def build_and_train_model(self,n_hu,n_hl):
print('Building Model')
input_phrase = T.imatrix('train_inputmatrix')
labels = T.imatrix('trainphrase_matrix')
network = self.define_layers(input_phrase,labels,n_hu,n_hl)
print("Defining loss")
#Prediction or loss
prediction = []
prediction.append(T.clip(lasagne.layers.get_output(network[0]),1.0e-7,1.0-1.0e-7))
prediction.append(T.clip(lasagne.layers.get_output(network[1]),1.0e-7,1.0-1.0e-7))
loss = l.define_loss(prediction[0],prediction[1])
self.model = network
#define params
params = lasagne.layers.get_all_params(network)
updates = lasagne.updates.adadelta(loss,params)
#run test
train_fn = theano.function([input_phrase,labels],[loss, prediction[0], prediction[1]],updates=updates,allow_input_downcast=True)
print("Model and params defined now training")
epoch = 0
for epoch in range(self.end_epoch):
train_loss = 0
train_pred = []
start_time = time.time()
loss, predicted, phrase = train_fn(self.train_inputmatrix,self.trainphrase_matrix)
print('Training Loss: ' + str(loss) + ' Train Epoch ' + str(epoch))
self.save_best(loss,predicted,network)
def _classify(self,dataset_static,dataset_nonstatic):
"""
Classify method for static or non-static models.
:param classifier: model
:param conv_layers: list of convPoolLayer objects
:param Words: Dictionary of word index to word vectors
:param dataset: Indices of words for the current sentence/dataset
:param dim: dimension of word vector
:param img_h: length of sentence vector after padding
:return: [y_pred,prob_pred] The probability for each class
"""
x_static = T.imatrix('x_static')
x_nonstatic = T.imatrix('x_nonstatic')
y = T.ivector('y')
Words_static = theano.shared(value = self.Words_static, name = "Words_static")
Words_nonstatic = theano.shared(value = self.Words_nonstatic, name = "Words_nonstatic")
test_pred_layers = []
test_size = np.shape(dataset_static)[0]
test_layer0_input_static = Words_static[T.cast(x_static.flatten(),dtype="int32")].reshape((test_size,1,self.img_h,self.Words_static.shape[1]))
test_layer0_input_nonstatic = Words_nonstatic[T.cast(x_nonstatic.flatten(),dtype="int32")].reshape((test_size,1,self.img_h,self.Words_nonstatic.shape[1]))
for i in range(len(self.conv_layers)/2):
test_layer0_output = self.conv_layers[i].predict(test_layer0_input_nonstatic, test_size)
test_pred_layers.append(test_layer0_output.flatten(2))
for i in range(len(self.conv_layers)/2,len(self.conv_layers)):
test_layer0_output = self.conv_layers[i].predict(test_layer0_input_static, test_size)
test_pred_layers.append(test_layer0_output.flatten(2))
test_layer1_input = T.concatenate(test_pred_layers, 1)
test_y_pred = self.classifier.predict(test_layer1_input)
test_prob_pred = self.classifier.predict_p(test_layer1_input)
test_model_all = theano.function([x_static,x_nonstatic], (test_y_pred,test_prob_pred))
return test_model_all(dataset_static,dataset_nonstatic)
def run():
batch_size = 16
prems = np.random.randint(low=0, high=99, size=(batch_size, 5), dtype='int32')
hypoes = np.random.randint(low=0, high=99, size=(batch_size, 3), dtype='int32')
labels = np.random.randint(low=0, high=3, size=(batch_size,), dtype='int32')
print prems
print hypoes
print labels
ematrix = np.random.uniform(low=-1, high=1, size=(100, 100)).astype(theano.config.floatX)
t_prems = T.imatrix('p')
t_hypoes = T.imatrix('h')
t_ematrix = theano.shared(ematrix, 't_ematrix')
r_prems = T.repeat(t_prems, 3, axis= 1)
r_hypoes = T.concatenate([t_hypoes]* 5, axis=1)
batch_prems = t_ematrix[r_prems]
batch_hypoes = t_ematrix[r_hypoes]
batch_prem_hypo = T.concatenate((batch_prems, batch_hypoes), axis=2)
get_b_prems = theano.function(inputs=[t_prems], outputs=batch_prems)
get_r_prems = theano.function(inputs=[t_prems], outputs=r_prems)
get_b_hypoes = theano.function(inputs=[t_hypoes], outputs=batch_hypoes)
get_r_hypoes = theano.function(inputs=[t_hypoes], outputs=r_hypoes)
get_b_ph = theano.function(inputs=[t_prems, t_hypoes], outputs=batch_prem_hypo)
# print get_b_prems(prems)
print get_r_prems(prems)
print get_r_hypoes(hypoes)
print get_b_prems(prems).shape
print get_b_hypoes(hypoes).shape
print get_b_ph(prems, hypoes).shape
W = theano.shared(
value=np.random.uniform(
low=-np.sqrt(1. / 6),
high=np.sqrt(1. / 6),
size=(200, 400)
).astype(theano.config.floatX),
name='W'
)
U = theano.shared(
value=np.random.uniform(
low=-np.sqrt(1. / 6),
high=np.sqrt(1. / 6),
size=(400,)
).astype(theano.config.floatX),
name='U'
)
result = T.dot(T.dot(batch_prem_hypo, W), U)
get_result = theano.function(inputs=[t_prems, t_hypoes], outputs=result)
print get_result(prems, hypoes).shape
def train_ready(self): print "adopt softmax model plus contractive regularization ........ " print "weight 1 : "+str(self.lowreg_weight) print "weight 2 : "+str(self.highreg_weight) print "variance : "+str(self.variance) print "nc : "+str(self.nc) var_x = T.imatrix() var_y = T.imatrix() loss = self.reg_logp(var_x,var_y, self.lowreg_weight, self.highreg_weight, self.variance, self.nc) witems = self.w.values() #ave_w = sum(T.sum(item**2) for item in witems)/len(witems) wg = T.grad(loss, witems) #ave_g = sum(T.sum(item**2) for item in wg) /len(wg) weight_up = self.upda(wg, witems, self.lrate, self.mweight, self.opt, self.gradbound) if not self.fix_emb: dicitems = self.dic.values() dg = T.grad(loss, dicitems) dic_up = self.upda(dg, dicitems, self.lrate/10., self.mweight, self.opt) weight_up.update(dic_up) up = weight_up self.updatefunc = theano.function([var_x, var_y], loss,updates = up)
def create_model(num_timesteps, num_blocks, hidden_size, learning_rate, \
grad_clip=10, dropout_p=0.5, num_lstm_layers=1, use_forward_and_backward_lstm=False):
'''
returns train function which reports both loss and accuracy
and test function, which also reports both loss and accuracy
'''
l_in, l_mask, l_out, l_out_slice, l_lstm, l_lstm_slice = \
_build_net_layers(num_timesteps, num_blocks, hidden_size, learning_rate, \
grad_clip, dropout_p, num_lstm_layers, use_forward_and_backward_lstm)
inp = T.tensor3('input')
truth = T.imatrix("truth")
mask = T.imatrix("mask")
# pred should be of shape (batchsize, num_timesteps, num_asts)
pred = lasagne.layers.get_output(l_out)
# pred_slice should be of shape (batchsize, num_asts), only contains
# predictions for the last timestep
pred_slice = lasagne.layers.get_output(l_out_slice)
# the hidden representations for the last timestep (batchsize, hidden_size)
hidden_slice = lasagne.layers.get_output(l_lstm_slice)
# truth should also be of shape (batchsize, num_timesteps, num_asts)
pred_2d = pred.reshape((-1, num_blocks))
truth_1d = truth.reshape((-1,))
# pred_2d_shape = T.shape(pred_2d)
# truth_1d_shape = T.shape(truth_1d)
# categorical_crossentropy
loss = T.nnet.categorical_crossentropy(pred_2d, truth_1d).mean()
# categorical accuracy
# acc = T.nnet.categorical_crossentropy(pred_2d, truth_1d).mean()
acc = lasagne.objectives.categorical_accuracy(pred_2d, truth_1d).mean()
# update function
print("Computing updates ...")
all_params = lasagne.layers.get_all_params(l_out)
updates = lasagne.updates.adam(loss, all_params, learning_rate)
# training function
print("Compiling functions ...")
train_loss = theano.function([l_in.input_var, l_mask.input_var, truth], loss, updates=updates, allow_input_downcast=True)
compute_loss = theano.function([l_in.input_var, l_mask.input_var, truth], loss, allow_input_downcast=True)
# training function, returns loss and acc
compute_pred = theano.function([l_in.input_var, l_mask.input_var, truth], [pred_2d, truth_1d], updates=updates, allow_input_downcast=True)
train_loss_acc = theano.function([l_in.input_var, l_mask.input_var, truth], [loss, acc, pred], updates=updates, allow_input_downcast=True)
# computes loss and accuracy, without training
compute_loss_acc = theano.function([l_in.input_var, l_mask.input_var, truth], [loss, acc, pred], allow_input_downcast=True)
# In order to generate text from the network, we need the probability distribution of the next character given
# the state of the network and the input (a seed).
# In order to produce the probability distribution of the prediction, we compile a function called probs.
probs = theano.function([l_in.input_var, l_mask.input_var], pred_slice, allow_input_downcast=True)
generate_hidden_representations = theano.function([l_in.input_var, l_mask.input_var], hidden_slice, allow_input_downcast=True)
print("Compiling done!")
return train_loss_acc, compute_loss_acc, probs, generate_hidden_representations, compute_pred, l_out
def __init__( self,
config,
qvocab_len,
max_qlen,
num_ans,
num_qtypes,
l_saver):
self.config = config
self.qn = T.imatrix()
self.lstm_mask = T.imatrix()
self.iX = T.fmatrix()
self.Y = T.ivector()
self.qtype = T.ivector()
self.sparse_indices = T.ivector()
self.qembd = T.fmatrix()
self.ql_out = T.fmatrix()
self.timer = l.timer_type()
self.saver, self.exp_saver = l_saver
self.qlstm_hidden_dim = 300
self.qn_classifier_emb_size = 75
self.max_ql = max_qlen
self.qvocab_len = qvocab_len
self.bptt_trunk_steps = -1
self.mlp_input_dim = 1024
self.num_qtypes = num_qtypes
self.num_ans = num_ans
self.grad_clip = config['grad_clip']
self.params = {}
print "Models Initialization done ..."
def test_sparseblockgemvF(self):
"""
Test the fortan order for W (which can happen in the grad for some
graphs).
"""
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.imatrix()
oIdx = tensor.imatrix()
o = self.gemv_op(b.take(oIdx, axis=0),
tensor.DimShuffle((False, False, False, False),
(0, 1, 3, 2))
(tensor.as_tensor_variable(W)),
h, iIdx, oIdx)
f = theano.function([W, h, iIdx, b, oIdx], o, mode=self.mode)
W_val, h_val, iIdx_val, b_val, oIdx_val = \
BlockSparse_Gemv_and_Outer.gemv_data()
th_out = f(numpy.swapaxes(W_val, 2, 3), h_val, iIdx_val, b_val,
oIdx_val)
ref_out = BlockSparse_Gemv_and_Outer.gemv_numpy(
b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)
utt.assert_allclose(ref_out, th_out)
def __init__(self, size_vocab, size_embed, size, size_out, depth, network,
alpha=0.5,
gru_activation=clipped_rectify,
visual_activation=linear,
visual_encoder=StackedGRUH0,
cost_visual=CosineDistance,
max_norm=None,
lr=0.0002,
dropout_prob=0.0):
autoassign(locals())
self.network = network(self.size_vocab,
self.size_embed,
self.size,
self.size_out,
self.depth,
gru_activation=self.gru_activation,
visual_activation=self.visual_activation,
visual_encoder=self.visual_encoder,
dropout_prob=self.dropout_prob)
self.input = T.imatrix()
self.output_t_prev = T.imatrix()
self.output_t = T.imatrix()
self.output_v = T.fmatrix()
self.OH = OneHot(size_in=self.size_vocab)
self.output_t_oh = self.OH(self.output_t)
self.updater = util.Adam(max_norm=self.max_norm, lr=self.lr)
self.train = self._make_train()
self.loss_test = self._make_loss_test()
def set_model(argv, vocab_word, init_emb):
x_span = T.imatrix("x_span")
x_word = T.imatrix("x_word")
x_ctx = T.imatrix("x_ctx")
x_dist = T.imatrix("x_dist")
x_slen = T.imatrix("x_slen")
y = T.ivector("y")
""" Set params for the model """
n_vocab = vocab_word.size()
dim_x_word = argv.emb
dim_x_dist = 10 # (0, ..., 10-)
dim_h = argv.hidden
L2_reg = argv.reg
""" Instantiate the model """
return Model(
x_span=x_span,
x_word=x_word,
x_ctx=x_ctx,
x_dist=x_dist,
x_slen=x_slen,
y=y,
init_emb=init_emb,
n_vocab=n_vocab,
dim_w_p=dim_x_word,
dim_d=dim_x_dist,
dim_h=dim_h,
L2_reg=L2_reg,
)
def run():
# params
dims = 10
negrate = 1
batsize = 300
epochs = 300
#paths
datafileprefix = "../../data/nycfilms/"
dirfwdsuffix = "direct_forward.plustypes.ssd"
# get the data and split
dirfwdf = open(datafileprefix+dirfwdsuffix)
datadf = readdata(dirfwdf)
traind, validd, testd = datadf.split((70, 15, 15), random=True)
numents = int(datadf.ix[:, 0].max())+1
print numents
numrels = int(datadf.ix[:, 1].max())+1
print numrels
# define model
inp = Input(T.imatrix())
eemb = VectorEmbed.indim(numents).outdim(dims).Wreg(l2reg(0.00001))()
remb = VectorEmbed.indim(numrels).outdim(dims).Wreg(l2reg(0.00001))()
# for debugging
eembd = SymTensor(T.fmatrix())
rembd = SymTensor(T.fmatrix())
dotp = SymTensor(T.fmatrix())
out = ((inp[:, 0] >> eemb >> eembd) & (inp[:, 1] >> remb >> rembd)) >> DotProduct() >> dotp >> Tanh()
# for plotting purposes: relation to relation dot product (or relation-type)
r2rinp = Input(T.imatrix())
rel2rel = ((r2rinp[:, 0] >> remb) & (r2rinp[:, 1] >> remb)) >> DotProduct()
outtest = Output(T.fvector())
loss = (out & outtest) >> HingeLoss()
trainer = Trainer\
.batsize(batsize)\
.epochs(epochs)\
.onrun(getonrun())\
.offrun(offrun)\
.offepoch(getoffepoch(out, rel2rel))\
.onbatch(getonbatch(negrate, numents, numrels))\
.optimizer(sgd(lr=1.))\
.batchtransformer(transbat)
trainer\
.loss(loss)\
trainer.train(traind.values, validd.values)\
.test(testd.values)
explore(eemb, remb)
# functions for interactive exploration
embed()
def train_model_func(self, batch_size, num_batches, summary_sz, input_sz):
summaries = T.imatrix('summaries')
docs = T.imatrix('docs')
s = np.zeros((batch_size * num_batches, summary_sz))
d = np.zeros((batch_size * num_batches, input_sz))
summary_superbatch = theano.shared( s.astype(theano.config.floatX),
name = 's_summs', borrow = True )
doc_superbatch = theano.shared( d.astype(theano.config.floatX),
name = 's_docs', borrow = True )
self.ssb = summary_superbatch
self.dsb = doc_superbatch
cost = self.negative_log_likelihood_batch(docs, summaries, batch_size)
regularization_cost = self.l2_coefficient * sum([(p ** 2).sum() for p in self.params])
self.get_batch_cost_unregularized = theano.function([docs, summaries], cost, allow_input_downcast=True)
#theano.printing.debugprint(cost)
cost = cost + regularization_cost
params = {p.name: p for p in self.params}
grads = T.grad(cost, self.params)
#grads = theano.printing.Print("grads")(grads)
# learning rate
lr = T.scalar(name='lr')
gradient_update = optimisers.sgd_(lr, self.params, grads, docs, summaries,
cost, self.dsb, self.ssb, batch_size)
return gradient_update
def build_model_1(self):
x = T.imatrix('x').astype(theano.config.floatX)
drop_masks = T.imatrix('drop_masks').astype(theano.config.floatX)
y = T.ivector('y')
self.layers[0] = LSTMLayer(random_state=self.random_state,input=x,drop_masks=drop_masks,input_dim=self.input_dim,output_dim=self.hidden_dims[0])
params = self.layers[0].params
self.layers[1] = OutputLayer(input=self.layers[0].output,
input_dim=self.layers[0].output_dim, output_dim=self.output_dim,random_state=self.random_state)
params += self.layers[1].params
_EPSILON = 10e-8
L1 = 0.001 * T.sum([T.sum(param) for param in params])
L2 = 0.001 * T.sum([T.sum(param ** param) for param in params])
cost = T.sum(T.nnet.categorical_crossentropy(T.clip(self.layers[self.number_of_layers].probabilities[-1], _EPSILON, 1.0 - _EPSILON),y)) + L1 + L2
#grads = T.grad(cost, params)
#updates = [(param_i, param_i - self.learning_rate * grad_i) for param_i,grad_i in zip(params,grads)]
updates = LearningAlgorithms.adam(cost,params,learning_rate=0.001)
self.sgd_step = theano.function([x,drop_masks, y], L1, updates=updates)
self.predict = theano.function([x,drop_masks],self.layers[self.number_of_layers].probabilities[-1])
self.test_model = theano.function([x,drop_masks, y], cost)
def _make_stack(self, seq_length=4):
self.embedding_dim = embedding_dim = 3
self.vocab_size = vocab_size = 10
self.seq_length = seq_length
def compose_network(inp, inp_dim, outp_dim, vs, name="compose"):
# Just add the two embeddings!
W = T.concatenate([T.eye(outp_dim), T.eye(outp_dim)], axis=0)
return inp.dot(W)
X = T.imatrix("X")
transitions = T.imatrix("transitions")
apply_dropout = T.scalar("apply_dropout")
vs = VariableStore()
self.stack = HardStack(
embedding_dim,
embedding_dim,
vocab_size,
seq_length,
compose_network,
IdentityLayer,
apply_dropout,
vs,
X=X,
transitions=transitions,
make_test_fn=True,
)
# Swap in our own dummy embeddings and weights.
embeddings = np.arange(vocab_size).reshape((vocab_size, 1)).repeat(embedding_dim, axis=1)
self.stack.embeddings.set_value(embeddings)
def build_model(self):
print '\n... building the model with unroll=%d, backroll=%d' \
% (self.source.unroll, self.source.backroll)
x = T.imatrix('x')
y = T.imatrix('y')
reset = T.scalar('reset')
hiddens = [h['init'] for h in self.hiddens.values()]
outputs_info = [None] * 3 + hiddens
[losses, probs, errors, hids], updates = \
theano.scan(self.step, sequences=[x, y], outputs_info=outputs_info)
loss = losses.sum()
error = errors.sum() / T.cast((T.neq(y, 255).sum()), floatX)
hidden_updates_train = []
hidden_updates_test = []
for h in self.hiddens.values():
h_train = ifelse(T.eq(reset, 0), \
hids[-1-self.source.backroll, :], T.ones_like(h['init']))
h_test = ifelse(T.eq(reset, 0), \
hids[-1, :], T.ones_like(h['init']))
hidden_updates_train.append((h['init'], h_train))
hidden_updates_test.append((h['init'], h_test))
updates = self.source.get_updates(loss, self.sgd_params)
updates += hidden_updates_train
rets = [loss, probs[-1, :], error]
mode = theano.Mode(linker='cvm')
train_model = theano.function([x, y, reset, self.lr], rets, \
updates=updates, mode=mode)
test_model = theano.function([x, y, reset], rets, \
updates=hidden_updates_test, mode=mode)
return train_model, test_model
def setup_encode(self):
# dimensions: (batch, time, 12)
chord_types = T.btensor3()
# dimensions: (batch, time)
chord_roots = T.imatrix()
# dimensions: (batch, time)
relative_posns = [T.imatrix() for _ in self.encodings]
# dimesions: (batch, time, output_data)
encoded_melodies = [T.btensor3() for _ in self.encodings]
n_batch, n_time = chord_roots.shape
all_activations = []
for encoding, enc_lstmstack, encoded_melody, relative_pos in zip(self.encodings, self.enc_lstmstacks, encoded_melodies, relative_posns):
activations = enc_lstmstack.do_preprocess_scan( timestep=T.tile(T.arange(n_time), (n_batch,1)) ,
relative_position=relative_pos,
cur_chord_type=chord_types,
cur_chord_root=chord_roots,
cur_input=encoded_melody,
deterministic_dropout=True )
all_activations.append(activations)
reduced_activations = functools.reduce((lambda x,y: x+y), all_activations)
strengths, vects = self.qman.get_strengths_and_vects(reduced_activations)
self.encode_fun = theano.function(
inputs=[chord_types, chord_roots] + relative_posns + encoded_melodies,
outputs=[strengths, vects],
allow_input_downcast=True,
mode=(NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True) if self.nanguard else None))
def _get_input_tensor_variables(self):
# x_w: 1D: batch, 2D: n_words, 3D: 5 + window; word id
# x_p: 1D: batch, 2D: n_words; posit id
# y: 1D: batch, 2D: n_words; label id
if self.argv.mark_phi:
return [T.itensor3('x_w'), T.imatrix('x_p'), T.imatrix('y')]
return [T.itensor3('x_w'), T.imatrix('y')]
def build(self):
"""build the model. This method should be called after self.add_data.
