def gru_cell(state_size):
_cell = tf.nn.rnn_cell.GRUCell(
state_size,
activation=get_activation_by_name(
self.args.activation))
# _cell = tf.nn.rnn_cell.DropoutWrapper(_cell, output_keep_prob=0.5)
return _cell
def lstm_cell(state_size):
_cell = tf.nn.rnn_cell.LSTMCell(
state_size,
state_is_tuple=True,
activation=get_activation_by_name(
self.args.activation))
# _cell = tf.nn.rnn_cell.DropoutWrapper(_cell, output_keep_prob=0.5)
return _cell
def ready(self, args, train):
# len * batch
self.idxs = T.imatrix()
self.idys = T.imatrix()
self.init_state = T.matrix(dtype=theano.config.floatX)
dropout_prob = np.float64(args["dropout"]).astype(theano.config.floatX)
self.dropout = theano.shared(dropout_prob)
self.n_d = args["hidden_dim"]
embedding_layer = EmbeddingLayer(n_d=self.n_d,
vocab=set(w for w in train))
self.n_V = embedding_layer.n_V
say("Vocab size: {}\tHidden dim: {}\n".format(self.n_V, self.n_d))
activation = get_activation_by_name(args["activation"])
rnn_layer = LSTM(n_in=self.n_d, n_out=self.n_d, activation=activation)
output_layer = Layer(
n_in=self.n_d,
n_out=self.n_V,
activation=T.nnet.softmax,
)
# (len*batch) * n_d
x_flat = embedding_layer.forward(self.idxs.ravel())
# len * batch * n_d
x = apply_dropout(x_flat, self.dropout)
x = x.reshape((self.idxs.shape[0], self.idxs.shape[1], self.n_d))
# len * batch * (n_d+n_d)
h = rnn_layer.forward_all(x, self.init_state, return_c=True)
self.last_state = h[-1]
h = h[:, :, self.n_d:]
h = apply_dropout(h, self.dropout)
self.p_y_given_x = output_layer.forward(h.reshape(x_flat.shape))
idys = self.idys.ravel()
self.nll = -T.log(self.p_y_given_x[T.arange(idys.shape[0]), idys])
#self.nll = T.nnet.categorical_crossentropy(
# self.p_y_given_x,
# idys
# )
self.layers = [embedding_layer, rnn_layer, output_layer]
#self.params = [ x_flat ] + rnn_layer.params + output_layer.params
self.params = embedding_layer.params + rnn_layer.params + output_layer.params
self.num_params = sum(
len(x.get_value(borrow=True).ravel()) for l in self.layers
for x in l.params)
say("# of params in total: {}\n".format(self.num_params))
def ready(self):
args = self.args
#n_domain = 2
accum_dict = self.accum_dict = {}
# len(sent) * len(doc) * batch
s_idxs = self.s_idxs = T.itensor3()
t_idxs = self.t_idxs = T.itensor3()
# batch
s_idys = self.s_idys = T.ivector()
t_idys = self.t_idys = T.ivector()
# batch
s_dom_ids = self.s_dom_ids = T.ivector()
t_dom_ids = self.t_dom_ids = T.ivector()
# len(doc) * batch, 0: negative, 1: positive, -1: REL_UNK, -2, REL_PAD
s_gold_rels = self.s_gold_rels = T.imatrix()
t_gold_rels = self.t_gold_rels = T.imatrix()
# has label flag, 0: no, 1: yes
s_has_lab = self.s_has_lab = T.iscalar()
t_has_lab = self.t_has_lab = T.iscalar()
self.dropout = theano.shared(np.float64(args.dropout).astype(
theano.config.floatX))
embedding_layer = self.embedding_layer
if not embedding_layer.fix_init_embs:
accum_dict[embedding_layer] = self.create_accumulators(embedding_layer)
activation = get_activation_by_name(args.activation)
n_d = self.n_d = args.hidden_dim
n_e = self.n_e = embedding_layer.n_d
n_c = self.nclasses
self.rho = theano.shared(np.float64(0.0).astype(theano.config.floatX))
self.source_k = 2
# CNN to encode sentence into embedding
cnn_layer = self.cnn_layer = LeCNN(
n_in = n_e,
n_out = n_d,
activation=activation,
order = args.cnn_window_size,
BN = True,
)
accum_dict[cnn_layer] = self.create_accumulators(cnn_layer)
# softmax layer to predict the label of the document
self.lab_hid_layer = lab_hid_layer = Layer(
n_in = n_d,
n_out = n_d,
activation = activation,
)
accum_dict[lab_hid_layer] = self.create_accumulators(lab_hid_layer)
self.lab_out_layer = lab_out_layer = Layer(
n_in = n_d,
n_out = n_c,
activation = logsoftmax,
)
accum_dict[lab_out_layer] = self.create_accumulators(lab_out_layer)
# hidden layer to predict the domain of the document
dom_hid_layer = self.dom_hid_layer = Layer(
n_in = n_d,
n_out = n_d,
activation = activation,
)
accum_dict[dom_hid_layer] = self.create_accumulators(dom_hid_layer)
# softmax layer to predict the domain of the document
dom_out_layer = self.dom_out_layer = Layer(
n_in = n_d,
n_out = 2,
activation = logsoftmax,
)
accum_dict[dom_out_layer] = self.create_accumulators(dom_out_layer)
# for each domain, a vector parameter to compute the relevance score
rel_hid_layer = self.rel_hid_layer = Layer(
n_in = n_d,
n_out = n_d,
activation = activation,
)
accum_dict[rel_hid_layer] = self.create_accumulators(rel_hid_layer)
s_rel_out_layer = self.s_rel_out_layer = Layer(
n_in = n_d,
n_out = 1,
activation = sigmoid,
)
accum_dict[s_rel_out_layer] = self.create_accumulators(s_rel_out_layer)
t_rel_out_layer = self.t_rel_out_layer = Layer(
n_in = n_d,
n_out = 1,
activation = sigmoid,
)
accum_dict[t_rel_out_layer] = self.create_accumulators(t_rel_out_layer)
# transformation to domain independent layer
trans_layer = self.trans_layer = Layer(
n_in = n_d,
n_out = n_d,
activation = activation,
has_bias=False,
init_zero=True,
)
accum_dict[trans_layer] = self.create_accumulators(trans_layer)
val = np.eye(n_d, dtype=theano.config.floatX)
identity_mat = theano.shared(val)
trans_layer.W.set_value(val)
# reconstruction layer
recon_layer = self.recon_layer = Layer(
n_in = n_d,
n_out = n_e,
activation = tanh,
)
accum_dict[recon_layer] = self.create_accumulators(recon_layer)
# construct network
s_lab_loss, s_rel_loss, s_dom_loss, s_adv_loss, s_lab_prob, s_recon_loss = self.ready_one_domain(
s_idxs, s_idys, s_dom_ids, s_gold_rels, \
cnn_layer, rel_hid_layer, s_rel_out_layer, trans_layer, \
dom_hid_layer, dom_out_layer, lab_hid_layer, lab_out_layer)
self.s_lab_loss, self.s_rel_loss, self.s_dom_loss, self.s_adv_loss, self.s_lab_prob, self.s_recon_loss = \
s_lab_loss, s_rel_loss, s_dom_loss, s_adv_loss, s_lab_prob, s_recon_loss
t_lab_loss, t_rel_loss, t_dom_loss, t_adv_loss, t_lab_prob, t_recon_loss = self.ready_one_domain(
t_idxs, t_idys, t_dom_ids, t_gold_rels, \
cnn_layer, rel_hid_layer, t_rel_out_layer, trans_layer, \
dom_hid_layer, dom_out_layer, lab_hid_layer, lab_out_layer)
self.t_lab_loss, self.t_rel_loss, self.t_dom_loss, self.t_adv_loss, self.t_lab_prob, self.t_recon_loss = \
t_lab_loss, t_rel_loss, t_dom_loss, t_adv_loss, t_lab_prob, t_recon_loss
# transformation regularization
trans_reg = self.trans_reg = args.trans_reg * T.sum((trans_layer.W - identity_mat) ** 2)
# domain cost
layers = [ dom_out_layer, dom_hid_layer ]
self.dom_params = self.get_params(layers)
self.dom_accums = self.get_accumulators(layers, accum_dict)
self.dom_cost = s_dom_loss + t_dom_loss + args.l2_reg * self.get_l2_cost(self.dom_params)
# label cost
lab_layers = [ lab_out_layer, lab_hid_layer ]
lab_params = self.get_params(lab_layers)
lab_cost = s_has_lab * self.source_k * s_lab_loss + t_has_lab * t_lab_loss \
+ args.l2_reg * (s_has_lab + t_has_lab) * self.get_l2_cost(lab_params)
# total cost
other_layers = [ cnn_layer, s_rel_out_layer, t_rel_out_layer, rel_hid_layer, trans_layer, recon_layer ]
other_params = self.get_params(other_layers)
self.other_cost_except_dom = lab_cost + s_rel_loss + t_rel_loss + s_adv_loss + t_adv_loss + trans_reg \
+ s_recon_loss + t_recon_loss \
+ args.l2_reg * self.get_l2_cost(other_params)
self.other_params_except_dom = lab_params + other_params
self.other_accums_except_dom = self.get_accumulators(lab_layers + other_layers, accum_dict)
if not embedding_layer.fix_init_embs:
self.other_params_except_dom += embedding_layer.params
self.add_accumulators(self.other_accums_except_dom, embedding_layer, accum_dict)
# info
layers = lab_layers + other_layers + [ dom_out_layer, dom_hid_layer ]
params = self.params = self.get_params(layers)
if not embedding_layer.fix_init_embs:
self.params += embedding_layer.params
say("num of parameters: {}\n".format(
sum(len(x.get_value(borrow=True).ravel()) for x in params)
))
def ready(self, args, train):
# len * batch
depth = args["depth"]
self.args = args
self.idxs = T.imatrix()
self.idys = T.imatrix()
self.init_state = [
T.matrix(dtype=theano.config.floatX) for i in xrange(depth * 2)
]
dropout_prob = np.float64(args["dropout"]).astype(theano.config.floatX)
