class InputModule(MergeLayer):
# Input Module, which uses SemMemModule and GRULayer(lasgne)
def __init__(self, incomings, voc_size, hid_state_size,
SemMem=None, GRU=None, **kwargs):
super(InputModule, self).__init__(incomings, **kwargs)
if SemMem is not None:
self.SemMem = SemMem
else:
self.SemMem = SemMemModule(incomings[0], voc_size, hid_state_size, **kwargs)
if GRU is not None:
self.GRU = GRU
else:
self.GRU = GRULayer(SemMem, hid_state_size)
self.voc_size = voc_size
self.hid_state_size = hid_state_size
def get_params(self, **tags):
# Because InputModules uses external GRULayer's parameters,
# We have to notify this information to train the GRU's parameters.
return self.GRU.get_params(**tags)
def get_output_shape_for(self, input_shape):
return (None, None, self.hid_state_size)
def get_output_for(self, inputs, **kwargs):
# input with size (batch, sentences, words)
input = inputs[0]
# original size of input_word is (batch, sentences)
# input_word with size (batch x sentences, ) after flatten
input_word = T.flatten(inputs[1])
word_dropout = inputs[2]
# Apply word embedding
# With size (batch x sentence, word, emb_dim)
sentence_rep = self.SemMem.get_output_for([input, word_dropout])
# Apply GRU Layer
# 'gru_outs' with size (batch x sentence, word, hid_state_size)
gru_outs = self.GRU.get_output_for([sentence_rep])
# Extract candidate fact from GRU's output by input_word variable
# resolving input with additional word
# e.g. John went to the hallway nil nil nil -> [GRU1, ... ,GRU8] -> GRU5
#
# hid_extract with size (batch x sentence, hid_state_size)
hid_extract = gru_outs[T.arange(gru_outs.shape[0], dtype='int16'), input_word - 1]
# candidate_facts with size (batch, sentences, hid_state_size)
candidate_facts = T.reshape(x=hid_extract, newshape=(-1, input.shape[1], self.hid_state_size))
return candidate_facts
def test_gru_hid_init_layer_eval():
# Test `hid_init` as a `Layer` with some dummy input. Compare the output of
# a network with a `Layer` as input to `hid_init` to a network with a
# `np.array` as input to `hid_init`
n_units = 7
n_test_cases = 2
in_shp = (n_test_cases, 2, 3)
in_h_shp = (1, n_units)
# dummy inputs
X_test = np.ones(in_shp, dtype=theano.config.floatX)
Xh_test = np.ones(in_h_shp, dtype=theano.config.floatX)
Xh_test_batch = np.tile(Xh_test, (n_test_cases, 1))
# network with `Layer` initializer for hid_init
l_inp = InputLayer(in_shp)
l_inp_h = InputLayer(in_h_shp)
l_rec_inp_layer = GRULayer(l_inp, n_units, hid_init=l_inp_h)
# network with `np.array` initializer for hid_init
l_rec_nparray = GRULayer(l_inp, n_units, hid_init=Xh_test)
# copy network parameters from l_rec_inp_layer to l_rec_nparray
l_il_param = dict([(p.name, p) for p in l_rec_inp_layer.get_params()])
l_rn_param = dict([(p.name, p) for p in l_rec_nparray.get_params()])
for k, v in l_rn_param.items():
if k in l_il_param:
v.set_value(l_il_param[k].get_value())
# build the theano functions
X = T.tensor3()
Xh = T.matrix()
output_inp_layer = lasagne.layers.get_output(l_rec_inp_layer,
{l_inp: X, l_inp_h: Xh})
output_nparray = lasagne.layers.get_output(l_rec_nparray, {l_inp: X})
# test both nets with dummy input
output_val_inp_layer = output_inp_layer.eval({X: X_test,
Xh: Xh_test_batch})
output_val_nparray = output_nparray.eval({X: X_test})
# check output given `Layer` is the same as with `np.array`
assert np.allclose(output_val_inp_layer, output_val_nparray)
class InputModule(MergeLayer):
# Input Module, which uses SemMemModule and GRULayer(lasgne)
def __init__(self, incomings, voc_size, hid_state_size,
SemMem=None, GRU=None, **kwargs):
super(InputModule, self).__init__(incomings, **kwargs)
if SemMem is not None:
self.SemMem = SemMem
else:
self.SemMem = SemMemModule(incomings[0],voc_size,hid_state_size,**kwargs)
if GRU is not None:
self.GRU = GRU
else:
self.GRU = GRULayer(SemMem, hid_state_size)
self.voc_size = voc_size
self.hid_state_size = hid_state_size
def get_params(self, **tags):
# Because InputModules uses external GRULayer's parameters,
# We have to inform this information to train them.
return self.GRU.get_params(**tags)
def get_output_shape_for(self, input_shape):
return (None, None, self.hid_state_size)
def get_output_for(self, inputs, **kwargs):
input = inputs[0]
input_word = T.flatten(inputs[1])
word_dropout = inputs[2]
# Apply word embedding
sentence_rep = self.SemMem.get_output_for([input, word_dropout])
# Apply GRU Layer
gru_outs = self.GRU.get_output_for([sentence_rep])
# Extract candidate fact from GRU's output by input_word variable
# resolving input with adtional word
# e.g. John when to the hallway nil nil nil -> [GRU1, ... ,GRU8] -> GRU5
candidate_facts = T.reshape(
gru_outs[T.arange(gru_outs.shape[0],dtype='int32'), input_word-1],
(-1, input.shape[1], self.hid_state_size))
return candidate_facts
def test_gru_bck():
num_batch, seq_len, n_features1 = 2, 3, 4
num_units = 2
x = T.tensor3()
in_shp = (num_batch, seq_len, n_features1)
l_inp = InputLayer(in_shp)
x_in = np.ones(in_shp).astype('float32')
# need to set random seed.
lasagne.random.get_rng().seed(1234)
l_gru_fwd = GRULayer(l_inp, num_units=num_units, backwards=False)
lasagne.random.get_rng().seed(1234)
l_gru_bck = GRULayer(l_inp, num_units=num_units, backwards=True)
output_fwd = helper.get_output(l_gru_fwd, x)
output_bck = helper.get_output(l_gru_bck, x)
output_fwd_val = output_fwd.eval({x: x_in})
output_bck_val = output_bck.eval({x: x_in})
# test that the backwards model reverses its final input
np.testing.assert_almost_equal(output_fwd_val, output_bck_val[:, ::-1])
def test_gru_hid_init_mask():
# test that you can set hid_init to be a layer when a mask is provided
l_inp = InputLayer((2, 2, 3))
l_inp_h = InputLayer((2, 5))
l_inp_msk = InputLayer((2, 2))
l_gru = GRULayer(l_inp, 5, hid_init=l_inp_h, mask_input=l_inp_msk)
x = T.tensor3()
h = T.matrix()
msk = T.matrix()
inputs = {l_inp: x, l_inp_h: h, l_inp_msk: msk}
output = lasagne.layers.get_output(l_gru, inputs)
def test_gru_passthrough():
# Tests that the LSTM can simply pass through its input
l_in = InputLayer((4, 5, 6))
zero = lasagne.init.Constant(0.)
one = lasagne.init.Constant(1.)
