)).astype(dtype))
self.b_y = theano.shared(numpy.zeros(n_y, dtype=dtype))
self.c0 = theano.shared(numpy.zeros(n_hidden, dtype=dtype))
self.h0 = T.tanh(self.c0)
# bundle weights
self.params = [self.emb, self.W_xi, self.W_hi, self.W_ci, self.b_i, self.W_xf, self.W_hf, \
self.W_cf, self.b_f, self.W_xc, self.W_hc, self.b_c, self.W_xo, self.W_ho, \
self.W_co, self.b_o, self.W_hy, self.b_y, self.c0]
self.names = ['embeddings', 'W_xi', 'W_hi', 'W_ci', 'b_i', 'W_xf', 'W_hf', 'W_cf', 'b_f', \
'W_xc', 'W_hc', 'b_c', 'W_xo', 'W_ho', 'W_co', 'b_o', 'W_hy', 'b_y', 'c0']
idxs = T.imatrix(
) # as many columns as context window size/lines as words in the sentence
# print idxs.shape()
x = self.emb[idxs].reshape((idxs.shape[0], de * cs))
# print type(x), x.shape(), "details of x"
f = T.matrix('f')
f.reshape((idxs.shape[0], featdim))
# print type(f), f.shape(), "details of f"
y = T.iscalar('y') # label
# print type(y), y.shape(), "details of y"
def recurrence(x_t, feat_t, h_tm1, c_tm1):
i_t = sigma(
theano.dot(x_t, self.W_xi) + theano.dot(h_tm1, self.W_hi) +
theano.dot(c_tm1, self.W_ci) + self.b_i)
f_t = sigma(
theano.dot(x_t, self.W_xf) + theano.dot(h_tm1, self.W_hf) +
theano.dot(c_tm1, self.W_cf) + self.b_f)
c_t = f_t * c_tm1 + i_t * T.tanh(
theano.dot(x_t, self.W_xc) + theano.dot(h_tm1, self.W_hc) +
self.b_c)
o_t = sigma(
theano.dot(x_t, self.W_xo) + theano.dot(h_tm1, self.W_ho) +
theano.dot(c_t, self.W_co) + self.b_o)
h_t = o_t * T.tanh(c_t)
if self.featdim > 0:
all_t = T.concatenate([h_t, feat_t])
else:
all_t = h_t
# print "all_t", type(all_t), T.shape(all_t)
s_t = softmax(theano.dot(all_t, self.W_hy) + self.b_y)
# print T.shape(h_t), T.shape(c_t), T.shape(s_t)
return [h_t, c_t, s_t]
# Initialization occurs in outputs_info
# scan gives -- result, updates
[h, _, s], _ = theano.scan(fn=recurrence,
sequences=[x, f],
outputs_info=[self.h0, self.c0, None],
n_steps=x.shape[0])
p_y_given_x_lastword = s[-1, 0, :]
p_y_given_x_sentence = s[:, 0, :]
y_pred = T.argmax(p_y_given_x_sentence, axis=1)
# cost and gradients and learning rate
lr = T.scalar('lr')
nll = -T.mean(T.log(p_y_given_x_lastword)[y])
gradients = T.grad(nll, self.params)
updates = OrderedDict(
(p, p - lr * g) for p, g in zip(self.params, gradients))
# theano functions
self.classify = theano.function(inputs=[idxs, f], outputs=y_pred)
self.train = theano.function(inputs=[idxs, f, y, lr],
outputs=nll,
updates=updates)
self.normalize = theano.function(
inputs=[],
updates={
self.emb:
self.emb / T.sqrt(
(self.emb**2).sum(axis=1)).dimshuffle(0, 'x')
})
def test_grad_h(self):
"tests that the gradients with respect to h_i are 0 after doing a mean field update of h_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
init_H = e_step.init_H_hat(V=X)
init_Mu1 = e_step.init_S_hat(V=X)
prev_setting = config.compute_test_value
config.compute_test_value = 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0., 1., H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(
-5., 5., Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
new_H = e_step.infer_H_hat(V=X, H_hat=H_var, S_hat=Mu1_var)
h_idx = new_H[:, idx]
updates_func = function([H_var, Mu1_var, idx], h_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0,
var_s1_hat = Sigma1)
grad_H = T.grad(trunc_kl.sum(), H_var)
assert len(grad_H.type.broadcastable) == 2
#from theano.printing import min_informative_str
#print min_informative_str(grad_H)
#grad_H = Print('grad_H')(grad_H)
#grad_H_idx = grad_H[:,idx]
grad_func = function([H_var, Mu1_var], grad_H)
failed = False
for i in xrange(self.N):
rval = updates_func(H, Mu1, i)
H[:, i] = rval
g = grad_func(H, Mu1)[:, i]
assert not contains_nan(g)
g_abs_max = np.abs(g).max()
if g_abs_max > self.tol:
#print "new values of H"
#print H[:,i]
#print "gradient on new values of H"
#print g
failed = True
print 'iteration ', i
#print 'max value of new H: ',H[:,i].max()
#print 'H for failing g: '
failing_h = H[np.abs(g) > self.tol, i]
#print failing_h
#from matplotlib import pyplot as plt
#plt.scatter(H[:,i],g)
#plt.show()
#ignore failures extremely close to h=1
high_mask = failing_h > .001
low_mask = failing_h < .999
mask = high_mask * low_mask
print 'masked failures: ', mask.shape[0], ' err ', g_abs_max
if mask.sum() > 0:
print 'failing h passing the range mask'
print failing_h[mask.astype(bool)]
raise Exception(
'after mean field step, gradient of kl divergence'
' wrt freshly updated variational parameter should be 0, '
'but here the max magnitude of a gradient element is '
+ str(g_abs_max) + ' after updating h_' + str(i))
def __init__(self,
word_dim,
hidden_dim=5,
Nclass=4,
degree=2,
momentum=0.9,
trainable_embeddings=True,
labels_on_nonroot_nodes=False,
irregular_tree=True):
assert word_dim > 1 and hidden_dim > 1
self.word_dim = word_dim
self.hidden_dim = hidden_dim
self.Nclass = Nclass
self.degree = degree
#self.learning_rate = learning_rate
self.momentum = momentum
self.irregular_tree = irregular_tree
self.params = []
#self.x = T.ivector(name='x') # word indices
#self.x_word = T.matrix(dtype=theano.config.floatX) # word frequendtype=theano.config.floatX
self.x_word = T.matrix(name='x_word') # word frequent
self.x_index = T.imatrix(name='x_index') # word indices
self.tree = T.imatrix(name='tree') # shape [None, self.degree]
self.y = T.ivector(name='y') # output shape [self.output_dim]
self.num_parent = T.iscalar(name='num_parent')
self.num_nodes = self.x_word.shape[
0] # total number of nodes (leaves + internal) in tree
self.num_child = self.num_nodes - self.num_parent - 1
#emb_x = self.embeddings[self.x]
#emb_x = emb_x * T.neq(self.x, -1).dimshuffle(0, 'x') # zero-out non-existent embeddings
self.tree_states = self.compute_tree(self.x_word, self.x_index,
self.num_parent, self.tree)
#self.final_state = self.tree_states.mean(axis=0)#self.tree_states[-1]
#self.final_state = pool_2d(input=self.tree_states, ds=(self.num_child,1), ignore_border=True,mode='max')
self.final_state = self.tree_states.max(axis=0)
self.output_fn = self.create_output_fn()
self.pred_y = self.output_fn(self.final_state)
self.loss = self.loss_fn(self.y, self.pred_y)