"""
x_sym = sparse.csr_matrix('x', dtype = 'float32')
y_sym = T.imatrix('y')
g_sym = T.imatrix('g')
gy_sym = T.vector('gy')
ind_sym = T.ivector('ind')
l_x_in = lasagne.layers.InputLayer(shape = (None, self.x.shape[1]), input_var = x_sym)
l_g_in = lasagne.layers.InputLayer(shape = (None, 2), input_var = g_sym)
l_ind_in = lasagne.layers.InputLayer(shape = (None, ), input_var = ind_sym)
l_gy_in = lasagne.layers.InputLayer(shape = (None, ), input_var = gy_sym)
num_ver = max(self.graph.keys()) + 1
l_emb_in = lasagne.layers.SliceLayer(l_g_in, indices = 0, axis = 1)
l_emb_in = lasagne.layers.EmbeddingLayer(l_emb_in, input_size = num_ver, output_size = self.embedding_size)
l_emb_out = lasagne.layers.SliceLayer(l_g_in, indices = 1, axis = 1)
if self.neg_samp > 0:
l_emb_out = lasagne.layers.EmbeddingLayer(l_emb_out, input_size = num_ver, output_size = self.embedding_size)
l_emd_f = lasagne.layers.EmbeddingLayer(l_ind_in, input_size = num_ver, output_size = self.embedding_size, W = l_emb_in.W)
l_x_hid = layers.SparseLayer(l_x_in, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
if self.use_feature:
l_emd_f = layers.DenseLayer(l_emd_f, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
l_y = lasagne.layers.ConcatLayer([l_x_hid, l_emd_f], axis = 1)
l_y = layers.DenseLayer(l_y, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
else:
l_y = layers.DenseLayer(l_emd_f, self.y.shape[1], nonlinearity = lasagne.nonlinearities.softmax)
py_sym = lasagne.layers.get_output(l_y)
loss = lasagne.objectives.categorical_crossentropy(py_sym, y_sym).mean()
if self.layer_loss and self.use_feature:
hid_sym = lasagne.layers.get_output(l_x_hid)
loss += lasagne.objectives.categorical_crossentropy(hid_sym, y_sym).mean()
emd_sym = lasagne.layers.get_output(l_emd_f)
loss += lasagne.objectives.categorical_crossentropy(emd_sym, y_sym).mean()
if self.neg_samp == 0:
l_gy = layers.DenseLayer(l_emb_in, num_ver, nonlinearity = lasagne.nonlinearities.softmax)
pgy_sym = lasagne.layers.get_output(l_gy)
g_loss = lasagne.objectives.categorical_crossentropy(pgy_sym, lasagne.layers.get_output(l_emb_out)).sum()
else:
l_gy = lasagne.layers.ElemwiseMergeLayer([l_emb_in, l_emb_out], T.mul)
pgy_sym = lasagne.layers.get_output(l_gy)
g_loss = - T.log(T.nnet.sigmoid(T.sum(pgy_sym, axis = 1) * gy_sym)).sum()
params = [l_emd_f.W, l_emd_f.b, l_x_hid.W, l_x_hid.b, l_y.W, l_y.b] if self.use_feature else [l_y.W, l_y.b]
if self.update_emb:
params = lasagne.layers.get_all_params(l_y)
updates = lasagne.updates.sgd(loss, params, learning_rate = self.learning_rate)
self.train_fn = theano.function([x_sym, y_sym, ind_sym], loss, updates = updates, on_unused_input = 'ignore')
self.test_fn = theano.function([x_sym, ind_sym], py_sym, on_unused_input = 'ignore')
self.l = [l_gy, l_y]
g_params = lasagne.layers.get_all_params(l_gy, trainable = True)
g_updates = lasagne.updates.sgd(g_loss, g_params, learning_rate = self.g_learning_rate)
self.g_fn = theano.function([g_sym, gy_sym], g_loss, updates = g_updates, on_unused_input = 'ignore')
def __theano_build__(self):
params = self.params
param_names = self.param_names
hidden_dim = self.hidden_dim
x1 = T.imatrix('x1') # first sentence
x2 = T.imatrix('x2') # second sentence
x1_mask = T.fmatrix('x1_mask') #mask
x2_mask = T.fmatrix('x2_mask')
y = T.ivector('y') # label
y_c = T.ivector('y_c') # class weights
# Embdding words
_E1 = params["E"].dot(params["W"][0]) + params["B"][0]
_E2 = params["E"].dot(params["W"][1]) + params["B"][1]
statex1 = _E1[x1.flatten(), :].reshape([x1.shape[0], x1.shape[1], hidden_dim])
statex2 = _E2[x2.flatten(), :].reshape([x2.shape[0], x2.shape[1], hidden_dim])
def rnn_cell(x, mx, ph, Wh):
h = T.tanh(ph.dot(Wh) + x)
h = mx[:, None] * h + (1-mx[:, None]) * ph
return [h]
[h1], updates = theano.scan(
fn=rnn_cell,
sequences=[statex1, x1_mask],
truncate_gradient=self.truncate,
outputs_info=[dict(initial=T.zeros([self.batch_size, self.hidden_dim]))],
non_sequences=params["W"][2])
[h2], updates = theano.scan(
fn=rnn_cell,
sequences=[statex2, x2_mask],
truncate_gradient=self.truncate,
outputs_info=[dict(initial=h1[-1])],
non_sequences=params["W"][3])
#predict
_s = T.nnet.softmax(h1[-1].dot(params["lrW"][0]) + h2[-1].dot(params["lrW"][1]) + params["lrb"])
_p = T.argmax(_s, axis=1)
_c = T.nnet.categorical_crossentropy(_s, y)
_c = T.sum(_c * y_c)
_l = T.sum(params["lrW"]**2)
_cost = _c + 0.01 * _l
# SGD parameters
learning_rate = T.scalar('learning_rate')
decay = T.scalar('decay')
# Gradients and updates
_grads, _updates = rms_prop(_cost, param_names, params, learning_rate, decay)
# Assign functions
self.bptt = theano.function([x1, x2, x1_mask, x2_mask, y, y_c], _grads)
self.loss = theano.function([x1, x2, x1_mask, x2_mask, y, y_c], _c)
self.weights = theano.function([x1, x2, x1_mask, x2_mask], _s)
self.predictions = theano.function([x1, x2, x1_mask, x2_mask], _p)
self.sgd_step = theano.function(
[x1, x2, x1_mask, x2_mask, y, y_c, learning_rate, decay],
updates=_updates)
def __init__(self,rng,model_params):
self.input = T.itensor3('input') # the data is a minibatch
self.label = T.imatrix('label') # label's shape (mini_batch size, max_term_per_sent)
self.sent_length= T.ivector('sent_length') # sent_length is the number of terms in each sentence
self.masks = T.imatrix('masks') # masks which used in error and likelihood calculation
self.core = SentenceLevelNeuralModelCore(rng,self.input,self.label,self.sent_length,self.masks,model_params)
self.params = self.core.wordvec.params() \
+ self.core.POSvec.params() \
+ self.core.wordpos_vec.params() \
+ self.core.verbpos_vec.params() \
+ self.core.conv_word.params() \
+ self.core.conv_POS.params() \
+ self.core.conv_wordpos.params() \
+ self.core.conv_verbpos.params() \
+ self.core.hidden_layer.params
self.L2_sqr = (self.core.wordvec.embeddings ** 2).sum() \
+ (self.core.POSvec.embeddings ** 2).sum() \
+ (self.core.wordpos_vec.embeddings ** 2).sum() \
+ (self.core.verbpos_vec.embeddings ** 2).sum() \
+ (self.core.conv_word.W ** 2).sum() \
+ (self.core.conv_POS.W ** 2).sum() \
+ (self.core.conv_wordpos.W ** 2).sum() \
+ (self.core.conv_verbpos.W ** 2).sum() \
+ (self.core.hidden_layer.W ** 2).sum()
self.negative_log_likelihood = self.core.likelihood()
self.errors = self.core.errors()
# we only use L2 regularization
self.cost = self.negative_log_likelihood \
+ self.core.L2_reg * self.L2_sqr
self.gparams = []
for param in self.params:
gparam = T.grad(self.cost, param)
self.gparams.append(gparam)
self.updates = []
learning_rate = model_params['learning_rate']
for param, gparam in zip(self.params, self.gparams):
self.updates.append((param, param - learning_rate * gparam))
#self.train_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=self.core.conv_word.output,on_unused_input='ignore')
#self.train_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=self.core.conv_POS.output,on_unused_input='ignore')
#self.train_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=self.core.conv_verbpos.output,on_unused_input='ignore')
#self.train_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=self.core.conv_wordpos.output,on_unused_input='ignore')
#self.train_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=self.core.conv_out,on_unused_input='ignore')
#self.train_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=self.core.max_out,on_unused_input='ignore')
#self.train_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=self.core.hidden_layer.output,on_unused_input='ignore')
#self.train_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=self.core.negative_log_likelihood,on_unused_input='ignore')
#self.train_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=self.cost,on_unused_input='ignore')
self.train_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=self.cost,updates=self.updates,on_unused_input='ignore')
self.valid_model = theano.function(inputs=[self.input,self.label,self.masks], outputs=[self.errors,self.core.sentce_loglikelihood.y_pred_pointwise],on_unused_input='ignore')
def __init__(self, config):
autoassign(locals())
self.updater = util.Adam(max_norm=config['max_norm'], lr=config['lr'])
self.Decode = Decoder(config['size_vocab'],
config['size_embed'], config['size'], config['depth'])
self.ToTxt = Dense(config['size'], config['size_vocab'])
self.inputs = [T.imatrix()]
self.target = T.imatrix()
def ndim_itensor(ndim, name=None):
if ndim == 2:
return T.imatrix(name)
elif ndim == 3:
return T.itensor3(name)
elif ndim == 4:
return T.itensor4(name)
return T.imatrix(name=name)
def defmodel(self):
pathidxs = T.imatrix("pathidxs") # integers of (batsize, seqlen)
zidxs = T.imatrix("zidxs") # integers of (batsize, seqlen)
occluder = T.imatrix("occluder")
scores = self.definnermodel(pathidxs) # predictions, floats of (batsize, seqlen, vocabsize)
# probs = T.nnet.softmax(scores) # row-wise softmax; probs: (batsize, seqlen, vocabsize) #softmax doesn't work on tensor3D
probs, _ = theano.scan(fn=T.nnet.softmax, sequences=scores, outputs_info=[None])
return probs, zidxs, occluder, [pathidxs, zidxs, occluder]
def create_rnn(hidden_dim, vocab_dim,mode="rnn"):
# input
x = tensor.imatrix('inchar')
y = tensor.imatrix('outchar')
#
W = LookupTable(
name = "W1",
#dim = hidden_dim*4,
dim = hidden_dim,
length = vocab_dim,
weights_init = initialization.IsotropicGaussian(0.01),
biases_init = initialization.Constant(0)
)
if mode == "lstm":
# Long Short Term Memory
H = LSTM(
hidden_dim,
name = 'H',
weights_init = initialization.IsotropicGaussian(0.01),
biases_init = initialization.Constant(0.0)
)
else:
# recurrent history weight
H = SimpleRecurrent(
name = "H",
dim = hidden_dim,
activation = Tanh(),
weights_init = initialization.IsotropicGaussian(0.01)
)
#
S = Linear(
name = "W2",
input_dim = hidden_dim,
output_dim = vocab_dim,
weights_init = initialization.IsotropicGaussian(0.01),
biases_init = initialization.Constant(0)
)
A = NDimensionalSoftmax(
name = "softmax"
)
initLayers([W,H,S])
activations = W.apply(x)
hiddens = H.apply(activations)#[0]
activations2 = S.apply(hiddens)
y_hat = A.apply(activations2, extra_ndim=1)
cost = A.categorical_cross_entropy(y, activations2, extra_ndim=1).mean()
cg = ComputationGraph(cost)
#print VariableFilter(roles=[WEIGHT])(cg.variables)
#W1,H,W2 = VariableFilter(roles=[WEIGHT])(cg.variables)
layers = (x, W, H, S, A, y)
return cg, layers, y_hat, cost
def build_model(options):
print('Build model...')
sys.stdout.flush()
weights = None
if options['flag_random_lookup_table'] == False: weights = options['embedding']
embed_layer = Embedding(input_dim = options['embedding'].shape[0],
output_dim = options['embedding'].shape[1],
weights = weights)
dense_layers = []
dense_layers.append(Dense(input_dim = options['embedding'].shape[1] * 2, output_dim = options['size_hidden_layer'], activation = 'tanh'))
dense_layers.append(Dense(input_dim = options['size_hidden_layer'], output_dim = 1, activation = 'sigmoid'))
# for training
sentence1 = T.imatrix('s1') # sentence1, n_samples * len_sentence
sentence1_mask = T.matrix('s1_mask')
sentence2 = T.imatrix('s2') # sentence2, n_samples * len_sentence
sentence2_mask = T.matrix('s2_mask')
y = T.ivector('y1') # n_samples
embed_s1 = embed_layer.get_output(sentence1) # n_samples * len_sentence * embed_dim
embed_s2 = embed_layer.get_output(sentence2) # n_samples * len_sentence * embed_dim
if options['sentence_modeling'] == 'CBoW':
embed_s1 = ave_embed(embed_s1,sentence1_mask) # n_samples * embed_dim
embed_s2 = ave_embed(embed_s2,sentence2_mask) # n_samples * embed_dim
elif options['sentence_modeling'] == 'CNN':
sentence_encode_layer = Convolution1D(input_dim = options['embedding'].shape[1], activation = 'tanh',
nb_filter = options['embedding'].shape[1], filter_length = options['CNN_filter_length'],
border_mode = 'same')
embed_s1 = CNN_embed(embed_s1,sentence1_mask,sentence_encode_layer) # n_samples * embed_dim
embed_s2 = CNN_embed(embed_s2,sentence2_mask,sentence_encode_layer) # n_samples * embed_dim
elif options['sentence_modeling'] == 'LSTM':
sentence_encode_layer = LSTM(input_dim = options['embedding'].shape[1], output_dim = options['embedding'].shape[1])
embed_s1 = LSTM_embed(embed_s1,sentence1_mask,sentence_encode_layer,options) # n_samples * embed_dim
embed_s2 = LSTM_embed(embed_s2,sentence2_mask,sentence_encode_layer,options) # n_samples * embed_dim
else:
print 'Error: No model called %s available!' % options['sentence_modeling']
return
output = T.concatenate([embed_s1,embed_s2],axis = -1) # n_samples * (embed_dim * 2)
if options['flag_dropout'] == True:
output = dropout(output, level=options['dropoutRates'])
for dense_layer in dense_layers:
output = dense_layer.get_output(output)
f_pred = theano.function([sentence1,sentence1_mask,sentence2,sentence2_mask],output, allow_input_downcast=True)
output = output.reshape((output.shape[0],))
#y = y.reshape((output.shape[0],1))
cost = T.nnet.binary_crossentropy(output, y).mean()
f_debug = theano.function([sentence1,sentence1_mask,sentence2,sentence2_mask,y],[output,y,T.nnet.binary_crossentropy(output, y),cost], allow_input_downcast=True)
tparams = []
tparams += embed_layer.params
if options['sentence_modeling'] != 'CBoW':
tparams += sentence_encode_layer.params
for dense_layer in dense_layers: tparams += dense_layer.params
return sentence1,sentence1_mask,sentence2,sentence2_mask,y,cost,f_pred,tparams,f_debug
def __init__(self, voca_size, hidden_size, ydim, num_layers=2, learning_rate=0.1):
self.hidden_size = hidden_size
self.n_out = ydim
self.learning_rate = learning_rate
self.num_layers = num_layers
self.layers = []
self.params = []
self.emb = WordEmbeder(voca_size, hidden_size)
self.params += self.emb.params
x = tensor.imatrix() #symbolic
mask = tensor.imatrix()
y = tensor.ivector()
state_below = self.emb.embed_it(x)
for _ in range(self.num_layers):
binet = BiLSTM(self.hidden_size, self.learning_rate)
self.layers += binet,
self.params += binet.params
state_below = binet.forward(state_below, mask)
self.U = theano.shared(name="biU", value=utils.init_norm(self.hidden_size, self.n_out), borrow=True)
self.by = theano.shared(name="by", value=np.zeros(self.n_out), borrow=True)
self.params += [self.U, self.by]
#mean pooling
hs = state_below
mp = (hs*mask[:,:,None]).sum(axis=0)
mp = mp / mask.sum(axis=0)[:,None]
#classifier
pred_p = tensor.nnet.softmax(tensor.dot(mp, self.U) + self.by)
pred_y = pred_p.argmax(axis=1)
#nll
off_set = 1e-8
cost = -tensor.log( pred_p[tensor.arange(mask.shape[1]), y] + off_set ).mean()
gparams = [tensor.grad(cost, param) for param in self.params]
updates = [(param, param - self.learning_rate*gparam) for param, gparam in zip(self.params, gparams)]
vinputs = tensor.imatrix("vinputs")#variable
vmask = tensor.imatrix("vmask")
vy = tensor.ivector("vy")
self._train = theano.function(
inputs=[vinputs, vmask, vy],
outputs=cost,
updates=updates,
givens={x:vinputs, mask:vmask, y:vy}
)
self._predict = theano.function(
inputs=[vinputs, vmask],
outputs=pred_y,
givens={x:vinputs, mask:vmask}
)
def add_b():
w = T.imatrix('w')
a = T.imatrix('a')
y = w + a.repeat(4, 0)
f = theano.function(inputs=[w, a], outputs=[y])
e = np.asarray([[2, 4], [2, 1], [3, 2], [4, 1]], dtype='int32')
b = np.asarray([[2, 1]], dtype='int32')
print f(e, b)
def build_batch(self):
x = TT.imatrix('x') # 2D int32
x_mask = TT.fmatrix('x_mask') # float32
y = TT.imatrix('y') # 2D int32
y_given_x = self.fprop_batch(x) # 3D, shape (seq_len, bs, n_out)
self.get_y_given_x = theano.function(inputs = [x], outputs = y_given_x)
y_given_x_ = y_given_x.reshape((y_given_x.shape[0]*y_given_x.shape[1], y_given_x.shape[2]))
y_ = y.reshape((y.shape[0]*y.shape[1], ))
nll = -TT.sum(
TT.log( y_given_x_[TT.arange(y_.shape[0]), y_] ) *
x_mask.reshape( (x_mask.shape[0]*x_mask.shape[1], ) )
) / x_mask.shape[1] # nll is the sum of nll divided by batch size
cost = nll
# l2 norm cost
if self.l2_weight is not None:
L2 = 0
for p in self.params_l2:
L2 += TT.sum(p ** 2)
cost += self.l2_weight * L2
print '[SimpleRNNLM] L2 norm used %g' % self.l2_weight
else:
print '[SimpleRNNLM] L2 norm not used'
lr = TT.scalar('lr')
print '[SimpleRNNLM] ... get grads ...'
grads = TT.grad(cost, self.params)
grad_norm = TT.sqrt(sum([TT.sum(g**2) for g in grads]))
if self.grad_clip is not None:
grads = clip_grad(grads, grad_norm, self.grad_clip)
grad_norm = TT.sqrt(sum([TT.sum(g**2) for g in grads]))
else:
print '[SimpleRNNLM] no grad_clip is used'
print '[SimpleRNNLM] ... got grads ...'
print '[SimpleRNNLM] algo = ', self.algo
if self.algo == 'SGD':
updates = SGD(self.params, grads, lr)
else:
sys.stderr.write('Not recognized training algorithm')
sys.exit(1)
print '[SimpleRNNLM] ...build training function...'
self.train_batch_fn = theano.function(inputs = [x, x_mask, y, lr], outputs = nll, updates = updates)
print '[SimpleRNNLM] ...build training function done...'
