class FeedbackRNN(BaseRecurrent):
def __init__(self, dim, **kwargs):
super(FeedbackRNN, self).__init__(**kwargs)
self.dim = dim
self.first_recurrent_layer = SimpleRecurrent(
dim=self.dim,
activation=Identity(),
name='first_recurrent_layer',
weights_init=initialization.Identity())
self.second_recurrent_layer = SimpleRecurrent(
dim=self.dim,
activation=Identity(),
name='second_recurrent_layer',
weights_init=initialization.Identity())
self.children = [
self.first_recurrent_layer, self.second_recurrent_layer
]
@recurrent(sequences=['inputs'],
contexts=[],
states=['first_states', 'second_states'],
outputs=['first_states', 'second_states'])
def apply(self, inputs, first_states=None, second_states=None):
first_h = self.first_recurrent_layer.apply(inputs=inputs,
states=first_states +
second_states,
iterate=False)
second_h = self.second_recurrent_layer.apply(inputs=first_h,
states=second_states,
iterate=False)
return first_h, second_h
def get_dim(self, name):
return (self.dim if name in ('inputs', 'first_states', 'second_states')
else super(FeedbackRNN, self).get_dim(name))
def example():
""" Simple reccurent example. Taken from : https://github.com/mdda/pycon.sg-2015_deep-learning/blob/master/ipynb/blocks-recurrent-docs.ipynb """
x = tensor.tensor3('x')
rnn = SimpleRecurrent(dim=3, activation=Identity(), weights_init=initialization.Identity())
rnn.initialize()
h = rnn.apply(x)
f = theano.function([x], h)
print(f(np.ones((3, 1, 3), dtype=theano.config.floatX)))
doubler = Linear(
input_dim=3, output_dim=3, weights_init=initialization.Identity(2),
biases_init=initialization.Constant(0))
doubler.initialize()
h_doubler = rnn.apply(doubler.apply(x))
f = theano.function([x], h_doubler)
print(f(np.ones((3, 1, 3), dtype=theano.config.floatX)))
#Initial State
h0 = tensor.matrix('h0')
h = rnn.apply(inputs=x, states=h0)
f = theano.function([x, h0], h)
print(f(np.ones((3, 1, 3), dtype=theano.config.floatX),
np.ones((1, 3), dtype=theano.config.floatX)))
class FeedbackRNN(BaseRecurrent):
def __init__(self, dim, **kwargs):
super(FeedbackRNN, self).__init__(**kwargs)
self.dim = dim
self.first_recurrent_layer = SimpleRecurrent(
dim=self.dim, activation=Identity(), name='first_recurrent_layer',
weights_init=initialization.Identity())
self.second_recurrent_layer = SimpleRecurrent(
dim=self.dim, activation=Identity(), name='second_recurrent_layer',
weights_init=initialization.Identity())
self.children = [self.first_recurrent_layer,
self.second_recurrent_layer]
@recurrent(sequences=['inputs'], contexts=[],
states=['first_states', 'second_states'],
outputs=['first_states', 'second_states'])
def apply(self, inputs, first_states=None, second_states=None):
first_h = self.first_recurrent_layer.apply(
inputs=inputs, states=first_states + second_states, iterate=False)
second_h = self.second_recurrent_layer.apply(
inputs=first_h, states=second_states, iterate=False)
return first_h, second_h
def get_dim(self, name):
return (self.dim if name in ('inputs', 'first_states', 'second_states')
else super(FeedbackRNN, self).get_dim(name))
def rnn_layer(in_size, dim, x, h, n, first_layer=False):
if connect_h_to_h == 'all-previous':
if first_layer:
rnn_input = x
linear = Linear(input_dim=in_size,
output_dim=dim,
name='linear' + str(n) + '-' + str(task))
elif connect_x_to_h:
rnn_input = T.concatenate([x] + [hidden for hidden in h], axis=2)
linear = Linear(input_dim=in_size + dim * n,
output_dim=dim,
name='linear' + str(n) + '-' + str(task))
else:
rnn_input = T.concatenate([hidden for hidden in h], axis=2)
linear = Linear(input_dim=dim * n,
output_dim=dim,
name='linear' + str(n) + '-' + str(task))
elif connect_h_to_h == 'two-previous':
if first_layer:
rnn_input = x
linear = Linear(input_dim=in_size,
output_dim=dim,
name='linear' + str(n) + '-' + str(task))
elif connect_x_to_h:
rnn_input = T.concatenate([x] + h[max(0, n - 2):n], axis=2)
linear = Linear(input_dim=in_size + dim * 2 if n > 1 else in_size +
dim,
output_dim=dim,
name='linear' + str(n) + '-' + str(task))
else:
rnn_input = T.concatenate(h[max(0, n - 2):n], axis=2)
linear = Linear(input_dim=dim * 2 if n > 1 else dim,
output_dim=dim,
name='linear' + str(n) + '-' + str(task))
elif connect_h_to_h == 'one-previous':
if first_layer:
rnn_input = x
linear = Linear(input_dim=in_size,