self.dropout = theano.shared(dropout_prob)
rnn_dropout_prob = np.float64(args["rnn_dropout"]).astype(
theano.config.floatX)
self.rnn_dropout = theano.shared(rnn_dropout_prob)
self.n_d = args["hidden_dim"]
embedding_layer = EmbeddingLayer(n_d=self.n_d,
vocab=set(w for w in train))
self.n_V = embedding_layer.n_V
say("Vocab size: {}\tHidden dim: {}\n".format(self.n_V, self.n_d))
activation = get_activation_by_name(args["activation"])
layers = self.layers = []
for i in xrange(depth):
rnn_layer = KernelNN(n_in=self.n_d,
n_out=self.n_d,
activation=activation,
highway=args["highway"],
dropout=self.rnn_dropout)
layers.append(rnn_layer)
output_layer = Layer(
n_in=self.n_d,
n_out=self.n_V,
activation=T.nnet.softmax,
)
output_layer.W = embedding_layer.embeddings.T
# (len*batch) * n_d
x_flat = embedding_layer.forward(self.idxs.ravel())
# len * batch * n_d
x = apply_dropout(x_flat, self.dropout)
#x = x_flat
x = x.reshape((self.idxs.shape[0], self.idxs.shape[1], self.n_d))
# len * batch * (n_d+n_d)
self.last_state = []
prev_h = x
for i in xrange(depth):
hidden = self.init_state[i * 2:i * 2 + 2]
c, h = layers[i].forward_all(prev_h, hidden, return_c=True)
self.last_state += [c[-1], h[-1]]
prev_h = h
prev_h = apply_dropout(prev_h, self.dropout)
self.p_y_given_x = output_layer.forward(prev_h.reshape(x_flat.shape))
idys = self.idys.ravel()
self.nll = T.nnet.categorical_crossentropy(self.p_y_given_x, idys)
self.params = [x for l in layers for x in l.params]
self.params += [embedding_layer.embeddings, output_layer.b]
self.num_params = sum(
len(x.get_value(borrow=True).ravel()) for x in self.params)
say("# of params in total: {}\n".format(self.num_params))
layers += [embedding_layer, output_layer]
def ready(self):
global total_generate_time
#say("in generator ready: \n")
#start_generate_time = time.time()
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX))
# len*batch
x = self.x = T.imatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = []
layer_type = args.layer.lower()
for i in xrange(2):
if layer_type == "rcnn":
l = RCNN(n_in=n_e,
n_out=n_d,
activation=activation,
order=args.order)
elif layer_type == "lstm":
l = LSTM(n_in=n_e, n_out=n_d, activation=activation)
l = Layer(n_in=n_e, n_out=n_d, activation=sigmoid)
layers.append(l)
# len * batch
#masks = T.cast(T.neq(x, padding_id), theano.config.floatX)
masks = T.cast(T.neq(x, padding_id), theano.config.floatX).dimshuffle(
(0, 1, "x"))
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
self.word_embs = embs
flipped_embs = embs[::-1]
# len*bacth*n_d
h1 = layers[0].forward(embs)
h2 = layers[1].forward(flipped_embs)
h_final = T.concatenate([h1, h2[::-1]], axis=2)
h_final = apply_dropout(h_final, dropout)
size = n_d * 2
#size = n_e
output_layer = self.output_layer = Layer(n_in=size,
n_out=1,
activation=sigmoid)
# len*batch*1
probs = output_layer.forward(h_final)
#probs = output_layer.forward(embs)
#probs1 = probs.reshape(x.shape)
#probs_rev = output_layer.forward(flipped_embs)
#probs1_rev = probs.reshape(x.shape)
#probs = T.concatenate([probs1, probs1_rev[::-1]], axis=2)
# len*batch
probs2 = probs.reshape(x.shape)
if self.args.seed is not None:
self.MRG_rng = MRG_RandomStreams(self.args.seed)
else:
self.MRG_rng = MRG_RandomStreams()
z_pred = self.z_pred = T.cast(
self.MRG_rng.binomial(size=probs2.shape, p=probs2),
theano.config.floatX) #"int8")
# we are computing approximated gradient by sampling z;
# so should mark sampled z not part of the gradient propagation path
#
z_pred = self.z_pred = theano.gradient.disconnected_grad(z_pred)
#self.sample_updates = sample_updates
print "z_pred", z_pred.ndim
z2 = z_pred.dimshuffle((0, 1, "x"))
logpz = -T.nnet.binary_crossentropy(probs, z2) * masks
logpz = self.logpz = logpz.reshape(x.shape)
probs = self.probs = probs.reshape(x.shape)
# batch
z = z_pred
self.zsum = T.sum(z, axis=0, dtype=theano.config.floatX)
self.zdiff = T.sum(T.abs_(z[1:] - z[:-1]),
axis=0,
dtype=theano.config.floatX)
params = self.params = []
for l in layers + [output_layer]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
def ready(self):
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX)
)
# len*batch
x = self.x = T.imatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
layer_type = args.layer.lower()
for i in xrange(1):
l = CNN(
n_in = n_e,
n_out = n_d,
activation = activation,
order = args.order
)
layers.append(l)
# len * batch
masks = T.cast(T.neq(x, padding_id), "int8").dimshuffle((0,1,'x'))
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
self.word_embs = embs
# len*bacth*n_d
h1 = layers[0].forward_all(embs)
h_final = h1
size = n_d
h_final = apply_dropout(h_final, dropout)
output_layer = self.output_layer = Layer(
n_in = size,
n_out = 1,
activation = sigmoid
)
# len*batch*1
probs = output_layer.forward(h_final)
# len*batch
self.MRG_rng = MRG_RandomStreams()
z_pred_dim3 = self.MRG_rng.binomial(size=probs.shape, p=probs, dtype="int8")
z_pred = z_pred_dim3.reshape(x.shape)
# we are computing approximated gradient by sampling z;
# so should mark sampled z not part of the gradient propagation path
#
z_pred = self.z_pred = theano.gradient.disconnected_grad(z_pred)
print "z_pred", z_pred.ndim
#logpz = - T.nnet.binary_crossentropy(probs, z_pred_dim3) * masks
logpz = - T.nnet.binary_crossentropy(probs, z_pred_dim3)
logpz = self.logpz = logpz.reshape(x.shape)
probs = self.probs = probs.reshape(x.shape)
# batch
z = z_pred
self.zsum = T.sum(z, axis=0, dtype=theano.config.floatX)
self.zdiff = T.sum(T.abs_(z[1:]-z[:-1]), axis=0, dtype=theano.config.floatX)
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
def ready(self):
encoder = self.encoder
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = encoder.dropout
# len*batch
x = self.x = encoder.x
z = self.z = encoder.z
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = []
layer_type = args.layer.lower()
for i in range(2):
if layer_type == "rcnn":
l = RCNN(
n_in=n_e, # if i == 0 else n_d,
n_out=n_d,
activation=activation,
order=args.order)
elif layer_type == "lstm":
l = LSTM(
n_in=n_e, # if i == 0 else n_d,
n_out=n_d,
activation=activation)
layers.append(l)
# len * batch
#masks = T.cast(T.neq(x, padding_id), theano.config.floatX)
masks = T.cast(T.neq(x, padding_id), "int8").dimshuffle((0, 1, "x"))
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
flipped_embs = embs[::-1]
# len*bacth*n_d
h1 = layers[0].forward_all(embs)
h2 = layers[1].forward_all(flipped_embs)
h_final = T.concatenate([h1, h2[::-1]], axis=2)
h_final = apply_dropout(h_final, dropout)
size = n_d * 2
output_layer = self.output_layer = Layer(n_in=size,
n_out=1,
activation=sigmoid)
# len*batch*1
probs = output_layer.forward(h_final)
# len*batch
probs2 = probs.reshape(x.shape)
self.MRG_rng = MRG_RandomStreams()
z_pred = self.z_pred = T.cast(
self.MRG_rng.binomial(size=probs2.shape, p=probs2), "int8")
# we are computing approximated gradient by sampling z;
# so should mark sampled z not part of the gradient propagation path
#
self.z_pred = theano.gradient.disconnected_grad(z_pred)
z2 = z.dimshuffle((0, 1, "x"))
logpz = -T.nnet.binary_crossentropy(probs, z2) * masks
logpz = self.logpz = logpz.reshape(x.shape)
probs = self.probs = probs.reshape(x.shape)
# batch
zsum = T.sum(z, axis=0, dtype=theano.config.floatX)
zdiff_pre = (z[1:] - z[:-1]) * 1.0
zdiff = T.sum(abs(zdiff_pre), axis=0, dtype=theano.config.floatX)
loss_mat = encoder.loss_mat
if args.aspect < 0:
loss_vec = T.mean(loss_mat, axis=1)
else:
assert args.aspect < self.nclasses
loss_vec = loss_mat[:, args.aspect]
self.loss_vec = loss_vec
coherent_factor = args.sparsity * args.coherent
loss = self.loss = T.mean(loss_vec)
sparsity_cost = self.sparsity_cost = T.mean(zsum) * args.sparsity + \
T.mean(zdiff) * coherent_factor
cost_vec = loss_vec + zsum * args.sparsity + zdiff * coherent_factor
cost_logpz = T.mean(cost_vec * T.sum(logpz, axis=0))
self.obj = T.mean(cost_vec)
params = self.params = []
for l in layers + [output_layer]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
cost = self.cost = cost_logpz * 10 + l2_cost
print("cost.dtype", cost.dtype)
self.cost_e = loss * 10 + encoder.l2_cost