# valid_fn return nll
self.valid_batch_fn = theano.function(inputs = [x, x_mask, y], outputs = nll)
# detailed valid function return both nll and y_given_x
self.detailed_valid_batch_fn = theano.function(inputs = [x, x_mask, y], outputs = [nll, y_given_x])
print '[SimpleRNNLM] build train_fn and valid_fn done!'
return self.train_batch_fn, self.valid_batch_fn
def test_dot_infershape(self):
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.imatrix()
oIdx = tensor.imatrix()
self._compile_and_check(
[W, h, iIdx, b, oIdx], [sparse_block_dot(W, h, iIdx, b, oIdx)], self.gemv_data(), self.gemv_class
)
def test_outer_infershape(self):
o = tensor.ftensor4()
x = tensor.ftensor3()
y = tensor.ftensor3()
xIdx = tensor.imatrix()
yIdx = tensor.imatrix()
self._compile_and_check(
[o, x, y, xIdx, yIdx], [self.outer_op(o, x, y, xIdx, yIdx)], self.outer_data(), self.outer_class
)
def __init__(self, config):
autoassign(locals())
self.updater = util.Adam(max_norm=config['max_norm'], lr=config['lr'])
self.Decode = Decoder(config['size_vocab'],
config['size_embed'], config['size'], config['depth'],
activation=eval(config.get('activation','clipped_rectify')),
residual=config.get('residual', False))
self.ToTxt = Dense(config['size'], config['size_vocab'])
self.inputs = [T.imatrix()]
self.target = T.imatrix()
def __init__(self, dv, dh, dx, nc, alpha=1.0, init_scale=0.2, initial_embeddings=None, params_init=None,
update='adagrad', seed=None, drop_p=0.5, momentum=0.9):
self.dv = dv # vocabulary size
self.dh = dh # hidden node size
self.dx = dx # word embedding size
self.nc = nc # number of classes
self.alpha = alpha # regularization strength
self.drop_p = drop_p # probability of dropping an input with dropout
# adagrad parameters
self.epsilon = 0.00001
if initial_embeddings is None:
self.emb = theano.shared(name='embeddings',
value=init_scale * np.random.uniform(-1.0, 1.0,
(dv, dx)).astype(theano.config.floatX))
else:
self.emb = theano.shared(name='embeddings', value=initial_embeddings.astype(theano.config.floatX))
self.W_x_i = theano.shared(name='W_x_i', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, dh))
.astype(theano.config.floatX))
self.W_hl_i = theano.shared(name='W_hl_i', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, dh))
.astype(theano.config.floatX))
self.W_hr_i = theano.shared(name='W_hr_i', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, dh))
.astype(theano.config.floatX))
self.b_h_i = theano.shared(name='b_h_i', value=np.array(np.zeros(dh),
dtype=theano.config.floatX))
self.W_x_f = theano.shared(name='W_x_f', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, dh))
.astype(theano.config.floatX))
self.b_h_f = theano.shared(name='b_h_f', value=np.array(np.random.uniform(0.0, 1.0, dh),
dtype=theano.config.floatX))
self.W_hl_fl = theano.shared(name='W_hl_fl', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, dh))
.astype(theano.config.floatX))
self.W_hr_fl = theano.shared(name='W_hr_fl', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, dh))
.astype(theano.config.floatX))
self.W_hl_fr = theano.shared(name='W_hl_fr', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, dh))
.astype(theano.config.floatX))
self.W_hr_fr = theano.shared(name='W_hr_fr', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, dh))
.astype(theano.config.floatX))
self.W_x_o = theano.shared(name='W_x_o', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, dh))
.astype(theano.config.floatX))
self.W_hl_o = theano.shared(name='W_hl_o', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, dh))
.astype(theano.config.floatX))
self.W_hr_o = theano.shared(name='W_hr_o', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, dh))
.astype(theano.config.floatX))
self.b_h_o = theano.shared(name='b_h_o', value=np.array(np.zeros(dh),
dtype=theano.config.floatX))
self.W_x_u = theano.shared(name='W_x_u', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, dh))
.astype(theano.config.floatX))
self.W_hl_u = theano.shared(name='W_hl_u', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, dh))
.astype(theano.config.floatX))
self.W_hr_u = theano.shared(name='W_hr_u', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, dh))
.astype(theano.config.floatX))
self.b_h_u = theano.shared(name='b_h_u', value=np.array(np.zeros(dh),
dtype=theano.config.floatX))
self.W_z = theano.shared(name='W_z', value=init_scale * np.random.uniform(-1.0, 1.0, (dh, nc))
.astype(theano.config.floatX))
self.b_z = theano.shared(name='b_z', value=np.array(np.zeros(nc),
dtype=theano.config.floatX))
self.params = [self.W_x_i, self.W_hl_i, self.W_hr_i, self.b_h_i]
self.params += [self.W_x_f, self.W_hl_fl, self.W_hr_fl, self.b_h_f]
self.params += [self.W_hl_fr, self.W_hr_fr]
self.params += [self.W_x_o, self.W_hl_o, self.W_hr_o, self.b_h_o]
self.params += [self.W_x_u, self.W_hl_u, self.W_hr_u, self.b_h_u]
self.params += [self.W_z, self.b_z]
self.param_shapes = [(dx, dh), (dh, dh), (dh, dh), (dh,),
(dx, dh), (dh, dh), (dh, dh), (dh,),
(dh, dh), (dh, dh),
(dx, dh), (dh, dh), (dh, dh), (dh,),
(dx, dh), (dh, dh), (dh, dh), (dh,),
(dh, nc), (nc,)]
if update == 'adagrad':
self.grad_histories = [
theano.shared(
value=np.zeros(param_shape, dtype=theano.config.floatX),
borrow=True,
name="grad_hist:" + param.name
)
for param_shape, param in zip(self.param_shapes, self.params)
]
elif update == 'sgdm':
self.velocity = [
theano.shared(
value=np.zeros(param_shape, dtype=theano.config.floatX),
borrow=True,
name="momentum:" + param.name
)
for param_shape, param in zip(self.param_shapes, self.params)
]
self.momentum = momentum
self.theano_rng = RandomStreams(seed)
idxs = T.ivector()
sequence_length = T.shape(idxs)[0]
temp = self.emb[idxs]
x = temp.reshape([sequence_length, dx])
#counter = T.ivector('counter')
left_mask = T.imatrix('left_mask')
right_mask = T.imatrix('right_mask')
y = T.iscalar('y')
lr = T.scalar('lr', dtype=theano.config.floatX)
is_train = T.iscalar('is_train')
drop_x = T.iscalar('drop_x')
# This is a bit annoying; the 0th dimension of x needs to be sequence, so we can iterate over it
# but the 0th dimension of the hidden nodes needs to be hidden-node dimension, so that we can broadcast
# the mask out to it
def treefwd(x_t, left_mask_t, right_mask_t, counter_t, h_tm1, c_tm1):
h_t = h_tm1
c_t = c_tm1
# zero out the input unless this is a leaf node
input = T.switch(T.eq(T.sum(left_mask_t) + T.sum(right_mask_t), 0), x_t, x_t*0)
i_t = T.nnet.sigmoid(T.dot(input, self.W_x_i) + T.sum(T.dot(self.W_hl_i.T, (left_mask_t * h_tm1)).T, axis=0) + T.sum(T.dot(self.W_hr_i.T, (right_mask_t * h_tm1)).T, axis=0) + self.b_h_i)
fl_t = T.nnet.sigmoid(T.dot(input, self.W_x_f) + T.sum(T.dot(self.W_hl_fl.T, (left_mask_t * h_tm1)).T, axis=0) + T.sum(T.dot(self.W_hr_fl.T, (right_mask_t * h_tm1)).T, axis=0) + self.b_h_f)
fr_t = T.nnet.sigmoid(T.dot(input, self.W_x_f) + T.sum(T.dot(self.W_hl_fr.T, (left_mask_t * h_tm1)).T, axis=0) + T.sum(T.dot(self.W_hr_fr.T, (right_mask_t * h_tm1)).T, axis=0) + self.b_h_f)
o_t = T.nnet.sigmoid(T.dot(input, self.W_x_o) + T.sum(T.dot(self.W_hl_o.T, (left_mask_t * h_tm1)).T, axis=0) + T.sum(T.dot(self.W_hr_o.T, (right_mask_t * h_tm1)).T, axis=0) + self.b_h_o)
u_t = T.tanh(T.dot(input, self.W_x_u) + T.sum(T.dot(self.W_hl_u.T, (left_mask_t * h_tm1)).T, axis=0) + T.sum(T.dot(self.W_hr_u.T, (right_mask_t * h_tm1)).T, axis=0) + self.b_h_u)
c_temp = i_t * u_t + fl_t * T.sum((left_mask_t * c_tm1).T, axis=0) + fr_t * T.sum((right_mask_t * c_tm1).T, axis=0)
h_temp = o_t * T.tanh(c_temp)
h_t = T.set_subtensor(h_t[:, counter_t], h_temp)
c_t = T.set_subtensor(c_t[:, counter_t], c_temp)
return h_t, c_t
def drop(drop_input, drop_p, is_train):
mask = self.theano_rng.binomial(p=1.0-drop_p, size=drop_input.shape, dtype=theano.config.floatX)
return T.cast(T.switch(T.neq(is_train, 0), drop_input * mask, drop_input * (1.0-self.drop_p)), dtype=theano.config.floatX)
ds, dx = T.shape(x)
# do dropout on x, if specified
x = T.switch(T.neq(drop_x, 0), drop(x, self.drop_p, is_train), x)
output, _ = theano.scan(fn=treefwd, sequences=[x, left_mask, right_mask, T.arange(0, ds)], outputs_info=[T.zeros((dh, ds), dtype=theano.config.floatX), T.zeros((dh, ds), dtype=theano.config.floatX)])
full_h, full_c = output
h = full_h[-1, :, -1]
h = drop(h, self.drop_p, is_train)
temp = T.dot(h, self.W_z) + self.b_z
p_y_given_x = T.nnet.softmax(temp)[0]
pred_y = T.argmax(p_y_given_x)
log_loss = T.sum(-T.log(p_y_given_x[y]))
penalty = T.sum([T.sum(p ** 2) for p in self.params])
cost = log_loss + alpha * penalty / 2.0
gradients = [T.grad(cost, param) for param in self.params]
if update == 'adagrad':
new_grad_histories = [
T.cast(g_hist + g ** 2, dtype=theano.config.floatX)
for g_hist, g in zip(self.grad_histories, gradients)
]
grad_hist_update = zip(self.grad_histories, new_grad_histories)
param_updates = [(param, T.cast(param - lr / (T.sqrt(g_hist) + self.epsilon) * param_grad, dtype=theano.config.floatX))
for param, param_grad, g_hist in zip(self.params, gradients, new_grad_histories)]
updates = grad_hist_update + param_updates
# sgd with momentum
elif update == 'sgdm':
velocity_t = [momentum * v + lr * g for v, g in zip(self.velocity, gradients)]
velocity_updates = [(v, T.cast(v_t, theano.config.floatX)) for v, v_t in zip(self.velocity, velocity_t)]
param_updates = [(param, T.cast(param - v_t, theano.config.floatX)) for param, v_t in zip(self.params, velocity_t)]
updates = velocity_updates + param_updates
# else, basic sgd
else:
updates = OrderedDict((p, T.cast(p - lr * g, dtype=theano.config.floatX)) for p, g in zip(self.params, gradients))
self.train = theano.function(inputs=[idxs, left_mask, right_mask, y, lr, is_train, drop_x],
outputs=[pred_y, p_y_given_x, log_loss, cost], updates=updates,
on_unused_input='ignore')
self.predict = theano.function(inputs=[idxs, left_mask, right_mask, is_train, drop_x],
outputs=[pred_y, p_y_given_x])
# good example of how to see a value in a tensor; way easier than theano.printing.Print()
idx = T.iscalar('idx')
emb = self.emb[idx]
self.get_embedding = theano.function(inputs=[idx], outputs=emb)
RECURR_SGDM_LR.set_value(RECURR_SGDM_LR.get_value() *
EPOCH_LR_COEFF)
ADAM_EPOCHS = 0
else:
for _ in xrange(max_epoch):
RESNET_ADAM_LR.set_value(RESNET_ADAM_LR.get_value() *
EPOCH_LR_COEFF)
RECURR_ADAM_LR.set_value(RECURR_ADAM_LR.get_value() *
EPOCH_LR_COEFF)
param_values_file = 'param_values_{}.pkl'.format(max_epoch)
logger.info('Building the network.')
im_features = lasagne.layers.get_output(resnet['pool5'])
im_features = T.flatten(im_features,
outdim=2) # batch size, number of features
cap_out_var = T.imatrix('cap_out') # batch size, seq len
cap_in_var = T.imatrix('cap_in') # batch size, seq len
mask_var = T.bmatrix('mask_var') # batch size, seq len
gate = lasagne.layers.Gate(W_in=lasagne.init.Orthogonal(),
W_hid=lasagne.init.Orthogonal(),
W_cell=lasagne.init.Normal(),
b=lasagne.init.Constant(0.0))
cell_gate = lasagne.layers.Gate(W_in=lasagne.init.Orthogonal(),
W_hid=lasagne.init.Orthogonal(),
W_cell=None,
b=lasagne.init.Constant(0.0),
nonlinearity=lasagne.nonlinearities.tanh)
forget_gate = lasagne.layers.Gate(W_in=lasagne.init.Orthogonal(),
W_hid=lasagne.init.Orthogonal(),
W_cell=lasagne.init.Normal(),
b=lasagne.init.Constant(5.0))
def __init__(self, We_initial, params):
self.eta = params.eta
We = theano.shared(We_initial)
embsize = We_initial.shape[1]
hidden = params.hidden
g = T.imatrix()
gmask = T.fmatrix()
y = T.ivector()
idxs = T.ivector()
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_emb_word = lasagne.layers.EmbeddingLayer(l_in_word, input_size= We_initial.shape[0] , output_size = embsize , W =We)
#l_emb_word = lasagne_embedding_layer_2(l_in_word, embsize , We)
if params.dropout:
l_emb_word = lasagne.layers.DropoutLayer(l_emb_word, p=0.5)
if (params.inf == 0):
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)
l_reshapef = lasagne.layers.ReshapeLayer(l_lstm_wordf,
(-1, hidden))
l_reshapeb = lasagne.layers.ReshapeLayer(l_lstm_wordb,
(-1, hidden))
concat2 = lasagne.layers.ConcatLayer([l_reshapef, l_reshapeb])
elif (params.inf == 1):
l_cnn_input = lasagne.layers.DimshuffleLayer(l_emb_word, (0, 2, 1))
l_cnn_1 = lasagne.layers.Conv1DLayer(l_cnn_input,
hidden,
1,
1,
pad='same')
l_cnn_3 = lasagne.layers.Conv1DLayer(l_cnn_input,
hidden,
3,
1,
pad='same')
l_cnn = lasagne.layers.ConcatLayer([l_cnn_1, l_cnn_3], axis=1)
#l_cnn = lasagne.layers.Conv1DLayer(l_cnn_input, hidden, 1, 1, pad = 'same')
concat2 = lasagne.layers.DimshuffleLayer(l_cnn, (0, 2, 1))
#concat2 = lasagne.layers.ConcatLayer([l_emb_word, concat2], axis =2)
concat2 = lasagne.layers.ReshapeLayer(concat2, (-1, 2 * hidden))
else:
l_cnn_input = lasagne.layers.DimshuffleLayer(l_emb_word, (0, 2, 1))
l_cnn = lasagne.layers.Conv1DLayer(l_cnn_input,
hidden,
3,
1,
pad='same')
concat2 = lasagne.layers.DimshuffleLayer(l_cnn, (0, 2, 1))
concat2 = lasagne.layers.ReshapeLayer(concat2, (-1, hidden))
concat2 = lasagne.layers.DenseLayer(concat2, num_units=hidden)
if params.dropout:
concat2 = lasagne.layers.DropoutLayer(concat2, p=0.5)
#l_emb = lasagne.layers.DenseLayer(concat2, num_units=hidden, nonlinearity=lasagne.nonlinearities.tanh)
l_out = lasagne.layers.DenseLayer(
concat2,
num_units=params.num_labels,
nonlinearity=lasagne.nonlinearities.softmax)
output = lasagne.layers.get_output(l_out, {
l_in_word: g,
l_mask_word: gmask
})
output_1 = output[idxs]
test_output = lasagne.layers.get_output(l_out, {
l_in_word: g,
l_mask_word: gmask
},
deterministic=True)
test_output_1 = test_output[idxs]
model_params = lasagne.layers.get_all_params(l_out, trainable=True)
self.model_p = lasagne.layers.get_all_params(l_out, trainable=True)
reg = sum(lasagne.regularization.l2(x) for x in model_params)
cost = lasagne.objectives.categorical_crossentropy(output_1, y)
cost = T.mean(cost) + params.L2 * reg
#pred = T.argmax(output_1, axis=1)
final_pred = T.argmax(test_output_1, axis=1)
y1 = T.ones_like(y)
SUM = T.sum(y1)
acc = 1.0 * T.sum(T.eq(final_pred, y)) / SUM
self.acc_function = theano.function([g, gmask, y, idxs],
[acc, final_pred],
on_unused_input='warn')
#updates = lasagne.updates.adam(cost, model_params, self.eta)
#from adam import adam
#updates = adam(cost, model_params, self.eta)
updates = lasagne.updates.sgd(cost, model_params, self.eta)
updates = lasagne.updates.apply_momentum(updates,
model_params,
momentum=0.9)
self.train_function = theano.function([g, gmask, y, idxs], [cost, acc],
updates=updates,
on_unused_input='warn')
data_dir = "/nikel/dhpark/fundus/kaggle/original/training/train_medium"
label_file = "/nikel/dhpark/fundus/kaggle/original/training/trainLabels.csv"
#mean_file = ""
#model = "models/softmax_regression"
#model = "models/double_softmax"
model = "models/512x512_model"
#model = "models/vgg_bn_pairwise"
dst_path = "/nikel/dhpark/fundus_saved_weights/vgg_pairwise"
#dst_path = "/nikel/dhpark/fundus_saved_weights/multi_task_loss_oversampled"
#dst_path = "/nikel/dhpark/fundus_saved_weights/hybrid_loss"
#dst_path = "/nikel/dhpark/fundus_saved_weights/vgg_bn_pairwise"
# Load the model
x = T.tensor4('x')
y = T.imatrix('y')
input_layer, output_layer = load_model(model).build_model(x)
# Get batchiterator
#First load the files and split to train and validation set
#Then create a iterator using these
files = data_util.get_image_files(data_dir)
names = data_util.get_names(files)
labels = data_util.get_labels(names, label_file=label_file).astype(np.int32)
print('{} files loaded'.format(len(files)))
paired_files, paired_labels, merged_labels = data_util.pair_up(files, labels)
sss = StratifiedShuffleSplit(merged_labels, n_iter=1, test_size=0.1, random_state=123)
train_idx, valid_idx = next(iter(sss))
def __init__(self,
name='gnic',
nimg=2048,
nh=512,
nw=512,
nout=8843,
model_file=None):
self.name = name
if model_file is not None:
with h5py.File(model_file, 'r') as f:
nimg = f.attrs['nimg']
nh = f.attrs['nh']
nw = f.attrs['nw']
nout = f.attrs['nout']
self.config = {'nimg': nimg, 'nh': nh, 'nw': nw, 'nout': nout}
# word embedding layer
self.embedding = Embedding(n_emb=nout,
dim_emb=nw,
name=self.name + '@embedding')
# initialization mlp layer
self.proj_mlp = MLP(layer_sizes=[nimg, 2 * nh],
output_type='tanh',
name=self.name + '@proj_mlp')
# lstm
self.lstm = BasicLSTM(dim_x=nw, dim_h=nh, name=self.name + '@lstm')
# prediction mlp
self.pred_mlp = MLP(layer_sizes=[nh + nw, nout],
output_type='softmax',
name=self.name + '@pred_mlp')
# inputs
cap = T.imatrix('cap')
img = T.matrix('img')
self.inputs = [cap, img]
# go through sequence
init_state = self.proj_mlp.compute(img)
(state, self.p,
loss), _ = theano.scan(fn=self.scan_func,
sequences=[cap[0:-1, :], cap[1:, :]],
outputs_info=[init_state, None, None])
# loss function
loss = T.mean(loss)
self.costs = [loss]
# layers and parameters
self.layers = [self.embedding, self.proj_mlp, self.lstm, self.pred_mlp]
self.params = sum([l.params for l in self.layers], [])
# load weights from file, if model_file is not None
if model_file is not None:
self.load_weights(model_file)
# these functions are used in test stage
self._init_func = None
self._step_func = None
def __init__(self, input_width, input_height, num_actions,
num_frames, discount, learning_rate, rho,
rms_epsilon, momentum, clip_delta, freeze_interval,
batch_size, network_type, update_rule, lambda_reg,
batch_accumulator, pretrained_net, rng, input_scale=255.0):
self.input_width = input_width
self.input_height = input_height
self.num_actions = num_actions
self.num_frames = num_frames
self.batch_size = batch_size
self.discount = discount
self.rho = rho
self.lr = learning_rate
self.rms_epsilon = rms_epsilon
self.momentum = momentum
self.clip_delta = clip_delta
self.freeze_interval = freeze_interval
self.rng = rng
self.lambda_reg = lambda_reg
lasagne.random.set_rng(self.rng)
self.update_counter = 0
self.l_in, self.l_act_in, self.l_out, self.pred_z, self.true_z = \
self.build_network(network_type, \
input_width, input_height, num_actions,\
num_frames, batch_size)
if self.freeze_interval > 0:
self.next_l_in, self.next_l_act_in, self.next_l_out, _d, _d = \
self.build_network(network_type, input_width, \
input_height, num_actions, num_frames, batch_size)
self.reset_q_hat()
states = T.tensor4('states')
next_states = T.tensor4('next_states')
rewards = T.col('rewards')
actions = T.imatrix('actions')
terminals = T.icol('terminals')