output_dim=dim,
name='linear' + str(n) + '-' + str(task))
elif connect_x_to_h:
rnn_input = T.concatenate([x] + [h[n - 1]], axis=2)
linear = Linear(input_dim=in_size + dim,
output_dim=dim,
name='linear' + str(n) + '-' + str(task))
else:
rnn_input = h[n]
linear = Linear(input_dim=dim,
output_dim=dim,
name='linear' + str(n) + '-' + str(task))
rnn = SimpleRecurrent(dim=dim,
activation=Tanh(),
name=layer_models[n] + str(n) + '-' + str(task))
initialize([linear, rnn])
if layer_models[n] == 'rnn':
return rnn.apply(linear.apply(rnn_input))
elif layer_models[n] == 'mt_rnn':
return rnn.apply(linear.apply(rnn_input),
time_scale=layer_resolutions[n],
time_offset=layer_execution_time_offset[n])
def example():
""" Simple reccurent example. Taken from : https://github.com/mdda/pycon.sg-2015_deep-learning/blob/master/ipynb/blocks-recurrent-docs.ipynb """
x = tensor.tensor3('x')
rnn = SimpleRecurrent(dim=3,
activation=Identity(),
weights_init=initialization.Identity())
rnn.initialize()
h = rnn.apply(x)
f = theano.function([x], h)
print(f(np.ones((3, 1, 3), dtype=theano.config.floatX)))
doubler = Linear(input_dim=3,
output_dim=3,
weights_init=initialization.Identity(2),
biases_init=initialization.Constant(0))
doubler.initialize()
h_doubler = rnn.apply(doubler.apply(x))
f = theano.function([x], h_doubler)
print(f(np.ones((3, 1, 3), dtype=theano.config.floatX)))
#Initial State
h0 = tensor.matrix('h0')
h = rnn.apply(inputs=x, states=h0)
f = theano.function([x, h0], h)
print(
f(np.ones((3, 1, 3), dtype=theano.config.floatX),
np.ones((1, 3), dtype=theano.config.floatX)))
def test_similar_scans():
x = tensor.tensor3('x')
r1 = SimpleRecurrent(activation=Tanh(), dim=10)
y1 = r1.apply(x)
r2 = SimpleRecurrent(activation=Tanh(), dim=10)
y2 = r2.apply(x)
cg = ComputationGraph([y1, y2])
assert len(cg.scans) == 2
class RNNwMini(BaseRecurrent):
def __init__(self, dim, mini_dim, summary_dim, **kwargs):
super(RNNwMini, self).__init__(**kwargs)
self.dim = dim
self.mini_dim = mini_dim
self.summary_dim = summary_dim
self.recurrent_layer = SimpleRecurrent(
dim=self.summary_dim,
activation=Rectifier(),
name='recurrent_layer',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.mini_recurrent_layer = SimpleRecurrent(
dim=self.mini_dim,
activation=Rectifier(),
name='mini_recurrent_layer',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.mini_to_main = Linear(self.dim + self.mini_dim,
self.summary_dim,
name='mini_to_main',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.children = [
self.recurrent_layer, self.mini_recurrent_layer, self.mini_to_main
]
@recurrent(sequences=['x', 'xmini'],
contexts=[],
states=['states'],
outputs=['states'])
def apply(self, x, xmini, states=None):
mini_h_all = self.mini_recurrent_layer.apply(inputs=xmini,
states=None,
iterate=True)
#grab last hidden state
mini_h = mini_h_all[-1]
combInput = T.concatenate([x, mini_h], axis=1)
combTransform = self.mini_to_main.apply(combInput)
h = self.recurrent_layer.apply(inputs=combTransform,
states=states,
iterate=False)
return h
def get_dim(self, name):
dim = 1
if name == 'x':
dim = self.dim
elif name == 'states':
dim = self.summary_dim
else:
dim = super(RNNwMini, self).get_dim(name)
return dim
def rnn_layer(dim, h, n, x_mask, first, **kwargs):
linear = Linear(input_dim=dim, output_dim=dim, name='linear' + str(n))
rnn = SimpleRecurrent(dim=dim, activation=Rectifier(), name='rnn' + str(n))
initialize([linear, rnn])
applyLin = linear.apply(h)
if first:
rnnApply = rnn.apply(applyLin, mask=x_mask, **kwargs)
else:
rnnApply = rnn.apply(applyLin, **kwargs)
return rnnApply
class TestSimpleRecurrent(unittest.TestCase):
def setUp(self):
self.simple = SimpleRecurrent(dim=3,
weights_init=Constant(2),
activation=Tanh())
self.simple.initialize()
def test_one_step(self):
h0 = tensor.matrix('h0')
x = tensor.matrix('x')
mask = tensor.vector('mask')
h1 = self.simple.apply(x, h0, mask=mask, iterate=False)
next_h = theano.function(inputs=[h0, x, mask], outputs=[h1])
h0_val = 0.1 * numpy.array([[1, 1, 0], [0, 1, 1]],
dtype=theano.config.floatX)
x_val = 0.1 * numpy.array([[1, 2, 3], [4, 5, 6]],
dtype=theano.config.floatX)
mask_val = numpy.array([1, 0]).astype(theano.config.floatX)
h1_val = numpy.tanh(h0_val.dot(2 * numpy.ones((3, 3))) + x_val)