pass_gate = Gate(zero, zero, None, one, None)
no_gate = Gate(zero, zero, None, zero, None)
in_pass_gate = Gate(
np.eye(6).astype(theano.config.floatX), zero, None, zero, None)
l_rec = GRULayer(l_in, 6, no_gate, pass_gate, in_pass_gate)
out = lasagne.layers.get_output(l_rec)
inp = np.arange(4 * 5 * 6).reshape(4, 5, 6).astype(theano.config.floatX)
np.testing.assert_almost_equal(out.eval({l_in.input_var: inp}), inp)
def __init__(self, incomings, voc_size, hid_state_size,
SemMem=None, GRU=None, **kwargs):
super(InputModule, self).__init__(incomings, **kwargs)
if SemMem is not None:
self.SemMem = SemMem
else:
self.SemMem = SemMemModule(incomings[0],voc_size,hid_state_size,**kwargs)
if GRU is not None:
self.GRU = GRU
else:
self.GRU = GRULayer(SemMem, hid_state_size)
self.voc_size = voc_size
self.hid_state_size = hid_state_size
def test_gru_return_shape():
num_batch, seq_len, n_features1, n_features2 = 5, 3, 10, 11
num_units = 6
x = T.tensor4()
in_shp = (num_batch, seq_len, n_features1, n_features2)
l_inp = InputLayer(in_shp)
l_rec = GRULayer(l_inp, num_units=num_units)
x_in = np.random.random(in_shp).astype('float32')
output = helper.get_output(l_rec, x)
output_val = output.eval({x: x_in})
assert helper.get_output_shape(l_rec, x_in.shape) == output_val.shape
assert output_val.shape == (num_batch, seq_len, num_units)
def _add_decoder(self):
"""
Decoder returns the batch of sequences of thought vectors, each corresponds to a decoded token
reshapes this 3d tensor to 2d matrix so that the next Dense layer can convert each thought vector to
a probability distribution vector
"""
self._net['hid_states_decoder'] = InputLayer(
shape=(None, self._decoder_depth, None),
input_var=T.tensor3('hid_inits_decoder'),
name='hid_states_decoder')
# repeat along the sequence axis output_seq_len times, where output_seq_len is inferred from input tensor
self._net['enc_repeated'] = RepeatLayer(
incoming=self._net[
'enc_result'], # input shape = (batch_size, encoder_output_dimension)
n=self._output_seq_len,
name='repeat_layer')
self._net['emb_condition_id_repeated'] = RepeatLayer(
incoming=self._net['emb_condition_id'],
n=self._output_seq_len,
name='embedding_condition_id_repeated')
self._net['dec_concated_input'] = ConcatLayer(
incomings=[
self._net['emb_y'], self._net['enc_repeated'],
self._net['emb_condition_id_repeated']
],
axis=2,
name='decoder_concated_input')
# shape = (batch_size, input_seq_len, encoder_output_dimension)
self._net['dec_0'] = self._net['dec_concated_input']
for dec_layer_id in xrange(1, self._decoder_depth + 1):
# input shape = (batch_size, input_seq_len, embedding_dimension + hidden_dimension)
self._net['dec_' + str(dec_layer_id)] = GRULayer(
incoming=self._net['dec_' + str(dec_layer_id - 1)],
num_units=self._hidden_layer_dim,
grad_clipping=self._grad_clip,
only_return_final=False,
name='decoder_' + str(dec_layer_id),
mask_input=self._net['input_y_mask'],
hid_init=SliceLayer(self._net['hid_states_decoder'],
dec_layer_id - 1,
axis=1))
self._net['dec'] = self._net['dec_' + str(self._decoder_depth)]
def test_gru_precompute():
num_batch, seq_len, n_features1 = 2, 3, 4
num_units = 2
in_shp = (num_batch, seq_len, n_features1)
l_inp = InputLayer(in_shp)
l_mask_inp = InputLayer(in_shp[:2])
x_in = np.random.random(in_shp).astype('float32')
mask_in = np.ones((num_batch, seq_len), dtype='float32')
# need to set random seed.
lasagne.random.get_rng().seed(1234)
l_gru_precompute = GRULayer(l_inp,
num_units=num_units,
precompute_input=True,
mask_input=l_mask_inp)
lasagne.random.get_rng().seed(1234)
l_gru_no_precompute = GRULayer(l_inp,
num_units=num_units,
precompute_input=False,
mask_input=l_mask_inp)
output_precompute = helper.get_output(l_gru_precompute).eval({
l_inp.input_var:
x_in,
l_mask_inp.input_var:
mask_in
})
output_no_precompute = helper.get_output(l_gru_no_precompute).eval({
l_inp.input_var:
x_in,
l_mask_inp.input_var:
mask_in
})
# test that the backwards model reverses its final input
np.testing.assert_almost_equal(output_precompute, output_no_precompute)
def test_gru_variable_input_size():
# that seqlen and batchsize None works
num_batch, n_features1 = 6, 5
num_units = 13
x = T.tensor3()
in_shp = (None, None, n_features1)
l_inp = InputLayer(in_shp)
x_in1 = np.ones((num_batch + 1, 10, n_features1)).astype('float32')
x_in2 = np.ones((num_batch, 15, n_features1)).astype('float32')
l_rec = GRULayer(l_inp, num_units=num_units, backwards=False)
output = helper.get_output(l_rec, x)
output.eval({x: x_in1})
output.eval({x: x_in2})
def test_gru_unroll_scan_fwd():
num_batch, seq_len, n_features1 = 2, 3, 4
num_units = 2
in_shp = (num_batch, seq_len, n_features1)
l_inp = InputLayer(in_shp)
l_mask_inp = InputLayer(in_shp[:2])
x_in = np.random.random(in_shp).astype('float32')
mask_in = np.ones(in_shp[:2]).astype('float32')
# need to set random seed.
lasagne.random.get_rng().seed(1234)
l_gru_scan = GRULayer(l_inp,
num_units=num_units,
backwards=False,
unroll_scan=False,
mask_input=l_mask_inp)
lasagne.random.get_rng().seed(1234)
l_gru_unrolled = GRULayer(l_inp,
num_units=num_units,
backwards=False,
unroll_scan=True,
mask_input=l_mask_inp)
output_scan = helper.get_output(l_gru_scan)
output_unrolled = helper.get_output(l_gru_unrolled)
output_scan_val = output_scan.eval({
l_inp.input_var: x_in,
l_mask_inp.input_var: mask_in
})
output_unrolled_val = output_unrolled.eval({
l_inp.input_var: x_in,
l_mask_inp.input_var: mask_in
})
np.testing.assert_almost_equal(output_scan_val, output_unrolled_val)
def __init__(self, num_batch, max_len, n_features, hidden=[200, 200], **kwargs):
self.num_batch = num_batch
self.n_features = n_features
self.max_len = max_len
self.hidden = hidden
rng = np.random.RandomState(123)
self.drng = rng
self.rng = RandomStreams(rng.randint(2 ** 30))
# params
initial_W = np.asarray(
rng.uniform(
low=-4 * np.sqrt(6. / (self.hidden[1] + self.n_features)),
high=4 * np.sqrt(6. / (self.hidden[1] + self.n_features)),
size=(self.hidden[1], self.n_features)
),
dtype=theano.config.floatX
)
self.W = theano.shared(value=initial_W, name='W', borrow=True)
# # self.W_y_kappa = theano.shared(value=initial_W, name='W_y_kappa', borrow=True)
self.b = theano.shared(
value=np.zeros(
self.n_features,
dtype=theano.config.floatX
),
borrow=True
)
# self.b_y_kappa = theano.shared(
# value=np.zeros(
# self.n_features,
# dtype=theano.config.floatX
# ),
# name='b',
# borrow=True
# )
# I could directly create the model here since it is fixed
self.l_in = InputLayer(shape=(None, self.max_len, self.n_features))
self.mask_input = InputLayer(shape=(None, self.max_len))
first_hidden = GRULayer(self.l_in, mask_input=self.mask_input, num_units=hidden[0])
# l_shp = ReshapeLayer(first_hidden, (-1, hidden[0]))
# l_dense = DenseLayer(l_shp, num_units=self.hidden[0], nonlinearity=rectify)
# l_drop = DropoutLayer(l_dense, p=0.5)
# l_shp = ReshapeLayer(l_drop, (-1, self.max_len, self.hidden[0]))
self.model = GRULayer(first_hidden, num_units=hidden[1])
def gru_column(input, num_units, hidden, **kwargs):
kwargs.pop("only_return_final", None)
assert isinstance(hidden, (list, tuple))
name = kwargs.pop("name", "default")
column = [input]
for i, l_hidden in enumerate(hidden):
kwargs_ = kwargs.copy()
if isinstance(l_hidden, Layer):
kwargs_.pop("learn_init", None)
kwargs_["hid_init"] = l_hidden
layer = GRULayer(column[-1], num_units,
name=os.path.join(name, "gru_%02d" % i),
**kwargs_)
column.append(layer)
return column[1:]
def _add_utterance_encoder(self):
# input shape = (batch_size * input_context_size, input_seq_len, embedding_dimension)
self._add_forward_backward_encoder_layer()
for enc_layer_id in xrange(1, self._encoder_depth):
is_last_encoder_layer = enc_layer_id == self._encoder_depth - 1
return_only_final_state = is_last_encoder_layer
# input shape = (batch_size * input_context_size, input_seq_len, embedding_dimension)
self._net['enc_' + str(enc_layer_id)] = GRULayer(
incoming=self._net['enc_' + str(enc_layer_id - 1)],
num_units=self._hidden_layer_dim,
grad_clipping=self._grad_clip,
only_return_final=return_only_final_state,
name='encoder_' + str(enc_layer_id),
mask_input=self._net['input_x_mask'])
self._net['enc'] = self._net['enc_' + str(self._encoder_depth - 1)]
def test_gru_nparams_hid_init_layer():
# test that you can see layers through hid_init
l_inp = InputLayer((2, 2, 3))
l_inp_h = InputLayer((2, 5))
l_inp_h_de = DenseLayer(l_inp_h, 7)
l_gru = GRULayer(l_inp, 7, hid_init=l_inp_h_de)
# directly check the layers can be seen through hid_init
assert lasagne.layers.get_all_layers(l_gru) == [l_inp, l_inp_h, l_inp_h_de,
l_gru]