self.learning_rate = T.scalar('learning_rate')
#updates = self.gradient_descent(self.loss, self.learning_rate)
train_inputs = [
self.x_word, self.x_index, self.num_parent, self.tree, self.y,
self.learning_rate
]
updates = self.gradient_descent(self.loss)
#train_inputs = [self.x_word, self.x_index, self.tree, self.y]
self._train = theano.function(train_inputs, [self.loss, self.pred_y],
updates=updates)
self._evaluate = theano.function(
[self.x_word, self.x_index, self.num_parent, self.tree],
self.final_state)
self._evaluate2 = theano.function(
[self.x_word, self.x_index, self.num_parent, self.tree],
self.tree_states)
#self._state = theano.function([self.x_word, self.x_index, self.num_child, self.tree], self.tree_states)
self._predict = theano.function(
[self.x_word, self.x_index, self.num_parent, self.tree],
self.pred_y)
self.tree_states_test = self.compute_tree_test(self.x_word,
self.x_index, self.tree)
self._evaluate3 = theano.function(
[self.x_word, self.x_index, self.tree], self.tree_states_test)
def main(n_iter, n_batch, n_hidden, time_steps, learning_rate, savefile,
scale_penalty, use_scale, reload_progress, model, n_hidden_lstm,
n_gru_lr_proj, initial_b_u):
np.random.seed(1234)
#import pdb; pdb.set_trace()
# --- Set optimization params --------
# --- Set data params ----------------
n_input = 1
n_output = 10
##### MNIST processing ################################################
# load and preprocess the data
(train_x, train_y), (valid_x, valid_y), (test_x, test_y) = cPickle.load(
gzip.open("mnist.pkl.gz", 'rb'))
n_data = train_x.shape[0]
num_batches = n_data / n_batch
# shuffle data order
inds = range(n_data)
np.random.shuffle(inds)
train_x = np.ascontiguousarray(train_x[inds, :time_steps])
train_y = np.ascontiguousarray(train_y[inds])
n_data_valid = valid_x.shape[0]
inds_valid = range(n_data_valid)
np.random.shuffle(inds_valid)
valid_x = np.ascontiguousarray(valid_x[inds_valid, :time_steps])
valid_y = np.ascontiguousarray(valid_y[inds_valid])
# reshape x
train_x = np.reshape(train_x.T, (time_steps, n_data, 1))
valid_x = np.reshape(valid_x.T, (time_steps, valid_x.shape[0], 1))
# change y to one-hot encoding
temp = np.zeros((n_data, n_output))
# import pdb; pdb.set_trace()
temp[np.arange(n_data), train_y] = 1
train_y = temp.astype('float32')
temp = np.zeros((n_data_valid, n_output))
temp[np.arange(n_data_valid), valid_y] = 1
valid_y = temp.astype('float32')
# Random permutation of pixels
P = np.random.permutation(time_steps)
train_x = train_x[P, :, :]
valid_x = valid_x[P, :, :]
#######################################################################
# --- Compile theano graph and gradients
gradient_clipping = np.float32(1)
if (model == 'LSTM'):
#inputs, parameters, costs = LSTM(n_input, n_hidden_LSTM, n_output)
inputs, parameters, costs = LSTM(n_input,
n_hidden_lstm,
n_output,
initial_b_f=initial_b_u)
#by AnvaMiba
elif (model == 'GRU'):
inputs, parameters, costs = GRU(n_input,
n_hidden_lstm,
n_output,
initial_b_u=initial_b_u)
#by AnvaMiba
elif (model == 'GRU_LR'):
inputs, parameters, costs = GRU_LR(n_input,
n_hidden_lstm,
n_output,
n_gru_lr_proj,
initial_b_u=initial_b_u)
elif (model == 'complex_RNN'):
gradient_clipping = np.float32(100000)
inputs, parameters, costs = complex_RNN(n_input, n_hidden, n_output,
scale_penalty)
elif (model == 'complex_RNN_LSTM'):
inputs, parameters, costs = complex_RNN_LSTM(n_input, n_hidden,
n_hidden_lstm, n_output,
scale_penalty)
elif (model == 'IRNN'):
inputs, parameters, costs = IRNN(n_input, n_hidden, n_output)
elif (model == 'RNN'):
inputs, parameters, costs = RNN(n_input, n_hidden, n_output)
else:
print >> sys.stderr, "Unsuported model:", model
return
gradients = T.grad(costs[0], parameters)
# GRADIENT CLIPPING
gradients = gradients[:7] + [
T.clip(g, -gradient_clipping, gradient_clipping) for g in gradients[7:]
]
s_train_x = theano.shared(train_x)
s_train_y = theano.shared(train_y)
s_valid_x = theano.shared(valid_x)
s_valid_y = theano.shared(valid_y)
# --- Compile theano functions --------------------------------------------------
index = T.iscalar('i')
updates, rmsprop = rms_prop(learning_rate, parameters, gradients)
givens = {
inputs[0]: s_train_x[:, n_batch * index:n_batch * (index + 1), :],
inputs[1]: s_train_y[n_batch * index:n_batch * (index + 1), :]
}
givens_valid = {inputs[0]: s_valid_x, inputs[1]: s_valid_y}
train = theano.function([index], [costs[0], costs[2]],
givens=givens,
updates=updates)
valid = theano.function([], [costs[1], costs[2]], givens=givens_valid)
#import pdb; pdb.set_trace()
# --- Training Loop ---------------------------------------------------------------
train_loss = []
test_loss = []
test_acc = []
best_params = [p.get_value() for p in parameters]
best_test_loss = 1e6
for i in xrange(n_iter):
# pdb.set_trace()
[cross_entropy, acc] = train(i % num_batches)
train_loss.append(cross_entropy)
print >> sys.stderr, "Iteration:", i
print >> sys.stderr, "cross_entropy:", cross_entropy
print >> sys.stderr, "accurracy", acc * 100
print >> sys.stderr, ''
#if (i % 100==0):
if (i % 300 == 0):
[valid_cross_entropy, valid_acc] = valid()
print >> sys.stderr, ''
print >> sys.stderr, "VALIDATION"
print >> sys.stderr, "cross_entropy:", valid_cross_entropy
print >> sys.stderr, "accurracy", valid_acc * 100
print >> sys.stderr, ''
test_loss.append(valid_cross_entropy)
test_acc.append(valid_acc)
if valid_cross_entropy < best_test_loss:
print >> sys.stderr, "NEW BEST!"
best_params = [p.get_value() for p in parameters]
best_test_loss = valid_cross_entropy
save_vals = {
'parameters': [p.get_value() for p in parameters],
'rmsprop': [r.get_value() for r in rmsprop],
'train_loss': train_loss,
'test_loss': test_loss,
'best_params': best_params,
'test_acc': test_acc,
'best_test_loss': best_test_loss
}
cPickle.dump(save_vals, file(savefile, 'wb'),
cPickle.HIGHEST_PROTOCOL)
def build(self,
dropout,
char_dim,
char_hidden_dim,
char_bidirect,
word_dim,
word_hidden_dim,
word_bidirect,
tagger_hidden_dim,
hamming_cost,
L2_reg,
lr_method,
pre_word_emb,
pre_char_emb,
tagger,
use_gaze,
POS,
# plot_cost,
#cap_dim,
training=True,
**kwargs
):
"""
Build the network.