h1_val = mask_val[:, None] * h1_val + (1 - mask_val[:, None]) * h0_val
assert_allclose(h1_val, next_h(h0_val, x_val, mask_val)[0])
def test_many_steps(self):
x = tensor.tensor3('x')
mask = tensor.matrix('mask')
h = self.simple.apply(x, mask=mask, iterate=True)
calc_h = theano.function(inputs=[x, mask], outputs=[h])
x_val = 0.1 * numpy.asarray(list(itertools.permutations(range(4))),
dtype=theano.config.floatX)
x_val = numpy.ones(
(24, 4, 3), dtype=theano.config.floatX) * x_val[..., None]
mask_val = numpy.ones((24, 4), dtype=theano.config.floatX)
mask_val[12:24, 3] = 0
h_val = numpy.zeros((25, 4, 3), dtype=theano.config.floatX)
for i in range(1, 25):
h_val[i] = numpy.tanh(h_val[i - 1].dot(2 * numpy.ones((3, 3))) +
x_val[i - 1])
h_val[i] = (mask_val[i - 1, :, None] * h_val[i] +
(1 - mask_val[i - 1, :, None]) * h_val[i - 1])
h_val = h_val[1:]
assert_allclose(h_val, calc_h(x_val, mask_val)[0], rtol=1e-04)
# Also test that initial state is a parameter
initial_state, = VariableFilter(roles=[INITIAL_STATE])(
ComputationGraph(h))
assert is_shared_variable(initial_state)
assert initial_state.name == 'initial_state'
class MyRnn(BaseRecurrent): # Extend the base recurrent class to create one of your own
def __init__(self, dim, **kwargs):
super(MyRnn, self).__init__(**kwargs)
self.dim = dim
self.layer1 = SimpleRecurrent(dim=self.dim, activation=Identity(), name='recurrent layer 1', weights_init=initialization.Identity())
self.layer2 = SimpleRecurrent(dim=self.dim, activation=Identity(), name='recurrent layer 2', weights_init=initialization.Identity())
self.children = [self.layer1, self.layer2]
def apply(self, inputs, first_states=None, second_states=None):
first_h = self.layer1.apply(inputs=inputs, states=first_states, iterate=False)
second_h = self.layer2.apply(inputs=first_h, states=second_states, iterate=False)
return first_h, second_h
def get_dim(self):
pass
class TestSimpleRecurrent(unittest.TestCase):
def setUp(self):
self.simple = SimpleRecurrent(dim=3, weights_init=Constant(2),
activation=Tanh())
self.simple.initialize()
def test_one_step(self):
h0 = tensor.matrix('h0')
x = tensor.matrix('x')
mask = tensor.vector('mask')
h1 = self.simple.apply(x, h0, mask=mask, iterate=False)
next_h = theano.function(inputs=[h0, x, mask], outputs=[h1])
h0_val = 0.1 * numpy.array([[1, 1, 0], [0, 1, 1]],
dtype=theano.config.floatX)
x_val = 0.1 * numpy.array([[1, 2, 3], [4, 5, 6]],
dtype=theano.config.floatX)
mask_val = numpy.array([1, 0]).astype(theano.config.floatX)
h1_val = numpy.tanh(h0_val.dot(2 * numpy.ones((3, 3))) + x_val)
h1_val = mask_val[:, None] * h1_val + (1 - mask_val[:, None]) * h0_val
assert_allclose(h1_val, next_h(h0_val, x_val, mask_val)[0])
def test_many_steps(self):
x = tensor.tensor3('x')
mask = tensor.matrix('mask')
h = self.simple.apply(x, mask=mask, iterate=True)
calc_h = theano.function(inputs=[x, mask], outputs=[h])
x_val = 0.1 * numpy.asarray(list(itertools.permutations(range(4))),
dtype=theano.config.floatX)
x_val = numpy.ones((24, 4, 3),
dtype=theano.config.floatX) * x_val[..., None]
mask_val = numpy.ones((24, 4), dtype=theano.config.floatX)
mask_val[12:24, 3] = 0
h_val = numpy.zeros((25, 4, 3), dtype=theano.config.floatX)
for i in range(1, 25):
h_val[i] = numpy.tanh(h_val[i - 1].dot(
2 * numpy.ones((3, 3))) + x_val[i - 1])
h_val[i] = (mask_val[i - 1, :, None] * h_val[i] +
(1 - mask_val[i - 1, :, None]) * h_val[i - 1])
h_val = h_val[1:]
assert_allclose(h_val, calc_h(x_val, mask_val)[0], rtol=1e-04)
# Also test that initial state is a parameter
initial_state, = VariableFilter(roles=[INITIAL_STATE])(
ComputationGraph(h))
assert is_shared_variable(initial_state)
assert initial_state.name == 'initial_state'
class TestBidirectional(unittest.TestCase):
def setUp(self):
self.bidir = Bidirectional(weights_init=Orthogonal(),
prototype=SimpleRecurrent(
dim=3, activation=Tanh()))
self.simple = SimpleRecurrent(dim=3, weights_init=Orthogonal(),
activation=Tanh(), seed=1)
self.bidir.allocate()
self.simple.initialize()
self.bidir.children[0].params[0].set_value(
self.simple.params[0].get_value())
self.bidir.children[1].params[0].set_value(
self.simple.params[0].get_value())
self.x_val = 0.1 * numpy.asarray(
list(itertools.permutations(range(4))),
dtype=floatX)