# 3*n_gates + 2
# the 3 is because we have hid_to_gate, in_to_gate and bias for each gate
# 2 is for the W and b parameters in the DenseLayer
assert len(lasagne.layers.get_all_params(l_gru, trainable=True)) == 11
# GRU bias params(3) + Dense bias params(1)
assert len(lasagne.layers.get_all_params(l_gru, regularizable=False)) == 4
def __init__(self, num_batch, max_len, n_features, hidden=[200, 200], **kwargs):
self.num_batch = num_batch
self.n_features = n_features
self.max_len = max_len
self.hidden = hidden
rng = np.random.RandomState(123)
self.drng = rng
self.rng = RandomStreams(rng.randint(2 ** 30))
# params
initial_W = np.asarray(
rng.uniform(
low=1e-5,
high=1,
size=(self.hidden[1], self.n_features)
),
dtype=theano.config.floatX
)
self.W_y_theta = theano.shared(value=initial_W, name='W_y_theta', borrow=True)
# # self.W_y_kappa = theano.shared(value=initial_W, name='W_y_kappa', borrow=True)
self.b_y_theta = theano.shared(
value=np.zeros(
self.n_features,
dtype=theano.config.floatX
),
borrow=True
)
# self.b_y_kappa = theano.shared(
# value=np.zeros(
# self.n_features,
# dtype=theano.config.floatX
# ),
# name='b',
# borrow=True
# )
# I could directly create the model here since it is fixed
self.l_in = InputLayer(shape=(self.num_batch, self.max_len, self.n_features))
self.mask_input = InputLayer(shape=(self.num_batch, self.max_len))
first_hidden = GRULayer(self.l_in, mask_input=self.mask_input, num_units=hidden[0])
self.model =GRULayer(first_hidden, num_units=hidden[1])
def gated_layer(incoming,
num_units,
grad_clipping,
only_return_final,
backwards,
gated_layer_type,
mask_input=None,
cell_init=lasagne.init.Constant(0.),
hid_init=lasagne.init.Constant(0.),
resetgate=lasagne.layers.Gate(W_cell=None),
updategate=lasagne.layers.Gate(W_cell=None),
hidden_update=lasagne.layers.Gate(
W_cell=None, nonlinearity=lasagne.nonlinearities.tanh),
name=None):
if gated_layer_type == "gru":
return GRULayer(incoming,
num_units,
mask_input=mask_input,
grad_clipping=grad_clipping,
only_return_final=only_return_final,
backwards=backwards,
hid_init=hid_init,
resetgate=resetgate,
updategate=updategate,
hidden_update=hidden_update,
name=name)
else:
return LSTMLayer(incoming,
num_units,
mask_input=mask_input,
grad_clipping=grad_clipping,
nonlinearity=lasagne.nonlinearities.tanh,
only_return_final=only_return_final,
backwards=backwards,
cell_init=cell_init,
hid_init=hid_init,
resetgate=resetgate,
updategate=updategate,
hidden_update=hidden_update,
name=name)
def test_gru_hid_init_layer_eval():
# Test `hid_init` as a `Layer` with some dummy input. Compare the output of
# a network with a `Layer` as input to `hid_init` to a network with a
# `np.array` as input to `hid_init`
n_units = 7
n_test_cases = 2
in_shp = (n_test_cases, 2, 3)
in_h_shp = (1, n_units)
# dummy inputs
X_test = np.ones(in_shp, dtype=theano.config.floatX)
Xh_test = np.ones(in_h_shp, dtype=theano.config.floatX)
Xh_test_batch = np.tile(Xh_test, (n_test_cases, 1))
# network with `Layer` initializer for hid_init
l_inp = InputLayer(in_shp)
l_inp_h = InputLayer(in_h_shp)
l_rec_inp_layer = GRULayer(l_inp, n_units, hid_init=l_inp_h)
# network with `np.array` initializer for hid_init
l_rec_nparray = GRULayer(l_inp, n_units, hid_init=Xh_test)
# copy network parameters from l_rec_inp_layer to l_rec_nparray
l_il_param = dict([(p.name, p) for p in l_rec_inp_layer.get_params()])
l_rn_param = dict([(p.name, p) for p in l_rec_nparray.get_params()])
for k, v in l_rn_param.items():
if k in l_il_param:
v.set_value(l_il_param[k].get_value())
# build the theano functions
X = T.tensor3()
Xh = T.matrix()
output_inp_layer = lasagne.layers.get_output(l_rec_inp_layer, {
l_inp: X,
l_inp_h: Xh
})
output_nparray = lasagne.layers.get_output(l_rec_nparray, {l_inp: X})
# test both nets with dummy input
output_val_inp_layer = output_inp_layer.eval({
X: X_test,
Xh: Xh_test_batch
})
output_val_nparray = output_nparray.eval({X: X_test})
# check output given `Layer` is the same as with `np.array`
assert np.allclose(output_val_inp_layer, output_val_nparray)
def _add_context_encoder(self):
self._net['batched_enc'] = reshape(
self._net['enc'], (self._batch_size, self._input_context_size, get_output_shape(self._net['enc'])[-1]))
self._net['context_enc'] = GRULayer(
incoming=self._net['batched_enc'],
num_units=self._hidden_layer_dim,
grad_clipping=self._grad_clip,
only_return_final=True,
name='context_encoder')
self._net['switch_enc_to_tv'] = T.iscalar(name='switch_enc_to_tv')
self._net['thought_vector'] = InputLayer(
shape=(None, self._hidden_layer_dim), input_var=T.fmatrix(name='thought_vector'), name='thought_vector')
self._net['enc_result'] = SwitchLayer(
incomings=[self._net['thought_vector'], self._net['context_enc']], condition=self._net['switch_enc_to_tv'])
# We need the following to pass as 'givens' argument when compiling theano functions:
self._default_thoughts_vector = T.zeros((self._batch_size, self._hidden_layer_dim))
self._default_input_x = T.zeros(shape=(self._net['thought_vector'].input_var.shape[0], 1, 1), dtype=np.int32)
def __init__(self, in_path, concat=False, wsi_path=None, dat_path='data/dat.pkl', supp_path='data/supp.pkl'): self.in_path = in_path self.concat = concat self.wsi_path = wsi_path self.lm_mode = 'default' with open(self.in_path, 'rb') as f: p = pk.load(f) self.do_brnn = False if 'do_brnn' in p: self.do_brnn = p['do_brnn'] self.is_lstm = 'Wxo' in p['params'] self.is_gru = 'Whr' in p['params'] if 'Wt' not in p: self.lm_mode = 'iden' elif p['Wt'].get_value().ndim == 1: self.lm_mode = 'diag' self.params = p['params'] self.dwe = self.params['dwe'] # disambiguated word embeddings self.td = self.dwe.get_value().shape[1] self.hd = self.params['L'].get_value().shape[1] self.gc = 2 self.l_mask = InputLayer((None, None), trainable=False) if self.is_lstm: self.l_gru_emb = LSTMLayer((None, None, self.td), self.hd, grad_clipping=self.gc, \ ingate=Gate(W_in=self.params['Wxr'], W_hid=self.params['Whr'], b=self.params['br'], W_cell=None), \ forgetgate=Gate(W_in=self.params['Wxu'], W_hid=self.params['Whu'], b=self.params['bu'], W_cell=None), \ outgate=Gate(W_in=self.params['Wxo'], W_hid=self.params['Who'], b=self.params['bo'], W_cell=None), \ cell=Gate(W_in=self.params['Wxc'], W_hid=self.params['Whc'], b=self.params['bc'], W_cell=None,\ nonlinearity=nonlinearities.tanh),mask_input=self.l_mask, peepholes=False) if self.do_brnn: self.l_bgru_emb = LSTMLayer((None, None, self.td), self.hd, grad_clipping=self.gc, \ ingate=Gate(W_in=self.params['bWxr'], W_hid=self.params['bWhr'], b=self.params['bbr'], W_cell=None), \ forgetgate=Gate(W_in=self.params['bWxu'], W_hid=self.params['bWhu'], b=self.params['bbu'], W_cell=None), \ outgate=Gate(W_in=self.params['bWxo'], W_hid=self.params['bWho'], b=self.params['bbo'], W_cell=None), \ cell=Gate(W_in=self.params['bWxc'], W_hid=self.params['bWhc'], b=self.params['bbc'], W_cell=None,\ nonlinearity=nonlinearities.tanh),mask_input=self.l_mask, peepholes=False, backwards=True) elif self.is_gru: self.l_gru_emb = GRULayer((None, None, self.td), self.hd, grad_clipping=self.gc, \ resetgate=Gate(W_in=self.params['Wxr'], W_hid=self.params['Whr'], b=self.params['br'], W_cell=None), \ updategate=Gate(W_in=self.params['Wxu'], W_hid=self.params['Whu'], b=self.params['bu'], W_cell=None), \ hidden_update=Gate(W_in=self.params['Wxc'], W_hid=self.params['Whc'], b=self.params['bc'], W_cell=None,\ nonlinearity=nonlinearities.tanh),mask_input=self.l_mask) if self.do_brnn: self.l_bgru_emb = GRULayer((None, None, self.td), self.hd, grad_clipping=self.gc, \ resetgate=Gate(W_in=self.params['bWxr'], W_hid=self.params['bWhr'], b=self.params['bbr'], W_cell=None), \ updategate=Gate(W_in=self.params['bWxu'], W_hid=self.params['bWhu'], b=self.params['bbu'], W_cell=None), \ hidden_update=Gate(W_in=self.params['bWxc'], W_hid=self.params['bWhc'], b=self.params['bbc'], W_cell=None,\ nonlinearity=nonlinearities.tanh),mask_input=self.l_mask, backwards=True) else: self.is_nlm = True with open(dat_path, 'rb') as f: d = pk.load(f) self.nw, self.mw, self.ms = d['def'].shape # num words, max num of words, max num of senses self.dw = d['dw'] # dw to index self.aw = d['aw'] self.no = len(d['aw']) if 'spriors' in d: self.sense_priors = d['spriors'] else: self.sense_priors = np.ones((self.no, self.ms)) with open(supp_path, 'rb') as f: s = pk.load(f) self.id2aw = s['id2aw'] self.id2dw = s['id2dw'] self.aw2dw = s['aw2dw'] self.build_encoder()
def test_gru_grad_clipping():
# test that you can set grad_clip variable
x = T.tensor3()
l_rec = GRULayer(InputLayer((2, 2, 3)), 5, grad_clipping=1)
output = lasagne.layers.get_output(l_rec, x)
hidden.append(slice_)
return hidden
###############################################################################