"""
# Training parameters
n_words = len(self.id_to_word)
n_chars = len(self.id_to_char)
n_tags = len(self.id_to_tag)
# n_pos = len(self.id_to_pos) + 1
# Number of capitalization features
#if cap_dim:
# n_cap = 4
# Network variables
is_train = T.iscalar('is_train') # declare variable,声明整型变量is_train
word_ids = T.ivector(name='word_ids') #声明整型一维向量
char_for_ids = T.imatrix(name='char_for_ids') # 声明整型二维矩阵
char_rev_ids = T.imatrix(name='char_rev_ids')
char_pos_ids = T.ivector(name='char_pos_ids')
if use_gaze:
gaze = T.imatrix(name='gaze')
if POS:
# pos_ids = T.ivector(name='pos_ids')
pos_one_hot = T.imatrix(name= 'pos_one_hot')
#hamming_cost = T.matrix('hamming_cost', theano.config.floatX) # 声明整型二维矩阵
tag_ids = T.ivector(name='tag_ids')
#if cap_dim:
# cap_ids = T.ivector(name='cap_ids')
# Sentence length
s_len = (word_ids if word_dim else char_pos_ids).shape[0] #句子中的单词数
# Final input (all word features)
input_dim = 0
inputs = []
L2_norm = 0.0
theano.config.compute_test_value = 'off'
#
# Word inputs
#
if word_dim:
print("word_dim:", word_dim)
input_dim += word_dim
word_layer = EmbeddingLayer(n_words, word_dim, name='word_layer')
word_input = word_layer.link(word_ids)
inputs.append(word_input)
# Initialize with pretrained embeddings
if pre_word_emb and training:
new_weights = word_layer.embeddings.get_value()
print 'Loading pretrained word embeddings from %s...' % pre_word_emb
pretrained = {}
emb_invalid = 0
for i, line in enumerate(codecs.open(pre_word_emb, 'r', 'utf-8', 'ignore')):
line = line.rstrip().split()
if len(line) == word_dim + 1:
pretrained[line[0]] = np.array(
[float(x) for x in line[1:]]
).astype(np.float32)
else:
emb_invalid += 1
if emb_invalid > 0:
print 'WARNING: %i invalid word embedding lines' % emb_invalid
c_found = 0
c_lower = 0
c_zeros = 0
# Lookup table initialization
for i in xrange(n_words):
word = self.id_to_word[i]
if word in pretrained:
new_weights[i] = pretrained[word]
c_found += 1
elif word.lower() in pretrained:
new_weights[i] = pretrained[word.lower()]
c_lower += 1
elif re.sub('\d', '0', word) in pretrained:
new_weights[i] = pretrained[
re.sub('\d', '0', word)
]
c_zeros += 1
word_layer.embeddings.set_value(new_weights)
print 'Loaded %i pretrained word embeddings.' % len(pretrained)
print ('%i / %i (%.4f%%) words have been initialized with '
'pretrained word embeddings.') % (
c_found + c_lower + c_zeros, n_words,
100. * (c_found + c_lower + c_zeros) / n_words
)
print ('%i found directly, %i after lowercasing + zero.') % (c_found, c_lower + c_zeros)
L2_norm += (word_layer.embeddings ** 2).sum()
#
# Chars inputs
#
if char_dim:
print("char_dim:", char_dim)
input_dim += char_dim
char_layer = EmbeddingLayer(n_chars, char_dim, name='char_layer')
char_for_input = char_layer.link(char_for_ids)
# Initialize with pretrained char embeddings
if pre_char_emb and training:
new_weights = char_layer.embeddings.get_value()
print 'Loading pretrained char embeddings from %s...' % pre_char_emb
pretrained = {}
emb_invalid = 0
for i, line in enumerate(codecs.open(pre_char_emb, 'r', 'utf-8', 'ignore')):
line = line.rstrip().split()
if len(line) == char_dim + 1:
pretrained[line[0]] = np.array(
[float(x) for x in line[1:]]
).astype(np.float32)
else:
emb_invalid += 1
if emb_invalid > 0:
print 'WARNING: %i invalid char embedding lines' % emb_invalid
c_found = 0
c_lower = 0
c_zeros = 0
# Lookup table initialization
for i in xrange(n_chars):
char = self.id_to_char[i]
if char in pretrained:
new_weights[i] = pretrained[char]
c_found += 1
elif word.lower() in pretrained:
new_weights[i] = pretrained[word.lower()]
c_lower += 1
elif re.sub('\d', '0', char) in pretrained:
new_weights[i] = pretrained[
re.sub('\d', '0', char)
]
c_zeros += 1
char_layer.embeddings.set_value(new_weights)
print 'Loaded %i pretrained char embeddings.' % len(pretrained)
print ('%i / %i (%.4f%%) words have been initialized with '
'pretrained char embeddings.') % (
c_found + c_lower + c_zeros, n_chars,
100. * (c_found + +c_lower + c_zeros) / n_chars
)
print ('%i found directly, %i after lowercasing + zero.') % (c_found, c_lower + c_zeros)
L2_norm += (char_layer.embeddings ** 2).sum()
wc_layer = CW_EmbeddingLayer(char_dim, word_dim + char_dim, bias= True, name= 'wc_layer')
wc_comp_input = wc_layer.link(char_for_input, word_input)
for param in wc_layer.params:
L2_norm += (param ** 2).sum()
print(word_input.ndim)
print(wc_comp_input.ndim)
# new_word_input, _ = theano.scan(lambda x_t, y_t: T.max([x_t, y_t], axis= 0), sequences= [word_input, wc_comp_input], n_steps= word_input.shape[0])
# print(new_word_input.ndim)
inputs.append(wc_comp_input)
# if POS:
# pos_dim = 20
# input_dim += pos_dim
# pos_layer = EmbeddingLayer(n_pos, pos_dim, name='pos_layer')
# pos_input = pos_layer.link(pos_ids)
# inputs.append(pos_input)
# L2_norm += (pos_layer.embeddings ** 2).sum()
#if len(inputs) != 1:
inputs = T.concatenate(inputs, axis= 1)
#
# Dropout on final input
#
if dropout:
dropout_layer = DropoutLayer(p=dropout)
input_train = dropout_layer.link(inputs)
input_test = (1 - dropout) * inputs
inputs = T.switch(T.neq(is_train, 0), input_train, input_test) # 条件句
# if POS:
# inputs = T.concatenate([inputs, pos_one_hot], axis= 1)
# input_dim += 6
# LSTM for words
print("input_dim:", input_dim)
print("word_hidden_dim:", word_hidden_dim)
word_lstm_for = LSTM(input_dim, word_hidden_dim, with_batch=False,
name='word_lstm_for')
word_lstm_rev = LSTM(input_dim, word_hidden_dim, with_batch=False,
name='word_lstm_rev')
word_lstm_for.link(inputs) # 单词的顺序： I like dog
word_lstm_rev.link(inputs[::-1, :]) # 单词的顺序： dog like I
word_for_output = word_lstm_for.h
word_rev_output = word_lstm_rev.h[::-1, :]
for param in word_lstm_for.params[:8]:
L2_norm += (param ** 2).sum()
if word_bidirect:
final_output = T.concatenate(
[word_for_output, word_rev_output],
axis=1
)
tanh_layer = HiddenLayer(2 * word_hidden_dim, word_hidden_dim,
name='tanh_layer', activation='tanh')
final_output = tanh_layer.link(final_output)
for param in word_lstm_rev.params[:8]:
L2_norm += (param ** 2).sum()
else:
final_output = word_for_output
dims = word_hidden_dim
if use_gaze:
final_output = T.concatenate([final_output, gaze], axis= 1)
dims = word_hidden_dim + n_tags
if POS:
final_output = T.concatenate([final_output, pos_one_hot], axis=1)
dims += 6