self.x_val = (numpy.ones((24, 4, 3), dtype=floatX) *
self.x_val[..., None])
self.mask_val = numpy.ones((24, 4), dtype=floatX)
self.mask_val[12:24, 3] = 0
def test(self):
x = tensor.tensor3('x')
mask = tensor.matrix('mask')
calc_bidir = theano.function([x, mask],
[self.bidir.apply(x, mask=mask)])
calc_simple = theano.function([x, mask],
[self.simple.apply(x, mask=mask)])
h_bidir = calc_bidir(self.x_val, self.mask_val)[0]
h_simple = calc_simple(self.x_val, self.mask_val)[0]
h_simple_rev = calc_simple(self.x_val[::-1], self.mask_val[::-1])[0]
assert_allclose(h_simple, h_bidir[..., :3], rtol=1e-04)
assert_allclose(h_simple_rev, h_bidir[::-1, ..., 3:], rtol=1e-04)
def rnn_layer(in_dim, h, h_dim, n):
linear = Linear(input_dim=in_dim,
output_dim=h_dim,
name='linear' + str(n) + h.name)
rnn = SimpleRecurrent(dim=h_dim, name='rnn' + str(n))
initialize([linear, rnn])
return rnn.apply(linear.apply(h))
class CompositionalLayerToyWithTables(Initializable):
def __init__(self, batch_size, num_subwords, num_words, subword_embedding_size, input_vocab_size,
subword_RNN_hidden_state_size, **kwargs):
super(CompositionalLayerToyWithTables, self).__init__(**kwargs)
self.batch_size = batch_size
self.num_subwords = num_subwords # number of subwords which make up a word
self.num_words = num_words # number of words in the sentence
self.subword_embedding_size = subword_embedding_size
self.input_vocab_size = input_vocab_size
self.subword_RNN_hidden_state_size = subword_RNN_hidden_state_size
# create the look up table
self.lookup = LookupTable(length=self.input_vocab_size, dim=self.subword_embedding_size, name='input_lookup')
self.lookup.weights_init = Uniform(width=0.08)
self.lookup.biases_init = Constant(0)
# has one RNN which reads the subwords into a word embedding
self.compositional_subword_to_word_RNN = SimpleRecurrent(
dim=self.subword_RNN_hidden_state_size, activation=Identity(), name='subword_RNN',
weights_init=Identity_init())
self.children = [self.lookup, self.compositional_subword_to_word_RNN]
'''
subword_id_input_ is a 3d tensor with the dimensions of shape = (num_words, num_subwords, batch_size).
It is expected as a dtype=uint16 or equivalent
subword_id_input_mask_ is a 3d tensor with the dimensions of shape = (num_words, num_subwords, batch_size).
It is expected as a dtype=uint8 or equivalent and has binary values of 1 when there is data and zero otherwise.
The look up table will return a 4d tensor with shape = (num_words, num_subwords, batch_size, embedding size)
The RNN will eat up the subwords dimension, resulting in a
3d tensor of shape = (num_words, batch_size, RNN_hidden_value_size), which is returned as 'word_embeddings'
Also returned is a 2d tensor of shape = (num_words, batch_zize), which is the remaining mask indicated
the length of the sentence for each sentence in the batch. i.e., 1 when there is a word, 0 otherwise.
'''
@application(inputs=['subword_id_input_', 'subword_id_input_mask_'], outputs=['word_embeddings', 'word_embeddings_mask'])
def apply(self, subword_id_input_, subword_id_input_mask_):
##shape = (num_words, num_subwords, batch_size, embedding size)
subword_embeddings = self.lookup.apply(subword_id_input_)
result, updates = theano.scan( #loop over each word and have the rnn eat up the subwords
fn=lambda subword_embeddings, subword_id_input_mask_: self.compositional_subword_to_word_RNN.apply(subword_embeddings, mask=subword_id_input_mask_),
sequences= [subword_embeddings, subword_id_input_mask_])
word_embeddings = result.dimshuffle(1,0,2,3) #put the states as the last dimension
#remove this line to see the RNN states
word_embeddings = word_embeddings[-1] #take only the last state, since we dont need the others
#remove subword dim from mask
#if subword is empty then word is emptry the word is emptry, if not then the word is used
word_embeddings_mask = subword_id_input_mask_.max(axis=1)
return word_embeddings, word_embeddings_mask
def rnn_layer(in_dim, h, h_dim, n, pref=""):
linear = Linear(input_dim=in_dim,
output_dim=h_dim,
name='linear' + str(n) + pref)
rnn = SimpleRecurrent(dim=h_dim,
activation=Tanh(),
name='rnn' + str(n) + pref)
initialize([linear, rnn])
return rnn.apply(linear.apply(h))
def test_saved_inner_graph():
"""Make sure that the original inner graph is saved."""