# ENCODER #
###############################################################################
# Encoder's Recurrent subnetwork
l_encoder_mask = InputLayer((None, None), name="encoder/mask")
l_encoder_embed = InputLayer((None, None, n_embed_char), name="encoder/input")
bidi_gru = []
bidi_gru.append(
GRULayer(l_encoder_embed,
n_hidden_encoder,
learn_init=True,
name="encoder/gru_f",
backwards=False))
bidi_gru.append(
GRULayer(l_encoder_embed,
n_hidden_encoder,
learn_init=True,
name="encoder/gru_b",
backwards=True))
l_encoder_context = ConcatLayer(bidi_gru, axis=-1, name="encoder/cat")
###############################################################################
# DECODER #
###############################################################################
# Decoder's Recurrent subnetwork
class PRAE:
def __init__(self,
num_batch,
max_len,
n_features,
hidden=[200, 200],
**kwargs):
self.num_batch = num_batch
self.n_features = n_features
self.max_len = max_len
self.hidden = hidden
rng = np.random.RandomState(123)
self.drng = rng
self.rng = RandomStreams(rng.randint(2**30))
# params
initial_W = np.asarray(rng.uniform(low=1e-5,
high=1,
size=(self.hidden[1],
self.n_features)),
dtype=theano.config.floatX)
self.W_y_theta = theano.shared(value=initial_W,
name='W_y_theta',
borrow=True)
# # self.W_y_kappa = theano.shared(value=initial_W, name='W_y_kappa', borrow=True)
self.b_y_theta = theano.shared(value=np.zeros(
self.n_features, dtype=theano.config.floatX),
borrow=True)
# self.b_y_kappa = theano.shared(
# value=np.zeros(
# self.n_features,
# dtype=theano.config.floatX
# ),
# name='b',
# borrow=True
# )
# I could directly create the model here since it is fixed
self.l_in = InputLayer(shape=(self.num_batch, self.max_len,
self.n_features))
self.mask_input = InputLayer(shape=(self.num_batch, self.max_len))
first_hidden = GRULayer(self.l_in,
mask_input=self.mask_input,
num_units=hidden[0])
self.model = GRULayer(first_hidden, num_units=hidden[1])
# need some reshape voodoo
# l_shp = ReshapeLayer(second_hidden, (-1, hidden[1]))
# after the reshape I have batch*max_len X features
# self.model = DenseLayer(l_shp, num_units=self.n_features, nonlinearity=rectify)
# if now I put a dense layer this will collect all the output temporally which is what I want, I'll have to fix
# the dimensions probably later
# For every gaussian in the sum I need 3 values plus a value for the total scale
# the output of this layer will be (num_batch, num_units, max_len) TODO check size
def get_output_shape_for(self):
return self.model.get_output_shape_for(self.num_batch, self.max_len,
self.hidden[2])
def get_output_y(self, output):
# (batch, time, hidden) X (hidden, features) + (, features) => (batch, time, features)
theta_out = T.nnet.relu(T.dot(output, self.W_y_theta) + self.b_y_theta)
#kappa_out = T.nnet.relu(T.dot(output, self.W_y_kappa) + self.b_y_kappa)
return theta_out
def get_log_x(self, x, theta_out):
# DIM = (batch, time, hidden)
# everything is elementwise
log_x = T.log(theta_out + 1e-8) - theta_out * x
log_x = log_x.sum(axis=2, dtype=theano.config.floatX
) # sum over x cause I assume they are independent
return log_x
def build_model(self, train_x, train_mask_x, train_mask_out, train_target,
test_x, test_mask_x, test_mask_out, test_target):
self.train_x = train_x
self.train_mask_x = train_mask_x
self.train_mask_out = train_mask_out
self.train_target = train_target
self.test_x = test_x
self.test_mask_x = test_mask_x
self.test_mask_out = test_mask_out
self.test_target = test_target
self.index = T.iscalar('index')
self.num_batch_test = T.iscalar('index')
self.b_slice = slice(self.index * self.num_batch,
(self.index + 1) * self.num_batch)
sym_x = T.dtensor3()
sym_mask_x = T.dmatrix()
sym_target = T.dtensor3()
# sym_mask_out = T.dtensor3() should not be useful since output is still zero
# TODO think about this if it is true
output = lasagne.layers.get_output(self.model,
inputs={
self.l_in: sym_x,
self.mask_input: sym_mask_x
})
theta = self.get_output_y(output)
log_px = self.get_log_x(sym_target, theta)
log_px_sum_time = log_px.sum(axis=1,
dtype=theano.config.floatX) # sum over tx
loss = -T.sum(log_px_sum_time) / self.num_batch # average over batch
##
log_px_test = self.get_log_x(sym_target, theta)
log_px_sum_time_test = log_px_test.sum(
axis=1, dtype=theano.config.floatX) # sum over time
loss_test = -T.sum(
log_px_sum_time_test) / self.num_batch_test # average over batch
# loss = T.mean(lasagne.objectives.squared_error(mu, sym_target))
all_params = [self.W_y_theta] + [
self.b_y_theta
] + lasagne.layers.get_all_params(self.model)
all_grads_target = [T.clip(g, -3, 3) for g in T.grad(loss, all_params)]
all_grads_target = lasagne.updates.total_norm_constraint(
all_grads_target, 3)
updates_target = adam(all_grads_target, all_params)
train_model = theano.function(
[self.index], [loss, theta, log_px],
givens={
sym_x: self.train_x[self.b_slice],
sym_mask_x: self.train_mask_x[self.b_slice],
sym_target: self.train_target[self.b_slice]
},
updates=updates_target)
test_model = theano.function(
[self.num_batch_test], [loss_test, theta],
givens={
sym_x: self.test_x,
sym_mask_x: self.test_mask_x,
sym_target: self.test_target
})
return train_model, test_model
def test_memory_cells(batch_size=3, seq_len=50, input_dim=8, n_hidden=16):
# lasagne way
l_in = InputLayer(
(None, seq_len, input_dim),
input_var=theano.shared(
np.random.normal(size=[batch_size, seq_len, input_dim])),
name='input seq')
l_lstm0 = LSTMLayer(l_in, n_hidden, name='lstm')
l_gru0 = GRULayer(l_in, n_hidden, name='gru')
f_predict0 = theano.function([], get_output([l_lstm0, l_gru0]))
# agentnet way
s_in = InputLayer((None, input_dim), name='in')
s_prev_cell = InputLayer((None, n_hidden), name='cell')
s_prev_hid = InputLayer((None, n_hidden), name='hid')
s_lstm_cell, s_lstm_hid = LSTMCell(s_prev_cell,
s_prev_hid,
s_in,
name='lstm')
s_prev_gru = InputLayer((None, n_hidden), name='hid')
s_gru = GRUCell(s_prev_gru, s_in, name='gru')
rec = Recurrence(state_variables=OrderedDict({
s_lstm_cell: s_prev_cell,
s_lstm_hid: s_prev_hid,
s_gru: s_prev_gru
}),
input_sequences={s_in: l_in},
unroll_scan=False)
state_seqs, _ = rec.get_sequence_layers()
l_lstm1 = state_seqs[s_lstm_hid]
l_gru1 = state_seqs[s_gru]
f_predict1 = theano.function([], get_output([l_lstm1, l_gru1]))
# lstm param transfer
old_params = sorted(get_all_params(l_lstm0, trainable=True),
key=lambda p: p.name)
new_params = sorted(get_all_params(s_lstm_hid, trainable=True),
key=lambda p: p.name)
for old, new in zip(old_params, new_params):
print old.name, '<-', new.name
assert tuple(old.shape.eval()) == tuple(new.shape.eval())
old.set_value(new.get_value())
# gru param transfer
old_params = sorted(get_all_params(l_gru0, trainable=True),
key=lambda p: p.name)
new_params = sorted(get_all_params(s_gru, trainable=True),
key=lambda p: p.name)
for old, new in zip(old_params, new_params):
print old.name, '<-', new.name
assert tuple(old.shape.eval()) == tuple(new.shape.eval())
old.set_value(new.get_value())
lstm0_out, gru0_out = f_predict0()
lstm1_out, gru1_out = f_predict1()
assert np.allclose(lstm0_out, lstm1_out)
assert np.allclose(gru0_out, gru1_out)
class PRAE:
def __init__(self, num_batch, max_len, n_features, hidden=[200, 200], **kwargs):
self.num_batch = num_batch
self.n_features = n_features
self.max_len = max_len
self.hidden = hidden
rng = np.random.RandomState(123)
self.drng = rng
self.rng = RandomStreams(rng.randint(2 ** 30))
# params
initial_W = np.asarray(
rng.uniform(
low=-4 * np.sqrt(6. / (self.hidden[1] + self.n_features)),
high=4 * np.sqrt(6. / (self.hidden[1] + self.n_features)),
size=(self.hidden[1], self.n_features)
),
dtype=theano.config.floatX
)
self.W = theano.shared(value=initial_W, name='W', borrow=True)
# # self.W_y_kappa = theano.shared(value=initial_W, name='W_y_kappa', borrow=True)
self.b = theano.shared(
value=np.zeros(
self.n_features,
dtype=theano.config.floatX
),
borrow=True
)
# self.b_y_kappa = theano.shared(
# value=np.zeros(
# self.n_features,
# dtype=theano.config.floatX
# ),
# name='b',
# borrow=True
# )
# I could directly create the model here since it is fixed
self.l_in = InputLayer(shape=(None, self.max_len, self.n_features))
self.mask_input = InputLayer(shape=(None, self.max_len))
first_hidden = GRULayer(self.l_in, mask_input=self.mask_input, num_units=hidden[0])
# l_shp = ReshapeLayer(first_hidden, (-1, hidden[0]))
# l_dense = DenseLayer(l_shp, num_units=self.hidden[0], nonlinearity=rectify)
# l_drop = DropoutLayer(l_dense, p=0.5)
# l_shp = ReshapeLayer(l_drop, (-1, self.max_len, self.hidden[0]))
self.model = GRULayer(first_hidden, num_units=hidden[1])