# if word_bidirect:
# final_output = T.concatenate(
# [word_for_output, word_rev_output],
# axis=1
# )
# tanh_layer = HiddenLayer(2 * word_hidden_dim, word_hidden_dim,
# name='tanh_layer', activation='tanh')
# final_output = tanh_layer.link(final_output)
# else:
# final_output = word_for_output
# Sentence to Named Entity tags
## final_layer = HiddenLayer(dims, n_tags, name='final_layer',
## activation=(None if crf else 'softmax'))
# final_layer = HiddenLayer(word_hidden_dim, n_tags, name='final_layer',
# activation=(None if crf else 'softmax'))
## tags_scores = final_layer.link(final_output)
## L2_norm += (final_layer.params[0] ** 2).sum()
# No CRF
if tagger == 'lstm':
tagger_layer = LSTM_d(dims, tagger_hidden_dim, with_batch= False, name='LSTM_d')
tagger_layer.link(final_output)
final_output = tagger_layer.t
dims = tagger_hidden_dim
for param in tagger_layer.params[:8]:
L2_norm += (param ** 2).sum()
final_layer = HiddenLayer(dims, n_tags, name='final_layer',
activation=(None if tagger == 'crf' else 'softmax'))
tags_scores = final_layer.link(final_output)
L2_norm += (final_layer.params[0] ** 2).sum()
if tagger != 'crf':
cost = T.nnet.categorical_crossentropy(tags_scores, tag_ids).mean()
# CRF
else:
transitions = shared((n_tags + 2, n_tags + 2), 'transitions')
small = -1000
b_s = np.array([[small] * n_tags + [0, small]]).astype(np.float32)
e_s = np.array([[small] * n_tags + [small, 0]]).astype(np.float32)
observations = T.concatenate(
[tags_scores, small * T.ones((s_len, 2))],
axis=1
)
observations = T.concatenate(
[b_s, observations, e_s],
axis=0
)
# Score from tags
real_path_score = tags_scores[T.arange(s_len), tag_ids].sum() # P中对应元素的求和好
# Score from add_componentnsitions
b_id = theano.shared(value=np.array([n_tags], dtype=np.int32))
e_id = theano.shared(value=np.array([n_tags + 1], dtype=np.int32))
padded_tags_ids = T.concatenate([b_id, tag_ids, e_id], axis=0)
real_path_score += transitions[
padded_tags_ids[T.arange(s_len + 1)],
padded_tags_ids[T.arange(s_len + 1) + 1]
].sum() # A中对应元素的求和
all_paths_scores = forward(observations, transitions, hamming_cost=hamming_cost, n_tags=n_tags, padded_tags_ids=padded_tags_ids)
L2_norm += (transitions ** 2).sum()
cost = - (real_path_score - all_paths_scores) + L2_reg * L2_norm
# Network parameters
params = []
if word_dim:
self.add_component(word_layer)
params.extend(word_layer.params)
if char_dim:
self.add_component(wc_layer)
params.extend(wc_layer.params)
self.add_component(word_lstm_for)
params.extend(word_lstm_for.params)
# if POS:
# self.add_component(pos_layer)
# params.extend(pos_layer.params)
if word_bidirect:
self.add_component(word_lstm_rev)
params.extend(word_lstm_rev.params)
self.add_component(final_layer)
params.extend(final_layer.params)
if tagger == 'lstm':
self.add_component(tagger_layer)
params.extend(tagger_layer.params)
elif tagger == 'crf':
self.add_component(transitions)
params.append(transitions)
if word_bidirect:
self.add_component(tanh_layer)
params.extend(tanh_layer.params)
# Prepare train and eval inputs
eval_inputs = []
if word_dim:
eval_inputs.append(word_ids)
if use_gaze:
eval_inputs.append(gaze)
if POS:
# eval_inputs.append(pos_ids)
eval_inputs.append(pos_one_hot)
if char_dim:
eval_inputs.append(char_for_ids)
if char_bidirect:
eval_inputs.append(char_rev_ids)
eval_inputs.append(char_pos_ids)
#if cap_dim:
# eval_inputs.append(cap_ids)
train_inputs = eval_inputs + [tag_ids]
# Parse optimization method parameters
if "-" in lr_method:
lr_method_name = lr_method[:lr_method.find('-')]
lr_method_parameters = {}
for x in lr_method[lr_method.find('-') + 1:].split('-'):
split = x.split('_')
assert len(split) == 2
lr_method_parameters[split[0]] = float(split[1])
else:
lr_method_name = lr_method
lr_method_parameters = {}
# Compile training function
print 'Compiling...'
if training:
updates = Optimization(clip=5.0).get_updates(lr_method_name, cost, params, **lr_method_parameters)
f_train = theano.function(
inputs=train_inputs,
outputs=cost,
updates=updates,
givens=({is_train: np.cast['int32'](1)} if dropout else {}),
on_unused_input='warn'
)
else:
f_train = None
# if plot_cost:
# f_plot_cost = theano.function(
# inputs=train_inputs,
# outputs=cost,
# givens=({is_train: np.cast['int32'](1)} if dropout else {}),
# on_unused_input='warn'
# )
# else:
# f_plot_cost = None
# Compile evaluation function
if tagger != 'crf':
f_eval = theano.function(
inputs=eval_inputs,
outputs=tags_scores,
givens=({is_train: np.cast['int32'](0)} if dropout else {}),
on_unused_input='warn'
)
else:
f_eval = theano.function(
inputs=eval_inputs,
outputs=forward(observations, transitions, hamming_cost= 0, n_tags= None, padded_tags_ids= None, viterbi=True,
return_alpha=False, return_best_sequence=True),
givens=({is_train: np.cast['int32'](0)} if dropout else {}),
on_unused_input='warn'
)
return f_train, f_eval#, f_plot_cost
def __init__(self, nh, nc, ne, de, cs):
'''
nh :: dimension of the hidden layer
nc :: number of classes
ne :: number of word embeddings in the vocabulary 572
de :: dimension of the word embeddings 100
cs :: word window context size
'''
# parameters of the model
self.emb = theano.shared(0.2 * numpy.random.uniform(-1.0, 1.0,\
(ne+1, de)).astype(theano.config.floatX)) # add one for PADDING at the end
self.Wx = theano.shared(0.2 * numpy.random.uniform(-1.0, 1.0,\
(de * cs, nh)).astype(theano.config.floatX))
self.Wh = theano.shared(0.2 * numpy.random.uniform(-1.0, 1.0,\
(nh, nh)).astype(theano.config.floatX))
self.W = theano.shared(0.2 * numpy.random.uniform(-1.0, 1.0,\
(nh, nc)).astype(theano.config.floatX))
self.bh = theano.shared(numpy.zeros(nh, dtype=theano.config.floatX))
self.b = theano.shared(numpy.zeros(nc, dtype=theano.config.floatX))
self.h0 = theano.shared(numpy.zeros(nh, dtype=theano.config.floatX))
# bundle
self.params = [
self.emb, self.Wx, self.Wh, self.W, self.bh, self.b, self.h0
]
self.names = ['embeddings', 'Wx', 'Wh', 'W', 'bh', 'b', 'h0']
idxs = T.imatrix(
) # as many columns as context window size/lines as words in the sentence
x = self.emb[idxs].reshape((idxs.shape[0], de * cs))
y = T.iscalar('y') # label
def Relu(x):
out_dtype = scalar.upgrade_to_float(
scalar.Scalar(dtype=x.dtype))[0].dtype
a = T.constant(0.5, dtype=out_dtype)
# ab = T.constant(abs(x), dtype=out_dtype)
# x = (x * slope) + shift
y = (x + abs(x)) * a
r = T.clip(y, 0, 1)
return r
def PRelu(x):
out_dtype = scalar.upgrade_to_float(
scalar.Scalar(dtype=x.dtype))[0].dtype
a = T.constant(0.625, dtype=out_dtype)
b = T.constant(0.375, dtype=out_dtype)
# x = (x * slope) + shift
y = x * a + abs(x) * b
r = T.clip(y, 0, 1)
return r
def my_tanh(x):
#return 2*T.nnet.sigmoid(2*x)-1
return T.nnet.sigmoid(x)
def sigmoid_sigmoid(x):
return 0.8 * T.nnet.sigmoid(x) + 0.2 * T.nnet.hard_sigmoid(x)
def recurrence(x_t, h_tm1):