# Shared variables for training from a minibatch of replayed
# state transitions, each consisting of num_frames + 1 (due to
# overlap) images, along with the chosen action and resulting
# reward and terminal status.
self.imgs_shared = theano.shared(
np.zeros((batch_size, num_frames*2+1, input_height, input_width),
dtype=theano.config.floatX))
self.rewards_shared = theano.shared(
np.zeros((batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(
np.zeros((batch_size, num_frames), dtype='int32')
)
self.terminals_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
# Shared variable for a single state, to calculate q_vals.
self.state_shared = theano.shared(
np.zeros((num_frames*2, input_height, input_width),
dtype=theano.config.floatX))
q_vals, z_pred, z_true = lasagne.layers.get_output(
[self.l_out, self.pred_z, self.true_z],
inputs = {self.l_in: states / input_scale,
self.l_act_in: actions}
)
if self.freeze_interval > 0:
next_q_vals = lasagne.layers.get_output(
self.next_l_out,
{self.next_l_in: next_states / input_scale,
self.next_l_act_in: actions}
)
else:
next_q_vals = lasagne.layers.get_output(
self.l_out,
{self.l_in: next_states / input_scale,
self.l_act_in: actions}
)
next_q_vals = theano.gradient.disconnected_grad(next_q_vals)
terminalsX = terminals.astype(theano.config.floatX)
actionmask = T.eq(T.arange(num_actions).reshape((1, -1)),
actions[:, 0].reshape((-1, 1))).astype(theano.config.floatX)
target = (rewards +
(T.ones_like(terminalsX) - terminalsX) *
self.discount * T.max(next_q_vals, axis=1, keepdims=True))
output = (q_vals * actionmask).sum(axis=1).reshape((-1, 1))
diff = target - output
diff_reg = z_true - z_pred
if self.clip_delta > 0:
# If we simply take the squared clipped diff as our loss,
# then the gradient will be zero whenever the diff exceeds
# the clip bounds. To avoid this, we extend the loss
# linearly past the clip point to keep the gradient constant
# in that regime.
#
# This is equivalent to declaring d loss/d q_vals to be
# equal to the clipped diff, then backpropagating from
# there, which is what the DeepMind implementation does.
quadratic_part = T.minimum(abs(diff), self.clip_delta)
linear_part = abs(diff) - quadratic_part
loss = 0.5 * quadratic_part ** 2 + self.clip_delta * linear_part
else:
loss = 0.5 * diff ** 2
loss = loss + 0.5 * self.lambda_reg * (diff_reg ** 2).sum(axis=1)
if batch_accumulator == 'sum':
loss = T.sum(loss)
elif batch_accumulator == 'mean':
loss = T.mean(loss)
else:
raise ValueError("Bad accumulator: {}".format(batch_accumulator))
params = lasagne.layers.helper.get_all_params([self.l_out, self.pred_z, self.true_z])
train_givens = {
states: self.imgs_shared[:, :-1],
next_states: self.imgs_shared[:, 1:],
rewards: self.rewards_shared,
actions: self.actions_shared,
terminals: self.terminals_shared
}
if update_rule == 'deepmind_rmsprop':
updates = deepmind_rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'rmsprop':
updates = lasagne.updates.rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'sgd':
updates = lasagne.updates.sgd(loss, params, self.lr)
else:
raise ValueError("Unrecognized update: {}".format(update_rule))
if self.momentum > 0:
updates = lasagne.updates.apply_momentum(updates, None,
self.momentum)
self._train = theano.function([], [loss], updates=updates,
givens=train_givens)
q_givens = {
states: self.state_shared.reshape((1,
self.num_frames*2,
self.input_height,
self.input_width))
}
self._q_vals = theano.function([], q_vals[0], givens=q_givens)
config = importlib.import_module('configurations.%s' %
metadata['configuration'])
# samples dir
if not os.path.isdir('samples'):
os.makedirs('samples')
target_path = "samples/%s-s%d-%.2f-%s.txt" % (
metadata['experiment_id'], rng_seed, temperature,
time.strftime("%Y%m%d-%H%M%S", time.localtime()))
token2idx = metadata['token2idx']
idx2token = dict((v, k) for k, v in token2idx.iteritems())
vocab_size = len(token2idx)
print('Building the model')
x = T.imatrix('x')
l_inp = InputLayer((1, None), input_var=x)
W_emb = np.eye(
vocab_size,
dtype='float32') if config.one_hot else lasagne.init.Orthogonal()
emb_output_size = vocab_size if config.one_hot else config.embedding_size
l_emb = EmbeddingLayer(l_inp,
input_size=vocab_size,
output_size=emb_output_size,
W=W_emb)
main_layers = []
for _ in xrange(config.num_layers):
if not main_layers:
def ready(self):
args = self.args
w_emb_layer = self.w_emb_layer
c_emb_layer = self.c_emb_layer
r_emb_layers = self.r_emb_layers
r_matrix_layers = self.r_matrix_layers
char_dim = self.char_dim = args.char_dim
char_lstm_dim = self.char_lstm_dim = args.char_lstm_dim
word_dim = self.word_dim = args.word_dim
word_lstm_dim = self.word_lstm_dim = args.word_lstm_dim
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX))
word_ids = self.word_ids = T.ivector('word_ids')
char_ids = self.char_ids = T.imatrix('char_ids')
char_lens = self.char_lens = T.fvector('char_lens')
char_masks = self.char_masks = T.imatrix('char_masks')
up_ids = self.up_ids = T.imatrix('up_ids')
up_rels = self.up_rels = T.imatrix('up_rels')
up_id_masks = self.up_id_masks = T.imatrix('up_id_masks')
down_ids = self.down_ids = T.imatrix('down_ids')
down_rels = self.down_rels = T.imatrix('down_rels')
down_id_masks = self.down_id_masks = T.imatrix('down_id_masks')
tag_ids = self.tag_ids = T.ivector('tag_ids')
layers = self.layers = [w_emb_layer, c_emb_layer]
layers.extend(r_emb_layers)
layers.extend(r_matrix_layers)
inputs = self.inputs = []
inputs.append(self.word_ids)
inputs.append(self.char_ids)
inputs.append(self.char_lens)
inputs.append(self.char_masks)
inputs.append(self.up_ids)
inputs.append(self.up_rels)
inputs.append(self.up_id_masks)
inputs.append(self.down_ids)
inputs.append(self.down_rels)
inputs.append(self.down_id_masks)
inputs.append(self.tag_ids)
wslices = w_emb_layer.forward(word_ids)
cslices = c_emb_layer.forward(char_ids.ravel())
cslices = cslices.reshape(
(char_ids.shape[0], char_ids.shape[1], char_dim))
cslices = cslices.dimshuffle(1, 0, 2)
bv_ur_slicess = []
bv_dr_slicess = []
b_ur_slicess = []
b_dr_slicess = []
bv_ur_matrixss = []
bv_dr_matrixss = []
b_ur_matrixss = []
b_dr_matrixss = []
for r_matrix_layer in r_matrix_layers:
bv_ur_matrixs = r_matrix_layer.forward1(up_rels.ravel())
bv_dr_matrixs = r_matrix_layer.forward1(down_rels.ravel())
b_ur_matrixs = r_matrix_layer.forward2(up_rels.ravel())
b_dr_matrixs = r_matrix_layer.forward2(down_rels.ravel())
bv_ur_matrixss.append(
bv_ur_matrixs.reshape(
(up_rels.shape[0], up_rels.shape[1], word_dim, word_dim)))
bv_dr_matrixss.append(
bv_dr_matrixs.reshape((down_rels.shape[0], down_rels.shape[1],
word_dim, word_dim)))
b_ur_matrixss.append(
b_ur_matrixs.reshape(
(up_rels.shape[0], up_rels.shape[1], word_dim, word_dim)))
b_dr_matrixss.append(
b_dr_matrixs.reshape((down_rels.shape[0], down_rels.shape[1],
word_dim, word_dim)))
for r_emb_layer in r_emb_layers:
bv_ur_slices = r_emb_layer.forward(up_rels.ravel())
bv_dr_slices = r_emb_layer.forward(down_rels.ravel())
b_ur_slices = r_emb_layer.forward2(up_rels.ravel())
b_dr_slices = r_emb_layer.forward2(down_rels.ravel())
bv_ur_slicess.append(
bv_ur_slices.reshape(
(up_rels.shape[0], up_rels.shape[1], word_dim)))
bv_dr_slicess.append(
bv_dr_slices.reshape(
(down_rels.shape[0], down_rels.shape[1], word_dim)))
b_ur_slicess.append(
b_ur_slices.reshape(
(up_rels.shape[0], up_rels.shape[1], word_dim)))
b_dr_slicess.append(
b_dr_slices.reshape(
(down_rels.shape[0], down_rels.shape[1], word_dim)))
char_masks = char_masks.dimshuffle(1, 0)
prev_output = wslices
prev_size = word_dim
if char_dim:
layers.append(
LSTM(n_in=char_dim,
n_out=char_lstm_dim,
direction='bi' if args.char_bidirect else 'si'))
prev_output_2 = cslices
prev_output_2 = apply_dropout(prev_output_2, dropout, v2=True)
prev_output_2 = layers[-1].forward_all(cslices, char_masks)
prev_output_2 = T.sum(prev_output_2, axis=0)
prev_output_2 = prev_output_2 / (1e-6 * T.ones_like(char_lens) +
char_lens).dimshuffle(0, 'x')
prev_size += char_lstm_dim
prev_output = T.concatenate([prev_output, prev_output_2], axis=1)
prev_output = apply_dropout(prev_output, dropout)
if args.conv != 0:
for ind in range(args.clayer):
layers.append(GraphCNNTensor(
n_in=prev_size,
n_out=prev_size,
))
residual = True
if ind == 0:
residual = False
prev_output = layers[-1].forward_all(prev_output,
up_ids,
up_id_masks,
bv_ur_slicess[ind],
bv_ur_matrixss[ind],
b_ur_slicess[ind],
b_ur_matrixss[ind],
down_ids,
down_id_masks,
bv_dr_slicess[ind],
bv_dr_matrixss[ind],
b_dr_slicess[ind],
b_dr_matrixss[ind],
residual=residual)
prev_output = apply_dropout(prev_output, dropout)
prev_size *= 3
layers.append(
LSTM(n_in=prev_size,
n_out=word_lstm_dim,
direction='bi' if args.word_bidirect else 'si'))
prev_output = prev_output.dimshuffle(0, 'x', 1)
prev_output = layers[-1].forward_all(prev_output)
prev_output = prev_output.reshape(
(prev_output.shape[0], prev_output.shape[-1]))
prev_size = word_lstm_dim
layers.append(
Layer(
n_in=prev_size,
n_out=args.classes,
activation=linear, #ReLU,
has_bias=False))
n_tags = args.classes
s_len = char_ids.shape[0]
tags_scores = layers[-1].forward(prev_output)
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)
real_path_score = tags_scores[T.arange(s_len), tag_ids].sum()
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)
pre_ids = T.arange(s_len + 1)
s_ids = T.arange(s_len + 1) + 1
real_path_score += transitions[padded_tags_ids[pre_ids],
padded_tags_ids[s_ids]].sum()
all_paths_scores = CRFForward(observations, transitions)
self.nll_loss = nll_loss = -(real_path_score - all_paths_scores)
preds = CRFForward(observations,
transitions,
viterbi=True,
return_alpha=False,
return_best_sequence=True)
self.pred = preds[1:-1]
self.l2_sqr = None
params = self.params = [transitions]
for layer in layers:
self.params += layer.params
for p in self.params:
if self.l2_sqr is None:
self.l2_sqr = args.l2_reg * T.sum(p**2)
else:
self.l2_sqr += args.l2_reg * T.sum(p**2)
#for l, i in zip(layers[3:], range(len(layers[3:]))):
for l, i in zip(
layers[2 + len(r_emb_layers) + len(r_matrix_layers):],
range(
len(layers[2 + len(r_emb_layers) +
len(r_matrix_layers):]))):
say("layer {}: n_in={}\tn_out={}\n".format(i, l.n_in, l.n_out))
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in self.params)
say("total # parameters: {}\n".format(nparams))
cost = self.nll_loss + self.l2_sqr
lr_method_name = args.learning
lr_method_parameters = {}
lr_method_parameters['lr'] = args.learning_rate
updates = Optimization(clip=5.0).get_updates(lr_method_name, cost,
params,
**lr_method_parameters)
f_train = theano.function(inputs=self.inputs,
outputs=[cost, nll_loss],
updates=updates,
allow_input_downcast=True)
f_eval = theano.function(inputs=self.inputs[:-1],
outputs=self.pred,
allow_input_downcast=True)
return f_train, f_eval
def build(self,
dropout,
char_dim,
char_lstm_dim,
char_bidirect,
word_dim,
word_lstm_dim,
word_bidirect,
lr_method,
pre_emb,
crf,
cap_dim,
training=True,
hard_training_labels=True,
crf_probs=False,
**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)
# Number of capitalization features
if cap_dim:
n_cap = 4
# Network variables
is_train = T.iscalar('is_train')
word_ids = T.ivector(name='word_ids')
char_for_ids = T.imatrix(name='char_for_ids')
char_rev_ids = T.imatrix(name='char_rev_ids')
char_pos_ids = T.ivector(name='char_pos_ids')
if hard_training_labels:
tag_ids = T.ivector(name='tag_ids')
else:
tag_dist = T.imatrix(name='tag_dist')
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]
# 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 from %s...' % pre_emb)
pretrained = {}
emb_invalid = 0
for i, line in enumerate(codecs.open(pre_emb, 'r', 'utf-8')):
if i > 100000:
break # we don't need all the embeddings
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)
c_found = 0
c_lower = 0
c_zeros = 0
# Lookup table initialization
for i in range(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.lower()) in pretrained:
new_weights[i] = pretrained[re.sub(
'\d', '0', word.lower())]
c_zeros += 1
word_layer.embeddings.set_value(new_weights)
print('Loaded %i pretrained embeddings.' % len(pretrained))
print(('%i / %i (%.4f%%) words have been initialized with '
'pretrained 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, '
'%i after lowercasing + zero.') %
(c_found, c_lower, c_zeros))
#
# Chars inputs
#
if char_dim:
input_dim += char_lstm_dim
char_layer = EmbeddingLayer(n_chars, char_dim, name='char_layer')
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_output = char_lstm_for.h.dimshuffle(
(1, 0, 2))[T.arange(s_len), char_pos_ids]
char_rev_output = char_lstm_rev.h.dimshuffle(
(1, 0, 2))[T.arange(s_len), char_pos_ids]
inputs.append(char_for_output)
if char_bidirect:
inputs.append(char_rev_output)
input_dim += char_lstm_dim
#
# Capitalization feature
#
if cap_dim:
input_dim += cap_dim
cap_layer = EmbeddingLayer(n_cap, cap_dim, name='cap_layer')
inputs.append(cap_layer.link(cap_ids))
# Prepare final input
inputs = T.concatenate(inputs,
axis=1) if len(inputs) != 1 else 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:
# Here we pass in hard labels as 'tag_ids'. We can also pass in a matrix in this place where each row is
# a categorical distribution to provide a soft label.
if hard_training_labels:
cost = T.nnet.categorical_crossentropy(tags_scores,
tag_ids).mean()
else:
cost = T.nnet.categorical_crossentropy(tags_scores,
tag_dist).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 -- uses tag_ids as hard labels here.
if hard_training_labels:
real_path_score = tags_scores[T.arange(s_len), tag_ids].sum()
else: # soft training labels (probabilities)
real_path_score = (tags_scores * tag_dist).sum()
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))
if hard_training_labels:
padded_tags_ids = T.concatenate([b_id, tag_ids, e_id], axis=0)
else:
padded_tags_ids = T.concatenate([b_id, tag_dist, 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 char_dim:
self.add_component(char_layer)
self.add_component(char_lstm_for)
params.extend(char_layer.params)
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 cap_dim:
self.add_component(cap_layer)
params.extend(cap_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 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)
if hard_training_labels:
train_inputs = eval_inputs + [tag_ids]
else:
train_inputs = eval_inputs + [tag_dist]
# 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=crf_probs,
return_best_sequence=not crf_probs),
givens=({
is_train: np.cast['int32'](0)
} if dropout else {}))
return f_train, f_eval
def build_model(options):
print('Build model...')