def ready(self):
args = self.args
embedding_layer = self.embedding_layer
self.n_hidden = args.hidden_dim
self.n_in = embedding_layer.n_d
dropout = self.dropout = theano.shared(
np.float64(args.dropout_rate).astype(theano.config.floatX))
# x is length * batch_size
# y is batch_size
self.x = T.imatrix('x')
self.y = T.ivector('y')
x = self.x
y = self.y
n_hidden = self.n_hidden
n_in = self.n_in
# fetch word embeddings
# (len * batch_size) * n_in
slices = embedding_layer.forward(x.ravel())
self.slices = slices
# 3-d tensor, len * batch_size * n_in
slices = slices.reshape((x.shape[0], x.shape[1], n_in))
# stacking the feature extraction layers
pooling = args.pooling
depth = args.depth
layers = self.layers = []
prev_output = slices
prev_output = apply_dropout(prev_output, dropout, v2=True)
size = 0
softmax_inputs = []
activation = get_activation_by_name(args.act)
for i in range(depth):
if args.layer.lower() == "lstm":
layer = LSTM(n_in=n_hidden if i > 0 else n_in, n_out=n_hidden)
elif args.layer.lower() == "strcnn":
layer = StrCNN(n_in=n_hidden if i > 0 else n_in,
n_out=n_hidden,
activation=activation,
decay=args.decay,
order=args.order)
elif args.layer.lower() == "rcnn":
layer = RCNN(n_in=n_hidden if i > 0 else n_in,
n_out=n_hidden,
activation=activation,
order=args.order,
mode=args.mode)
else:
raise Exception("unknown layer type: {}".format(args.layer))
layers.append(layer)
prev_output = layer.forward_all(prev_output)
if pooling:
softmax_inputs.append(T.sum(prev_output,
axis=0)) # summing over columns
else:
softmax_inputs.append(prev_output[-1])
prev_output = apply_dropout(prev_output, dropout)
size += n_hidden
# final feature representation is the concatenation of all extraction layers
if pooling:
softmax_input = T.concatenate(softmax_inputs, axis=1) / x.shape[0]
else:
softmax_input = T.concatenate(softmax_inputs, axis=1)
softmax_input = apply_dropout(softmax_input, dropout, v2=True)
# feed the feature repr. to the softmax output layer
layers.append(
Layer(n_in=size,
n_out=self.nclasses,
activation=softmax,
has_bias=False))
for l, i in zip(layers, range(len(layers))):
say("layer {}: n_in={}\tn_out={}\n".format(i, l.n_in, l.n_out))
# unnormalized score of y given x
self.p_y_given_x = layers[-1].forward(softmax_input)
self.pred = T.argmax(self.p_y_given_x, axis=1)
self.nll_loss = T.mean(
T.nnet.categorical_crossentropy(self.p_y_given_x, y))
# adding regularizations
self.l2_sqr = None
self.params = []
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)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in self.params)
say("total # parameters: {}\n".format(nparams))
def ready(self):
args = self.args
weights = self.weights
# len(title) * batch
idts = self.idts = T.imatrix()
# len(body) * batch
idbs = self.idbs = T.imatrix()
# num pairs * 3, or num queries * candidate size
idps = self.idps = T.imatrix()
dropout = self.dropout = theano.shared(np.float64(args.dropout).astype(
theano.config.floatX))
dropout_op = self.dropout_op = Dropout(self.dropout)
embedding_layer = self.embedding_layer
activation = get_activation_by_name(args.activation)
n_d = self.n_d = args.hidden_dim
n_e = self.n_e = embedding_layer.n_d
if args.layer.lower() == "rcnn":
LayerType = RCNN
elif args.layer.lower() == "lstm":
LayerType = LSTM
elif args.layer.lower() == "gru":
LayerType = GRU
depth = self.depth = args.depth
layers = self.layers = [ ]
for i in range(depth):
if LayerType != RCNN:
feature_layer = LayerType(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation
)
else:
feature_layer = LayerType(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order,
mode = args.mode,
has_outgate = args.outgate
)
layers.append(feature_layer)
# feature computation starts here
# (len*batch)*n_e
xt = embedding_layer.forward(idts.ravel())
if weights is not None:
xt_w = weights[idts.ravel()].dimshuffle((0,'x'))
xt = xt * xt_w
# len*batch*n_e
xt = xt.reshape((idts.shape[0], idts.shape[1], n_e))
xt = apply_dropout(xt, dropout)
# (len*batch)*n_e
xb = embedding_layer.forward(idbs.ravel())
if weights is not None:
xb_w = weights[idbs.ravel()].dimshuffle((0,'x'))
xb = xb * xb_w
# len*batch*n_e
xb = xb.reshape((idbs.shape[0], idbs.shape[1], n_e))
xb = apply_dropout(xb, dropout)
prev_ht = self.xt = xt
prev_hb = self.xb = xb
for i in range(depth):
# len*batch*n_d
ht = layers[i].forward_all(prev_ht)
hb = layers[i].forward_all(prev_hb)
prev_ht = ht
prev_hb = hb
# normalize vectors
if args.normalize:
ht = self.normalize_3d(ht)
hb = self.normalize_3d(hb)
say("h_title dtype: {}\n".format(ht.dtype))
self.ht = ht
self.hb = hb
# average over length, ignore paddings
# batch * d
if args.average:
ht = self.average_without_padding(ht, idts)
hb = self.average_without_padding(hb, idbs)
else:
ht = ht[-1]
hb = hb[-1]
say("h_avg_title dtype: {}\n".format(ht.dtype))
# batch * d
h_final = (ht+hb)*0.5
h_final = apply_dropout(h_final, dropout)
h_final = self.normalize_2d(h_final)
self.h_final = h_final
say("h_final dtype: {}\n".format(ht.dtype))
# For testing:
# first one in batch is query, the rest are candidate questions
self.scores = T.dot(h_final[1:], h_final[0])
# For training:
xp = h_final[idps.ravel()]
xp = xp.reshape((idps.shape[0], idps.shape[1], n_d))
# num query * n_d
query_vecs = xp[:,0,:]
# num query
pos_scores = T.sum(query_vecs*xp[:,1,:], axis=1)
# num query * candidate size
neg_scores = T.sum(query_vecs.dimshuffle((0,'x',1))*xp[:,2:,:], axis=2)
# num query
neg_scores = T.max(neg_scores, axis=1)
diff = neg_scores - pos_scores + 1.0
loss = T.mean( (diff>0)*diff )
self.loss = loss
params = [ ]
for l in self.layers:
params += l.params
self.params = params
say("num of parameters: {}\n".format(
sum(len(x.get_value(borrow=True).ravel()) for x in params)
))
l2_reg = None
for p in params:
if l2_reg is None:
l2_reg = p.norm(2)
else:
l2_reg = l2_reg + p.norm(2)
l2_reg = l2_reg * args.l2_reg
self.cost = self.loss + l2_reg
def ready(self):
args = self.args
embedding_layer = self.embedding_layer
user_embedding_layer = self.user_embedding_layer
self.n_hidden = args.hidden_dim
self.n_in = embedding_layer.n_d
dropout = self.dropout = theano.shared(
np.float64(args.dropout_rate).astype(theano.config.floatX)
)
# x is length * batch_size
# y is batch_size
self.x = T.imatrix('x')
self.w_masks = T.fmatrix('mask')
self.w_lens = T.fvector('lens')
self.s_ml = T.iscalar('sent_maxlen')
self.s_num = T.iscalar('sent_num')
self.y = T.ivector('y')
self.usr = T.ivector('users')
x = self.x
y = self.y
usr = self.usr
w_masks = self.w_masks
w_lens = self.w_lens
s_ml = self.s_ml
s_num = self.s_num
n_hidden = self.n_hidden
n_emb = n_in = self.n_in
layers = self.layers = []
slicesu = user_embedding_layer.forward(usr)
slices = embedding_layer.forward(x.ravel())
self.slices = slices # important for updating word embeddings
# 3-d tensor, len * batch_size * n_in
slices = slices.reshape((x.shape[0], x.shape[1], n_in))
pooling = args.pooling
prev_output = slices
prev_output = apply_dropout(prev_output, dropout, v2=True)
size = 0
n_hidden_t = n_hidden
if args.direction == "bi":
n_hidden_t = 2 * n_hidden
softmax_inputs = []
activation = get_activation_by_name(args.act)
if args.layer.lower() == "lstm":
layer = LSTM(n_in=n_in,
n_out=n_hidden_t,
direction=args.direction
)
elif args.layer.lower() == "cnn":
layer = CNN(n_in=n_in,
n_out=n_hidden_t,
activation=activation,
order=args.order
)
else:
raise Exception("unknown layer type: {}".format(args.layer))
layers.append(layer)
prev_output = layer.forward_all(prev_output, masks=w_masks)
prev_output = apply_dropout(prev_output, dropout)
# final feature representation is the concatenation of all extraction layers
if args.user_atten:
layer = IterAttentionLayer(
n_in=n_emb,
n_out=n_hidden_t
)
layers.append(layer)
if args.user_atten_base:
slicesu = None
softmax_input = layers[-1].multi_hop_forward(
prev_output, user_embs=slicesu, isWord=True, masks=w_masks)
else:
if pooling:
softmax_input = T.sum(prev_output, axis=0) / w_lens.dimshuffle(0, 'x')
else:
ind = T.cast(w_lens - T.ones_like(w_lens), 'int32')
softmax_input = prev_output[T.arange(ind.shape[0]), ind]
softmax_input = apply_dropout(softmax_input, dropout, v2=True)