temp = T.dot(x_t, self.Wx) + T.dot(h_tm1, self.Wh) + self.bh
#h_t = T.nnet.hard_sigmoid(temp) # the t moment output of the hidden layer
#h_t = T.tanh(temp)
h_t = T.nnet.sigmoid(temp)
#h_t=T.nnet.relu(temp,0.2)#relu=T.maximum(0, temp)
s_t = T.nnet.softmax(
T.dot(h_t, self.W) +
self.b) # the t moment output of the output layer
return [h_t, s_t]
[h, s], _ = theano.scan(fn=recurrence, \
sequences=x, outputs_info=[self.h0, None], \
n_steps=x.shape[0])
p_y_given_x_lastword = s[-1, 0, :]
p_y_given_x_sentence = s[:, 0, :]
y_pred = T.argmax(p_y_given_x_sentence, axis=1)
#print 'y_pred', y_pred
#print ' p_y_given_x_sentence', p_y_given_x_sentence
# cost and gradients and learning rate
lr = T.scalar('lr')
nll = -T.log(p_y_given_x_lastword)[y] #negative log-likelihood(NLL)
gradients = T.grad(nll, self.params)
updates = OrderedDict(
(p, p - lr * g) for p, g in zip(self.params, gradients))
# theano functions
self.myclassify = theano.function(inputs=[idxs],
outputs=p_y_given_x_sentence)
self.classify = theano.function(inputs=[idxs], outputs=y_pred)
self.train = theano.function(inputs=[idxs, y, lr],
outputs=nll,
updates=updates)
self.normalize = theano.function( inputs = [],
updates = {self.emb:\
self.emb/T.sqrt((self.emb**2).sum(axis=1)).dimshuffle(0,'x')})
def main(n_iter, n_batch, n_hidden, time_steps, learning_rate, savefile, scale_penalty, use_scale,
model, n_hidden_lstm, loss_function):
#import pdb; pdb.set_trace()
# --- Set optimization params --------
gradient_clipping = np.float32(50000)
# --- Set data params ----------------
n_input = 2
n_output = 1
# --- Manage data --------------------
n_train = 1e5
n_test = 1e4
num_batches = n_train / n_batch
train_x = np.asarray(np.zeros((time_steps, n_train, 2)),
dtype=theano.config.floatX)
train_x[:,:,0] = np.asarray(np.random.uniform(low=0.,
high=1.,
size=(time_steps, n_train)),
dtype=theano.config.floatX)
# inds = np.asarray([np.random.choice(time_steps, 2, replace=False) for i in xrange(train_x.shape[1])])
inds = np.asarray(np.random.randint(time_steps/2, size=(train_x.shape[1],2)))
inds[:, 1] += time_steps/2
for i in range(train_x.shape[1]):
train_x[inds[i, 0], i, 1] = 1.0
train_x[inds[i, 1], i, 1] = 1.0
train_y = (train_x[:,:,0] * train_x[:,:,1]).sum(axis=0)
train_y = np.reshape(train_y, (n_train, 1))
test_x = np.asarray(np.zeros((time_steps, n_test, 2)),
dtype=theano.config.floatX)
test_x[:,:,0] = np.asarray(np.random.uniform(low=0.,
high=1.,
size=(time_steps, n_test)),
dtype=theano.config.floatX)
inds = np.asarray([np.random.choice(time_steps, 2, replace=False) for i in xrange(test_x.shape[1])])
for i in range(test_x.shape[1]):
test_x[inds[i, 0], i, 1] = 1.0
test_x[inds[i, 1], i, 1] = 1.0
test_y = (test_x[:,:,0] * test_x[:,:,1]).sum(axis=0)
test_y = np.reshape(test_y, (n_test, 1))
#######################################################################
gradient_clipping = np.float32(1)
if (model == 'LSTM'):
inputs, parameters, costs = LSTM(n_input, n_hidden_lstm, n_output, loss_function=loss_function)
gradients = T.grad(costs[0], parameters)
gradients = [T.clip(g, -gradient_clipping, gradient_clipping) for g in gradients]
elif (model == 'complex_RNN'):
inputs, parameters, costs = complex_RNN(n_input, n_hidden, n_output, scale_penalty, loss_function=loss_function)
if use_scale is False:
parameters.pop()
gradients = T.grad(costs[0], parameters)
elif (model == 'complex_RNN_LSTM'):
inputs, parameters, costs = complex_RNN_LSTM(n_input, n_hidden, n_hidden_lstm, n_output, scale_penalty, loss_function=loss_function)
elif (model == 'IRNN'):
inputs, parameters, costs = IRNN(n_input, n_hidden, n_output, loss_function=loss_function)
gradients = T.grad(costs[0], parameters)
gradients = [T.clip(g, -gradient_clipping, gradient_clipping) for g in gradients]
elif (model == 'RNN'):
inputs, parameters, costs = tanhRNN(n_input, n_hidden, n_output, loss_function=loss_function)
gradients = T.grad(costs[0], parameters)
gradients = [T.clip(g, -gradient_clipping, gradient_clipping) for g in gradients]
else:
print "Unsuported model:", model
return
s_train_x = theano.shared(train_x)
s_train_y = theano.shared(train_y)
s_test_x = theano.shared(test_x)
s_test_y = theano.shared(test_y)
# --- Compile theano functions --------------------------------------------------
index = T.iscalar('i')
updates, rmsprop = rms_prop(learning_rate, parameters, gradients)
givens = {inputs[0] : s_train_x[:, n_batch * index : n_batch * (index + 1), :],
inputs[1] : s_train_y[n_batch * index : n_batch * (index + 1), :]}
givens_test = {inputs[0] : s_test_x,
inputs[1] : s_test_y}
train = theano.function([index], costs[0], givens=givens, updates=updates)
test = theano.function([], costs[1], givens=givens_test)
# --- Training Loop ---------------------------------------------------------------
# f1 = file('/data/lisatmp3/shahamar/adding/complexRNN_400.pkl', 'rb')
# data1 = cPickle.load(f1)
# f1.close()
# train_loss = data1['train_loss']
# test_loss = data1['test_loss']
# best_params = data1['best_params']
# best_test_loss = data1['best_test_loss']
# for i in xrange(len(parameters)):
# parameters[i].set_value(data1['parameters'][i])
# for i in xrange(len(parameters)):
# rmsprop[i].set_value(data1['rmsprop'][i])
# import pdb; pdb.set_trace()
train_loss = []
test_loss = []
best_params = [p.get_value() for p in parameters]
best_test_loss = 1e6
for i in xrange(n_iter):
# start_time = timeit.default_timer()
if (n_iter % int(num_batches) == 0):
#import pdb; pdb.set_trace()
inds = np.random.permutation(int(n_train))
data_x = s_train_x.get_value()
s_train_x.set_value(data_x[:,inds,:])
data_y = s_train_y.get_value()
s_train_y.set_value(data_y[inds,:])
mse = train(i % int(num_batches))
train_loss.append(mse)
print "Iteration:", i
print "mse:", mse
print
if (i % 50==0):
mse = test()
print
print "TEST"
print "mse:", mse
print
test_loss.append(mse)
if mse < best_test_loss:
best_params = [p.get_value() for p in parameters]
best_test_loss = mse
save_vals = {'parameters': [p.get_value() for p in parameters],
'rmsprop': [r.get_value() for r in rmsprop],
'train_loss': train_loss,
'test_loss': test_loss,
'best_params': best_params,
'best_test_loss': best_test_loss,
'model': model,
'time_steps': time_steps}
cPickle.dump(save_vals,
file(savefile, 'wb'),
cPickle.HIGHEST_PROTOCOL)
def test_notex_print():
tt_normalrv_noname_expr = tt.scalar("b") * NormalRV(
tt.scalar("\\mu"), tt.scalar("\\sigma"))
expected = textwrap.dedent(r"""
b in R, \mu in R, \sigma in R
a ~ N(\mu, \sigma**2) in R
(b * a)
""")
assert tt_pprint(tt_normalrv_noname_expr) == expected.strip()