# self.model = ConcatLayer([first_hidden, second_hidden], axis=2)
# l_shp = ReshapeLayer(second_hidden, (-1, hidden[1]))
# l_dense = DenseLayer(l_shp, num_units=self.n_features, nonlinearity=rectify)
# To reshape back to our original shape, we can use the symbolic shape
# variables we retrieved above.
#self.model = ReshapeLayer(l_dense, (-1, self.max_len, self.n_features))
# if now I put a dense layer this will collect all the output temporally which is what I want, I'll have to fix
# the dimensions probably later
# For every gaussian in the sum I need 3 values plus a value for the total scale
# the output of this layer will be (num_batch, num_units, max_len) TODO check size
def get_output_shape_for(self):
return self.model.get_output_shape_for(self.num_batch, self.max_len, self.hidden[1])
def get_output_y(self, x):
return T.nnet.relu(T.dot(x, self.W) + self.b)
def build_model(self, train_x, train_mask_x, train_mask_out, train_target,
test_x, test_mask_x, test_mask_out, test_target):
self.train_x = train_x
self.train_mask_x = train_mask_x
self.train_mask_out = train_mask_out
self.train_target = train_target
self.test_x = test_x
self.test_mask_x = test_mask_x
self.test_mask_out = test_mask_out
self.test_target = test_target
self.index = T.iscalar('index')
self.num_batch_test = T.iscalar('index')
self.b_slice = slice(self.index * self.num_batch, (self.index + 1) * self.num_batch)
sym_x = T.dtensor3()
sym_mask_x = T.dmatrix()
sym_target = T.dtensor3()
sym_mask_out = T.dtensor3()
# sym_mask_out = T.dtensor3() should not be useful since output is still zero
# TODO think about this if it is true
out = lasagne.layers.get_output(self.model, inputs={self.l_in: sym_x, self.mask_input: sym_mask_x})
out_out = self.get_output_y(out)
loss = T.mean(lasagne.objectives.squared_error(out_out, sym_target)) / self.num_batch
out_test = lasagne.layers.get_output(self.model, inputs={self.l_in: sym_x, self.mask_input: sym_mask_x})
out_out_test = self.get_output_y(out_test)
loss_test = T.mean(lasagne.objectives.squared_error(out_out_test, sym_target)) / self.num_batch_test
all_params = [self.W] + [self.b] +lasagne.layers.get_all_params(self.model)
all_grads_target = [T.clip(g, -3, 3) for g in T.grad(loss, all_params)]
all_grads_target = lasagne.updates.total_norm_constraint(all_grads_target, 3)
updates_target = adam(all_grads_target, all_params)
train_model = theano.function([self.index],
[loss, out_out],
givens={sym_x: self.train_x[self.b_slice],
sym_mask_x: self.train_mask_x[self.b_slice],
sym_target: self.train_target[self.b_slice],
},
updates=updates_target)
test_model = theano.function([self.num_batch_test],
[loss_test, out_out_test],
givens={sym_x: self.test_x,
sym_mask_x: self.test_mask_x,
sym_target: self.test_target,
})
return train_model, test_model
def test_gru_init_val_error():
# check if errors are raised when init is non matrix tensorVariable
vector = T.vector()
with pytest.raises(ValueError):
l_rec = GRULayer(InputLayer((2, 2, 3)), 5, hid_init=vector)
def __init__(self, model_name, max_seq_len, num_features_pitch, num_features_duration, num_gru_layer_units=25, set_x_input_to_zero=False, in_dropout_p=0, out_dropout_p=0, use_l2_penalty=False):
super(GRU_Network, self).__init__(model_name, max_seq_len, num_features_pitch, num_features_duration, num_gru_layer_units, set_x_input_to_zero, in_dropout_p, use_l2_penalty)
self.out_dropout_p = out_dropout_p
##### THE LAYERS OF THE NEXT-STEP PREDICTION GRU NETWORK #####
### INPUT NETWORK ###
# Two input layers receiving Onehot-encoded data
l_in_pitch = InputLayer((None, None, self.num_features_pitch), name="l_in_pitch")
l_in_duration = InputLayer((None, None, self.num_features_duration), name="l_in_duration")
# Layer merging the two input layers
l_in_merge = ConcatLayer([l_in_pitch, l_in_duration], axis=2, name="l_in_merge")
# Dropout in input network
l_in_intermediate = l_in_merge
if self.in_dropout_p > 0:
l_in_intermediate = DropoutLayer(l_in_intermediate, rescale=False, p=self.in_dropout_p, shared_axes=(1,2))
# The mask layer for ignoring time-steps after <eos> in the GRU layer
l_in_mask = InputLayer((None, self.max_seq_len), name="l_in_mask")
### OUTPUT NETWORK ###
# A normal GRU layer
self.l_out_gru = GRULayer(l_in_intermediate, num_units=self.num_gru_layer_units, name='GRULayer', mask_input=l_in_mask)
# Dropout in output network
l_out_intermediate = self.l_out_gru
if self.out_dropout_p > 0:
l_out_intermediate = DropoutLayer(l_out_intermediate, rescale=False, p=self.out_dropout_p)
# We need to do some reshape voodo to connect a softmax layer.
# See http://lasagne.readthedocs.org/en/latest/modules/layers/recurrent.html#examples
# In short this line changes the shape from
# (batch_size, decode_len, num_dec_units) -> (batch_size*decodelen,num_dec_units).
# We need to do this since the softmax is applied to the last dimension and we want to
# softmax the output at each position individually
l_out_reshape = ReshapeLayer(l_out_intermediate, (-1, [2]), name="l_out_reshape")
# Setting up the output-layers as softmax-encoded pitch and duration vectors from the dense layers. (Two dense layers with softmax output, e.g. prediction probabilities for next note in melody)
l_out_softmax_pitch = DenseLayer(l_out_reshape, num_units=self.num_features_pitch, nonlinearity=lasagne.nonlinearities.softmax, name='SoftmaxOutput_pitch')
l_out_softmax_duration = DenseLayer(l_out_reshape, num_units=self.num_features_duration, nonlinearity=lasagne.nonlinearities.softmax, name='SoftmaxOutput_duration')
# reshape back to 3d format (batch_size, decode_len, num_dec_units). Here we tied the batch size to the shape of the symbolic variable for X allowing
#us to use different batch sizes in the model.