n_in = n_hidden_t
size = 0
softmax_inputs = []
[sentlen, emblen] = T.shape(softmax_input)
prev_output = softmax_input.reshape(
(sentlen / s_num, s_num, emblen)).dimshuffle(1, 0, 2)
if args.layer.lower() == "lstm":
layer = LSTM(n_in=n_in,
n_out=n_hidden_t,
direction=args.direction
)
elif args.layer.lower() == "cnn":
layer = CNN(n_in=n_in,
n_out=n_hidden_t,
activation=activation,
order=args.order,
)
else:
raise Exception("unknown layer type: {}".format(args.layer))
layers.append(layer)
prev_output = layer.forward_all(prev_output)
prev_output = apply_dropout(prev_output, dropout)
if args.user_atten:
layer = IterAttentionLayer(
n_in=n_emb,
n_out=n_hidden_t
)
layers.append(layer)
if args.user_atten_base:
slicesu = None
softmax_input = layers[-1].multi_hop_forward(
prev_output, user_embs=slicesu, isWord=False)
else:
if pooling:
softmax_input = T.sum(prev_output, axis=0) / \
T.cast(s_num, 'float32')
else:
softmax_input = prev_output[-1]
softmax_input = apply_dropout(softmax_input, dropout, v2=True)
size = n_hidden_t
layers.append(Layer(
n_in=size,
n_out=self.nclasses,
activation=softmax,
has_bias=False
))
if not args.fix_emb:
for l, i in zip(layers, range(len(layers))):
say("layer {}: n_in={}\tn_out={}\n".format(
i, l.n_in, l.n_out
))
else:
for l, i in zip(layers[1:], range(len(layers[1:]))):
say("layer {}: n_in={}\tn_out={}\n".format(
i, l.n_in, l.n_out
))
# unnormalized score of y given x
self.p_y_given_x = layers[-1].forward(softmax_input)
self.pred = T.argmax(self.p_y_given_x, axis=1)
self.nll_loss = T.mean(T.nnet.categorical_crossentropy(
self.p_y_given_x,
y
))
# adding regularizations
self.l2_sqr = None
self.params = []
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)
nparams = sum(len(x.get_value(borrow=True).ravel())
for x in self.params)
say("total # parameters: {}\n".format(nparams))
def ready(self):
encoder = self.encoder
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = encoder.dropout
# len*batch
x = self.x = encoder.x
z = self.z = encoder.z
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
layer_type = args.layer.lower()
for i in xrange(2):
if layer_type == "rcnn":
l = RCNN(
n_in = n_e,# if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order
)
elif layer_type == "lstm":
l = LSTM(
n_in = n_e,# if i == 0 else n_d,
n_out = n_d,
activation = activation
)
layers.append(l)
# len * batch
#masks = T.cast(T.neq(x, padding_id), theano.config.floatX)
masks = T.cast(T.neq(x, padding_id), "int8").dimshuffle((0,1,"x"))
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
flipped_embs = embs[::-1]
# len*bacth*n_d
h1 = layers[0].forward_all(embs)
h2 = layers[1].forward_all(flipped_embs)
h_final = T.concatenate([h1, h2[::-1]], axis=2)
h_final = apply_dropout(h_final, dropout)
size = n_d * 2
output_layer = self.output_layer = Layer(
n_in = size,
n_out = 1,
activation = sigmoid
)
# len*batch*1
probs = output_layer.forward(h_final)
# len*batch
probs2 = probs.reshape(x.shape)
self.MRG_rng = MRG_RandomStreams()
z_pred = self.z_pred = T.cast(self.MRG_rng.binomial(size=probs2.shape, p=probs2), "int8")
# we are computing approximated gradient by sampling z;
# so should mark sampled z not part of the gradient propagation path
#
self.z_pred = theano.gradient.disconnected_grad(z_pred)
z2 = z.dimshuffle((0,1,"x"))
logpz = - T.nnet.binary_crossentropy(probs, z2) * masks
logpz = self.logpz = logpz.reshape(x.shape)
probs = self.probs = probs.reshape(x.shape)
# batch
zsum = T.sum(z, axis=0, dtype=theano.config.floatX)
zdiff = T.sum(T.abs_(z[1:]-z[:-1]), axis=0, dtype=theano.config.floatX)
loss_mat = encoder.loss_mat
if args.aspect < 0:
loss_vec = T.mean(loss_mat, axis=1)
else:
assert args.aspect < self.nclasses
loss_vec = loss_mat[:,args.aspect]
self.loss_vec = loss_vec
coherent_factor = args.sparsity * args.coherent
loss = self.loss = T.mean(loss_vec)
sparsity_cost = self.sparsity_cost = T.mean(zsum) * args.sparsity + \
T.mean(zdiff) * coherent_factor
cost_vec = loss_vec + zsum * args.sparsity + zdiff * coherent_factor
cost_logpz = T.mean(cost_vec * T.sum(logpz, axis=0))
self.obj = T.mean(cost_vec)
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
cost = self.cost = cost_logpz * 10 + l2_cost
print "cost.dtype", cost.dtype
self.cost_e = loss * 10 + encoder.l2_cost
def ready(self):
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX)
)
# len*batch
x = self.x = T.imatrix()
z = self.z = T.bmatrix()
z = z.dimshuffle((0,1,"x"))
# batch*nclasses
y = self.y = T.fmatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
depth = args.depth
layer_type = args.layer.lower()
for i in xrange(depth):
if layer_type == "rcnn":
l = ExtRCNN(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order
)
elif layer_type == "lstm":
l = ExtLSTM(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation
)
layers.append(l)
# len * batch * 1
masks = T.cast(T.neq(x, padding_id).dimshuffle((0,1,"x")) * z, theano.config.floatX)
# batch * 1
cnt_non_padding = T.sum(masks, axis=0) + 1e-8
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
pooling = args.pooling
lst_states = [ ]
h_prev = embs
for l in layers:
# len*batch*n_d
h_next = l.forward_all(h_prev, z)
if pooling:
# batch * n_d
masked_sum = T.sum(h_next * masks, axis=0)
lst_states.append(masked_sum/cnt_non_padding) # mean pooling
else:
lst_states.append(h_next[-1]) # last state
h_prev = apply_dropout(h_next, dropout)
if args.use_all:
size = depth * n_d
# batch * size (i.e. n_d*depth)
h_final = T.concatenate(lst_states, axis=1)
else:
size = n_d
h_final = lst_states[-1]
h_final = apply_dropout(h_final, dropout)
output_layer = self.output_layer = Layer(
n_in = size,
n_out = self.nclasses,
activation = sigmoid
)
# batch * nclasses
preds = self.preds = output_layer.forward(h_final)
# batch
loss_mat = self.loss_mat = (preds-y)**2
loss = self.loss = T.mean(loss_mat)
pred_diff = self.pred_diff = T.mean(T.max(preds, axis=1) - T.min(preds, axis=1))
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
cost = self.cost = loss * 10 + l2_cost
def _initialize_encoder_graph(self):
with tf.name_scope('embeddings'):
self.titles = tf.nn.embedding_lookup(
self.embeddings, self.titles_words_ids_placeholder)
self.bodies = tf.nn.embedding_lookup(
self.embeddings, self.bodies_words_ids_placeholder)
if self.weights is not None:
titles_weights = tf.nn.embedding_lookup(
self.weights, self.titles_words_ids_placeholder)
titles_weights = tf.expand_dims(titles_weights, axis=2)
self.titles = self.titles * titles_weights
bodies_weights = tf.nn.embedding_lookup(
self.weights, self.bodies_words_ids_placeholder)
bodies_weights = tf.expand_dims(bodies_weights, axis=2)
self.bodies = self.bodies * bodies_weights
self.titles = tf.nn.dropout(self.titles, 1.0 - self.dropout_prob)
self.bodies = tf.nn.dropout(self.bodies, 1.0 - self.dropout_prob)
with tf.name_scope('CNN'):
print 'ignoring depth at the moment !!'
self.embedded_titles_expanded = tf.expand_dims(self.titles, -1)
self.embedded_bodies_expanded = tf.expand_dims(self.bodies, -1)
# TensorFlow's convolutional conv2d operation expects a 4-dimensional
# tensor with dimensions corresponding to batch, width, height and channel
# The result of our embedding doesn't contain the channel dimension,
# so we add it manually, leaving us with a layer of
# shape [batch/None, sequence_length, embedding_size, 1].
# So, each element of the word vector is a list of size 1 instead of a real number.
# CONVOLUTION AND MAXPOOLING
pooled_outputs_t = []
pooled_outputs_b = []
filter_sizes = [3]
for i, filter_size in enumerate(filter_sizes):
with tf.name_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [
filter_size, self.embedding_layer.n_d, 1,
self.args.hidden_dim
]
print 'assuming num filters = hidden dim. IS IT CORRECT? '
w_vals, b_vals = init_w_b_vals(filter_shape,
[self.args.hidden_dim],
self.args.activation)
W = tf.Variable(w_vals, name="conv-W")
b = tf.Variable(b_vals, name="conv-b")
# self.W = W
with tf.name_scope('titles_output'):
conv_t = tf.nn.conv2d(
self.embedded_titles_expanded,
W,
strides=[
1, 1, 1, 1
], # how much the window shifts by in each of the dimensions.