sys.stdout.flush()
weights = None
if options['flag_random_lookup_table'] == False:
weights = options['embedding']
embed_layer = Embedding(input_dim=options['embedding'].shape[0],
output_dim=options['embedding'].shape[1],
weights=weights)
dense_layers = []
dense_layers.append(
Dense(input_dim=options['embedding'].shape[1] * 2,
output_dim=options['size_hidden_layer'],
activation='tanh'))
dense_layers.append(
Dense(input_dim=options['size_hidden_layer'],
output_dim=1,
activation='sigmoid'))
# for training
sentence1 = T.imatrix('s1') # sentence1, n_samples * len_sentence
sentence1_mask = T.matrix('s1_mask')
sentence2 = T.imatrix('s2') # sentence2, n_samples * len_sentence
sentence2_mask = T.matrix('s2_mask')
y = T.ivector('y1') # n_samples
embed_s1 = embed_layer.get_output(
sentence1) # n_samples * len_sentence * embed_dim
embed_s2 = embed_layer.get_output(
sentence2) # n_samples * len_sentence * embed_dim
if options['sentence_modeling'] == 'CBoW':
embed_s1 = ave_embed(embed_s1, sentence1_mask) # n_samples * embed_dim
embed_s2 = ave_embed(embed_s2, sentence2_mask) # n_samples * embed_dim
elif options['sentence_modeling'] == 'CNN':
sentence_encode_layer = Convolution1D(
input_dim=options['embedding'].shape[1],
activation='tanh',
nb_filter=options['embedding'].shape[1],
filter_length=options['CNN_filter_length'],
border_mode='same')
embed_s1 = CNN_embed(embed_s1, sentence1_mask,
sentence_encode_layer) # n_samples * embed_dim
embed_s2 = CNN_embed(embed_s2, sentence2_mask,
sentence_encode_layer) # n_samples * embed_dim
elif options['sentence_modeling'] == 'LSTM':
sentence_encode_layer = LSTM(input_dim=options['embedding'].shape[1],
output_dim=options['embedding'].shape[1])
embed_s1 = LSTM_embed(embed_s1, sentence1_mask, sentence_encode_layer,
options) # n_samples * embed_dim
embed_s2 = LSTM_embed(embed_s2, sentence2_mask, sentence_encode_layer,
options) # n_samples * embed_dim
else:
print 'Error: No model called %s available!' % options[
'sentence_modeling']
return
output = T.concatenate([embed_s1, embed_s2],
axis=-1) # n_samples * (embed_dim * 2)
if options['flag_dropout'] == True:
output = dropout(output, level=options['dropoutRates'])
for dense_layer in dense_layers:
output = dense_layer.get_output(output)
f_pred = theano.function(
[sentence1, sentence1_mask, sentence2, sentence2_mask],
output,
allow_input_downcast=True)
output = output.reshape((output.shape[0], ))
#y = y.reshape((output.shape[0],1))
cost = T.nnet.binary_crossentropy(output, y).mean()
f_debug = theano.function(
[sentence1, sentence1_mask, sentence2, sentence2_mask, y],
[output, y, T.nnet.binary_crossentropy(output, y), cost],
allow_input_downcast=True)
tparams = []
tparams += embed_layer.params
if options['sentence_modeling'] != 'CBoW':
tparams += sentence_encode_layer.params
for dense_layer in dense_layers:
tparams += dense_layer.params
return sentence1, sentence1_mask, sentence2, sentence2_mask, y, cost, f_pred, tparams, f_debug
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
print('----got to T var---')
sequences = T.imatrix('sequences')
h0 = T.tensor3('h0')
reset = T.iscalar('reset')
mask = T.matrix('mask')
#sequences_lab = T.tensor3('sequences_lab')
sequences_lab = T.itensor3('sequences_lab')
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),
def run_epoch():
# define symbolic variables
q = T.imatrix('q')
q_mask = T.matrix('q_mask', dtype=theano.config.floatX)
l = T.imatrix('l')
l_mask = T.matrix('l_mask', dtype=theano.config.floatX)
a = T.imatrix('a')
a_mask = T.matrix('a_mask', dtype=theano.config.floatX)
y = T.ivector('y')
lr = T.scalar(name='lr')
np_emb = get_embedding_matrix_from_param_file(config.embedding_param_file)
# build model
print '...building model'
model = DMN(q, q_mask, l, l_mask, a, a_mask, y, np_emb,
options['word_size'], options['hidden_size'],
options['use_dropout'], options['drop_p'])
cost = model.loss
grads = T.grad(cost, wrt=list(model.params.values()))
optimizer = options['optimizer']
f_grad_shared, f_update = optimizer(lr, model.params, grads,
[q, q_mask, l, l_mask, a, a_mask, y],
cost)
detector = theano.function(inputs=[q, q_mask, l, l_mask, a, a_mask, y],
outputs=model.error,
on_unused_input='ignore')
p_predictor = theano.function(inputs=[q, q_mask, l, l_mask, a, a_mask],
outputs=model.p_d,
on_unused_input='ignore')
# load parameters from specified file
if not options['loaded_params'] is None:
print '...loading parameters from ' + options['loaded_params']
file_name = options['loaded_params']
with open(file_name, 'rb') as f:
param_dict = cPickle.load(f)
for k, v in model.params.items():
v.set_value(param_dict[k])
# test the performance of initialized parameters
print '...testing the performance of initialized parameters'
p_ds = []
ys = []
for q_, q_mask_, l_, l_mask_, a_, a_mask_, y_ in gkhmc_qla_iterator(
path=config.dataset,
batch_size=options['valid_batch_size'],
is_train=False):
p_d = p_predictor(q_, q_mask_, l_, l_mask_, a_, a_mask_)
p_ds.extend(p_d)
ys.extend(y_)
right_num, total_num, _ = pred_check(p_ds, ys)
print right_num, '/', total_num
best_perform = -np.inf
print '...training model'
for i in xrange(options['max_epochs']):
total_loss = 0.
idx = 0
for q_, q_mask_, l_, l_mask_, a_, a_mask_, y_ in gkhmc_qla_iterator(
path=config.dataset,
batch_size=options['batch_size'],
is_train=True):
model.emb_set_value_zero()
this_cost = f_grad_shared(q_, q_mask_, l_, l_mask_, a_, a_mask_,
y_)
f_update(options['lrate'])
total_loss += this_cost
print '\r', 'epoch:', i, ', idx:', idx, ', this_loss:', this_cost,
idx += 1
print ', total loss:', total_loss
# validate model performance when necessary
if (i + 1) % options['valid_freq'] == 0:
# test performance on train set
errors = []
for q_, q_mask_, l_, l_mask_, a_, a_mask_, y_ in gkhmc_qla_iterator(
path=config.dataset,
batch_size=options['valid_batch_size'],
is_train=True):
error = detector(q_, q_mask_, l_, l_mask_, a_, a_mask_, y_)
errors.append(error)
print '\ttrain error of epoch ' + str(i) + ': ' + str(
np.mean(errors) * 100) + '%'
# test performance on test set
p_ds = []
ys = []
for q_, q_mask_, l_, l_mask_, a_, a_mask_, y_ in gkhmc_qla_iterator(
path=config.dataset,
batch_size=options['valid_batch_size'],
is_train=False):
p_d = p_predictor(q_, q_mask_, l_, l_mask_, a_, a_mask_)
p_ds.extend(p_d)
ys.extend(y_)
right_num, total_num, _ = pred_check(p_ds, ys)
# judge whether it's necessary to save the parameters
save = False
if float(right_num) / float(total_num) > best_perform:
best_perform = float(right_num) / float(total_num)
save = True
print '\ttest performance of epoch', i, ':', right_num, '/', total_num, '\t', \
float(right_num * 10000 / total_num) / 100., '%', '\tbest through:', float(int(best_perform * 10000)) / 100.
# save parameters if need
if save:
print '\t...saving parameters'
file_name = options['param_path'] + model.name + '_hidden' + str(options['hidden_size']) + '_lrate' + \
str(options['lrate']) + '_batch' + str(options['batch_size']) + '_epoch' + str(i+1) + \
'_perform' + str(float(int(best_perform * 10000)) / 100.) + '.pickle'
with open(file_name, 'wb') as f:
new_dict = {}
for k, v in model.params.items():
new_dict[k] = v.get_value()
cPickle.dump(new_dict, f)
def build_network_from_ae(classn):
input_var = T.tensor4('input_var')
target_var = T.imatrix('targets')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
120,
filter_size=(4, 4),
stride=1,
nonlinearity=leaky_rectify))
layer = layers.MaxPool2DLayer(layer, pool_size=(3, 3), stride=2)
layer = batch_norm(
layers.Conv2DLayer(layer,
240,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
480,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
480,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
480,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
480,
filter_size=(3, 3),
stride=1,
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(20, 20),
stride=20,
mode='average_inc_pad')
network = layers.DenseLayer(layer, classn, nonlinearity=sigmoid)
return network, input_var, target_var
def __init__(self,
args,
sent_emb_dim,
flic_dim,
load_model=None,
epochs_done=0):
"""Initializes the model and constructs the Theano Computation Graph."""
self.args = args
self.sig_handler = GracefullExit()
self.best_val_error = sys.float_info.max
if self.args.sample_all_sentences:
print("We sample all items from the generator in each iteration.")
else:
print("We sample {} sets from the generator in each iteration.".
format(args.num_samples))
self.flic_dim = flic_dim
self.sent_emb_dim = sent_emb_dim
# TODO: Implement or remove
self.dropout_encoder = theano.shared(
np.float64(args.dropout_encoder).astype(theano.config.floatX))
self.dropout_generator = theano.shared(
np.float64(args.dropout_generator).astype(theano.config.floatX))
# Generator and Encoder Layers
self.generator = Generator(args, None, self.sent_emb_dim, flic_dim)
if self.args.context == 'train_context':
self.encoder = Encoder(args, None, self.sent_emb_dim)
else:
self.encoder = Encoder(args, None, flic_dim)
#---------------------------------------------------------------------------------------------------------------
# Construct computation graph
#---------------------------------------------------------------------------------------------------------------
print("Constructing computation graph.")
# (Input) Tensors
sent_embs_t = T.matrix('sent_embs', dtype=theano.config.floatX)
context_t = T.vector('context', dtype=theano.config.floatX)
sample_sentences_padded_t = T.tensor3('sample_sent_embeddings',
dtype=theano.config.floatX)
item_counts_t = T.ivector('item_counts')
y_t = T.scalar('y', dtype=theano.config.floatX)
samples_t = T.imatrix('samples') # Sentence embedding
max_num_sents_t = T.iscalar('max_num_sents')
transformed_context_t = self.generator.transform_context(
context_t, normalize_embeddings=True)
transformed_sent_embs_t = self.generator.transform_sent_embs(
sent_embs_t, normalize_embeddings=True)
if not self.args.sample_all_sentences:
# Construct L to sample from the DPP.
L_t = self.generator.get_L(transformed_sent_embs_t,
transformed_context_t)
self.get_L_t = theano.function(
inputs=[sent_embs_t, context_t],
outputs=L_t,
#mode='DebugMode',
#profile=True,
allow_input_downcast=True,
#mode=NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True),
on_unused_input='warn')
# The samples will be passed into the Deep Set Layer to calculate the final cost.
# Encoder cost & updates
padded_sents_t, sents_count_t = self.generator.get_padded_embeddings_from_samples_t(
samples_t, transformed_sent_embs_t, max_num_sents_t)
probs_t, costs_encoder_t, \
probs_mean_t, costs_encoder_mean_t, rand_updates = self.encoder.cost(padded_sents_t, sents_count_t,
transformed_context_t, y_t)
# Generator cost & updates
lprob_t, cost_enc_t, cost_generator_t = self.generator.cost(
L_t, samples_t, costs_encoder_t, sents_count_t)
# Sample all sentences
# TODO: Implement or remove
else:
probs_mean_t, costs_encoder_mean_t = self.encoder.cost_all_sentences(
transformed_sent_embs_t, transformed_context_t, y_t)
cost_generator_t = costs_encoder_mean_t
# Updates of the Generator and Encoder Parameters
updates_e, self.lr_e, gnorm_e, self.params_opt_e = create_optimization_updates(
cost=costs_encoder_mean_t,
params=self.encoder.get_params(),
method=self.args.learning,
lr=self.args.learning_rate_encoder)[:4]
updates_g, self.lr_g, gnorm_g, self.params_opt_g = create_optimization_updates(
cost=cost_generator_t,
params=self.generator.get_params(),
method=self.args.learning,
lr=self.args.learning_rate_generator)[:4]
if self.args.adaptive_lrs:
self.adaptive_learning_rate = adaptive_learning_rate(
lr_1=self.lr_e, lr_2=self.lr_g)
else:
self.adaptive_learning_rate = adaptive_learning_rate()
if not self.args.sample_all_sentences:
# Compile training graph
self.train_model_t = theano.function(
inputs=[
sent_embs_t, samples_t, max_num_sents_t, context_t, y_t
],
outputs=[probs_mean_t, costs_encoder_mean_t],
updates=updates_e.items() + updates_g.items() + rand_updates,
allow_input_downcast=True,
#mode=NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=False),
#mode='DebugMode',
on_unused_input='warn')
# Compile graph for validation data
self.validate_t = theano.function(
inputs=[
sent_embs_t, samples_t, max_num_sents_t, context_t, y_t
],
outputs=[probs_mean_t, costs_encoder_mean_t],
updates=rand_updates,
allow_input_downcast=True,
on_unused_input='warn')
else:
# Compile train graph
self.train_model_t = theano.function(
inputs=[sent_embs_t, context_t, y_t],
outputs=[probs_mean_t, costs_encoder_mean_t],
updates=updates_g.items() + updates_e.items(),
allow_input_downcast=True,
# mode=NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True),
on_unused_input='warn')
# Compile graph for validation data
self.validate_t = theano.function(
inputs=[sent_embs_t, context_t, y_t],
outputs=[probs_mean_t, costs_encoder_mean_t],
updates=[],
allow_input_downcast=True,
on_unused_input='warn')
# Load pretrained model
if load_model:
self.load(load_model)
elif self.args.load_model:
self.load(args.load_model)
self.epochs_done = epochs_done
#
#####################################
# Create the train and predict_labels function
n_in = 2*windowSize+1
n_hidden = numHiddenUnits
n_out = len(label2Idx)
number_of_epochs = 10
minibatch_size = 35
embedding_size = embeddings.shape[1]
dim_case = 6
x = T.imatrix('x') # the data, one word+context per row
y = T.ivector('y') # the labels are presented as 1D vector of [int] labels
print "Embeddings shape",embeddings.shape
words = Sequential()
words.add(Embedding(output_dim=embeddings.shape[1], input_dim=embeddings.shape[0], input_length=n_in, weights=[embeddings]))
words.layers[0].trainable_weights = [] #Fixed Embedding layer
words.add(Flatten())
casing = Sequential()
casing.add(Embedding(output_dim=caseMatrix.shape[1], input_dim=caseMatrix.shape[0], input_length=n_in, weights=[caseMatrix]))
casing.layers[0].trainable_weights = [] #Fixed Embedding layer
casing.add(Flatten())
def setupSetMacroBatchSubset(self):
if isinstance(self.tvsData_x, list):
data_block = [
T.tensor4('data_block_{}'.format(i))
for i in range(len(self.tvsData_x))
]
data_updates = [(dx, T.set_subtensor(dx[:db.shape[0]], db))
for (dx, db) in zip(self.tvsData_x, data_block)]
else:
data_block = T.tensor4('data_block')
data_updates = [
(self.tvsData_x,
T.set_subtensor(self.tvsData_x[:data_block.shape[0]],
data_block))
]
self.tfSetMacroBatchSubsetData = theano.function(inputs=[data_block],
updates=data_updates)
if self.cfgParams.use_labels:
y_block = T.ivector('y_block')
y_updates = [(self.tvsData_y,
T.set_subtensor(self.tvsData_y[:y_block.shape[0]],
y_block))]
self.tfSetMacroBatchSubsetY = theano.function(inputs=[y_block],
updates=y_updates)
if self.cfgParams.use_regtargets:
yr_block = T.vector('yr_block')
yr_updates = [(self.tvsData_yr,
T.set_subtensor(self.tvsData_yr[:yr_block.shape[0]],
yr_block))]
self.tfSetMacroBatchSubsetYR = theano.function(inputs=[yr_block],
updates=yr_updates)
if self.cfgParams.use_pairs:
pairIdx_block = T.imatrix('pairIdx_block')
pairLabels_block = T.ivector('pairLabels_block')
pair_updates = [
(self.tvsData_pairIdx,
T.set_subtensor(self.tvsData_pairIdx[:pairIdx_block.shape[0]],
pairIdx_block)),
(self.tvsData_pairLabels,
T.set_subtensor(
self.tvsData_pairLabels[:pairLabels_block.shape[0]],
pairLabels_block))
]
self.tfSetMacroBatchSubsetPairs = theano.function(
inputs=[pairIdx_block, pairLabels_block], updates=pair_updates)
if self.cfgParams.use_triplets:
tripletIdx_block = T.imatrix('tripletIdx_block')
triplets_updates = [
(self.tvsData_tripletIdx,
T.set_subtensor(
self.tvsData_tripletIdx[:tripletIdx_block.shape[0]],
tripletIdx_block))
]
self.tfSetMacroBatchSubsetTriplets = theano.function(
inputs=[tripletIdx_block], updates=triplets_updates)
if self.cfgParams.use_tripletPools:
tripletPoolIdx_block = T.imatrix('tripletPoolIdx_block')
tripletPools_updates = [
(self.tvsData_tripletPoolIdx,
T.set_subtensor(
self.tvsData_tripletPoolIdx[:tripletPoolIdx_block.