self.l_out_pitch = ReshapeLayer(l_out_softmax_pitch, (-1, self.max_seq_len, self.num_features_pitch), name="l_out_pitch")
self.l_out_duration = ReshapeLayer(l_out_softmax_duration, (-1, self.max_seq_len, self.num_features_duration), name="l_out_duration")
### NETWORK OUTPUTS ###
# Setting up the output as softmax-encoded pitch and duration vectors from the dense softmax layers.
# (OBS: This is bypassing the onehot layers, so we evaluate the model on the softmax-outputs directly)
output_pitch_train = get_output(self.l_out_pitch, {l_in_pitch: self.x_pitch_sym, l_in_duration: self.x_duration_sym, l_in_mask: self.x_mask_sym}, deterministic = False)
output_duration_train = get_output(self.l_out_duration, {l_in_pitch: self.x_pitch_sym, l_in_duration: self.x_duration_sym, l_in_mask: self.x_mask_sym}, deterministic = False)
output_pitch_eval = get_output(self.l_out_pitch, {l_in_pitch: self.x_pitch_sym, l_in_duration: self.x_duration_sym, l_in_mask: self.x_mask_sym}, deterministic = True)
output_duration_eval = get_output(self.l_out_duration, {l_in_pitch: self.x_pitch_sym, l_in_duration: self.x_duration_sym, l_in_mask: self.x_mask_sym}, deterministic = True)
output_gru = get_output(self.l_out_gru, {l_in_pitch: self.x_pitch_sym, l_in_duration: self.x_duration_sym, l_in_mask: self.x_mask_sym}, deterministic = True)
#Get parameters from all layers except nondeterministic (dropout)
all_parameters = get_all_params([self.l_out_pitch, self.l_out_duration], trainable=True)
print "Trainable Model Parameters"
print "-"*40
for param in all_parameters:
print param, param.get_value().shape
print "-"*40
# Compute costs
# For indeterministic training
cost_pitch_train, acc_pitch_train = eval(output_pitch_train, self.y_pitch_sym, self.num_features_pitch, self.y_mask_sym)
cost_duration_train, acc_duration_train = eval(output_duration_train, self.y_duration_sym, self.num_features_duration, self.y_mask_sym)
if self.use_l2_penalty:
l2_penalty = regularize_layer_params([self.l_out_pitch, self.l_out_duration], l2)
else:
l2_penalty = 0
total_cost = cost_pitch_train + cost_duration_train + l2_penalty
# and deterministic evaluation
cost_pitch_eval, acc_pitch_eval = eval(output_pitch_eval, self.y_pitch_sym, self.num_features_pitch, self.y_mask_sym)
cost_duration_eval, acc_duration_eval = eval(output_duration_eval, self.y_duration_sym, self.num_features_duration, self.y_mask_sym)
#add grad clipping to avoid exploding gradients
all_grads = [T.clip(g,-3,3) for g in T.grad(total_cost, all_parameters)]
all_grads = lasagne.updates.total_norm_constraint(all_grads,3)
#Compile Theano functions.
updates = lasagne.updates.adam(all_grads, all_parameters, learning_rate=0.005)
self.f_train = theano.function([self.x_pitch_sym, self.y_pitch_sym, self.x_duration_sym, self.y_duration_sym, self.x_mask_sym, self.y_mask_sym], [cost_pitch_train, acc_pitch_train, output_pitch_train, cost_duration_train, acc_duration_train, output_duration_train], updates=updates)
#since we have stochasticity in the network when dropout is used we will use the evaluation graph without any updates given and deterministic=True.
self.f_eval = theano.function([self.x_pitch_sym, self.y_pitch_sym, self.x_duration_sym, self.y_duration_sym, self.x_mask_sym, self.y_mask_sym], [cost_pitch_eval, acc_pitch_eval, output_pitch_eval, cost_duration_eval, acc_duration_eval, output_duration_eval])
self.f_eval_gru = theano.function([self.x_pitch_sym, self.x_duration_sym, self.x_mask_sym], output_gru)
class Sent2Vec:
def __init__(self, in_path, concat=False, wsi_path=None, dat_path='data/dat.pkl', supp_path='data/supp.pkl'):
self.in_path = in_path
self.concat = concat
self.wsi_path = wsi_path
self.lm_mode = 'default'
with open(self.in_path, 'rb') as f:
p = pk.load(f)
self.do_brnn = False
if 'do_brnn' in p:
self.do_brnn = p['do_brnn']
self.is_lstm = 'Wxo' in p['params']
self.is_gru = 'Whr' in p['params']
if 'Wt' not in p: self.lm_mode = 'iden'
elif p['Wt'].get_value().ndim == 1: self.lm_mode = 'diag'
self.params = p['params']
self.dwe = self.params['dwe'] # disambiguated word embeddings
self.td = self.dwe.get_value().shape[1]
self.hd = self.params['L'].get_value().shape[1]
self.gc = 2
self.l_mask = InputLayer((None, None), trainable=False)
if self.is_lstm:
self.l_gru_emb = LSTMLayer((None, None, self.td), self.hd, grad_clipping=self.gc, \
ingate=Gate(W_in=self.params['Wxr'], W_hid=self.params['Whr'], b=self.params['br'], W_cell=None), \
forgetgate=Gate(W_in=self.params['Wxu'], W_hid=self.params['Whu'], b=self.params['bu'], W_cell=None), \
outgate=Gate(W_in=self.params['Wxo'], W_hid=self.params['Who'], b=self.params['bo'], W_cell=None), \
cell=Gate(W_in=self.params['Wxc'], W_hid=self.params['Whc'], b=self.params['bc'], W_cell=None,\
nonlinearity=nonlinearities.tanh),mask_input=self.l_mask, peepholes=False)
if self.do_brnn:
self.l_bgru_emb = LSTMLayer((None, None, self.td), self.hd, grad_clipping=self.gc, \
ingate=Gate(W_in=self.params['bWxr'], W_hid=self.params['bWhr'], b=self.params['bbr'], W_cell=None), \
forgetgate=Gate(W_in=self.params['bWxu'], W_hid=self.params['bWhu'], b=self.params['bbu'], W_cell=None), \
outgate=Gate(W_in=self.params['bWxo'], W_hid=self.params['bWho'], b=self.params['bbo'], W_cell=None), \
cell=Gate(W_in=self.params['bWxc'], W_hid=self.params['bWhc'], b=self.params['bbc'], W_cell=None,\
nonlinearity=nonlinearities.tanh),mask_input=self.l_mask, peepholes=False, backwards=True)
elif self.is_gru:
self.l_gru_emb = GRULayer((None, None, self.td), self.hd, grad_clipping=self.gc, \
resetgate=Gate(W_in=self.params['Wxr'], W_hid=self.params['Whr'], b=self.params['br'], W_cell=None), \
updategate=Gate(W_in=self.params['Wxu'], W_hid=self.params['Whu'], b=self.params['bu'], W_cell=None), \
hidden_update=Gate(W_in=self.params['Wxc'], W_hid=self.params['Whc'], b=self.params['bc'], W_cell=None,\
nonlinearity=nonlinearities.tanh),mask_input=self.l_mask)
if self.do_brnn:
self.l_bgru_emb = GRULayer((None, None, self.td), self.hd, grad_clipping=self.gc, \
resetgate=Gate(W_in=self.params['bWxr'], W_hid=self.params['bWhr'], b=self.params['bbr'], W_cell=None), \
updategate=Gate(W_in=self.params['bWxu'], W_hid=self.params['bWhu'], b=self.params['bbu'], W_cell=None), \
hidden_update=Gate(W_in=self.params['bWxc'], W_hid=self.params['bWhc'], b=self.params['bbc'], W_cell=None,\
nonlinearity=nonlinearities.tanh),mask_input=self.l_mask, backwards=True)
else:
self.is_nlm = True
with open(dat_path, 'rb') as f:
d = pk.load(f)
self.nw, self.mw, self.ms = d['def'].shape # num words, max num of words, max num of senses
self.dw = d['dw'] # dw to index
self.aw = d['aw']
self.no = len(d['aw'])
if 'spriors' in d:
self.sense_priors = d['spriors']
else:
self.sense_priors = np.ones((self.no, self.ms))
with open(supp_path, 'rb') as f:
s = pk.load(f)
self.id2aw = s['id2aw']
self.id2dw = s['id2dw']
self.aw2dw = s['aw2dw']
self.build_encoder()
# assume xml-style input
# output: 'lemma.pos instance-id sense-name/rating'
def perform_wsi(self):
expr = '[' + string.punctuation + ']'
jaccard = False
for d in os.listdir(self.wsi_path):
f = os.path.join(self.wsi_path, d)
if not os.path.isfile(f): continue
with open(f) as fin:
wsi = xd.parse(fin.read())
for inst in wsi['instances']['instance']:
tok = inst['@token']
txt = re.sub(expr, ' ', inst['#text'])
lemma = inst['@lemma']
pos = inst['@partOfSpeech']
inst_id = inst['@id']
ind = txt.split().index(tok)
s, m, ptmp = self.to_indexes(txt, token=tok, pos=pos, lem=lemma)