x = tensor.tensor3()
recurrent = SimpleRecurrent(dim=3, activation=Tanh())
y = recurrent.apply(x)
application_call = get_application_call(y)
assert application_call.inner_inputs
assert application_call.inner_outputs
cg = ComputationGraph(application_call.inner_outputs)
# Check that the inner scan graph is annotated
# with `recurrent.apply`
assert len(VariableFilter(application=recurrent.apply)(cg)) == 3
# Check that the inner graph is equivalent to the one
# produced by a stand-alone of `recurrent.apply`
assert is_same_graph(application_call.inner_outputs[0],
recurrent.apply(*application_call.inner_inputs,
iterate=False))
class LanguageModelToy(Initializable):
"""
This takes the word embeddings from CompositionalLayerToyWithTables and creates sentence embeddings
Input is a 3d tensor with the dimensions of (num_words, num_subwords, batch_size) and
a 3d tensor a mask of size (num_words, num_subwords, batch_size)
All hidden state sizes are the same as the subword embedding size
This returns a 3d tensor with dimenstions of (num_words = num RNN states, batch_size, sentence embedding size)
"""
def __init__(self, batch_size, num_subwords, num_words, subword_embedding_size, input_vocab_size,
subword_RNN_hidden_state_size, LM_RNN_hidden_state_size, **kwargs):
super(LanguageModelToy, self).__init__(**kwargs)
self.batch_size = batch_size
self.num_subwords = num_subwords # number of subwords which make up a word
self.num_words = num_words # number of words in the sentence
self.subword_embedding_size = subword_embedding_size
self.input_vocab_size = input_vocab_size
self.subword_RNN_hidden_state_size = subword_RNN_hidden_state_size
self.LM_RNN_hidden_state_size = LM_RNN_hidden_state_size
self.compositional_layer = CompositionalLayerToyWithTables(self.batch_size, self.num_subwords, self.num_words,
self.subword_embedding_size, self.input_vocab_size,
self.subword_RNN_hidden_state_size, name='compositional_layer')
# has one RNN which reads the word embeddings into a sentence embedding
self.language_model_RNN = SimpleRecurrent(
dim=self.LM_RNN_hidden_state_size, activation=Identity(), name='language_model_RNN',
weights_init=Identity_init())
self.children = [self.compositional_layer, self.language_model_RNN]
@application(inputs=['subword_id_input_', 'subword_id_input_mask_'], outputs=['sentence_embeddings', 'sentence_embeddings_mask'])
def apply(self, subword_id_input_, subword_id_input_mask_):
"""
subword_id_input_ is a 3d tensor with the dimensions of shape = (num_words, num_subwords, batch_size).
It is expected as a dtype=uint16 or equivalent
subword_id_input_mask_ is a 3d tensor with the dimensions of shape = (num_words, num_subwords, batch_size).
It is expected as a dtype=uint8 or equivalent and has binary values of 1 when there is data and zero otherwise.
Returned is a 3d tensor of size (num_words = num RNN states, batch_size, sentence embedding size)
Also returned is a 1d tensor of size (batch_size) describing if the sentence is valid of empty in the batch
"""
word_embeddings, word_embeddings_mask = self.compositional_layer.apply(subword_id_input_, subword_id_input_mask_)
sentence_embeddings = self.language_model_RNN.apply(word_embeddings, mask=word_embeddings_mask)
sentence_embeddings_mask = word_embeddings_mask.max(axis=0).T
return sentence_embeddings, sentence_embeddings_mask
def example5():
"""Bidir + simplereccurent. Adaptation from a unittest in blocks """
bidir = Bidirectional(weights_init=Orthogonal(),
prototype=SimpleRecurrent(
dim=3, activation=Tanh()))
simple = SimpleRecurrent(dim=3, weights_init=Orthogonal(),
activation=Tanh(), seed=1)
bidir.allocate()
simple.initialize()
bidir.children[0].parameters[0].set_value(
simple.parameters[0].get_value())
bidir.children[1].parameters[0].set_value(
simple.parameters[0].get_value())
#Initialize theano variables and functions
x = tensor.tensor3('x')
mask = tensor.matrix('mask')
calc_bidir = theano.function([x, mask],
[bidir.apply(x, mask=mask)])
calc_simple = theano.function([x, mask],
[simple.apply(x, mask=mask)])
#Testing time
x_val = 0.1 * np.asarray(
list(itertools.permutations(range(4))),
dtype=theano.config.floatX)
x_val = (np.ones((24, 4, 3), dtype=theano.config.floatX) *
x_val[..., None])
mask_val = np.ones((24, 4), dtype=theano.config.floatX)
mask_val[12:24, 3] = 0
h_bidir = calc_bidir(x_val, mask_val)[0]
h_simple = calc_simple(x_val, mask_val)[0]
h_simple_rev = calc_simple(x_val[::-1], mask_val[::-1])[0]
print(h_bidir)
print(h_simple)
print(h_simple_rev)
def example5():
"""Bidir + simplereccurent. Adaptation from a unittest in blocks """
bidir = Bidirectional(weights_init=Orthogonal(),
prototype=SimpleRecurrent(dim=3, activation=Tanh()))
simple = SimpleRecurrent(dim=3,
weights_init=Orthogonal(),
activation=Tanh(),
seed=1)
bidir.allocate()
simple.initialize()
bidir.children[0].parameters[0].set_value(simple.parameters[0].get_value())
bidir.children[1].parameters[0].set_value(simple.parameters[0].get_value())
#Initialize theano variables and functions
x = tensor.tensor3('x')
mask = tensor.matrix('mask')
calc_bidir = theano.function([x, mask], [bidir.apply(x, mask=mask)])
calc_simple = theano.function([x, mask], [simple.apply(x, mask=mask)])
#Testing time
x_val = 0.1 * np.asarray(list(itertools.permutations(range(4))),
dtype=theano.config.floatX)
x_val = (np.ones(
(24, 4, 3), dtype=theano.config.floatX) * x_val[..., None])
mask_val = np.ones((24, 4), dtype=theano.config.floatX)
mask_val[12:24, 3] = 0