padding="VALID",
name="conv-titles")
# Apply nonlinearity
nl_fun = get_activation_by_name(self.args.activation)
h_t = nl_fun(tf.nn.bias_add(conv_t, b),
name="act-titles")
if self.args.average:
pooled_t = tf.reduce_mean(h_t,
axis=1,
keep_dims=True)
else:
pooled_t = tf.reduce_max(h_t,
axis=1,
keep_dims=True)
# self.pooled_t = pooled_t
pooled_outputs_t.append(pooled_t)
with tf.name_scope('bodies_output'):
conv_b = tf.nn.conv2d(self.embedded_bodies_expanded,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv-bodies")
# self.conv_b = conv_b
nl_fun = get_activation_by_name(self.args.activation)
h_b = nl_fun(tf.nn.bias_add(conv_b, b),
name="act-bodies")
if self.args.average:
pooled_b = tf.reduce_mean(h_b,
axis=1,
keep_dims=True)
else:
pooled_b = tf.reduce_max(h_b,
axis=1,
keep_dims=True)
# self.pooled_b = pooled_b
pooled_outputs_b.append(pooled_b)
# Combine all the pooled features
num_filters_total = self.args.hidden_dim * len(filter_sizes)
self.t_pool = tf.concat(pooled_outputs_t, 3)
self.t_state = tf.reshape(self.t_pool, [-1, num_filters_total])
# reshape so that we have shape [batch, num_features_total]
self.b_pool = tf.concat(pooled_outputs_b, 3)
self.b_state = tf.reshape(self.b_pool, [-1, num_filters_total])
with tf.name_scope('outputs'):
# batch * d
h_final = (self.t_state + self.b_state) * 0.5
h_final = tf.nn.dropout(h_final, 1.0 - self.dropout_prob)
self.h_final = self.normalize_2d(h_final)
def lstm_cell():
_cell = tf.nn.rnn_cell.LSTMCell(
self.args.hidden_dim,
state_is_tuple=True,
activation=get_activation_by_name(self.args.activation))
return _cell
def ready(self):
args = self.args
weights = self.weights
# len(source) * batch
idxs = self.idxs = T.imatrix()
# len(target) * batch
idys = self.idys = T.imatrix()
idts = idys[:-1]
idgs = idys[1:]
dropout = self.dropout = theano.shared(np.float64(args.dropout).astype(
theano.config.floatX))
embedding_layer = self.embedding_layer
activation = get_activation_by_name(args.activation)
n_d = self.n_d = args.hidden_dim
n_e = self.n_e = embedding_layer.n_d
n_V = self.n_V = embedding_layer.n_V
if args.layer.lower() == "rcnn":
LayerType = RCNN
elif args.layer.lower() == "lstm":
LayerType = LSTM
elif args.layer.lower() == "gru":
LayerType = GRU
depth = self.depth = args.depth
layers = self.layers = [ ]
for i in range(depth*2):
if LayerType != RCNN:
feature_layer = LayerType(
n_in = n_e if i/2 == 0 else n_d,
n_out = n_d,
activation = activation
)
else:
feature_layer = LayerType(
n_in = n_e if i/2 == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order,
mode = args.mode,
has_outgate = args.outgate
)
layers.append(feature_layer)
self.output_layer = output_layer = Layer(
n_in = n_d,
n_out = n_V,
activation = T.nnet.softmax,
)
# feature computation starts here
# (len*batch)*n_e
xs_flat = embedding_layer.forward(idxs.ravel())
xs_flat = apply_dropout(xs_flat, dropout)
if weights is not None:
xs_w = weights[idxs.ravel()].dimshuffle((0,'x'))
xs_flat = xs_flat * xs_w
# len*batch*n_e
xs = xs_flat.reshape((idxs.shape[0], idxs.shape[1], n_e))
# (len*batch)*n_e
xt_flat = embedding_layer.forward(idts.ravel())
xt_flat = apply_dropout(xt_flat, dropout)
if weights is not None:
xt_w = weights[idts.ravel()].dimshuffle((0,'x'))
xt_flat = xt_flat * xt_w
# len*batch*n_e
xt = xt_flat.reshape((idts.shape[0], idts.shape[1], n_e))
prev_hs = xs
prev_ht = xt
for i in range(depth):
# len*batch*n_d
hs = layers[i*2].forward_all(prev_hs, return_c=True)
ht = layers[i*2+1].forward_all(prev_ht, hs[-1])
hs = hs[:,:,-n_d:]
ht = ht[:,:,-n_d:]
prev_hs = hs
prev_ht = ht
prev_hs = apply_dropout(hs, dropout)
prev_ht = apply_dropout(ht, dropout)
self.p_y_given_x = output_layer.forward(prev_ht.reshape(
(xt_flat.shape[0], n_d)
))
h_final = hs[-1]
self.scores2 = -(h_final[1:]-h_final[0]).norm(2,axis=1)
h_final = self.normalize_2d(h_final)
self.scores = T.dot(h_final[1:], h_final[0])
# (len*batch)
nll = T.nnet.categorical_crossentropy(
self.p_y_given_x,
idgs.ravel()
)
nll = nll.reshape(idgs.shape)
self.nll = nll
self.mask = mask = T.cast(T.neq(idgs, self.padding_id), theano.config.floatX)
nll = T.sum(nll*mask, axis=0)
#layers.append(embedding_layer)
layers.append(output_layer)
params = [ ]
for l in self.layers:
params += l.params
self.params = params
say("num of parameters: {}\n".format(
sum(len(x.get_value(borrow=True).ravel()) for x in params)
))
l2_reg = None
for p in params:
if l2_reg is None:
l2_reg = p.norm(2)
else:
l2_reg = l2_reg + p.norm(2)
l2_reg = l2_reg * args.l2_reg
self.loss = T.mean(nll)
self.cost = self.loss + l2_reg
def ready(self):
args = self.args
embedding_layer = self.embedding_layer
self.n_hidden = args.hidden_dim
self.n_in = embedding_layer.n_d
dropout = self.dropout = theano.shared(
np.float64(args.dropout_rate).astype(theano.config.floatX)
)
# x is length * batch_size
# y is batch_size
self.x = T.imatrix('x')
self.y = T.ivector('y')
x = self.x
y = self.y
n_hidden = self.n_hidden
n_in = self.n_in
# fetch word embeddings
# (len * batch_size) * n_in
slices = embedding_layer.forward(x.ravel())
self.slices = slices
# 3-d tensor, len * batch_size * n_in
slices = slices.reshape( (x.shape[0], x.shape[1], n_in) )
# stacking the feature extraction layers
pooling = args.pooling
depth = args.depth
layers = self.layers = [ ]
prev_output = slices
prev_output = apply_dropout(prev_output, dropout, v2=True)
size = 0
softmax_inputs = [ ]
activation = get_activation_by_name(args.act)
for i in range(depth):
if args.layer.lower() == "lstm":
layer = LSTM(
n_in = n_hidden if i > 0 else n_in,
n_out = n_hidden
)
elif args.layer.lower() == "strcnn":
layer = StrCNN(
n_in = n_hidden if i > 0 else n_in,
n_out = n_hidden,
activation = activation,
decay = args.decay,
order = args.order
)
elif args.layer.lower() == "rcnn":
layer = RCNN(
n_in = n_hidden if i > 0 else n_in,
n_out = n_hidden,
activation = activation,
order = args.order,
mode = args.mode
)
else:
raise Exception("unknown layer type: {}".format(args.layer))
layers.append(layer)
prev_output = layer.forward_all(prev_output)
if pooling:
softmax_inputs.append(T.sum(prev_output, axis=0)) # summing over columns
else:
softmax_inputs.append(prev_output[-1])
prev_output = apply_dropout(prev_output, dropout)
size += n_hidden
# final feature representation is the concatenation of all extraction layers
if pooling:
softmax_input = T.concatenate(softmax_inputs, axis=1) / x.shape[0]
else:
softmax_input = T.concatenate(softmax_inputs, axis=1)
softmax_input = apply_dropout(softmax_input, dropout, v2=True)
# feed the feature repr. to the softmax output layer
layers.append( Layer(
n_in = size,
n_out = self.nclasses,
activation = softmax,
has_bias = False
) )
for l,i in zip(layers, range(len(layers))):
say("layer {}: n_in={}\tn_out={}\n".format(
i, l.n_in, l.n_out
))
# unnormalized score of y given x
self.p_y_given_x = layers[-1].forward(softmax_input)
self.pred = T.argmax(self.p_y_given_x, axis=1)
self.nll_loss = T.mean( T.nnet.categorical_crossentropy(
self.p_y_given_x,
y
))
# adding regularizations
self.l2_sqr = None
self.params = [ ]
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)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in self.params)
say("total # parameters: {}\n".format(nparams))
def ready(self):
generator = self.generator
args = self.args
weights = self.weights
dropout = generator.dropout
# len(text) * batch
idts = generator.x
z = generator.z_pred
z = z.dimshuffle((0, 1, "x"))
# batch * 2
pairs = self.pairs = T.imatrix()
# num pairs * 3, or num queries * candidate size
triples = self.triples = T.imatrix()
embedding_layer = self.embedding_layer
activation = get_activation_by_name(args.activation)
n_d = self.n_d = args.hidden_dim
n_e = self.n_e = embedding_layer.n_d
if args.layer.lower() == "rcnn":
LayerType = RCNN
LayerType2 = ExtRCNN
elif args.layer.lower() == "lstm":
LayerType = LSTM
LayerType2 = ExtLSTM
#elif args.layer.lower() == "gru":
# LayerType = GRU
depth = self.depth = args.depth
layers = self.layers = []
for i in range(depth):
if LayerType != RCNN:
feature_layer = LayerType(n_in=n_e if i == 0 else n_d,
n_out=n_d,
activation=activation)
else:
feature_layer = LayerType(n_in=n_e if i == 0 else n_d,
n_out=n_d,
activation=activation,
order=args.order,
mode=args.mode,
has_outgate=args.outgate)
layers.append(feature_layer)
extlayers = self.extlayers = []
for i in range(depth):
if LayerType != RCNN:
feature_layer = LayerType2(n_in=n_e if i == 0 else n_d,
n_out=n_d,
activation=activation)
else:
feature_layer = LayerType2(n_in=n_e if i == 0 else n_d,
n_out=n_d,
activation=activation,
order=args.order,
mode=args.mode,
has_outgate=args.outgate)
feature_layer.copy_params(layers[i])
extlayers.append(feature_layer)
# feature computation starts here
xt = generator.word_embs
# encode full text into representation
prev_ht = self.xt = xt
for i in range(depth):
# len*batch*n_d
ht = layers[i].forward_all(prev_ht)
prev_ht = ht
# encode selected text into representation
prev_htz = self.xt = xt
for i in range(depth):
# len*batch*n_d
htz = extlayers[i].forward_all(prev_htz, z)
prev_htz = htz
# normalize vectors
if args.normalize:
ht = self.normalize_3d(ht)
htz = self.normalize_3d(htz)
say("h_title dtype: {}\n".format(ht.dtype))
self.ht = ht
self.htz = htz
# average over length, ignore paddings
# batch * d
if args.average:
ht = self.average_without_padding(ht, idts)
htz = self.average_without_padding(htz, idts, z)
else:
ht = ht[-1]
htz = htz[-1]
say("h_avg_title dtype: {}\n".format(ht.dtype))
# batch * d
h_final = apply_dropout(ht, dropout)
h_final = self.normalize_2d(h_final)
hz_final = apply_dropout(htz, dropout)
hz_final = self.normalize_2d(hz_final)
self.h_final = h_final
self.hz_final = hz_final
say("h_final dtype: {}\n".format(ht.shape))
# For testing:
# first one in batch is query, the rest are candidate questions
self.scores = T.dot(h_final[1:], h_final[0])
self.scores_z = T.dot(hz_final[1:], hz_final[0])
# For training encoder:
xp = h_final[triples.ravel()]
xp = xp.reshape((triples.shape[0], triples.shape[1], n_d))
# num query * n_d
query_vecs = xp[:, 0, :]
# num query
pos_scores = T.sum(query_vecs * xp[:, 1, :], axis=1)
# num query * candidate size
neg_scores = T.sum(query_vecs.dimshuffle((0, 'x', 1)) * xp[:, 2:, :],
axis=2)
# num query
neg_scores = T.max(neg_scores, axis=1)
diff = neg_scores - pos_scores + 1.0
hinge_loss = T.mean((diff > 0) * diff)
# For training generator
# batch
self_cosine_distance = 1.0 - T.sum(hz_final * h_final, axis=1)
pair_cosine_distance = 1.0 - T.sum(hz_final * h_final[pairs[:, 1]],
axis=1)
alpha = args.alpha
loss_vec = self_cosine_distance * alpha + pair_cosine_distance * (
1 - alpha)
#loss_vec = self_cosine_distance*0.2 + pair_cosine_distance*0.8
zsum = generator.zsum
zdiff = generator.zdiff
logpz = generator.logpz
sfactor = args.sparsity
cfactor = args.sparsity * args.coherent
scost_vec = zsum * sfactor + zdiff * cfactor
# batch
cost_vec = loss_vec + scost_vec
cost_logpz = T.mean(cost_vec * T.sum(logpz, axis=0))
loss = self.loss = T.mean(loss_vec)
sparsity_cost = self.sparsity_cost = T.mean(scost_vec)
self.obj = loss + sparsity_cost
params = []
for l in self.layers:
params += l.params
self.params = params
say("num of parameters: {}\n".format(
sum(len(x.get_value(borrow=True).ravel()) for x in params)))
l2_reg = None
for p in params:
if l2_reg is None:
l2_reg = T.sum(p**2) #p.norm(2)
else:
l2_reg = l2_reg + T.sum(p**2) #p.norm(2)
l2_reg = l2_reg * args.l2_reg
self.l2_cost = l2_reg
beta = args.beta
self.cost_g = cost_logpz + generator.l2_cost
self.cost_e = hinge_loss + loss * beta + l2_reg
def ready(self):
args = self.args
weights = self.weights
# len(source) * batch
idxs = self.idxs = T.imatrix()
# len(target) * batch
idys = self.idys = T.imatrix()
idts = idys[:-1]
idgs = idys[1:]
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX))
embedding_layer = self.embedding_layer
activation = get_activation_by_name(args.activation)
n_d = self.n_d = args.hidden_dim
n_e = self.n_e = embedding_layer.n_d
n_V = self.n_V = embedding_layer.n_V
if args.layer.lower() == "rcnn":
LayerType = RCNN
elif args.layer.lower() == "lstm":
LayerType = LSTM
elif args.layer.lower() == "gru":
LayerType = GRU
depth = self.depth = args.depth
layers = self.layers = []
for i in range(depth * 2):
if LayerType != RCNN:
feature_layer = LayerType(n_in=n_e if i / 2 == 0 else n_d,
n_out=n_d,
activation=activation)
else:
feature_layer = LayerType(n_in=n_e if i / 2 == 0 else n_d,
n_out=n_d,
activation=activation,
order=args.order,
mode=args.mode,
has_outgate=args.outgate)
layers.append(feature_layer)
self.output_layer = output_layer = Layer(
n_in=n_d,
n_out=n_V,
activation=T.nnet.softmax,
)
# feature computation starts here
# (len*batch)*n_e
xs_flat = embedding_layer.forward(idxs.ravel())
xs_flat = apply_dropout(xs_flat, dropout)
if weights is not None:
xs_w = weights[idxs.ravel()].dimshuffle((0, 'x'))
xs_flat = xs_flat * xs_w
# len*batch*n_e
xs = xs_flat.reshape((idxs.shape[0], idxs.shape[1], n_e))
# (len*batch)*n_e
xt_flat = embedding_layer.forward(idts.ravel())
xt_flat = apply_dropout(xt_flat, dropout)
if weights is not None:
xt_w = weights[idts.ravel()].dimshuffle((0, 'x'))
xt_flat = xt_flat * xt_w
# len*batch*n_e
xt = xt_flat.reshape((idts.shape[0], idts.shape[1], n_e))
prev_hs = xs
prev_ht = xt
for i in range(depth):
# len*batch*n_d
hs = layers[i * 2].forward_all(prev_hs, return_c=True)
ht = layers[i * 2 + 1].forward_all(prev_ht, hs[-1])
hs = hs[:, :, -n_d:]
ht = ht[:, :, -n_d:]
prev_hs = hs
prev_ht = ht
prev_hs = apply_dropout(hs, dropout)
prev_ht = apply_dropout(ht, dropout)
self.p_y_given_x = output_layer.forward(
prev_ht.reshape((xt_flat.shape[0], n_d)))
h_final = hs[-1]
self.scores2 = -(h_final[1:] - h_final[0]).norm(2, axis=1)
h_final = self.normalize_2d(h_final)
self.scores = T.dot(h_final[1:], h_final[0])
# (len*batch)
nll = T.nnet.categorical_crossentropy(self.p_y_given_x, idgs.ravel())
nll = nll.reshape(idgs.shape)
self.nll = nll
self.mask = mask = T.cast(T.neq(idgs, self.padding_id),
theano.config.floatX)
nll = T.sum(nll * mask, axis=0)
#layers.append(embedding_layer)
layers.append(output_layer)
params = []
for l in self.layers:
params += l.params
self.params = params
say("num of parameters: {}\n".format(
sum(len(x.get_value(borrow=True).ravel()) for x in params)))
l2_reg = None
for p in params:
if l2_reg is None:
l2_reg = p.norm(2)
else:
l2_reg = l2_reg + p.norm(2)
l2_reg = l2_reg * args.l2_reg
self.loss = T.mean(nll)
self.cost = self.loss + l2_reg
def ready(self):
generator = self.generator
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = generator.dropout
# len*batch
x = generator.x
z = generator.z_pred
z = z.dimshuffle((0,1,"x"))
# batch*nclasses
y = self.y = T.fmatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
depth = args.depth
layer_type = args.layer.lower()
for i in xrange(depth):
if layer_type == "rcnn":
l = ExtRCNN(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order
)
elif layer_type == "lstm":
l = ExtLSTM(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation
)
layers.append(l)
# len * batch * 1
masks = T.cast(T.neq(x, padding_id).dimshuffle((0,1,"x")) * z, theano.config.floatX)
# batch * 1
cnt_non_padding = T.sum(masks, axis=0) + 1e-8
# len*batch*n_e
embs = generator.word_embs
pooling = args.pooling
lst_states = [ ]
h_prev = embs
for l in layers:
# len*batch*n_d
h_next = l.forward_all(h_prev, z)
if pooling:
# batch * n_d
masked_sum = T.sum(h_next * masks, axis=0)
lst_states.append(masked_sum/cnt_non_padding) # mean pooling
else:
lst_states.append(h_next[-1]) # last state
h_prev = apply_dropout(h_next, dropout)
if args.use_all:
size = depth * n_d
# batch * size (i.e. n_d*depth)
h_final = T.concatenate(lst_states, axis=1)
else:
size = n_d
h_final = lst_states[-1]
h_final = apply_dropout(h_final, dropout)
output_layer = self.output_layer = Layer(
n_in = size,
n_out = self.nclasses,
activation = sigmoid
)
# batch * nclasses
preds = self.preds = output_layer.forward(h_final)
# batch
loss_mat = self.loss_mat = (preds-y)**2
pred_diff = self.pred_diff = T.mean(T.max(preds, axis=1) - T.min(preds, axis=1))
if args.aspect < 0:
loss_vec = T.mean(loss_mat, axis=1)
else:
assert args.aspect < self.nclasses
loss_vec = loss_mat[:,args.aspect]
self.loss_vec = loss_vec
zsum = generator.zsum
zdiff = generator.zdiff
logpz = generator.logpz
coherent_factor = args.sparsity * args.coherent
loss = self.loss = T.mean(loss_vec)
sparsity_cost = self.sparsity_cost = T.mean(zsum) * args.sparsity + \
T.mean(zdiff) * coherent_factor
cost_vec = loss_vec + zsum * args.sparsity + zdiff * coherent_factor
cost_logpz = T.mean(cost_vec * T.sum(logpz, axis=0))
self.obj = T.mean(cost_vec)
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
self.cost_g = cost_logpz * 10 + generator.l2_cost
self.cost_e = loss * 10 + l2_cost
def ready(self):
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX))
# len*batch
x = self.x = T.imatrix()
z = self.z = T.bmatrix()
z = z.dimshuffle((0, 1, "x"))
# batch*nclasses
y = self.y = T.fmatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = []
depth = args.depth
layer_type = args.layer.lower()
for i in range(depth):
if layer_type == "rcnn":
l = ExtRCNN(n_in=n_e if i == 0 else n_d,
n_out=n_d,
activation=activation,
order=args.order)
elif layer_type == "lstm":
l = ExtLSTM(n_in=n_e if i == 0 else n_d,
n_out=n_d,
activation=activation)
layers.append(l)
# len * batch * 1
masks = T.cast(
T.neq(x, padding_id).dimshuffle((0, 1, "x")) * z,
theano.config.floatX)
# batch * 1
cnt_non_padding = T.sum(masks, axis=0) + 1e-8
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
pooling = args.pooling
lst_states = []
h_prev = embs
for l in layers:
# len*batch*n_d
h_next = l.forward_all(h_prev, z)
if pooling:
# batch * n_d
masked_sum = T.sum(h_next * masks, axis=0)
lst_states.append(masked_sum / cnt_non_padding) # mean pooling
else:
lst_states.append(h_next[-1]) # last state
h_prev = apply_dropout(h_next, dropout)
if args.use_all:
size = depth * n_d
# batch * size (i.e. n_d*depth)
h_final = T.concatenate(lst_states, axis=1)
else:
size = n_d
h_final = lst_states[-1]
h_final = apply_dropout(h_final, dropout)
output_layer = self.output_layer = Layer(n_in=size,