'''s = s.reshape(1, *s.shape)
m = m.reshape(1, *m.shape)
fu = np.asarray([ptmp]).astype(np.int32)
weights = self.get_weights(s, m, fu, np.ones_like(s).astype(np.float32)) # mw x ms'''
weights = self.get_vector([txt], mode='w', token=tok, pos=pos, lem=lemma)
senses = s[ind, :]
sweight = weights[0][ind, :]
ratings = [(self.id2dw[senses[i]], sweight[i]) \
for i in range(len(sweight)) \
if self.id2dw[senses[i]].split('.')[0] == lemma and sweight[i] > 0.02]
ratings.sort(key=lambda k: k[1], reverse=True)
if len(ratings) == 0: pdb.set_trace()
l = min(3, len(ratings))
if jaccard:
r = [k[0] for k in ratings[0:2]]
else:
r = [k[0] + '/' + str(k[1]) for k in ratings[0:l]]
print '{}.{} {} {}'.format(lemma, pos, inst_id, ' '.join(r))
def build_encoder(self):
def to_vect(d, m, p):
L0 = self.params['L0']
hid_inp = self.dwe[d, :] # mw x ms x hd
logit = T.exp(T.dot(hid_inp, L0)[:,:,p])# (mw x ms) x mw
mk = T.switch(T.lt(p, 0), 0, 1) # mw: word-level mask (different mask from m)
mask = mk.dimshuffle(0, 'x', 'x')
l2 = logit * mask # mw x ms x mw
l2 = T.sum(l2 * mk.dimshuffle('x', 'x', 0), axis=2) * m # mw x ms
w0 = l2 / T.sum(l2, axis=1).dimshuffle(0, 'x')
w1 = T.switch(T.isnan(w0), 0, w0)
w = w1.dimshuffle(0, 1, 'x') # mw x ms x 1
res = T.sum(w * hid_inp, axis=1) # mw x hd
return res #, logit, weights
def to_weights(d, m, p, prior):
hid_inp = self.dwe[d, :] # mw x ms x hd
if self.is_lstm or self.is_gru:
logit = T.exp(T.dot(hid_inp, L0)[:,:,p])# (mw x ms) x mw
mk = T.switch(T.lt(p, 0), 0, 1) # mw: word-level mask (different mask from m)
mask = mk.dimshuffle(0, 'x', 'x')
l2 = logit * mask # mw x ms x mw
l2 = T.sum(l2 * mk.dimshuffle('x', 'x', 0), axis=2) * m # mw x ms
w0 = l2 / T.sum(l2, axis=1).dimshuffle(0, 'x')
w1 = T.switch(T.isnan(w0), 0, w0)
else:
if self.lm_mode == 'diag':
B = hid_inp * Wt.dimshuffle('x', 'x', 0)
tmp = T.tensordot(B, B.T, axes = 1)
elif self.lm_mode == 'iden':
logit = T.tensordot(self.dwe[d, :], self.dwe.T, axes=1)[:,:,d] # mw x ms x mw x ms
cnt = T.sum(m, axis=1).dimshuffle('x', 'x', 0) # 1 x 1 x mw
logit = T.sum(logit * m.dimshuffle('x', 'x', 0, 1), axis=3) / cnt # mw x ms x mw
logit = T.exp(10*T.switch(T.isnan(logit), 0, logit)) # mw x ms x mw
logit = T.prod(logit, axis=2) * prior # mw x ms
sm = T.sum(logit * m, axis=1, keepdims=True) # mw x 1
logit = (logit * m) / sm # mw x ms
return T.switch(T.or_(T.isnan(logit), T.isinf(logit)), 0, logit)
else:
tmp = T.tensordot(T.dot(hid_inp, self.params['Wt']), hid_inp.T, axes=1) # mw x ms x ms x mw
tmp = T.exp(tmp.dimshuffle(0, 1, 3, 2)) # mw x ms x mw x ms
tmp = tmp * m.dimshuffle('x', 'x', 0, 1)
nrm = T.sum(tmp, axis=3)
tmp = tmp / nrm.dimshuffle(0, 1, 2, 'x')
tmp = T.switch(T.isnan(tmp), 0, tmp)
mk = T.switch(T.lt(p, 0), 0, 1) # mw: word-level mask (different mask from m)
tmp = T.max(tmp, axis=3) * mk.dimshuffle('x', 'x', 0) # mw x ms x mw
tmp = T.exp(T.sum(T.log(T.switch(T.eq(tmp, 0), 1, tmp)), axis=2)) * m # mw x ms
tmp = tmp * prior
tmp = tmp / T.sum(tmp, axis=1).dimshuffle(0, 'x')
w1 = T.switch(T.isnan(tmp), 0, tmp)
return w1
st = T.itensor3('st') # bs x len x ms
pd = T.imatrix('wi') # bs x len
mk = T.itensor3('mk') # bs x len x ms
wv = T.dmatrix('wv') # bs x hd
pe = T.imatrix('pe') # bs x mew
pr = T.tensor3('pr') # bs x len x ms
weights, _ = theano.scan(fn = to_weights, sequences = [st, mk, pd, pr]) # bs x mw x ms
mask = T.ones_like(pd).astype(theano.config.floatX) # bs x len
if self.is_lstm or self.is_gru:
enc, _ = theano.scan(fn = to_vect, sequences = [st, mk, pd]) # bs x mw x hd
enc = enc.astype(theano.config.floatX)
fdef_emb = self.l_gru_emb.get_output_for([enc, mask]) # bs x hd
if self.do_brnn:
bdef_emb = self.l_bgru_emb.get_output_for([enc, mask])
if self.concat:
def_emb = T.concatenate([fdef_emb[:,-1,:], bdef_emb[:,0,:]], axis=1)
else:
def_emb = T.dot(fdef_emb[:, -1, :], self.params['Wf']) + \
T.dot(bdef_emb[:, 0, :], self.params['Wb']) + \
self.params['by'].dimshuffle('x', 0) # bs x hd
else:
def_emb = fdef_emb[:, -1, :]
else:
hid_inp = self.dwe[st, :]
dat = T.sum(weights.dimshuffle(0, 1, 2, 'x') * hid_inp, axis=2)
def_emb = T.sum(T.dot(dat, self.params['L']), axis = 1)
self.encode = theano.function([st, mk, pd, pr], def_emb)
self.get_weights = theano.function([st, mk, pd, pr], weights)
def preproc_word(self, w, pos=None):
if pos == 'j': pos = 'a'
w = re.sub(r'[\$,\{\}\[\]\(\)`\'\":;!\?\.]', '', w).lower()
w = re.sub(r'\-', '_', w) # hyphen -> underscore
if w == 'an': w = 'a' # dirty hack....
if w == 'oclock': w = 'o\'clock'
if w.isdigit(): w = '<NUM>'
wp = wn.morphy(w, pos=pos)
if wp is None: wp = w
return wp
# 'sents' is a list of sentences
def get_vector(self, sents, mode='v', token=None, pos='any', lem=None):
mw = max([len(s.split()) for s in sents])
s = np.ones((len(sents), mw, self.ms), dtype=np.int32) * -1
m = np.zeros(s.shape, dtype=np.int32)
p = np.ones((len(sents), mw), dtype=np.int32) * -1
pr = np.ones((len(sents), mw, self.ms), dtype=np.float32)
sp = self.sense_priors # no x ms
for (si, sn) in enumerate(sents):
s[si], m[si], p_tmp = self.to_indexes(sn, mw, token=token, pos=pos, lem=lem)
p[si][0:len(p_tmp)] = p_tmp
for i in range(mw):
if i >= len(sn): break
pwid = p[si][i]
pr[si][i] = sp[pwid, :]
if mode == 'v':
return self.encode(s, m, p, pr)
else:
return self.get_weights(s, m, p, pr)
# 'sent' is a single string
# mw is the maximum number of words (if called from get_vector())
# Setting token = w and pos = p will restrict the processing of 'w' to ones having POS tag 'p'
def to_indexes(self, sent, mw = None, token = None, pos = None, lem = None):
def same_pos(a, b):
if a is None or b is None or a == b: return True
if (a == 'a' or a == 's') and b == 'j': return True
return False
p_tmp = []
sn = sent.split()
if mw is None:
mw = len(sn)
s = np.ones((mw, self.ms), dtype=np.int32) * -1
m = np.zeros(s.shape, dtype=np.int32)
for (ind, w) in enumerate(sn):
filt = (token is not None) and (w == token) #filter the token using pos
if filt: _pos = pos
else: _pos = None
w = self.preproc_word(w, pos=_pos)
if w not in self.aw2dw or len(self.aw2dw[w]) == 0:
s[ind, 0] = self.dw['<UNK>']
m[ind, 0] = 1.0
else:
l = min(10, len(self.aw2dw[w]))
if filt:
cands = []
if lem is not None: w = lem
for wp in self.aw2dw[w]:
try:
if same_pos(wn.synset(wp).pos(), pos) and wp.split('.')[0] == w:
cands.append(wp)
except:
continue
#cands = [wp for wp in self.aw2dw[w] if same_pos(wn.synset(wp).pos(), pos)]
l = min(25, len(cands))
s[ind][0:l] = [self.dw[wp] for wp in cands][0:25]
else:
s[ind][0:l] = [self.dw[wp] for wp in self.aw2dw[w][0:l]]
m[ind][0:l] = np.ones((l,))
if l == 0: pdb.set_trace()
if w in self.aw:
p_tmp.append(self.aw[w])
else:
p_tmp.append(0)
return s, m, p_tmp
class PRAE:
def __init__(self, num_batch, max_len, n_features, hidden=[200, 200], **kwargs):
self.num_batch = num_batch
self.n_features = n_features
self.max_len = max_len
self.hidden = hidden
rng = np.random.RandomState(123)
self.drng = rng
self.rng = RandomStreams(rng.randint(2 ** 30))
# params
initial_W = np.asarray(
rng.uniform(
low=1e-5,
high=1,
size=(self.hidden[1], self.n_features)
),
dtype=theano.config.floatX
)
self.W_y_theta = theano.shared(value=initial_W, name='W_y_theta', borrow=True)
# # self.W_y_kappa = theano.shared(value=initial_W, name='W_y_kappa', borrow=True)
self.b_y_theta = theano.shared(
value=np.zeros(
self.n_features,
dtype=theano.config.floatX
),
borrow=True
)