# Make sure the constant shape is show in values and not symbols.
tt_normalrv_name_expr = tt.scalar("b") * NormalRV(
tt.scalar("\\mu"), tt.scalar("\\sigma"), size=[2, 1], name="X")
expected = textwrap.dedent(r"""
b in R, \mu in R, \sigma in R
X ~ N(\mu, \sigma**2) in R**(2 x 1)
(b * X)
""")
assert tt_pprint(tt_normalrv_name_expr) == expected.strip()
tt_2_normalrv_noname_expr = tt.matrix("M") * NormalRV(
tt.scalar("\\mu_2"), tt.scalar("\\sigma_2"))
tt_2_normalrv_noname_expr *= tt.scalar("b") * NormalRV(
tt_2_normalrv_noname_expr, tt.scalar("\\sigma")) + tt.scalar("c")
expected = textwrap.dedent(r"""
M in R**(N^M_0 x N^M_1), \mu_2 in R, \sigma_2 in R
b in R, \sigma in R, c in R
a ~ N(\mu_2, \sigma_2**2) in R, d ~ N((M * a), \sigma**2) in R**(N^d_0 x N^d_1)
((M * a) * ((b * d) + c))
""")
assert tt_pprint(tt_2_normalrv_noname_expr) == expected.strip()
expected = textwrap.dedent(r"""
b in Z, c in Z, M in R**(N^M_0 x N^M_1)
M[b, c]
""")
# TODO: "c" should be "1".
assert (tt_pprint(
tt.matrix("M")[tt.iscalar("a"),
tt.constant(1, dtype="int")]) == expected.strip())
expected = textwrap.dedent(r"""
M in R**(N^M_0 x N^M_1)
M[1]
""")
assert tt_pprint(tt.matrix("M")[1]) == expected.strip()
expected = textwrap.dedent(r"""
M in N**(N^M_0)
M[2:4:0]
""")
assert tt_pprint(tt.vector("M", dtype="uint32")[0:4:2]) == expected.strip()
norm_rv = NormalRV(tt.scalar("\\mu"), tt.scalar("\\sigma"))
rv_obs = observed(tt.constant(1.0, dtype=norm_rv.dtype), norm_rv)
expected = textwrap.dedent(r"""
\mu in R, \sigma in R
a ~ N(\mu, \sigma**2) in R
a = 1.0
""")
assert tt_pprint(rv_obs) == expected.strip()
def __init__(self,
n_in,
hidden_layer_size,
n_out,
L1_reg,
L2_reg,
hidden_layer_type,
output_type='LINEAR',
dropout_rate=0.0,
optimizer='sgd',
loss_function='MMSE',
rnn_batch_training=False):
""" This function initialises a neural network
:param n_in: Dimensionality of input features
:type in: Integer
:param hidden_layer_size: The layer size for each hidden layer
:type hidden_layer_size: A list of integers
:param n_out: Dimensionality of output features
:type n_out: Integrer
:param hidden_layer_type: the activation types of each hidden layers, e.g., TANH, LSTM, GRU, BLSTM
:param L1_reg: the L1 regulasation weight
:param L2_reg: the L2 regulasation weight
:param output_type: the activation type of the output layer, by default is 'LINEAR', linear regression.
:param dropout_rate: probability of dropout, a float number between 0 and 1.
"""
logger = logging.getLogger("DNN initialization")
self.n_in = int(n_in)
self.n_out = int(n_out)
self.n_layers = len(hidden_layer_size)
self.dropout_rate = dropout_rate
self.optimizer = optimizer
self.loss_function = loss_function
self.is_train = T.iscalar('is_train')
self.rnn_batch_training = rnn_batch_training
assert len(hidden_layer_size) == len(hidden_layer_type)
self.list_of_activations = [
'TANH', 'SIGMOID', 'SOFTMAX', 'RELU', 'RESU'
]
if self.rnn_batch_training:
self.x = T.tensor3('x')
self.y = T.tensor3('y')
else:
self.x = T.matrix('x')
self.y = T.matrix('y')
self.L1_reg = L1_reg
self.L2_reg = L2_reg
self.rnn_layers = []
self.params = []
self.delta_params = []
rng = np.random.RandomState(123)
for i in range(self.n_layers):
if i == 0:
input_size = n_in
else:
input_size = hidden_layer_size[i - 1]
if i == 0:
layer_input = self.x
else:
layer_input = self.rnn_layers[i - 1].output
if hidden_layer_type[i - 1] == 'BSLSTM' or hidden_layer_type[
i - 1] == 'BLSTM':
input_size = hidden_layer_size[i - 1] * 2
if hidden_layer_type[i] in self.list_of_activations:
hidden_activation = hidden_layer_type[i].lower()
hidden_layer = GeneralLayer(rng,
layer_input,
input_size,
hidden_layer_size[i],
activation=hidden_activation,
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'TANH_LHUC':
hidden_layer = SigmoidLayer_LHUC(rng,
layer_input,
input_size,
hidden_layer_size[i],
activation=T.tanh,
p=self.dropout_rate,
training=self.is_train)
elif hidden_layer_type[i] == 'SLSTM':
hidden_layer = SimplifiedLstm(
rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'SGRU':
hidden_layer = SimplifiedGRU(
rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'GRU':
hidden_layer = GatedRecurrentUnit(
rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_NFG':
hidden_layer = LstmNFG(
rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_NOG':
hidden_layer = LstmNOG(
rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_NIG':
hidden_layer = LstmNIG(
rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_NPH':
hidden_layer = LstmNoPeepholes(
rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM':
hidden_layer = VanillaLstm(
rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'BSLSTM':
hidden_layer = BidirectionSLstm(
rng,
layer_input,
input_size,
hidden_layer_size[i],
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'BLSTM':
hidden_layer = BidirectionLstm(
rng,
layer_input,
input_size,
hidden_layer_size[i],
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'RNN':
hidden_layer = VanillaRNN(
rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
elif hidden_layer_type[i] == 'LSTM_LHUC':
hidden_layer = VanillaLstm_LHUC(
rng,
layer_input,
input_size,
hidden_layer_size[i],
p=self.dropout_rate,
training=self.is_train,
rnn_batch_training=self.rnn_batch_training)
else:
logger.critical(