n_out=self.nclasses,
activation=sigmoid)
# batch * nclasses
preds = self.preds = output_layer.forward(h_final)
# batch
loss_mat = self.loss_mat = (preds - y)**2
loss = self.loss = T.mean(loss_mat)
pred_diff = self.pred_diff = T.mean(
T.max(preds, axis=1) - T.min(preds, axis=1))
params = self.params = []
for l in layers + [output_layer]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
cost = self.cost = loss * 10 + l2_cost
def ready(self):
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX)
)
# len*batch
x = self.x = T.imatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
layer_type = args.layer.lower()
for i in xrange(2):
if layer_type == "rcnn":
l = RCNN(
n_in = n_e,
n_out = n_d,
activation = activation,
order = args.order
)
elif layer_type == "lstm":
l = LSTM(
n_in = n_e,
n_out = n_d,
activation = activation
)
layers.append(l)
# len * batch
masks = T.cast(T.neq(x, padding_id), theano.config.floatX)
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
self.word_embs = embs
flipped_embs = embs[::-1]
# len*bacth*n_d
h1 = layers[0].forward_all(embs)
h2 = layers[1].forward_all(flipped_embs)
h_final = T.concatenate([h1, h2[::-1]], axis=2)
h_final = apply_dropout(h_final, dropout)
size = n_d * 2
output_layer = self.output_layer = ZLayer(
n_in = size,
n_hidden = args.hidden_dimension2,
activation = activation
)
# sample z given text (i.e. x)
z_pred, sample_updates = output_layer.sample_all(h_final)
# we are computing approximated gradient by sampling z;
# so should mark sampled z not part of the gradient propagation path
#
z_pred = self.z_pred = theano.gradient.disconnected_grad(z_pred)
self.sample_updates = sample_updates
print "z_pred", z_pred.ndim
probs = output_layer.forward_all(h_final, z_pred)
print "probs", probs.ndim
logpz = - T.nnet.binary_crossentropy(probs, z_pred) * masks
logpz = self.logpz = logpz.reshape(x.shape)
probs = self.probs = probs.reshape(x.shape)
# batch
z = z_pred
self.zsum = T.sum(z, axis=0, dtype=theano.config.floatX)
self.zdiff = T.sum(T.abs_(z[1:]-z[:-1]), axis=0, dtype=theano.config.floatX)
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
def ready(self):
generator = self.generator
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
unk_id = embedding_layer.vocab_map["<unk>"]
unk_vec = embedding_layer.embeddings[unk_id]
dropout = generator.dropout
# len*batch
x = generator.x
z = generator.z_pred
z = z.dimshuffle((0,1,"x"))
# batch*nclasses
y = self.y = T.fmatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
depth = args.depth
layer_type = args.layer.lower()
for i in xrange(depth):
l = CNN(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order
)
layers.append(l)
# len * batch * 1
masks = T.cast(T.neq(x, padding_id).dimshuffle((0,1,"x")) * z, theano.config.floatX)
# batch * 1
cnt_non_padding = T.sum(masks, axis=0) + 1e-8
# len*batch*n_e
embs = generator.word_embs*z + unk_vec.dimshuffle(('x','x',0))*(1-z)
pooling = args.pooling
lst_states = [ ]
h_prev = embs
for l in layers:
# len*batch*n_d
h_next = l.forward_all(h_prev)
if pooling:
# batch * n_d
masked_sum = T.sum(h_next * masks, axis=0)
lst_states.append(masked_sum/cnt_non_padding) # mean pooling
else:
lst_states.append(T.max(h_next, axis=0))
h_prev = apply_dropout(h_next, dropout)
if args.use_all:
size = depth * n_d
# batch * size (i.e. n_d*depth)
h_final = T.concatenate(lst_states, axis=1)
else:
size = n_d
h_final = lst_states[-1]
h_final = apply_dropout(h_final, dropout)
output_layer = self.output_layer = Layer(
n_in = size,
n_out = self.nclasses,
activation = sigmoid
)
# batch * nclasses
p_y_given_x = self.p_y_given_x = output_layer.forward(h_final)
preds = self.preds = p_y_given_x > 0.5
print preds, preds.dtype
print self.nclasses
# batch
loss_mat = T.nnet.binary_crossentropy(p_y_given_x, y)
if args.aspect < 0:
loss_vec = T.mean(loss_mat, axis=1)
else:
assert args.aspect < self.nclasses
loss_vec = loss_mat[:,args.aspect]
self.loss_vec = loss_vec
self.true_pos = T.sum(preds*y)
self.tot_pos = T.sum(preds)
self.tot_true = T.sum(y)
zsum = generator.zsum
zdiff = generator.zdiff
logpz = generator.logpz
coherent_factor = args.sparsity * args.coherent
loss = self.loss = T.mean(loss_vec)
sparsity_cost = self.sparsity_cost = T.mean(zsum) * args.sparsity + \
T.mean(zdiff) * coherent_factor
cost_vec = loss_vec + zsum * args.sparsity + zdiff * coherent_factor
cost_logpz = T.mean(cost_vec * T.sum(logpz, axis=0))
self.obj = T.mean(cost_vec)
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
if not args.fix_emb:
params += embedding_layer.params
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
self.cost_g = cost_logpz + generator.l2_cost
self.cost_e = loss + l2_cost
def ready(self):
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX))
# len*batch
x = self.x = T.imatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = []
layer_type = args.layer.lower()
for i in xrange(2):
if layer_type == "rcnn":
l = RCNN(n_in=n_e,
n_out=n_d,
activation=activation,
order=args.order)
elif layer_type == "lstm":
l = LSTM(n_in=n_e, n_out=n_d, activation=activation)
layers.append(l)
# len * batch
masks = T.cast(T.neq(x, padding_id), theano.config.floatX)
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
self.word_embs = embs
flipped_embs = embs[::-1]
# len*bacth*n_d
h1 = layers[0].forward_all(embs)
h2 = layers[1].forward_all(flipped_embs)
h_final = T.concatenate([h1, h2[::-1]], axis=2)
h_final = apply_dropout(h_final, dropout)
size = n_d * 2
output_layer = self.output_layer = ZLayer(
n_in=size, n_hidden=args.hidden_dimension2, activation=activation)
# sample z given text (i.e. x)
z_pred, sample_updates = output_layer.sample_all(h_final)
# we are computing approximated gradient by sampling z;
# so should mark sampled z not part of the gradient propagation path
#
z_pred = self.z_pred = theano.gradient.disconnected_grad(z_pred)
self.sample_updates = sample_updates
print "z_pred", z_pred.ndim
probs = output_layer.forward_all(h_final, z_pred)
print "probs", probs.ndim
logpz = -T.nnet.binary_crossentropy(probs, z_pred) * masks
logpz = self.logpz = logpz.reshape(x.shape)
probs = self.probs = probs.reshape(x.shape)
# batch
z = z_pred
self.zsum = T.sum(z, axis=0, dtype=theano.config.floatX)
self.zdiff = T.sum(T.abs_(z[1:] - z[:-1]),
axis=0,
dtype=theano.config.floatX)
params = self.params = []
for l in layers + [output_layer]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
def ready(self):
global total_encode_time
#say("in encoder ready: \n")
#start_encode_time = time.time()
generator = self.generator
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = generator.dropout
# len*batch
x = generator.x
z = generator.z_pred
z = z.dimshuffle((0, 1, "x"))
# batch*nclasses
y = self.y = T.fmatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = []
depth = args.depth
layer_type = args.layer.lower()
for i in xrange(depth):
if layer_type == "rcnn":
l = ExtRCNN(n_in=n_e if i == 0 else n_d,
n_out=n_d,
activation=activation,
order=args.order)
elif layer_type == "lstm":
l = ExtLSTM(n_in=n_e if i == 0 else n_d,
n_out=n_d,
activation=activation)
layers.append(l)
# len * batch * 1
masks = T.cast(
T.neq(x, padding_id).dimshuffle((0, 1, "x")) * z,
theano.config.floatX)
# batch * 1
cnt_non_padding = T.sum(masks, axis=0) + 1e-8
# len*batch*n_e
embs = generator.word_embs
pooling = args.pooling
lst_states = []
h_prev = embs
for l in layers:
# len*batch*n_d
h_next = l.forward_all(h_prev, z)
if pooling:
# batch * n_d
masked_sum = T.sum(h_next * masks, axis=0)
lst_states.append(masked_sum / cnt_non_padding) # mean pooling
else:
lst_states.append(h_next[-1]) # last state
h_prev = apply_dropout(h_next, dropout)
if args.use_all:
size = depth * n_d
# batch * size (i.e. n_d*depth)
h_final = T.concatenate(lst_states, axis=1)
else:
size = n_d
h_final = lst_states[-1]
h_final = apply_dropout(h_final, dropout)
output_layer = self.output_layer = Layer(n_in=size,
n_out=self.nclasses,
activation=sigmoid)
# batch * nclasses
preds = self.preds = output_layer.forward(h_final)
# batch
loss_mat = self.loss_mat = (preds - y)**2
pred_diff = self.pred_diff = T.mean(
T.max(preds, axis=1) - T.min(preds, axis=1))
if args.aspect < 0:
loss_vec = T.mean(loss_mat, axis=1)
else:
assert args.aspect < self.nclasses
loss_vec = loss_mat[:, args.aspect]
self.loss_vec = loss_vec
zsum = generator.zsum
zdiff = generator.zdiff
logpz = generator.logpz
coherent_factor = args.sparsity * args.coherent
loss = self.loss = T.mean(loss_vec)
sparsity_cost = self.sparsity_cost = T.mean(zsum) * args.sparsity + \
T.mean(zdiff) * coherent_factor
cost_vec = loss_vec + zsum * args.sparsity + zdiff * coherent_factor
cost_logpz = T.mean(cost_vec * T.sum(logpz, axis=0))
self.obj = T.mean(cost_vec)
params = self.params = []
for l in layers + [output_layer]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
self.cost_g = cost_logpz * 10 + generator.l2_cost
self.cost_e = loss * 10 + l2_cost
def ready(self, args, train):
# len * batch
self.idxs = T.imatrix()
self.idys = T.imatrix()
self.init_state = T.matrix(dtype=theano.config.floatX)