h_bidir = calc_bidir(x_val, mask_val)[0]
h_simple = calc_simple(x_val, mask_val)[0]
h_simple_rev = calc_simple(x_val[::-1], mask_val[::-1])[0]
print(h_bidir)
print(h_simple)
print(h_simple_rev)
class TextRNN(object):
def __init__(self, dim_in, dim_hidden, dim_out, **kwargs):
self.dim_in = dim_in
self.dim_hidden = dim_hidden
self.dim_out = dim_out
self.input_layer = Linear(input_dim=self.dim_in, output_dim=self.dim_hidden,
weights_init=initialization.IsotropicGaussian(),
biases_init=initialization.Constant(0))
self.input_layer.initialize()
sparse_init = initialization.Sparse(num_init=15, weights_init=initialization.IsotropicGaussian())
self.recurrent_layer = SimpleRecurrent(
dim=self.dim_hidden, activation=Tanh(), name="first_recurrent_layer",
weights_init=sparse_init,
biases_init=initialization.Constant(0.01))
'''
self.recurrent_layer = LSTM(dim=self.dim_hidden, activation=Tanh(),
weights_init=initialization.IsotropicGaussian(std=0.001),
biases_init=initialization.Constant(0.01))
'''
self.recurrent_layer.initialize()
self.output_layer = Linear(input_dim=self.dim_hidden, output_dim=self.dim_out,
weights_init=initialization.Uniform(width=0.01),
biases_init=initialization.Constant(0.01))
self.output_layer.initialize()
self.children = [self.input_layer, self.recurrent_layer, self.output_layer]
'''
@recurrent(sequences=['inputs'],
states=['states'],
contexts=[],
outputs=['states', 'output'])
'''
def run(self, inputs):
output = self.output_layer.apply( self.recurrent_layer.apply(self.input_layer.apply(inputs)) )
return output
# Computational Graph
input = T.tensor3('input')
mask = T.fmatrix('mask')
target = T.tensor3('target')
linear1 = Linear(name='linear1', input_dim=300, output_dim=128)
recurrent = SimpleRecurrent(name='recurrent', activation=Tanh(), dim=128)
linear2 = Linear(name='linear2', input_dim=128, output_dim=9)
softmax = Softmax()
bricks = [linear1, recurrent, linear2]
for brick in bricks:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0)
brick.initialize()
linear1_output = linear1.apply(input)
recurrent_output = recurrent.apply(linear1_output, mask=mask)
linear2_output = linear2.apply(recurrent_output)
shape = linear2_output.shape # 100 * 29*9
output = softmax.apply(linear2_output.reshape(
(-1,
9))).reshape(shape) # hameye dimension ha be gheyr az yeki k oon 9 hast.
# Cost and Functions
cost = T.nnet.categorical_crossentropy(output, target) # 100 x 29
cost = cost * mask
cost = cost.mean()
params = Model(cost).parameters
updates = sgd(cost, params)
f_train = theano.function(inputs=[input, mask, target],
outputs=cost,
activation=Tanh(),
weights_init=initialization.Uniform(width=0.01))
rnn.initialize()
linear_output = Linear(
name='linear_output',
input_dim=hidden_layer_dim,
output_dim=charset_size,
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
linear_output.initialize()
softmax = NDimensionalSoftmax(name='ndim_softmax')
activation_input = lookup_input.apply(x)
hidden = rnn.apply(linear_input.apply(activation_input))
activation_output = linear_output.apply(hidden)
y_est = softmax.apply(activation_output, extra_ndim=1)
cost = softmax.categorical_cross_entropy(y, activation_output, extra_ndim=1).mean()
from blocks.graph import ComputationGraph
from blocks.algorithms import GradientDescent, Adam
cg = ComputationGraph([cost])
step_rules = [RMSProp(learning_rate=0.002, decay_rate=0.95), StepClipping(1.0)]
algorithm = GradientDescent(
def rnn_layer(dim, h, n):
linear = Linear(input_dim=dim, output_dim=dim, name='linear' + str(n))
rnn = SimpleRecurrent(dim=dim, activation=Tanh(), name='rnn' + str(n))
initialize([linear, rnn])
return rnn.apply(linear.apply(h))
class BaselineCompositionalLayerToyBidirectional(Initializable):
def __init__(self, batch_size, num_subwords, num_words, subword_embedding_size, input_vocab_size,
subword_RNN_hidden_state_size, add_one = True, **kwargs):
super(BaselineCompositionalLayerToyBidirectional, self).__init__(**kwargs)
self.batch_size = batch_size
self.num_subwords = num_subwords # number of subwords which make up a word
self.num_words = num_words # number of words in the sentence
self.subword_embedding_size = subword_embedding_size
self.input_vocab_size = input_vocab_size
self.subword_RNN_hidden_state_size = subword_RNN_hidden_state_size
self.add_one = add_one # adds 1 to the backwards embeddings
# create the look up table
self.lookup = LookupTable(length=self.input_vocab_size, dim=self.subword_embedding_size, name='input_lookup')
self.lookup.weights_init = Uniform(width=0.08)
self.lookup.biases_init = Constant(0)
# has one RNN which reads the subwords into a word embedding
self.compositional_subword_to_word_RNN_forward = SimpleRecurrent(
dim=self.subword_RNN_hidden_state_size, activation=Identity(), name='subword_RNN_forward',
weights_init=Identity_init())
self.compositional_subword_to_word_RNN_backward = SimpleRecurrent(
dim=self.subword_RNN_hidden_state_size, activation=Identity(), name='subword_RNN_backward',
weights_init=Identity_init())
self.children = [self.lookup, self.compositional_subword_to_word_RNN_forward,
self.compositional_subword_to_word_RNN_backward]
'''
The RNN will eat up the subwords dimension, resulting in a
3d tensor of shape = (num_subwords, batch_size, RNN_hidden_value_size * 2), which is returned as 'word_embeddings'
NOTE: That it is the shape of num_subwords not num_words
The backwords embbedding elements are +1, to show them as different from the forward ones.