"This hidden layer type: %s is not supported right now! \n Please use one of the following: SLSTM, BSLSTM, TANH, SIGMOID\n"
% (hidden_layer_type[i]))
sys.exit(1)
self.rnn_layers.append(hidden_layer)
self.params.extend(hidden_layer.params)
input_size = hidden_layer_size[-1]
if hidden_layer_type[-1] == 'BSLSTM' or hidden_layer_type[
-1] == 'BLSTM':
input_size = hidden_layer_size[-1] * 2
output_activation = output_type.lower()
if output_activation == 'linear':
self.final_layer = LinearLayer(rng, self.rnn_layers[-1].output,
input_size, self.n_out)
elif output_activation == 'recurrent':
self.final_layer = RecurrentOutputLayer(
rng,
self.rnn_layers[-1].output,
input_size,
self.n_out,
rnn_batch_training=self.rnn_batch_training)
elif output_type.upper() in self.list_of_activations:
self.final_layer = GeneralLayer(rng,
self.rnn_layers[-1].output,
input_size,
self.n_out,
activation=output_activation)
else:
logger.critical(
"This output layer type: %s is not supported right now! \n Please use one of the following: LINEAR, BSLSTM\n"
% (output_type))
sys.exit(1)
self.params.extend(self.final_layer.params)
self.updates = {}
for param in self.params:
self.updates[param] = theano.shared(
value=np.zeros(param.get_value(borrow=True).shape,
dtype=theano.config.floatX),
name='updates')
if self.loss_function == 'CCE':
self.finetune_cost = self.categorical_crossentropy_loss(
self.final_layer.output, self.y)
self.errors = self.categorical_crossentropy_loss(
self.final_layer.output, self.y)
elif self.loss_function == 'Hinge':
self.finetune_cost = self.multiclass_hinge_loss(
self.final_layer.output, self.y)
self.errors = self.multiclass_hinge_loss(self.final_layer.output,
self.y)
elif self.loss_function == 'MMSE':
if self.rnn_batch_training:
self.y_mod = T.reshape(self.y, (-1, n_out))
self.final_layer_output = T.reshape(self.final_layer.output,
(-1, n_out))
nonzero_rows = T.any(self.y_mod, 1).nonzero()
self.y_mod = self.y_mod[nonzero_rows]
self.final_layer_output = self.final_layer_output[nonzero_rows]
self.finetune_cost = T.mean(
T.sum((self.final_layer_output - self.y_mod)**2, axis=1))
self.errors = T.mean(
T.sum((self.final_layer_output - self.y_mod)**2, axis=1))
else:
self.finetune_cost = T.mean(
T.sum((self.final_layer.output - self.y)**2, axis=1))
self.errors = T.mean(
T.sum((self.final_layer.output - self.y)**2, axis=1))
def test_value_s(self):
"tests that the value of the kl divergence decreases with each update to s_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
init_H = e_step.init_H_hat(V=X)
init_Mu1 = e_step.init_S_hat(V=X)
prev_setting = config.compute_test_value
config.compute_test_value = 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0., 1., H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(
-5., 5., Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
S = e_step.infer_S_hat(V=X, H_hat=H_var, S_hat=Mu1_var)
s_idx = S[:, idx]
s_i_func = function([H_var, Mu1_var, idx], s_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0, var_s1_hat = Sigma1)
trunc_kl_func = function([H_var, Mu1_var], trunc_kl)
for i in xrange(self.N):
prev_kl = trunc_kl_func(H, Mu1)
Mu1[:, i] = s_i_func(H, Mu1, i)
new_kl = trunc_kl_func(H, Mu1)
increase = new_kl - prev_kl
mx = increase.max()
if mx > 1e-3:
raise Exception(
'after mean field step in s, kl divergence should decrease, but some elements increased by as much as '
+ str(mx) + ' after updating s_' + str(i))
from __future__ import print_function
import numpy as np
import theano
import theano.tensor as T
N = T.iscalar('N')
def calc(n, fn1, fn2):
return fn1 + fn2, fn1
outputs, _ = theano.scan(fn=calc,
sequences=T.arange(N),
n_steps=N,
outputs_info=[1., 1.])
fibonacci = theano.function(inputs=[N], outputs=outputs)
print(fibonacci(8))
def build(self,
dropout,
char_dim,
char_lstm_dim,
char_bidirect,
slb_dim,
slb_lstm_dim,
slb_bidirect,
word_dim,
word_lstm_dim,
word_bidirect,
lr_method,
pre_emb,
crf,
pos_dim,
lexicon_dim,
training=True,
**kwargs):
"""
Build the network.
"""
# Training parameters
n_words = len(self.id_to_word)
n_tags = len(self.id_to_tag)
# Number of features
if slb_dim:
n_slbs = len(self.id_to_slb)
if char_dim:
n_chars = len(self.id_to_char)
if pos_dim:
n_pos = len(self.id_to_pos) + 2
if lexicon_dim:
n_lex = lexicon_dim
# Network variables
is_train = T.iscalar('is_train')
word_ids = T.ivector(name='word_ids')
if slb_dim:
slb_for_ids = T.imatrix(name='slb_for_ids')
if slb_lstm_dim:
slb_rev_ids = T.imatrix(name='slb_rev_ids')
if slb_bidirect:
slb_pos_ids = T.ivector(name='slb_pos_ids')
if char_dim:
char_for_ids = T.imatrix(name='char_for_ids')
if char_lstm_dim:
char_rev_ids = T.imatrix(name='char_rev_ids')
if char_bidirect:
char_pos_ids = T.ivector(name='char_pos_ids')
if pos_dim:
pos_ids = T.ivector(name='pos_ids')
if lexicon_dim:
lex_ids = T.fmatrix(name='lex_ids')
tag_ids = T.ivector(name='tag_ids')
# Sentence length
s_len = (word_ids if word_dim else char_pos_ids).shape[0]
# Final input (all word features)
input_dim = 0
inputs = []
#
# Word inputs
#
if word_dim:
input_dim += word_dim
word_layer = EmbeddingLayer(n_words, word_dim, name='word_layer')
word_input = word_layer.link(word_ids)
inputs.append(word_input)
# Initialize with pretrained embeddings
if pre_emb and training:
new_weights = word_layer.embeddings.get_value()
print 'Loading pretrained embeddings...'