dropout_prob = np.float64(args["dropout"]).astype(theano.config.floatX)
self.dropout = theano.shared(dropout_prob)
self.n_d = args["hidden_dim"]
embedding_layer = EmbeddingLayer(
n_d = self.n_d,
vocab = set(w for w in train)
)
self.n_V = embedding_layer.n_V
say("Vocab size: {}\tHidden dim: {}\n".format(
self.n_V, self.n_d
))
activation = get_activation_by_name(args["activation"])
rnn_layer = LSTM(
n_in = self.n_d,
n_out = self.n_d,
activation = activation
)
output_layer = Layer(
n_in = self.n_d,
n_out = self.n_V,
activation = T.nnet.softmax,
)
# (len*batch) * n_d
x_flat = embedding_layer.forward(self.idxs.ravel())
# len * batch * n_d
x = apply_dropout(x_flat, self.dropout)
x = x.reshape( (self.idxs.shape[0], self.idxs.shape[1], self.n_d) )
# len * batch * (n_d+n_d)
h = rnn_layer.forward_all(x, self.init_state, return_c=True)
self.last_state = h[-1]
h = h[:,:,self.n_d:]
h = apply_dropout(h, self.dropout)
self.p_y_given_x = output_layer.forward(h.reshape(x_flat.shape))
idys = self.idys.ravel()
self.nll = -T.log(self.p_y_given_x[T.arange(idys.shape[0]), idys])
#self.nll = T.nnet.categorical_crossentropy(
# self.p_y_given_x,
# idys
# )
self.layers = [ embedding_layer, rnn_layer, output_layer ]
#self.params = [ x_flat ] + rnn_layer.params + output_layer.params
self.params = embedding_layer.params + rnn_layer.params + output_layer.params
self.num_params = sum(len(x.get_value(borrow=True).ravel())
for l in self.layers for x in l.params)
say("# of params in total: {}\n".format(self.num_params))
def ready(self):
args = self.args
weights = self.weights
# len(title) * batch
idts = self.idts = T.imatrix()
# len(body) * batch
idbs = self.idbs = T.imatrix()
# num pairs * 3, or num queries * candidate size
idps = self.idps = T.imatrix()
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX))
dropout_op = self.dropout_op = Dropout(self.dropout)
embedding_layer = self.embedding_layer
activation = get_activation_by_name(args.activation)
n_d = self.n_d = args.hidden_dim
n_e = self.n_e = embedding_layer.n_d
if args.layer.lower() == "rcnn":
LayerType = RCNN
elif args.layer.lower() == "lstm":
LayerType = LSTM
elif args.layer.lower() == "gru":
LayerType = GRU
depth = self.depth = args.depth
layers = self.layers = []
for i in range(depth):
if LayerType != RCNN:
feature_layer = LayerType(n_in=n_e if i == 0 else n_d,
n_out=n_d,
activation=activation)
else:
feature_layer = LayerType(n_in=n_e if i == 0 else n_d,
n_out=n_d,
activation=activation,
order=args.order,
mode=args.mode,
has_outgate=args.outgate)
layers.append(feature_layer)
# feature computation starts here
# (len*batch)*n_e
xt = embedding_layer.forward(idts.ravel())
if weights is not None:
xt_w = weights[idts.ravel()].dimshuffle((0, 'x'))
xt = xt * xt_w
# len*batch*n_e
xt = xt.reshape((idts.shape[0], idts.shape[1], n_e))
xt = apply_dropout(xt, dropout)
# (len*batch)*n_e
xb = embedding_layer.forward(idbs.ravel())
if weights is not None:
xb_w = weights[idbs.ravel()].dimshuffle((0, 'x'))
xb = xb * xb_w
# len*batch*n_e
xb = xb.reshape((idbs.shape[0], idbs.shape[1], n_e))
xb = apply_dropout(xb, dropout)
prev_ht = self.xt = xt
prev_hb = self.xb = xb
for i in range(depth):
# len*batch*n_d
ht = layers[i].forward_all(prev_ht)
hb = layers[i].forward_all(prev_hb)
prev_ht = ht
prev_hb = hb
# normalize vectors
if args.normalize:
ht = self.normalize_3d(ht)
hb = self.normalize_3d(hb)
say("h_title dtype: {}\n".format(ht.dtype))
self.ht = ht
self.hb = hb
# average over length, ignore paddings
# batch * d
if args.average:
ht = self.average_without_padding(ht, idts)
hb = self.average_without_padding(hb, idbs)
else:
ht = ht[-1]
hb = hb[-1]
say("h_avg_title dtype: {}\n".format(ht.dtype))
# batch * d
h_final = (ht + hb) * 0.5
h_final = apply_dropout(h_final, dropout)
h_final = self.normalize_2d(h_final)
self.h_final = h_final
say("h_final dtype: {}\n".format(ht.dtype))
# For testing:
# first one in batch is query, the rest are candidate questions
self.scores = T.dot(h_final[1:], h_final[0])
# For training:
xp = h_final[idps.ravel()]
xp = xp.reshape((idps.shape[0], idps.shape[1], n_d))
# num query * n_d
query_vecs = xp[:, 0, :]
# num query
pos_scores = T.sum(query_vecs * xp[:, 1, :], axis=1)
# num query * candidate size
neg_scores = T.sum(query_vecs.dimshuffle((0, 'x', 1)) * xp[:, 2:, :],
axis=2)
# num query
neg_scores = T.max(neg_scores, axis=1)
diff = neg_scores - pos_scores + 1.0
loss = T.mean((diff > 0) * diff)
self.loss = loss
params = []
for l in self.layers:
params += l.params
self.params = params
say("num of parameters: {}\n".format(
sum(len(x.get_value(borrow=True).ravel()) for x in params)))
l2_reg = None
for p in params:
if l2_reg is None:
l2_reg = p.norm(2)
else:
l2_reg = l2_reg + p.norm(2)
l2_reg = l2_reg * args.l2_reg
self.cost = self.loss + l2_reg
def ready(self):
generator = self.generator
args = self.args
weights = self.weights
dropout = generator.dropout
# len(text) * batch
idts = generator.x
z = generator.z_pred
z = z.dimshuffle((0,1,"x"))
# batch * 2
pairs = self.pairs = T.imatrix()
# num pairs * 3, or num queries * candidate size
triples = self.triples = T.imatrix()
embedding_layer = self.embedding_layer
activation = get_activation_by_name(args.activation)
n_d = self.n_d = args.hidden_dim
n_e = self.n_e = embedding_layer.n_d
if args.layer.lower() == "rcnn":
LayerType = RCNN
LayerType2 = ExtRCNN
elif args.layer.lower() == "lstm":
LayerType = LSTM
LayerType2 = ExtLSTM
#elif args.layer.lower() == "gru":
# LayerType = GRU
depth = self.depth = args.depth
layers = self.layers = [ ]
for i in range(depth):
if LayerType != RCNN:
feature_layer = LayerType(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation
)
else:
feature_layer = LayerType(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order,
mode = args.mode,
has_outgate = args.outgate
)
layers.append(feature_layer)
extlayers = self.extlayers = [ ]
for i in range(depth):
if LayerType != RCNN:
feature_layer = LayerType2(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation
)
else:
feature_layer = LayerType2(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order,
mode = args.mode,
has_outgate = args.outgate
)
feature_layer.copy_params(layers[i])
extlayers.append(feature_layer)
# feature computation starts here
xt = generator.word_embs
# encode full text into representation
prev_ht = self.xt = xt
for i in range(depth):
# len*batch*n_d
ht = layers[i].forward_all(prev_ht)
prev_ht = ht
# encode selected text into representation
prev_htz = self.xt = xt
for i in range(depth):
# len*batch*n_d
htz = extlayers[i].forward_all(prev_htz, z)
prev_htz = htz
# normalize vectors
if args.normalize:
ht = self.normalize_3d(ht)
htz = self.normalize_3d(htz)
say("h_title dtype: {}\n".format(ht.dtype))
self.ht = ht
self.htz = htz
# average over length, ignore paddings
# batch * d
if args.average:
ht = self.average_without_padding(ht, idts)
htz = self.average_without_padding(htz, idts, z)
else:
ht = ht[-1]
htz = htz[-1]
say("h_avg_title dtype: {}\n".format(ht.dtype))
# batch * d
h_final = apply_dropout(ht, dropout)
h_final = self.normalize_2d(h_final)
hz_final = apply_dropout(htz, dropout)
hz_final = self.normalize_2d(hz_final)
self.h_final = h_final
self.hz_final = hz_final
say("h_final dtype: {}\n".format(ht.dtype))
# For testing:
# first one in batch is query, the rest are candidate questions
self.scores = T.dot(h_final[1:], h_final[0])
self.scores_z = T.dot(hz_final[1:], hz_final[0])
# For training encoder:
xp = h_final[triples.ravel()]
xp = xp.reshape((triples.shape[0], triples.shape[1], n_d))
# num query * n_d
query_vecs = xp[:,0,:]
# num query
pos_scores = T.sum(query_vecs*xp[:,1,:], axis=1)
# num query * candidate size
neg_scores = T.sum(query_vecs.dimshuffle((0,'x',1))*xp[:,2:,:], axis=2)
# num query
neg_scores = T.max(neg_scores, axis=1)
diff = neg_scores - pos_scores + 1.0
hinge_loss = T.mean( (diff>0)*diff )
# For training generator
# batch
self_cosine_distance = 1.0 - T.sum(hz_final * h_final, axis=1)
pair_cosine_distance = 1.0 - T.sum(hz_final * h_final[pairs[:,1]], axis=1)
alpha = args.alpha
loss_vec = self_cosine_distance*alpha + pair_cosine_distance*(1-alpha)
#loss_vec = self_cosine_distance*0.2 + pair_cosine_distance*0.8
zsum = generator.zsum
zdiff = generator.zdiff
logpz = generator.logpz
sfactor = args.sparsity
cfactor = args.sparsity * args.coherent
scost_vec = zsum*sfactor + zdiff*cfactor
# batch
cost_vec = loss_vec + scost_vec
cost_logpz = T.mean(cost_vec * T.sum(logpz, axis=0))
loss = self.loss = T.mean(loss_vec)
sparsity_cost = self.sparsity_cost = T.mean(scost_vec)
self.obj = loss + sparsity_cost
params = [ ]
for l in self.layers:
params += l.params
self.params = params
say("num of parameters: {}\n".format(
sum(len(x.get_value(borrow=True).ravel()) for x in params)
))
l2_reg = None
for p in params:
if l2_reg is None:
l2_reg = T.sum(p**2) #p.norm(2)
else:
l2_reg = l2_reg + T.sum(p**2) #p.norm(2)
l2_reg = l2_reg * args.l2_reg
self.l2_cost = l2_reg
beta = args.beta
self.cost_g = cost_logpz + generator.l2_cost
self.cost_e = hinge_loss + loss*beta + l2_reg
print "cost dtype", self.cost_g.dtype, self.cost_e.dtype