'''
@application(inputs=['subword_id_input_', 'subword_id_input_mask_'], outputs=['word_embeddings', 'word_embeddings_with_states', 'word_embeddings_mask'])
def apply(self, subword_id_input_, subword_id_input_mask_):
##shape = (num_words, num_subwords, batch_size, embedding size)
subword_embeddings = self.lookup.apply(subword_id_input_)
#forward sequence
forward_result, updates = theano.scan( #loop over each word and have the rnn eat up the subwords
fn=lambda subword_embeddings, subword_id_input_mask_: self.compositional_subword_to_word_RNN_forward.apply(subword_embeddings, mask=subword_id_input_mask_),
sequences= [subword_embeddings, subword_id_input_mask_])
forward_word_embeddings_with_states = forward_result.dimshuffle(1,0,2,3) # keep to check for values as output
#DO NOT DIMSHUFFLE AS YOU WANT IT TO BE state 1 then state 2 then state 3 etc.
s = forward_result.shape
forward_word_embeddings = T.reshape(forward_result, (s[0]*s[1], s[2], s[3]))
#backward sequence
backward_result, updates = theano.scan( #loop over each word and have the rnn eat up the subwords
fn=lambda subword_embeddings, subword_id_input_mask_: self.compositional_subword_to_word_RNN_backward.apply(subword_embeddings, mask=subword_id_input_mask_),
sequences= [subword_embeddings[:,::-1,:], subword_id_input_mask_[:,::-1,:]])
#NOTE! added + 1 to backword embeddings to show them as different from forward embeddings
if self.add_one:
backward_result = backward_result + 1
backward_word_embeddings_with_states = backward_result.dimshuffle(1,0,2,3) # keep to check for values as output
backward_word_embeddings = T.reshape(backward_result, (s[0]*s[1], s[2], s[3]))
word_embeddings_with_states = T.concatenate([forward_word_embeddings_with_states, backward_word_embeddings_with_states], axis=3)
word_embeddings_with_states = word_embeddings_with_states.dimshuffle(2,0,1,3)[-1]
word_embeddings = T.concatenate([forward_word_embeddings, backward_word_embeddings], axis=2)
#remove subword dim from mask
#if subword is empty then word is emptry the word is emptry, if not then the word is used
word_embeddings_mask_with_states = subword_id_input_mask_.max(axis=1)
return word_embeddings, word_embeddings_with_states, word_embeddings_mask_with_states
dim=hidden_layer_dim,
activation=Tanh(),
weights_init=initialization.Uniform(width=0.01))
rnn.initialize()
linear_output = Linear(name='linear_output',
input_dim=hidden_layer_dim,
output_dim=train_dataset.durations_vocab_size(),
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
linear_output.initialize()
softmax = NDimensionalSoftmax(name='ndim_softmax')
activation_input = lookup_input.apply(x)
hidden = rnn.apply(linear_input.apply(activation_input))
activation_output = linear_output.apply(hidden)
y_est = softmax.apply(activation_output, extra_ndim=1)
cost = softmax.categorical_cross_entropy(y, activation_output,
extra_ndim=1).mean()
from blocks.graph import ComputationGraph
from blocks.algorithms import GradientDescent, Adam
cg = ComputationGraph([cost])
step_rules = [RMSProp(learning_rate=0.002, decay_rate=0.95), StepClipping(1.0)]
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
def __init__(self, rnn_dims, num_actions, data_X_np=None, data_y_np=None, width=32, height=32):
###############################################################
#
# Network and data setup
#
##############################################################
RNN_DIMS = 100
NUM_ACTIONS = num_actions
tensor5 = T.TensorType('float32', [False, True, True, True, True])
self.x = T.tensor4('features')
self.reward = T.tensor3('targets', dtype='float32')
self.state = T.matrix('states', dtype='float32')
self.hidden_states = [] # holds hidden states in np array form
#data_X & data_Y supplied in init function now...
if data_X_np is None or data_y_np is None:
print 'you did not supply data at init'
data_X_np = np.float32(np.random.normal(size=(1280, 1,1, width, height)))
data_y_np = np.float32(np.random.normal(size=(1280, 1,1,1)))
#data_states_np = np.float32(np.ones((1280, 1, 100)))
state_shape = (data_X_np.shape[0],rnn_dims)
self.data_states_np = np.float32(np.zeros(state_shape))
self.datastream = IterableDataset(dict(features=data_X_np,
targets=data_y_np,
states=self.data_states_np)).get_example_stream()
self.datastream_test = IterableDataset(dict(features=data_X_np,
targets=data_y_np,
states=self.data_states_np)).get_example_stream()
data_X = self.datastream
# 2 conv inputs
# we want to take our sequence of input images and convert them to convolutional
# representations
conv_layers = [ConvolutionalLayer(Rectifier().apply, (3, 3), 16, (2, 2), name='l1'),
ConvolutionalLayer(Rectifier().apply, (3, 3), 32, (2, 2), name='l2'),
ConvolutionalLayer(Rectifier().apply, (3, 3), 64, (2, 2), name='l3'),
ConvolutionalLayer(Rectifier().apply, (3, 3), 128, (2, 2), name='l4'),
ConvolutionalLayer(Rectifier().apply, (3, 3), 128, (2, 2), name='l5'),
ConvolutionalLayer(Rectifier().apply, (3, 3), 128, (2, 2), name='l6')]
convnet = ConvolutionalSequence(conv_layers, num_channels=4,
image_size=(width, height),
weights_init=init.Uniform(0, 0.01),
biases_init=init.Constant(0.0),
tied_biases=False,
border_mode='full')
convnet.initialize()
output_dim = np.prod(convnet.get_dim('output'))
conv_out = convnet.apply(self.x)
reshape_dims = (conv_out.shape[0], conv_out.shape[1]*conv_out.shape[2]*conv_out.shape[3])
hidden_repr = conv_out.reshape(reshape_dims)
conv2rnn = Linear(input_dim=output_dim, output_dim=RNN_DIMS,
weights_init=init.Uniform(width=0.01),
biases_init=init.Constant(0.))