# self.b_y_kappa = theano.shared(
# value=np.zeros(
# self.n_features,
# dtype=theano.config.floatX
# ),
# name='b',
# borrow=True
# )
# I could directly create the model here since it is fixed
self.l_in = InputLayer(shape=(self.num_batch, self.max_len, self.n_features))
self.mask_input = InputLayer(shape=(self.num_batch, self.max_len))
first_hidden = GRULayer(self.l_in, mask_input=self.mask_input, num_units=hidden[0])
self.model =GRULayer(first_hidden, num_units=hidden[1])
# need some reshape voodoo
# l_shp = ReshapeLayer(second_hidden, (-1, hidden[1]))
# after the reshape I have batch*max_len X features
# self.model = DenseLayer(l_shp, num_units=self.n_features, nonlinearity=rectify)
# if now I put a dense layer this will collect all the output temporally which is what I want, I'll have to fix
# the dimensions probably later
# For every gaussian in the sum I need 3 values plus a value for the total scale
# the output of this layer will be (num_batch, num_units, max_len) TODO check size
def get_output_shape_for(self):
return self.model.get_output_shape_for(self.num_batch, self.max_len, self.hidden[2])
def get_output_y(self, output):
# (batch, time, hidden) X (hidden, features) + (, features) => (batch, time, features)
theta_out = T.nnet.relu(T.dot(output, self.W_y_theta) + self.b_y_theta)
#kappa_out = T.nnet.relu(T.dot(output, self.W_y_kappa) + self.b_y_kappa)
return theta_out
def get_log_x(self, x, theta_out):
# DIM = (batch, time, hidden)
# everything is elementwise
log_x = T.log(theta_out + 1e-8) - theta_out * x
log_x = log_x.sum(axis=2, dtype=theano.config.floatX) # sum over x cause I assume they are independent
return log_x
def build_model(self, train_x, train_mask_x, train_mask_out, train_target,
test_x, test_mask_x, test_mask_out, test_target):
self.train_x = train_x
self.train_mask_x = train_mask_x
self.train_mask_out = train_mask_out
self.train_target = train_target
self.test_x = test_x
self.test_mask_x = test_mask_x
self.test_mask_out = test_mask_out
self.test_target = test_target
self.index = T.iscalar('index')
self.num_batch_test = T.iscalar('index')
self.b_slice = slice(self.index * self.num_batch, (self.index + 1) * self.num_batch)
sym_x = T.dtensor3()
sym_mask_x = T.dmatrix()
sym_target = T.dtensor3()
# sym_mask_out = T.dtensor3() should not be useful since output is still zero
# TODO think about this if it is true
output = lasagne.layers.get_output(self.model, inputs={self.l_in: sym_x, self.mask_input: sym_mask_x})
theta = self.get_output_y(output)
log_px = self.get_log_x(sym_target, theta)
log_px_sum_time = log_px.sum(axis=1, dtype=theano.config.floatX) # sum over tx
loss = - T.sum(log_px_sum_time) / self.num_batch # average over batch
##
log_px_test = self.get_log_x(sym_target, theta)
log_px_sum_time_test = log_px_test.sum(axis=1, dtype=theano.config.floatX) # sum over time
loss_test = - T.sum(log_px_sum_time_test) / self.num_batch_test # average over batch
# loss = T.mean(lasagne.objectives.squared_error(mu, sym_target))
all_params = [self.W_y_theta] + [self.b_y_theta] + lasagne.layers.get_all_params(self.model)
all_grads_target = [T.clip(g, -3, 3) for g in T.grad(loss, all_params)]
all_grads_target = lasagne.updates.total_norm_constraint(all_grads_target, 3)
updates_target = adam(all_grads_target, all_params)
train_model = theano.function([self.index],
[loss, theta, log_px],
givens={sym_x: self.train_x[self.b_slice],
sym_mask_x: self.train_mask_x[self.b_slice],
sym_target: self.train_target[self.b_slice]},
updates=updates_target)
test_model = theano.function([self.num_batch_test],
[loss_test, theta],
givens={sym_x: self.test_x,
sym_mask_x: self.test_mask_x,
sym_target: self.test_target})
return train_model, test_model
def recurrence(i, h_tm1, w_a, M_a, *args, **kwargs):
"""
notes
Headers from paper in all caps
mem = n_article slots if is_article else n_title_slots
:param i: center index of sliding window
:param h_tm1: h_{t-1} (hidden state)
:param w_a: attention weights for article memory
:param M_a: article memory
:param args: gru_weights, maybe w_t, maybe M_t
gru_weights: weights with which to initialize GRULayer on each time step
w_t: attention weights for titles memory
M_t: titles memory
:param kwargs: is_training, is_article
is_training:
is_article: we use different parts of memory when working with a article
:return: [y = model outputs,
i + 1 = increment index,
h w, M (see above)]
"""
is_training = kwargs['is_training']
is_article = kwargs['is_article']
gru_weights = args[:depth]
if len(args) > depth:
w_t = args[depth]
M_t = args[depth + 1]
i_type = T.iscalar if is_article or is_training else T.ivector
assert i.type == i_type
if not is_article:
assert w_t is not None and M_t is not None
word_idxs = i
if is_article or is_training:
# get representation of word window
document = articles if is_article else titles # [instances, bucket_width]
word_idxs = document[:, i:i + 1] # [instances, 1]
# x_i = self.emb[word_idxs].flatten(ndim=2) # [instances, embedding_dim]
input = InputLayer(shape=(None, 1), input_var=word_idxs)
embed = EmbeddingLayer(input, num_embeddings, embedding_dim)
gru = GRULayer(incoming=embed,
num_units=embedding_dim,
hid_init=self.gru0)
for weight in gru_weights:
gru = GRULayer(incoming=gru,
num_units=embedding_dim,
hid_init=weight)
x_i = get_output(gru).flatten(ndim=2)
x_i = Print('x_i')(x_i) # [instances, embedding_dim]
gru_weights = []
if is_article:
M_read = M_a # [instances, memory_size, n_article_slots]
w_read = w_a # [instances, n_article_slots]
else:
M_read = T.concatenate(
[M_a, M_t],
axis=2) # [instances, memory_size, n_title_slots]
w_read = T.concatenate([w_a, w_t],
axis=1) # [instances, n_title_slots]
# eqn 15
c = T.batched_dot(M_read, w_read) # [instances, memory_size]
# EXTERNAL MEMORY READ
def get_attention(Wg, bg, M, w):
g = T.nnet.sigmoid(T.dot(x_i, Wg) + bg) # [instances, mem]
# eqn 11
k = T.dot(h_tm1, self.Wk) + self.bk # [instances, memory_size]
# eqn 13
beta = T.dot(h_tm1, self.Wb) + self.bb
beta = T.nnet.softplus(beta)
beta = T.addbroadcast(beta, 1) # [instances, 1]
# eqn 12
w_hat = T.nnet.softmax(beta * cosine_dist(M, k))
# eqn 14
return (1 - g) * w + g * w_hat # [instances, mem]
w_a = get_attention(self.Wg_a, self.bg_a, M_a,
w_a) # [instances, n_article_slots]
if not is_article:
w_t = get_attention(self.Wg_t, self.bg_t, M_t,
w_t) # [instances, n_title_slots]
# MODEL INPUT AND OUTPUT
# eqn 9
h = T.dot(c, self.Wh) + T.dot(
x_i, self.Wx) + self.bh # [instances, hidden_size]
# eqn 10
y = T.nnet.softmax(T.dot(h, self.W) +
self.b) # [instances, nclasses]
# EXTERNAL MEMORY UPDATE
def update_memory(We, be, w_update, M_update):
# eqn 17
e = T.nnet.sigmoid(T.dot(h_tm1, We) + be) # [instances, mem]
f = 1. - w_update * e # [instances, mem]
# eqn 16
v = T.tanh(T.dot(h, self.Wv) +
self.bv) # [instances, memory_size]
# need to add broadcast layers for memory update
f = f.dimshuffle(0, 'x', 1) # [instances, 1, mem]
u = w_update.dimshuffle(0, 'x', 1) # [instances, 1, mem]
v = v.dimshuffle(0, 1, 'x') # [instances, memory_size, 1]
# eqn 19
return M_update * f + T.batched_dot(v, u) * (
1 - f) # [instances, memory_size, mem]
M_a = update_memory(self.We_a, self.be_a, w_a, M_a)
attention_and_memory = [w_a, M_a]
if not is_article:
M_t = update_memory(self.We_t, self.be_t, w_t, M_t)
attention_and_memory += [w_t, M_t]
y_max = y.argmax(axis=1).astype(int32)
next_idxs = i + 1 if is_training or is_article else y_max
return [y, y_max, next_idxs, h] + attention_and_memory