shape[0]],
tripletPoolIdx_block))
]
self.tfSetMacroBatchSubsetTripletPools = theano.function(
inputs=[tripletPoolIdx_block], updates=tripletPools_updates)
import theano import theano.tensor as T import numpy as np a = T.imatrix() b = T.imatrix() ok = T.horizontal_stack(a, b) myfunc = theano.function([a, b], ok) a_init = np.reshape(np.arange(10, dtype='int32'), (2, 5)) b_init = np.reshape(np.arange(10, 20, dtype='int32'), (2, 5)) ok = myfunc(a_init, b_init) print ok
which_set=args.train_dataset,
batch_size=args.batch_size)
valid_datastream = get_datastream(path=args.data_path,
which_set=args.valid_dataset,
batch_size=args.batch_size)
test_datastream = get_datastream(path=args.data_path,
which_set=args.test_dataset,
batch_size=args.batch_size)
#################
# build network #
#################
print('Building and compiling network')
input_data = T.ftensor3('input_data')
input_mask = T.fmatrix('input_mask')
target_data = T.imatrix('target_data')
target_mask = T.fmatrix('target_mask')
network_output = deep_prj_lstm_model_v1(input_var=input_data,
mask_var=input_mask,
num_inputs=input_dim,
num_outputs=output_dim,
num_layers=args.num_layers,
num_units=args.num_units,
num_prjs=args.num_prjs,
grad_clipping=args.grad_clipping,
dropout=args.dropout)
network = network_output
network_params = get_all_params(network, trainable=True)
network_reg_params = get_all_params(network,
trainable=True,
def __init__(self):
super(SimpleVLblNceTrainer, self).__init__()
self.h_indices = debug_print(T.imatrix('h'), 'h_indices')
self.w_indices = debug_print(T.ivector(name='w'), 'w_indices')
self.inputs = [self.h_indices, self.w_indices]
def build_network_from_ae(classn):
input_var = T.tensor4('input_var')
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var)
layer = batch_norm(
layers.Conv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
240,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Conv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
layer = layers.Pool2DLayer(layer,
pool_size=(2, 2),
stride=2,
mode='average_inc_pad')
layer = batch_norm(
layers.Conv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
prely = batch_norm(
layers.Conv2DLayer(layer,
1024,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
featm = batch_norm(
layers.Conv2DLayer(prely,
640,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
feat_map = batch_norm(
layers.Conv2DLayer(featm,
100,
filter_size=(1, 1),
nonlinearity=rectify,
name="feat_map"))
maskm = batch_norm(
layers.Conv2DLayer(prely,
100,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
mask_rep = batch_norm(layers.Conv2DLayer(maskm,
1,
filter_size=(1, 1),
nonlinearity=None),
beta=None,
gamma=None)
mask_map = SoftThresPerc(mask_rep,
perc=97.0,
alpha=0.1,
beta=init.Constant(0.5),
tight=100.0,
name="mask_map")
enlyr = ChInnerProdMerge(feat_map, mask_map, name="encoder")
layer = batch_norm(
layers.Deconv2DLayer(enlyr,
1024,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
640,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
320,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
240,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
120,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = batch_norm(
layers.Deconv2DLayer(layer,
100,
filter_size=(5, 5),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
layer = layers.Deconv2DLayer(layer,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
glblf = batch_norm(
layers.Conv2DLayer(prely,
128,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
glblf = layers.Pool2DLayer(glblf,
pool_size=(5, 5),
stride=5,
mode='average_inc_pad')
glblf = batch_norm(
layers.Conv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
pad='same',
nonlinearity=leaky_rectify))
gllyr = batch_norm(layers.Conv2DLayer(glblf,
5,
filter_size=(1, 1),
nonlinearity=rectify),
name="global_feature")
glblf = batch_norm(
layers.Deconv2DLayer(gllyr,
256,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(9, 9),
stride=5,
crop=(2, 2),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
128,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
64,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(4, 4),
stride=2,
crop=(1, 1),
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = batch_norm(
layers.Deconv2DLayer(glblf,
32,
filter_size=(3, 3),
stride=1,
crop='same',
nonlinearity=leaky_rectify))
glblf = layers.Deconv2DLayer(glblf,
3,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=identity)
layer = layers.ElemwiseSumLayer([layer, glblf])
network = ReshapeLayer(layer, ([0], -1))
layers.set_all_param_values(network,
pickle.load(open(filename_model_ae, 'rb')))
mask_map.beta.set_value(np.float32(0.8 * mask_map.beta.get_value()))
old_params = layers.get_all_params(network, trainable=True)
# Adding more layers
aug_var = T.matrix('aug_var')
target_var = T.imatrix('targets')
add_a = batch_norm(
layers.Conv2DLayer(enlyr,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_b = batch_norm(
layers.Conv2DLayer(add_a,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_c = batch_norm(
layers.Conv2DLayer(add_b,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_d = batch_norm(
layers.Conv2DLayer(add_c,
320,
filter_size=(1, 1),
nonlinearity=leaky_rectify))
add_0 = layers.Pool2DLayer(add_d,
pool_size=(25, 25),
stride=25,
mode='average_inc_pad')
add_1 = batch_norm(
layers.DenseLayer(add_0, 100, nonlinearity=leaky_rectify))
add_2 = batch_norm(
layers.DenseLayer(gllyr, 320, nonlinearity=leaky_rectify))
add_3 = batch_norm(
layers.DenseLayer(add_2, 320, nonlinearity=leaky_rectify))
add_4 = batch_norm(
layers.DenseLayer(add_3, 100, nonlinearity=leaky_rectify))
aug_layer = layers.InputLayer(shape=(None, aug_fea_n), input_var=aug_var)
cat_layer = lasagne.layers.ConcatLayer([add_1, add_4, aug_layer], axis=1)
hidden_layer = layers.DenseLayer(cat_layer, 80, nonlinearity=leaky_rectify)
network = layers.DenseLayer(hidden_layer, classn, nonlinearity=sigmoid)
all_params = layers.get_all_params(network, trainable=True)
new_params = [x for x in all_params if x not in old_params]
return network, new_params, input_var, aug_var, target_var
def __init__(self, num_hidden, num_classes, context_win_size, embeddings,
featdim=0, fine_tuning=False, truncate_gradient=-1):
"""
num_hidden :: dimension of the hidden layer
num_classes :: number of classes
context_win_size :: word window context size
embeddings :: matrix
"""
# hyper parameters of the model
self.hyperparams = {}
# nh :: dimension of the hidden layer
nh = num_hidden
self.hyperparams['nh'] = nh
# nc :: number of classes
nc = num_classes
self.hyperparams['nc'] = nc
# de :: dimension of the word embeddings
de = embeddings.shape[1]
self.hyperparams['de'] = de
# cs :: word window context size
cs = context_win_size
self.hyperparams['cs'] = cs
self.hyperparams['featdim'] = featdim
self.hyperparams['fine_tuning'] = fine_tuning
self.hyperparams['truncate_gradient'] = truncate_gradient
# parameters of the model
self.emb = theano.shared(embeddings.astype(theano.config.floatX))
# inputs
idxs = T.imatrix()
w = T.fscalar('w')
x = self.emb[idxs].reshape((idxs.shape[0], de * cs))*w
y = T.iscalar('y')
y_sentence = T.ivector('y_sentence')
f = T.matrix('f')
f.reshape((idxs.shape[0], featdim))
# forward parameters of the model
self.fWx = theano.shared(0.2 * np.random.uniform(-1.0, 1.0,
(de * cs, nh)).astype(theano.config.floatX))
self.fWh = theano.shared(0.2 * np.random.uniform(-1.0, 1.0,
(nh, nh)).astype(theano.config.floatX))
self.fbh = theano.shared(np.zeros(nh, dtype=theano.config.floatX))
self.fh0 = theano.shared(np.zeros(nh, dtype=theano.config.floatX))
fparams = [self.fWx, self.fWh, self.fbh, self.fh0]
fnames = ['fWx', 'fWh', 'fbh', 'fh0']
def frecurrence(x_t, h_tm1):
h_t = T.nnet.sigmoid(T.dot(x_t, self.fWx) + T.dot(h_tm1, self.fWh) + self.fbh)
return h_t
fh, _ = theano.scan(fn=frecurrence,
sequences=x,
outputs_info=[self.fh0],
n_steps=x.shape[0],
truncate_gradient=truncate_gradient)
# backwards parameters of the model
self.bWx = theano.shared(0.2 * np.random.uniform(-1.0, 1.0,
(de * cs, nh)).astype(theano.config.floatX))
self.bWh = theano.shared(0.2 * np.random.uniform(-1.0, 1.0,
(nh, nh)).astype(theano.config.floatX))
self.bbh = theano.shared(np.zeros(nh, dtype=theano.config.floatX))
self.bh0 = theano.shared(np.zeros(nh, dtype=theano.config.floatX))
bparams = [self.bWx, self.bWh, self.bbh, self.bh0]
bnames = ['bWx', 'bWh', 'bbh', 'bh0']
def brecurrence(x_t, h_tm1):
h_t = T.nnet.sigmoid(T.dot(x_t, self.bWx) + T.dot(h_tm1, self.bWh) + self.bbh)
return h_t
bh, _ = theano.scan(fn=brecurrence,
sequences=x,
outputs_info=[self.bh0],
n_steps=x.shape[0],
go_backwards=True,
truncate_gradient=truncate_gradient)
# inverting backwards hidden
bh = bh[::-1]
# concatenation parameters
self.bW = theano.shared(0.2 * np.random.uniform(-1.0, 1.0,
(nh+featdim, nc)).astype(theano.config.floatX))
self.fW = theano.shared(0.2 * np.random.uniform(-1.0, 1.0,
(nh+featdim, nc)).astype(theano.config.floatX))
self.b = theano.shared(np.zeros(nc, dtype=theano.config.floatX))
# adding features
if featdim > 0:
fh_final = T.concatenate([fh, f], axis=1)
bh_final = T.concatenate([bh, f], axis=1)
else:
fh_final = fh
bh_final = bh
# "concatenating" forward and backward hidden states
h = T.dot(bh_final, self.bW) + T.dot(fh_final, self.fW)
s = T.nnet.softmax(h + self.b)
p_y_given_x_lastword = s[-1, :]
p_y_given_x_sentence = s
self.params = fparams + bparams + [self.bW, self.fW, self.b]
self.names = fnames + bnames + ['bW', 'fW', 'b']
if fine_tuning:
self.params.append(self.emb)
self.names.append("emb")
# prediction
y_pred = T.argmax(p_y_given_x_sentence, axis=1)
# cost functions
sentence_nll = -T.mean(T.log(p_y_given_x_sentence)
[T.arange(x.shape[0]), y_sentence])
nll = -T.mean(T.log(p_y_given_x_lastword)[y])
# gradients
sentence_gradients = T.grad(sentence_nll, self.params)
gradients = T.grad(nll, self.params)
# learning rate
lr = T.scalar('lr')
# updates
sentence_updates = OrderedDict((p, p - lr * g)
for p, g in
zip(self.params, sentence_gradients))
updates = OrderedDict((p, p - lr * g)
for p, g in
zip(self.params, gradients))
# theano functions
self.classify = theano.function(inputs=[idxs, f, In(w, value=1.0)],
outputs=y_pred,
on_unused_input='ignore')
self.sentence_train = theano.function(inputs=[idxs, f, y_sentence, lr, In(w, value=1.0)],
outputs=sentence_nll,
updates=sentence_updates,
on_unused_input='ignore')
self.train = theano.function(inputs=[idxs, f, y, lr, In(w, value=1.0)],
outputs=nll,
updates=updates,
on_unused_input='ignore')
self.predict = theano.function(inputs=[idxs, f, In(w, value=1.0)],
outputs=p_y_given_x_sentence,
on_unused_input='ignore')
self.normalize = theano.function(inputs=[],
updates={self.emb:\
self.emb/T.sqrt((self.emb**2).sum(axis=1)).dimshuffle(0, 'x')})
def main():
parser = argparse.ArgumentParser(description='Tuning with bi-directional LSTM-CNN-CRF')
parser.add_argument('--fine_tune', action='store_true', help='Fine tune the word embeddings')
parser.add_argument('--embedding', choices=['word2vec', 'glove', 'senna', 'random'], help='Embedding for words',
required=True)
parser.add_argument('--embedding_dict', default=None, help='path for embedding dict')
parser.add_argument('--batch_size', type=int, default=10, help='Number of sentences in each batch')
parser.add_argument('--num_units', type=int, default=100, help='Number of hidden units in LSTM')
parser.add_argument('--num_filters', type=int, default=20, help='Number of filters in CNN')
parser.add_argument('--learning_rate', type=float, default=0.1, help='Learning rate')
parser.add_argument('--decay_rate', type=float, default=0.1, help='Decay rate of learning rate')
parser.add_argument('--grad_clipping', type=float, default=0, help='Gradient clipping')
parser.add_argument('--gamma', type=float, default=1e-6, help='weight for regularization')
parser.add_argument('--peepholes', action='store_true', help='Peepholes for LSTM')
parser.add_argument('--oov', choices=['random', 'embedding'], help='Embedding for oov word', required=True)
parser.add_argument('--update', choices=['sgd', 'momentum', 'nesterov', 'adadelta'], help='update algorithm',
default='sgd')
parser.add_argument('--regular', choices=['none', 'l2'], help='regularization for training', required=True)
parser.add_argument('--dropout', action='store_true', help='Apply dropout layers')
parser.add_argument('--patience', type=int, default=5, help='Patience for early stopping')
parser.add_argument('--output_prediction', action='store_true', help='Output predictions to temp files')
parser.add_argument('--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
parser.add_argument('--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
parser.add_argument('--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args = parser.parse_args()
def construct_input_layer():
if fine_tune:
layer_input = lasagne.layers.InputLayer(shape=(None, max_length), input_var=input_var, name='input')
layer_embedding = lasagne.layers.EmbeddingLayer(layer_input, input_size=alphabet_size,
output_size=embedd_dim,
W=embedd_table, name='embedding')
return layer_embedding
else:
layer_input = lasagne.layers.InputLayer(shape=(None, max_length, embedd_dim), input_var=input_var,
name='input')
return layer_input
def construct_char_input_layer():
layer_char_input = lasagne.layers.InputLayer(shape=(None, max_sent_length, max_char_length),
input_var=char_input_var, name='char-input')
layer_char_input = lasagne.layers.reshape(layer_char_input, (-1, [2]))
layer_char_embedding = lasagne.layers.EmbeddingLayer(layer_char_input, input_size=char_alphabet_size,
output_size=char_embedd_dim, W=char_embedd_table,
name='char_embedding')
layer_char_input = lasagne.layers.DimshuffleLayer(layer_char_embedding, pattern=(0, 2, 1))
return layer_char_input
logger = utils.get_logger("BiLSTM-CNN-CRF")
fine_tune = args.fine_tune
oov = args.oov
regular = args.regular
embedding = args.embedding
embedding_path = args.embedding_dict
train_path = args.train
dev_path = args.dev
test_path = args.test
update_algo = args.update
grad_clipping = args.grad_clipping
peepholes = args.peepholes
num_filters = args.num_filters
gamma = args.gamma
output_predict = args.output_prediction
dropout = args.dropout
X_train, Y_train, mask_train, X_dev, Y_dev, mask_dev, X_test, Y_test, mask_test, \
embedd_table, label_alphabet, \
C_train, C_dev, C_test, char_embedd_table = data_processor.load_dataset_sequence_labeling(train_path, dev_path,
test_path,word_column=0, label_column=1, oov=oov, fine_tune=fine_tune,embedding=embedding, embedding_path=embedding_path, use_character=True)
num_labels = label_alphabet.size() - 1
logger.info("constructing network...")
# create variables
target_var = T.imatrix(name='targets')
mask_var = T.matrix(name='masks', dtype=theano.config.floatX)
if fine_tune:
input_var = T.imatrix(name='inputs')
num_data, max_length = X_train.shape
alphabet_size, embedd_dim = embedd_table.shape
else:
input_var = T.tensor3(name='inputs', dtype=theano.config.floatX)
num_data, max_length, embedd_dim = X_train.shape
char_input_var = T.itensor3(name='char-inputs')
num_data_char, max_sent_length, max_char_length = C_train.shape
char_alphabet_size, char_embedd_dim = char_embedd_table.shape
assert (max_length == max_sent_length)
assert (num_data == num_data_char)
# construct input and mask layers
layer_incoming1 = construct_char_input_layer()
layer_incoming2 = construct_input_layer()
layer_mask = lasagne.layers.InputLayer(shape=(None, max_length), input_var=mask_var, name='mask')
# construct bi-rnn-cnn
num_units = args.num_units
bi_lstm_cnn_crf = build_BiLSTM_CNN_CRF(layer_incoming1, layer_incoming2, num_units, num_labels, mask=layer_mask,
grad_clipping=grad_clipping, peepholes=peepholes, num_filters=num_filters,
dropout=dropout)
logger.info("Network structure: hidden=%d, filter=%d" % (num_units, num_filters))
# compute loss
num_tokens = mask_var.sum(dtype=theano.config.floatX)
# get outpout of bi-lstm-cnn-crf shape [batch, length, num_labels, num_labels]
energies_train = lasagne.layers.get_output(bi_lstm_cnn_crf)
energies_eval = lasagne.layers.get_output(bi_lstm_cnn_crf, deterministic=True)
loss_train = crf_loss(energies_train, target_var, mask_var).mean()
loss_eval = crf_loss(energies_eval, target_var, mask_var).mean()
# l2 regularization?
if regular == 'l2':
l2_penalty = lasagne.regularization.regularize_network_params(bi_lstm_cnn_crf, lasagne.regularization.l2)
loss_train = loss_train + gamma * l2_penalty
_, corr_train = crf_accuracy(energies_train, target_var)
corr_train = (corr_train * mask_var).sum(dtype=theano.config.floatX)
prediction_eval, corr_eval = crf_accuracy(energies_eval, target_var)
corr_eval = (corr_eval * mask_var).sum(dtype=theano.config.floatX)
debug_out = crf_nbest_debug(energies_eval,target_var)
# crf_para = crf_parameter(energies_eval)
# Create update expressions for training.
# hyper parameters to tune: learning rate, momentum, regularization.
batch_size = args.batch_size
learning_rate = 1.0 if update_algo == 'adadelta' else args.learning_rate
decay_rate = args.decay_rate
momentum = 0.9
params = lasagne.layers.get_all_params(bi_lstm_cnn_crf, trainable=True)
updates = utils.create_updates(loss_train, params, update_algo, learning_rate, momentum=momentum)
# Compile a function performing a training step on a mini-batch
train_fn = theano.function([input_var, target_var, mask_var, char_input_var], [loss_train, corr_train, num_tokens],
updates=updates)
# Compile a second function evaluating the loss and accuracy of network
eval_fn = theano.function([input_var, target_var, mask_var, char_input_var],
[loss_eval, corr_eval, num_tokens, prediction_eval, energies_eval])
debug_fn = theano.function([input_var, target_var, mask_var, char_input_var], debug_out,on_unused_input='ignore')
# Finally, launch the training loop.
logger.info(
"Start training: %s with regularization: %s(%f), dropout: %s, fine tune: %s (#training data: %d, batch size: %d, clip: %.1f, peepholes: %s)..." \
% (
update_algo, regular, (0.0 if regular == 'none' else gamma), dropout, fine_tune, num_data, batch_size,
grad_clipping,
peepholes))
num_batches = num_data / batch_size
num_epochs = 3
best_loss = 1e+12
best_acc = 0.0
best_epoch_loss = 0
best_epoch_acc = 0
best_loss_test_err = 0.
best_loss_test_corr = 0.
best_acc_test_err = 0.
best_acc_test_corr = 0.
stop_count = 0
lr = learning_rate
patience = args.patience
for epoch in range(1, num_epochs + 1):
print 'Epoch %d (learning rate=%.4f, decay rate=%.4f): ' % (epoch, lr, decay_rate)
train_err = 0.0
train_corr = 0.0
train_total = 0
train_inst = 0
start_time = time.time()
num_back = 0
train_batches = 0
for batch in utils.iterate_minibatches(X_train, Y_train, masks=mask_train, char_inputs=C_train,
batch_size=batch_size, shuffle=True):
inputs, targets, masks, char_inputs = batch
err, corr, num = train_fn(inputs, targets, masks, char_inputs)
train_err += err * inputs.shape[0]
train_corr += corr
train_total += num
train_inst += inputs.shape[0]
train_batches += 1
time_ave = (time.time() - start_time) / train_batches
time_left = (num_batches - train_batches) * time_ave
# update log
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, acc: %.2f%%, time left (estimated): %.2fs' % (
min(train_batches * batch_size, num_data), num_data,
train_err / train_inst, train_corr * 100 / train_total, time_left)
sys.stdout.write(log_info)
num_back = len(log_info)
# update training log after each epoch
assert train_inst == num_data
sys.stdout.write("\b" * num_back)
print 'train: %d/%d loss: %.4f, acc: %.2f%%, time: %.2fs' % (
min(train_batches * batch_size, num_data), num_data,
train_err / num_data, train_corr * 100 / train_total, time.time() - start_time)
# evaluate performance on dev data
dev_err = 0.0
dev_corr = 0.0
dev_total = 0
dev_inst = 0
for batch in utils.iterate_minibatches(X_dev, Y_dev, masks=mask_dev, char_inputs=C_dev, batch_size=batch_size):
inputs, targets, masks, char_inputs = batch
err, corr, num, predictions, crf_para = eval_fn(inputs, targets, masks, char_inputs)
dev_err += err * inputs.shape[0]
dev_corr += corr
dev_total += num
dev_inst += inputs.shape[0]
if output_predict:
utils.output_predictions(predictions, targets, masks, 'tmp/dev%d' % epoch, label_alphabet,
is_flattened=False)
# debug_out = debug_fn(inputs, targets, masks, char_inputs)
# print "debug out:", debug_out[1]
crf_nbest.write_nbest(inputs, targets, masks, crf_para,label_alphabet,'tmp/dev_nbest%d' % epoch, 10, is_flattened=False)
print 'dev loss: %.4f, corr: %d, total: %d, acc: %.2f%%' % (
dev_err / dev_inst, dev_corr, dev_total, dev_corr * 100 / dev_total)
if best_loss < dev_err and best_acc > dev_corr / dev_total:
stop_count += 1
else:
update_loss = False
update_acc = False
stop_count = 0
if best_loss > dev_err:
update_loss = True
best_loss = dev_err
best_epoch_loss = epoch
if best_acc < dev_corr / dev_total:
update_acc = True
best_acc = dev_corr / dev_total
best_epoch_acc = epoch
# evaluate on test data when better performance detected
test_err = 0.0
test_corr = 0.0
test_total = 0
test_inst = 0
for batch in utils.iterate_minibatches(X_test, Y_test, masks=mask_test, char_inputs=C_test,
batch_size=batch_size):
inputs, targets, masks, char_inputs = batch
err, corr, num, predictions,crf_para = eval_fn(inputs, targets, masks, char_inputs)
test_err += err * inputs.shape[0]
test_corr += corr
test_total += num
test_inst += inputs.shape[0]
if output_predict:
utils.output_predictions(predictions, targets, masks, 'tmp/test%d' % epoch, label_alphabet,
is_flattened=False)
print 'test loss: %.4f, corr: %d, total: %d, acc: %.2f%%' % (
test_err / test_inst, test_corr, test_total, test_corr * 100 / test_total)
if update_loss:
best_loss_test_err = test_err
best_loss_test_corr = test_corr
if update_acc:
best_acc_test_err = test_err
best_acc_test_corr = test_corr
# stop if dev acc decrease 3 time straightly.
if stop_count == patience:
break
# re-compile a function with new learning rate for training
if update_algo != 'adadelta':
lr = learning_rate / (1.0 + epoch * decay_rate)
updates = utils.create_updates(loss_train, params, update_algo, lr, momentum=momentum)
train_fn = theano.function([input_var, target_var, mask_var, char_input_var],
[loss_train, corr_train, num_tokens],
updates=updates)
# print best performance on test data.
logger.info("final best loss test performance (at epoch %d)" % best_epoch_loss)
print 'test loss: %.4f, corr: %d, total: %d, acc: %.2f%%' % (
best_loss_test_err / test_inst, best_loss_test_corr, test_total, best_loss_test_corr * 100 / test_total)
logger.info("final best acc test performance (at epoch %d)" % best_epoch_acc)
print 'test loss: %.4f, corr: %d, total: %d, acc: %.2f%%' % (
best_acc_test_err / test_inst, best_acc_test_corr, test_total, best_acc_test_corr * 100 / test_total)
print "BiLSTM-CNN-CRF model finished!"