pretrained = {}
emb_invalid = 0
for i, line in enumerate(codecs.open(pre_emb, 'r', 'utf-8')):
line = line.rstrip().split()
if len(line) == word_dim + 1:
pretrained[line[0]] = np.array(
[float(x) for x in line[1:]]).astype(np.float32)
else:
emb_invalid += 1
# if emb_invalid > 0:
# print 'WARNING: %i invalid lines' % emb_invalid
# Lookup table initialization
for i in xrange(n_words):
word = self.id_to_word[i]
if word in pretrained:
# print word
new_weights[i] = pretrained[word]
word_layer.embeddings.set_value(new_weights)
#
# Syllable inputs
#
if slb_dim:
slb_layer = EmbeddingLayer(n_slbs, slb_dim, name='slb_layer')
if slb_lstm_dim:
input_dim += slb_lstm_dim
slb_lstm_for = LSTM(slb_dim,
slb_lstm_dim,
with_batch=True,
name='slb_lstm_for')
slb_lstm_rev = LSTM(slb_dim,
slb_lstm_dim,
with_batch=True,
name='slb_lstm_rev')
slb_lstm_for.link(slb_layer.link(slb_for_ids))
slb_lstm_rev.link(slb_layer.link(slb_rev_ids))
slb_for_input = slb_lstm_for.h.dimshuffle(
(1, 0, 2))[T.arange(s_len), slb_pos_ids]
slb_rev_input = slb_lstm_rev.h.dimshuffle(
(1, 0, 2))[T.arange(s_len), slb_pos_ids]
inputs.append(slb_for_input)
if slb_bidirect:
inputs.append(slb_rev_input)
input_dim += slb_lstm_dim
#
# Chars inputs
#
if char_dim:
char_layer = EmbeddingLayer(n_chars, char_dim, name='char_layer')
if char_lstm_dim:
input_dim += char_lstm_dim
char_lstm_for = LSTM(char_dim,
char_lstm_dim,
with_batch=True,
name='char_lstm_for')
char_lstm_rev = LSTM(char_dim,
char_lstm_dim,
with_batch=True,
name='char_lstm_rev')
char_lstm_for.link(char_layer.link(char_for_ids))
char_lstm_rev.link(char_layer.link(char_rev_ids))
char_for_input = char_lstm_for.h.dimshuffle(
(1, 0, 2))[T.arange(s_len), char_pos_ids]
char_rev_input = char_lstm_rev.h.dimshuffle(
(1, 0, 2))[T.arange(s_len), char_pos_ids]
inputs.append(char_for_input)
if char_bidirect:
inputs.append(char_rev_input)
input_dim += char_lstm_dim
#
# PoS & Lexicon feature
#
if pos_dim:
input_dim += pos_dim
pos_layer = EmbeddingLayer(n_pos, pos_dim, name='pos_layer')
inputs.append(pos_layer.link(pos_ids))
if lexicon_dim:
input_dim += lexicon_dim
lex_layer = HiddenLayer(n_lex,
lexicon_dim,
name='lex_layer',
activation=None)
inputs.append(lex_layer.link(lex_ids))
# Prepare final input
if len(inputs) != 1:
inputs = T.concatenate(inputs, axis=1)
else:
inputs = inputs[0]
#
# Dropout on final input
#
if dropout:
dropout_layer = DropoutLayer(p=dropout)
input_train = dropout_layer.link(inputs)
input_test = (1 - dropout) * inputs
inputs = T.switch(T.neq(is_train, 0), input_train, input_test)
# LSTM for words
word_lstm_for = LSTM(input_dim,
word_lstm_dim,
with_batch=False,
name='word_lstm_for')
word_lstm_rev = LSTM(input_dim,
word_lstm_dim,
with_batch=False,
name='word_lstm_rev')
word_lstm_for.link(inputs)
word_lstm_rev.link(inputs[::-1, :])
word_for_output = word_lstm_for.h
word_rev_output = word_lstm_rev.h[::-1, :]
if word_bidirect:
final_output = T.concatenate([word_for_output, word_rev_output],
axis=1)
tanh_layer = HiddenLayer(2 * word_lstm_dim,
word_lstm_dim,
name='tanh_layer',
activation='tanh')
final_output = tanh_layer.link(final_output)
else:
final_output = word_for_output
# Sentence to Named Entity tags - Score
final_layer = HiddenLayer(word_lstm_dim,
n_tags,
name='final_layer',
activation=(None if crf else 'softmax'))
tags_scores = final_layer.link(final_output)
# No CRF
if not crf:
cost = T.nnet.categorical_crossentropy(tags_scores, tag_ids).mean()
# CRF
else:
transitions = shared((n_tags + 2, n_tags + 2), 'transitions')
small = -1000
b_s = np.array([[small] * n_tags + [0, small]]).astype(np.float32)
e_s = np.array([[small] * n_tags + [small, 0]]).astype(np.float32)
observations = T.concatenate(
[tags_scores, small * T.ones((s_len, 2))], axis=1)
observations = T.concatenate([b_s, observations, e_s], axis=0)
# Score from tags
real_path_score = tags_scores[T.arange(s_len), tag_ids].sum()
# Score from transitions
b_id = theano.shared(value=np.array([n_tags], dtype=np.int32))
e_id = theano.shared(value=np.array([n_tags + 1], dtype=np.int32))
padded_tags_ids = T.concatenate([b_id, tag_ids, e_id], axis=0)
real_path_score += transitions[padded_tags_ids[T.arange(s_len +
1)],
padded_tags_ids[T.arange(s_len + 1)
+ 1]].sum()
all_paths_scores = forward(observations, transitions)
cost = -(real_path_score - all_paths_scores)
# Network parameters
params = []
if word_dim:
self.add_component(word_layer)
params.extend(word_layer.params)
if slb_dim:
self.add_component(slb_layer)
params.extend(slb_layer.params)
if slb_lstm_dim:
self.add_component(slb_lstm_for)
params.extend(slb_lstm_for.params)
if slb_bidirect:
self.add_component(slb_lstm_rev)
params.extend(slb_lstm_rev.params)
if char_dim:
self.add_component(char_layer)
params.extend(char_layer.params)
if char_lstm_dim:
self.add_component(char_lstm_for)
params.extend(char_lstm_for.params)
if char_bidirect:
self.add_component(char_lstm_rev)
params.extend(char_lstm_rev.params)
self.add_component(word_lstm_for)
params.extend(word_lstm_for.params)
if word_bidirect:
self.add_component(word_lstm_rev)
params.extend(word_lstm_rev.params)
if pos_dim:
self.add_component(pos_layer)
params.extend(pos_layer.params)
if lexicon_dim:
self.add_component(lex_layer)
params.extend(lex_layer.params)
self.add_component(final_layer)
params.extend(final_layer.params)
if crf:
self.add_component(transitions)
params.append(transitions)
if word_bidirect:
self.add_component(tanh_layer)
params.extend(tanh_layer.params)
# Prepare train and eval inputs
eval_inputs = []
if word_dim:
eval_inputs.append(word_ids)
if slb_dim:
eval_inputs.append(slb_for_ids)
if slb_lstm_dim:
if slb_bidirect:
eval_inputs.append(slb_rev_ids)
eval_inputs.append(slb_pos_ids)
if char_dim:
eval_inputs.append(char_for_ids)
if char_lstm_dim:
if char_bidirect:
eval_inputs.append(char_rev_ids)
eval_inputs.append(char_pos_ids)
if pos_dim:
eval_inputs.append(pos_ids)
if lexicon_dim:
eval_inputs.append(lex_ids)
train_inputs = eval_inputs + [tag_ids]
# Parse optimization method parameters
if "-" in lr_method:
lr_method_name = lr_method[:lr_method.find('-')]
lr_method_parameters = {}
for x in lr_method[lr_method.find('-') + 1:].split('-'):
split = x.split('_')
assert len(split) == 2
lr_method_parameters[split[0]] = float(split[1])
else:
lr_method_name = lr_method
lr_method_parameters = {}
# Compile training function
print 'Compiling...'
if training:
updates = Optimization(clip=5.0).get_updates(
lr_method_name, cost, params, **lr_method_parameters)
f_train = theano.function(inputs=train_inputs,
outputs=cost,
updates=updates,
givens=({
is_train: np.cast['int32'](1)
} if dropout else {}))
else:
f_train = None
# Compile evaluation function
if not crf:
f_eval = theano.function(inputs=eval_inputs,
outputs=tags_scores,
givens=({
is_train: np.cast['int32'](0)
} if dropout else {}))
else:
f_eval = theano.function(inputs=eval_inputs,
outputs=forward(
observations,
transitions,
viterbi=True,
return_alpha=False,
return_best_sequence=True),
givens=({
is_train: np.cast['int32'](0)
} if dropout else {}))
return f_train, f_eval