def __init__(self,
ne,
de,
na,
n_lstm,
n_out,
cs,
npos,
lr=0.05,
single_output=True,
output_activation=T.nnet.softmax,
cost_function='nll'):
'''
ne :: number of word embeddings in the vocabulary
de :: dimension of the word embeddings
na :: number of acoustic or language model features at each word step
(acoustic context size in frames * number of features)
n_lstm :: dimension of the lstm layer
n_out :: number of classes
cs :: word window context size
npos :: number of pos tags
'''
# add one to ne for PADDING
self.emb = init_weight((ne + 1, de), 'emb')
self.n_in = (de * cs) + (npos * cs)
self.n_lstm = n_lstm
self.n_out = n_out
self.W_xi = init_weight((self.n_in, self.n_lstm), 'W_xi')
self.W_hi = init_weight((self.n_lstm, self.n_lstm), 'W_hi', 'svd')
self.W_ci = init_weight((self.n_lstm, self.n_lstm), 'W_ci', 'svd')
# bias to the input:
self.b_i = shared(np.cast[dtype](np.random.uniform(-0.5,
.5,
size=n_lstm)))
# forget gate weights:
self.W_xf = init_weight((self.n_in, self.n_lstm), 'W_xf')
self.W_hf = init_weight((self.n_lstm, self.n_lstm), 'W_hf', 'svd')
self.W_cf = init_weight((self.n_lstm, self.n_lstm), 'W_cf', 'svd')
# bias
self.b_f = shared(np.cast[dtype](np.random.uniform(0, 1.,
size=n_lstm)))
# memory cell gate weights:
self.W_xc = init_weight((self.n_in, self.n_lstm), 'W_xc')
self.W_hc = init_weight((self.n_lstm, self.n_lstm), 'W_hc', 'svd')
# bias to the memory cell:
self.b_c = shared(np.zeros(n_lstm, dtype=dtype))
# output gate weights:
self.W_xo = init_weight((self.n_in, self.n_lstm), 'W_xo')
self.W_ho = init_weight((self.n_lstm, self.n_lstm), 'W_ho', 'svd')
self.W_co = init_weight((self.n_lstm, self.n_lstm), 'W_co', 'svd')
# bias on output gate:
self.b_o = shared(np.cast[dtype](np.random.uniform(-0.5,
.5,
size=n_lstm)))
# hidden to y matrix weights:
self.W_hy = init_weight((self.n_lstm, self.n_out), 'W_hy')
self.b_y = shared(np.zeros(n_out, dtype=dtype)) # output bias
# Weights for L1 and L2
self.L1_reg = 0.0
self.L2_reg = 0.00001
self.params = [
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_ho,
self.W_co, self.W_co, self.b_o, self.W_hy, self.b_y, self.emb
]
self.names = [
"W_xi", "W_hi", "W_ci", "b_i", "W_xf", "W_hf", "W_cf", "b_f",
"W_xc", "W_hc", "b_c", "W_ho", "W_co", "W_co", "b_o", "W_hy",
"b_y", "embeddings"
]
def step_lstm(x_t, h_tm1, c_tm1):
i_t = T.nnet.sigmoid(
T.dot(x_t, self.W_xi) + T.dot(h_tm1, self.W_hi) +
T.dot(c_tm1, self.W_ci) + self.b_i)
f_t = T.nnet.sigmoid(
T.dot(x_t, self.W_xf) + T.dot(h_tm1, self.W_hf) +
T.dot(c_tm1, self.W_cf) + self.b_f)
c_t = f_t * c_tm1 + i_t * T.tanh(
T.dot(x_t, self.W_xc) + T.dot(h_tm1, self.W_hc) + self.b_c)
o_t = T.nnet.sigmoid(
T.dot(x_t, self.W_xo) + T.dot(h_tm1, self.W_ho) +
T.dot(c_t, self.W_co) + self.b_o)
h_t = o_t * T.tanh(c_t)
y_t = T.nnet.softmax(T.dot(h_t, self.W_hy) + self.b_y)
return [h_t, c_t, y_t]
# batch of sequence of vectors
self.idxs = T.imatrix()
self.pos_idxs = T.imatrix()
# The eye function (diagonal 1s) for the POS, small in memory
self.pos = T.eye(npos, npos, 0)
# TODO No pos
# x = self.emb[self.idxs].reshape((self.idxs.shape[0], de*cs))
# POS version
x = T.concatenate((self.emb[self.idxs].reshape(
(self.idxs.shape[0], de * cs)), self.pos[self.pos_idxs].reshape(
(self.pos_idxs.shape[0], npos * cs))), 1)
self.y = T.iscalar('y')
# initial hidden state
self.h0 = shared(np.zeros(shape=self.n_lstm, dtype=dtype))
self.c0 = shared(np.zeros(shape=self.n_lstm, dtype=dtype))
self.lr = T.scalar('lr')
[h_vals, c_vals,
y_vals], _ = theano.scan(fn=step_lstm,
sequences=x,
outputs_info=[self.h0, self.c0, None],
n_steps=x.shape[0])
self.output = y_vals
p_y_given_x_lastword = self.output[-1, 0, :]
p_y_given_x_sentence = self.output[:, 0, :]
p_y_given_x_sentence_hidden = (h_vals, c_vals, self.output[:, 0, :])
y_pred = T.argmax(p_y_given_x_sentence, axis=1)
# y_pred_word = T.argmax(p_y_given_x_lastword)
self.cxe = T.mean(T.nnet.binary_crossentropy(self.output, self.y))
self.nll = -T.mean(T.log(p_y_given_x_lastword)[self.y])
self.mse = T.mean((self.output - self.y)**2)
self.sentence_nll = -T.mean(
T.log(p_y_given_x_sentence)[T.arange(x.shape[0]), self.y])
self.L2_sqr = sum([(p**2).sum() for p in self.params])
self.cost = self.nll + self.L2_reg * self.L2_sqr
if cost_function == 'mse':
self.cost = self.mse + self.L2_reg * self.L2_sqr
elif cost_function == 'cxe':
self.cost = self.cxe + self.L2_reg * self.L2_sqr
self.debug = theano.function(
inputs=[x, self.y],
outputs=[x.shape, self.y.shape, y_vals.shape, self.cost.shape])
gradients = T.grad(self.cost, self.params)
self.updates = OrderedDict(
(p, p - self.lr * g) for p, g in zip(self.params, gradients))
self.loss = theano.function(inputs=[x, self.y], outputs=self.cost)
# if na == 0: #assume no acoustic features for now
# simply outputs the soft_max distribution for each word in utterance
self.soft_max = theano.function(inputs=[self.idxs, self.pos_idxs],
outputs=p_y_given_x_sentence)
self.soft_max_return_hidden_layer = theano.function(
inputs=[self.idxs, self.pos_idxs],
outputs=p_y_given_x_sentence_hidden)
if na == 0:
self.train = theano.function(
inputs=[self.idxs, self.pos_idxs, self.y, self.lr],
outputs=self.cost,
updates=self.updates)
self.classify = theano.function(inputs=[self.idxs, self.pos_idxs],
outputs=y_pred)
else:
self.train = theano.function(inputs=[
self.idxs, self.pos_idxs, self.acoustic, self.y, self.lr
],
outputs=self.cost,
updates=self.updates)
self.classify = theano.function(
inputs=[self.idxs, self.pos_idxs, self.acoustic],
outputs=y_pred)
self.normalize = theano.function(
inputs=[],
updates={
self.emb:
self.emb / T.sqrt(
(self.emb**2).sum(axis=1)).dimshuffle(0, 'x')
})
def evaluate_lenet5(learning_rate=0.02,
n_epochs=4,
L2_weight=1e-5,
extra_size=4,
emb_size=300,
batch_size=20,
filter_size=[3, 3],
maxSentLen=40,
hidden_size=[300, 300],
max_term_len=4):
model_options = locals().copy()
print "model options", model_options
seed = 1234
np.random.seed(seed)
rng = np.random.RandomState(
seed) #random seed, control the model generates the same results
all_sentences_l, all_masks_l, all_sentences_r, all_masks_r, all_word1, all_word2, all_word1_mask, all_word2_mask, all_labels, word2id = load_wordnet_hyper_vs_all_with_words(
maxlen=maxSentLen, wordlen=max_term_len
) #minlen, include one label, at least one word in the sentence
# test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_labels, word2id =load_ACE05_dataset(maxSentLen, word2id)
test_sents_l, test_masks_l, test_sents_r, test_masks_r, test_word1, test_word2, test_word1_mask, test_word2_mask, test_labels, word2id = load_EVAlution_hyper_vs_all_with_words(
maxSentLen, word2id, wordlen=max_term_len)
total_size = len(all_sentences_l)
hold_test_size = 10000
train_size = total_size - hold_test_size
train_sents_l = np.asarray(all_sentences_l[:train_size], dtype='int32')
# dev_sents_l=np.asarray(all_sentences_l[1], dtype='int32')
# test_sents_l=np.asarray(all_sentences_l[-test_size:], dtype='int32')
test_sents_l = np.asarray(test_sents_l, dtype='int32')
train_masks_l = np.asarray(all_masks_l[:train_size],
dtype=theano.config.floatX)
# dev_masks_l=np.asarray(all_masks_l[1], dtype=theano.config.floatX)
# test_masks_l=np.asarray(all_masks_l[-test_size:], dtype=theano.config.floatX)
test_masks_l = np.asarray(test_masks_l, dtype=theano.config.floatX)
train_sents_r = np.asarray(all_sentences_r[:train_size], dtype='int32')
# dev_sents_r=np.asarray(all_sentences_r[1] , dtype='int32')
# test_sents_r=np.asarray(all_sentences_r[-test_size:], dtype='int32')
test_sents_r = np.asarray(test_sents_r, dtype='int32')
train_masks_r = np.asarray(all_masks_r[:train_size],
dtype=theano.config.floatX)
# dev_masks_r=np.asarray(all_masks_r[1], dtype=theano.config.floatX)
# test_masks_r=np.asarray(all_masks_r[-test_size:], dtype=theano.config.floatX)
test_masks_r = np.asarray(test_masks_r, dtype=theano.config.floatX)
train_word1 = np.asarray(all_word1[:train_size], dtype='int32')
train_word2 = np.asarray(all_word2[:train_size], dtype='int32')
test_word1 = np.asarray(test_word1, dtype='int32')
test_word2 = np.asarray(test_word2, dtype='int32')
train_word1_mask = np.asarray(all_word1_mask[:train_size],
dtype=theano.config.floatX)
train_word2_mask = np.asarray(all_word2_mask[:train_size],
dtype=theano.config.floatX)
test_word1_mask = np.asarray(test_word1_mask, dtype=theano.config.floatX)
test_word2_mask = np.asarray(test_word2_mask, dtype=theano.config.floatX)
train_labels_store = np.asarray(all_labels[:train_size], dtype='int32')
# dev_labels_store=np.asarray(all_labels[1], dtype='int32')
# test_labels_store=np.asarray(all_labels[-test_size:], dtype='int32')
test_labels_store = np.asarray(test_labels, dtype='int32')
# train_size=len(train_labels_store)
# dev_size=len(dev_labels_store)
test_size = len(test_labels_store)
print 'train size: ', train_size, ' test size: ', test_size
vocab_size = len(word2id) + 1
rand_values = rng.normal(
0.0, 0.01,
(vocab_size, emb_size)) #generate a matrix by Gaussian distribution
#here, we leave code for loading word2vec to initialize words
rand_values[0] = np.array(np.zeros(emb_size), dtype=theano.config.floatX)
id2word = {y: x for x, y in word2id.iteritems()}
word2vec = load_word2vec()
rand_values = load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings = theano.shared(
value=np.array(rand_values, dtype=theano.config.floatX), borrow=True
) #wrap up the python variable "rand_values" into theano variable
#now, start to build the input form of the model
sents_ids_l = T.imatrix()
sents_mask_l = T.fmatrix()
sents_ids_r = T.imatrix()
sents_mask_r = T.fmatrix()
word1_ids = T.imatrix()
word2_ids = T.imatrix()
word1_mask = T.fmatrix()
word2_mask = T.fmatrix()
labels = T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
def embed_input(emb_matrix, sent_ids):
return emb_matrix[sent_ids.flatten()].reshape(
(batch_size, maxSentLen, emb_size)).dimshuffle(0, 2, 1)
embed_input_l = embed_input(
init_embeddings, sents_ids_l
) #embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_r = embed_input(
init_embeddings, sents_ids_r
) #embeddings[sents_ids_r.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1)
embed_word1 = init_embeddings[word1_ids.flatten()].reshape(
(batch_size, max_term_len, emb_size))
embed_word2 = init_embeddings[word2_ids.flatten()].reshape(
(batch_size, max_term_len, emb_size))
word1_embedding = T.sum(embed_word1 * word1_mask.dimshuffle(0, 1, 'x'),
axis=1)
word2_embedding = T.sum(embed_word2 * word2_mask.dimshuffle(0, 1, 'x'),
axis=1)
'''create_AttentiveConv_params '''
conv_W, conv_b = create_conv_para(rng,
filter_shape=(hidden_size[1], 1,
emb_size, filter_size[0]))
conv_W_context, conv_b_context = create_conv_para(
rng, filter_shape=(hidden_size[1], 1, emb_size, 1))
NN_para = [conv_W, conv_b, conv_W_context]
'''
attentive convolution function
'''
attentive_conv_layer = Conv_for_Pair(
rng,
origin_input_tensor3=embed_input_l,
origin_input_tensor3_r=embed_input_r,
input_tensor3=embed_input_l,
input_tensor3_r=embed_input_r,
mask_matrix=sents_mask_l,
mask_matrix_r=sents_mask_r,
image_shape=(batch_size, 1, emb_size, maxSentLen),
image_shape_r=(batch_size, 1, emb_size, maxSentLen),
filter_shape=(hidden_size[1], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[1], 1, emb_size, 1),
W=conv_W,
b=conv_b,
W_context=conv_W_context,
b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
"form input to LR classifier"
LR_input = T.concatenate([
attentive_sent_embeddings_l, attentive_sent_embeddings_r,
attentive_sent_embeddings_l * attentive_sent_embeddings_r,
attentive_sent_embeddings_l - attentive_sent_embeddings_r,
word1_embedding, word2_embedding, word1_embedding * word2_embedding
],
axis=1)
LR_input_size = 4 * hidden_size[1] + 3 * emb_size
U_a = create_ensemble_para(
rng, 2, LR_input_size) # the weight matrix hidden_size*2
LR_b = theano.shared(value=np.zeros((2, ), dtype=theano.config.floatX),
name='LR_b',
borrow=True) #bias for each target class
LR_para = [U_a, LR_b]
layer_LR = LogisticRegression(
rng, input=LR_input, n_in=LR_input_size, n_out=2, W=U_a, b=LR_b
) #basically it is a multiplication between weight matrix and input feature vector
loss = layer_LR.negative_log_likelihood(
labels
) #for classification task, we usually used negative log likelihood as loss, the lower the better.
# L2_reg = (conv_W**2).sum()+(conv_W_context**2).sum()+(U_a**2).sum()
params = NN_para + LR_para #[init_embeddings]
cost = loss #+L2_weight*L2_reg
updates = Gradient_Cost_Para(cost, params, learning_rate)
train_model = theano.function([
sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, word1_ids,
word2_ids, word1_mask, word2_mask, labels
],
cost,
updates=updates,
allow_input_downcast=True,
on_unused_input='ignore')
test_model = theano.function([
sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, word1_ids,
word2_ids, word1_mask, word2_mask, labels
], [layer_LR.errors(labels), layer_LR.y_pred, layer_LR.prop_for_posi],
allow_input_downcast=True,
on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time = mid_time
epoch = 0
done_looping = False
n_train_batches = train_size / batch_size
train_batch_start = list(
np.arange(n_train_batches) * batch_size) + [train_size - batch_size]
# n_dev_batches=dev_size/batch_size
# dev_batch_start=list(np.arange(n_dev_batches)*batch_size)+[dev_size-batch_size]
n_test_batches = test_size / batch_size
n_test_remain = test_size % batch_size
if n_test_remain != 0:
test_batch_start = list(
np.arange(n_test_batches) * batch_size) + [test_size - batch_size]
else:
test_batch_start = list(np.arange(n_test_batches) * batch_size)
# max_acc_dev=0.0
max_ap_test = 0.0
max_ap_topk_test = 0.0
max_f1 = 0.0
cost_i = 0.0
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(
train_indices
) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
iter_accu = 0
for batch_id in train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * n_train_batches + iter_accu + 1
iter_accu += 1
train_id_batch = train_indices[batch_id:batch_id + batch_size]
cost_i += train_model(
train_sents_l[train_id_batch], train_masks_l[train_id_batch],
train_sents_r[train_id_batch], train_masks_r[train_id_batch],
train_word1[train_id_batch], train_word2[train_id_batch],
train_word1_mask[train_id_batch],
train_word2_mask[train_id_batch],
train_labels_store[train_id_batch])
#after each 1000 batches, we test the performance of the model on all test data
if iter % 100 == 0:
print 'Epoch ', epoch, 'iter ' + str(
iter) + ' average cost: ' + str(cost_i / iter), 'uses ', (
time.time() - past_time) / 60.0, 'min'
past_time = time.time()
pred_labels = []
probs = []
gold_labels = []
error_sum = 0.0
for idd, test_batch_id in enumerate(
test_batch_start): # for each test batch
error_i, pred_i, prob_i = test_model(
test_sents_l[test_batch_id:test_batch_id + batch_size],
test_masks_l[test_batch_id:test_batch_id + batch_size],
test_sents_r[test_batch_id:test_batch_id + batch_size],
test_masks_r[test_batch_id:test_batch_id + batch_size],
test_word1[test_batch_id:test_batch_id + batch_size],
test_word2[test_batch_id:test_batch_id + batch_size],
test_word1_mask[test_batch_id:test_batch_id +
batch_size],
test_word2_mask[test_batch_id:test_batch_id +
batch_size],
test_labels_store[test_batch_id:test_batch_id +
batch_size])
error_sum += error_i
pred_labels += list(pred_i)
probs += list(prob_i)
if n_test_remain != 0:
probs = probs[:(len(test_batch_start) - 1) *
batch_size] + probs[-n_test_remain:]
assert len(test_labels) == len(probs)
# test_acc=1.0-error_sum/(len(test_batch_start))
test_ap = apk(test_labels, probs, k=len(test_labels))
test_ap_top100 = apk(test_labels, probs, k=100)
if test_ap > max_ap_test:
max_ap_test = test_ap
if test_ap_top100 > max_ap_topk_test:
max_ap_topk_test = test_ap_top100
print '\t\tcurrent ap:', test_ap, ' ; ', '\t\tmax_ap: ', max_ap_test, 'ap@100: ', test_ap_top100, '\tmax_ap@100:', max_ap_topk_test
print 'Epoch ', epoch, 'uses ', (time.time() - mid_time) / 60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def _InitializeModelThatPredictsCharsMultiSoftmax(self,learning_rate, num_softmaxes=5):
image_input = T.tensor4('image_input')
print ("num_of_softmax: " + str(num_softmaxes))
#prediction_layer = self._BuildModelToPredictFirstChar(image_input)
prediction_layer = self._BuildModelToPredictCharsMultiSoftmax(
image_input, num_softmaxes=num_softmaxes)
target_chars_input = T.imatrix('target_chars_input')
target_chars = target_chars_input[:, :num_softmaxes].reshape(shape=(-1,))
# Create a loss expression for training, Using cross-entropy loss.
prediction = lasagne.layers.get_output(prediction_layer)
l_loss = lasagne.objectives.categorical_crossentropy(prediction, target_chars)
loss = l_loss.mean()
# Create update expressions for training, i.e., how to modify the
# parameters at each training step. Here, we'll use Stochastic Gradient
# Descent (SGD) with Nesterov momentum.
params = lasagne.layers.get_all_params(prediction_layer, trainable=True)
updates = lasagne.updates.nesterov_momentum(
loss, params, learning_rate, momentum=0.9)
#updates = lasagne.updates.adagrad(loss, params, learning_rate=0.0001)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(prediction_layer, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_chars)
test_loss = test_loss.mean()
predicted_chars = T.argmax(test_prediction, axis=1)
correctly_predicted_chars = T.eq(predicted_chars, target_chars)
# An expression for the classification accuracy:
test_acc = T.mean(correctly_predicted_chars,
dtype=theano.config.floatX)
predicted_chars = predicted_chars.reshape(shape=(-1, num_softmaxes))
correctly_predicted_chars = correctly_predicted_chars.reshape(shape=(-1, num_softmaxes))
num_chars_matched = T.sum(correctly_predicted_chars, axis=1, dtype=theano.config.floatX)
seq_test_acc = T.mean(T.eq(num_chars_matched, T.fill(num_chars_matched, num_softmaxes)),
dtype=theano.config.floatX)
test_prediction = test_prediction.reshape(shape=(-1, num_softmaxes, len(self.CHARS)))
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function(
[image_input, target_chars_input],
loss,
updates=updates,
allow_input_downcast=True)
# Compile a second function computing the prediction, validation loss and accuracy:
test_fn = theano.function([image_input, target_chars_input],
[test_loss, test_acc, seq_test_acc],
allow_input_downcast=True)
# Compile a third function computing the prediction.
inference_fn = theano.function([image_input],
[predicted_chars, test_prediction],
allow_input_downcast=True)
return prediction_layer, train_fn, test_fn, inference_fn
def _get_input_tensor_variables():
# x_w: 1D: batch, 2D: n_words, 3D: 5 + window; word id
# x_p: 1D: batch, 2D: n_words; posit id
# y: 1D: batch, 2D: n_words; label id
return T.itensor3('x_w'), T.imatrix('x_p'), T.imatrix('y')
def _InitializeModelThatPredictsAllChars(
self, learning_rate, bidirectional_rnn=False, use_mask_input=False,
lstm_layer_units=256, cnn_dense_layer_sizes = 256, lstm_grad_clipping=False):
image_input = T.tensor4('image_input')
num_rnn_steps = self.num_rnn_steps
target_chars_input = T.imatrix('target_chars')
target_chars = target_chars_input[:, :num_rnn_steps]
target_chars = target_chars.reshape(shape=(-1,))
mask_input_input = None
mask_input = None
if use_mask_input:
mask_input_input = T.imatrix('mask_input')
mask_input = mask_input_input[:, :num_rnn_steps]
#mask_input = mask_input.reshape(shape=(-1,))
prediction_layer, l_cnn, l_lstm = self._BuildModelToPredictAllChars(
image_input, num_rnn_steps=num_rnn_steps, mask_input=mask_input,
bidirectional_rnn=bidirectional_rnn, lstm_layer_units=lstm_layer_units,
cnn_dense_layer_sizes= cnn_dense_layer_sizes,
lstm_grad_clipping=lstm_grad_clipping)
#lstm_grad_clipping=False)
# Create a loss expression for training, Using cross-entropy loss.
#prediction = lasagne.layers.get_output(prediction_layer)
prediction, l_cnn, l_lstm = tuple(
lasagne.layers.get_output([prediction_layer, l_cnn, l_lstm]))
l_loss = lasagne.objectives.categorical_crossentropy(prediction, target_chars)
print ("prediction",prediction.shape,"target_char",target_chars.shape,"$$$$$$$$")
if use_mask_input:
l_loss = l_loss.reshape(shape=(-1, num_rnn_steps))
l_loss *= mask_input
loss = l_loss.sum() / mask_input.sum()
else:
loss = l_loss.mean()
# Create update expressions for training, i.e., how to modify the
# parameters at each training step. Here, we'll use Stochastic Gradient
# Descent (SGD) with Nesterov momentum.
params = lasagne.layers.get_all_params(prediction_layer, trainable=True)
#grads = theano.grad(loss, params)
if lstm_grad_clipping:
print('doing grad clipping')
max_grad_norm = 15.0
grads = theano.grad(loss, params)
grads = [grad.clip(-5., 5.) for grad in grads]
#grads, norm = lasagne.updates.total_norm_constraint(
# grads, max_grad_norm, return_norm=True)
grads = [lasagne.updates.norm_constraint(
grad, max_grad_norm, range(grad.ndim))
for grad in grads]
updates = lasagne.updates.adam(grads, params, learning_rate=learning_rate)
else:
updates = lasagne.updates.nesterov_momentum(
loss, params, learning_rate, momentum=0.9)
#updates = lasagne.updates.adagrad(loss, params, learning_rate=0.001)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(prediction_layer, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_chars)
test_loss = test_loss.mean()
predicted_chars = T.argmax(test_prediction, axis=1)
correctly_predicted_chars = T.eq(predicted_chars, target_chars)
# An expression for the classification accuracy:
test_acc = T.mean(correctly_predicted_chars,
dtype=theano.config.floatX)
predicted_chars = predicted_chars.reshape(shape=(-1, num_rnn_steps))
correctly_predicted_chars = correctly_predicted_chars.reshape(shape=(-1, num_rnn_steps))
num_chars_matched = T.sum(correctly_predicted_chars, axis=1, dtype=theano.config.floatX)
seq_test_acc = T.mean(T.eq(num_chars_matched, T.fill(num_chars_matched, num_rnn_steps)),
dtype=theano.config.floatX)
test_prediction = test_prediction.reshape(shape=(-1, num_rnn_steps, len(self.CHARS)))
mask_input_vec = [mask_input_input] if use_mask_input else []
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
print ("target chars",image_input)
train_fn = theano.function(
[image_input, target_chars_input] + mask_input_vec,
loss,
updates=updates,
allow_input_downcast=True)
# Compile a second function computing the prediction, validation loss and accuracy:
test_fn = theano.function([image_input, target_chars_input] + mask_input_vec,
[test_loss, test_acc, seq_test_acc,predicted_chars,target_chars,correctly_predicted_chars],
allow_input_downcast=True)
# Compile a third function computing the prediction.
inference_fn = theano.function([image_input] + mask_input_vec,
[predicted_chars, test_prediction],
allow_input_downcast=True)
return prediction_layer, train_fn, test_fn, inference_fn
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('stream1'))
print(config.items('lstm_classifier'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('stream1', 'data'))
has_encoder = config.getboolean('stream1', 'has_encoder')
stream1_dim = config.getint('stream1', 'input_dimensions')
imagesize = tuple([int(d) for d in config.get('stream1', 'imagesize').split(',')])
if has_encoder:
stream1 = config.get('stream1', 'model')
stream1_shape = config.get('stream1', 'shape')
stream1_nonlinearities = config.get('stream1', 'nonlinearities')
# lstm classifier
output_classes = config.getint('lstm_classifier', 'output_classes')
output_classnames = config.get('lstm_classifier', 'output_classnames').split(',')
lstm_size = config.getint('lstm_classifier', 'lstm_size')
matlab_target_offset = config.getboolean('lstm_classifier', 'matlab_target_offset')
# lstm classifier configurations
weight_init = options['weight_init'] if 'weight_init' in options else config.get('lstm_classifier', 'weight_init')
use_peepholes = options['use_peepholes'] if 'use_peepholes' in options else config.getboolean('lstm_classifier',
'use_peepholes')
use_blstm = True if config.has_option('lstm_classifier', 'use_blstm') else False
windowsize = config.getint('lstm_classifier', 'windowsize')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(options['num_epoch']) if 'num_epoch' in options else config.getint('training', 'num_epoch')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
epochsize = config.getint('training', 'epochsize')
batchsize = config.getint('training', 'batchsize')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
data_matrix = data['dataMatrix'].astype('float32')
targets_vec = data['targetsVec'].reshape((-1,))
subjects_vec = data['subjectsVec'].reshape((-1,))
vidlen_vec = data['videoLengthVec'].reshape((-1,))
iter_vec = data['iterVec'].reshape((-1,))
data_matrix = presplit_dataprocessing(data_matrix, vidlen_vec, config, 'stream1', imagesize=imagesize)
indexes = create_split_index(len(data_matrix), vidlen_vec, iter_vec)
train_vidlen_vec, test_vidlen_vec = split_videolen(vidlen_vec, iter_vec)
if matlab_target_offset:
targets_vec -= 1
# split the data
train_data = data_matrix[indexes == True]
train_targets = targets_vec[indexes == True]
train_targets = train_targets.reshape((len(train_targets),))
test_data = data_matrix[indexes == False]
test_targets = targets_vec[indexes == False]
test_targets = test_targets.reshape((len(test_targets),))
train_data, test_data = postsplit_datapreprocessing(train_data, test_data, config, 'stream1')
inputs = T.tensor3('inputs', dtype='float32')
window = T.iscalar('theta')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
if not has_encoder:
network = deltanet_v1.create_model((None, None, stream1_dim), inputs,
(None, None), mask, window,
lstm_size, output_classes, weight_init_fn, use_peepholes, use_blstm)
else:
ae1 = load_decoder(stream1, stream1_shape, stream1_nonlinearities)
network = deltanet_majority_vote.create_model(ae1, (None, None, stream1_dim), inputs,
(None, None), mask,
lstm_size, window, output_classes, weight_init_fn, use_peepholes)
print_network(network)
draw_to_file(las.layers.get_all_layers(network), 'network.png', verbose=True)
# exit()
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = las.updates.adam(cost, all_params, learning_rate)
train = theano.function(
[inputs, targets, mask, window],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask, window], cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[inputs, targets, mask, window], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs, mask, window], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_data, train_targets, train_vidlen_vec, batchsize=batchsize)
val_datagen = gen_lstm_batch_random(test_data, test_targets, test_vidlen_vec,
batchsize=len(test_vidlen_vec))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(epochsize):
X, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, epochsize, len(X), learning_rate)
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, windowsize)
print('\r', end='')
cost = compute_train_cost(X, y, m, windowsize)
val_cost = compute_test_cost(X_val, y_val, mask_val, windowsize)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(X_val, y_val_evaluate, mask_val, windowsize, val_fn)
class_rate.append(cr)
print("Epoch {} train cost = {}, validation cost = {}, "
"generalization loss = {:.3f}, GQ = {:.3f}, classification rate = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if val_cost < best_val:
best_val = val_cost
best_conf = val_conf
best_cr = cr
if epoch >= validation_window and early_stop2(val_window, best_val, validation_window):
break
print('Best Model')
print('classification rate: {}, validation loss: {}'.format(best_cr, best_val))
print('confusion matrix: ')
plot_confusion_matrix(best_conf, output_classnames, fmt='latex')
plot_validation_cost(cost_train, cost_val, class_rate)
def train_lbl(train_data, dev_data, test_data=[],
K=20, context_sz=2, learning_rate=1.0,
rate_update='simple', epochs=10,
batch_size=1, rng=None, patience=None,
patience_incr=2, improvement_thrs=0.995,
validation_freq=1000):
""" Train log-bilinear model """
# create vocabulary from train data, plus <s>, </s>
logger.info("Creating vocabulary dictionary...")
vocab = Dictionary.from_corpus(train_data, unk='<unk>')
logger.info("Creating tag dictionary...")
vocab_tags = Dictionary.from_corpus_tags(train_data, unk='<unk>')
vocab.add_word('<s>')
vocab.add_word('</s>')
V = vocab.size()
vocab_tags.add_word('<s>')
vocab_tags.add_word('</s>')
V_tag = vocab_tags.size()
#print train_data
# initialize random generator if not provided
rng = np.random.RandomState() if not rng else rng
logger.info("Making instances...")
# generate (context, target) pairs of word ids
train_set_x, train_set_y, train_set_tags = make_instances(train_data, vocab, vocab_tags, context_sz)
dev_set_x, dev_set_y, dev_set_tags = make_instances(dev_data, vocab, vocab_tags, context_sz)
test_set_x, test_set_y, test_set_tags = make_instances(test_data, vocab, vocab_tags, context_sz)
# make feature_matrix
# very sparse matrix...better way to do it?
feature_matrix = np.zeros((vocab_tags.size(),vocab_tags.num_sub_tags))
feature_matrix[(0,0)] = 1 # unk encoding
for tag,tag_id in vocab_tags:
if tag == "<s>":
feature_matrix[(tag_id,1)] = 1
elif tag == "</s>":
feature_matrix[(tag_id,2)] = 1
else:
for sub_tag in vocab_tags.map_tag_to_sub_tags[tag]:
val = vocab_tags.map_sub_to_ids[sub_tag]
feature_matrix[(tag_id,val)] = 1
feature_matrix[1,:] = np.zeros((vocab_tags.num_sub_tags))
# number of minibatches for training
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_dev_batches = dev_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size
# build the model
logger.info("Build the model ...")
index = T.lscalar()
x = T.imatrix('x')
y = T.ivector('y')
t = T.ivector('t') # the tag vector
# create log-bilinear model
lbl = LogBilinearLanguageModel(x, V, K, vocab_tags.num_sub_tags, feature_matrix, context_sz, rng)
# cost function is negative log likelihood of the training data
cost = lbl.negative_log_likelihood(y,t)
# compute the gradient
gparams = []
for param in lbl.params:
gparam = T.grad(cost, param)
gparams.append(gparam)
# specify how to update the parameter of the model
updates = []
for param_i,(param, gparam) in enumerate(zip(lbl.params, gparams)):
updates.append((param, param-learning_rate*gparam))
# function that computes log-probability of the dev set
logprob_dev = theano.function(inputs=[index], outputs=cost,
givens={x: dev_set_x[index*batch_size:
(index+1)*batch_size],
y: dev_set_y[index*batch_size:
(index+1)*batch_size],
t: dev_set_tags[index*batch_size:(index+1)*batch_size]
})
# function that computes log-probability of the test set
logprob_test = theano.function(inputs=[index], outputs=cost,
givens={x: test_set_x[index*batch_size:
(index+1)*batch_size],
y: test_set_y[index*batch_size:
(index+1)*batch_size],
t: test_set_tags[index*batch_size:(index+1)*batch_size]
})
# function that returns the cost and updates the parameter
train_model = theano.function(inputs=[index], outputs=cost,
updates=updates,
givens={x: train_set_x[index*batch_size:
(index+1)*batch_size],
y: train_set_y[index*batch_size:
(index+1)*batch_size],
t: train_set_tags[index*batch_size:(index+1)*batch_size]
})
# perplexity functions
def compute_dev_logp():
return np.mean([logprob_dev(i) for i in xrange(n_dev_batches)])
def compute_test_logp():
return np.mean([logprob_test(i) for i in xrange(n_test_batches)])
def ppl(neg_logp):
return np.power(2.0, neg_logp)
# train model
logger.info("training model...")
best_params = None
last_epoch_dev_ppl = np.inf
best_dev_ppl = np.inf
test_ppl = np.inf
test_core = 0
start_time = time.clock()
done_looping = False
for epoch in xrange(epochs):
if done_looping:
break
logger.info('epoch %i' % epoch)
for minibatch_index in xrange(n_train_batches):
itr = epoch * n_train_batches + minibatch_index
train_logp = train_model(minibatch_index)
logger.info('epoch %i, minibatch %i/%i, train minibatch log prob %.4f ppl %.4f' %
(epoch, minibatch_index+1, n_train_batches,
train_logp, ppl(train_logp)))
if (itr+1) % validation_freq == 0:
# compute perplexity on dev set, lower is better
dev_logp = compute_dev_logp()
dev_ppl = ppl(dev_logp)
logger.debug('epoch %i, minibatch %i/%i, dev log prob %.4f ppl %.4f' %
(epoch, minibatch_index+1, n_train_batches,
dev_logp, ppl(dev_logp)))
# if we got the lowest perplexity until now
if dev_ppl < best_dev_ppl:
# improve patience if loss improvement is good enough
if patience and dev_ppl < best_dev_ppl * improvement_thrs:
patience = max(patience, itr * patience_incr)
best_dev_ppl = dev_ppl
test_logp = compute_test_logp()
test_ppl = ppl(test_logp)
logger.debug('epoch %i, minibatch %i/%i, test log prob %.4f ppl %.4f' %
(epoch, minibatch_index+1, n_train_batches,
test_logp, ppl(test_logp)))
# stop learning if no improvement was seen for a long time
if patience and patience <= itr:
done_looping = True
break
# adapt learning rate
if rate_update == 'simple':
# set learning rate to 1 / (epoch+1)
learning_rate = 1.0 / (epoch+1)
elif rate_update == 'adaptive':
# half learning rate if perplexity increased at end of epoch (Mnih and Teh 2012)
this_epoch_dev_ppl = ppl(compute_dev_logp())
if this_epoch_dev_ppl > last_epoch_dev_ppl:
learning_rate /= 2.0
last_epoch_dev_ppl = this_epoch_dev_ppl
elif rate_update == 'constant':
# keep learning rate constant
pass
else:
raise ValueError("Unknown learning rate update strategy: %s" %rate_update)
end_time = time.clock()
total_time = end_time - start_time
logger.info('Optimization complete with best dev ppl of %.4f and test ppl %.4f' %
(best_dev_ppl, test_ppl))
logger.info('Training took %d epochs, with %.1f epochs/sec' % (epoch+1,
float(epoch+1) / total_time))
logger.info("Total training time %d days %d hours %d min %d sec." %
(total_time/60/60/24, total_time/60/60%24, total_time/60%60, total_time%60))
# return model
return lbl
sys.exit("'Hidden layer size' argument missing!")
if len(sys.argv) > 3:
learning_rate = float(sys.argv[3])
else:
sys.exit("'Learning rate' argument missing!")
model_file_name = "Model_%s_h%d_lr%s.pcl" % (model_name, num_hidden,
learning_rate)
print num_hidden, learning_rate, model_file_name
word_vocabulary = data.read_vocabulary(data.WORD_VOCAB_FILE)
punctuation_vocabulary = data.iterable_to_dict(data.PUNCTUATION_VOCABULARY)
x = T.imatrix('x')
y = T.imatrix('y')
lr = T.scalar('lr')
if os.path.isfile(model_file_name):
print "Loading previous model state"
net, state = models.load(model_file_name, MINIBATCH_SIZE, x)
gsums, learning_rate, validation_ppl_history, starting_epoch, rng = state
best_ppl = min(validation_ppl_history)
else:
rng = np.random
rng.seed(1)
print "Building model..."