conv2rnn.initialize()
conv2rnn_output = conv2rnn.apply(hidden_repr)
# RNN hidden layer
# then we want to feed those conv representations into an RNN
rnn = SimpleRecurrent(dim=RNN_DIMS, activation=Rectifier(), weights_init=init.Uniform(width=0.01))
rnn.initialize()
self.learned_state = rnn.apply(inputs=conv2rnn_output, states=self.state, iterate=False)
# linear output from hidden layer
# the RNN has two outputs, but only this one has a target. That is, this is "expected return"
# which the network attempts to minimize difference between expected return and actual return
lin_output = Linear(input_dim=RNN_DIMS, output_dim=1,
weights_init=init.Uniform(width=0.01),
biases_init=init.Constant(0.))
lin_output.initialize()
self.exp_reward = lin_output.apply(self.learned_state)
self.get_exp_reward = theano.function([self.x, self.state], self.exp_reward)
# softmax output from hidden layer
# this provides a softmax of action recommendations
# the hypothesis is that adjusting the other outputs magically influences this set of outputs
# to suggest smarter (or more realistic?) moves
action_output = Linear(input_dim=RNN_DIMS, output_dim=NUM_ACTIONS,
weights_init=init.Constant(.001),
biases_init=init.Constant(0.))
action_output.initialize()
self.suggested_actions = Softmax().apply(action_output.apply(self.learned_state[-1]))
######################
# use this to get suggested actions... it requires the state of the hidden units from the previous
# timestep
#####################
self.get_suggested_actions = theano.function([self.x, self.state], [self.suggested_actions, self.learned_state])
def test_attention_recurrent():
rng = numpy.random.RandomState(1234)
dim = 5
batch_size = 4
input_length = 20
attended_dim = 10
attended_length = 15
wrapped = SimpleRecurrent(dim, Identity())
attention = SequenceContentAttention(state_names=wrapped.apply.states,
attended_dim=attended_dim,
match_dim=attended_dim)
recurrent = AttentionRecurrent(wrapped, attention, seed=1234)
recurrent.weights_init = IsotropicGaussian(0.5)
recurrent.biases_init = Constant(0)
recurrent.initialize()
attended = tensor.tensor3("attended")
attended_mask = tensor.matrix("attended_mask")
inputs = tensor.tensor3("inputs")
inputs_mask = tensor.matrix("inputs_mask")
outputs = recurrent.apply(inputs=inputs,
mask=inputs_mask,
attended=attended,
attended_mask=attended_mask)
states, glimpses, weights = outputs
assert states.ndim == 3
assert glimpses.ndim == 3
assert weights.ndim == 3
# For values.
def rand(size):
return rng.uniform(size=size).astype(theano.config.floatX)
# For masks.
def generate_mask(length, batch_size):
mask = numpy.ones((length, batch_size), dtype=theano.config.floatX)
# To make it look like read data
for i in range(batch_size):
mask[1 + rng.randint(0, length - 1):, i] = 0.0
return mask
input_vals = rand((input_length, batch_size, dim))
input_mask_vals = generate_mask(input_length, batch_size)
attended_vals = rand((attended_length, batch_size, attended_dim))
attended_mask_vals = generate_mask(attended_length, batch_size)
func = theano.function([inputs, inputs_mask, attended, attended_mask],
[states, glimpses, weights])
states_vals, glimpses_vals, weight_vals = func(input_vals, input_mask_vals,
attended_vals,
attended_mask_vals)
assert states_vals.shape == (input_length, batch_size, dim)
assert glimpses_vals.shape == (input_length, batch_size, attended_dim)
assert (len(ComputationGraph(outputs).shared_variables) == len(
Selector(recurrent).get_parameters()))
# Manual reimplementation
inputs2d = tensor.matrix()
states2d = tensor.matrix()
mask1d = tensor.vector()
weighted_averages = tensor.matrix()
distribute_func = theano.function([inputs2d, weighted_averages],
recurrent.distribute.apply(
inputs=inputs2d,
weighted_averages=weighted_averages))
wrapped_apply_func = theano.function([states2d, inputs2d, mask1d],
wrapped.apply(states=states2d,
inputs=inputs2d,
mask=mask1d,
iterate=False))
attention_func = theano.function([states2d, attended, attended_mask],
attention.take_glimpses(
attended=attended,
attended_mask=attended_mask,
states=states2d))
states_man = wrapped.initial_states(batch_size).eval()
glimpses_man = numpy.zeros((batch_size, attended_dim),
dtype=theano.config.floatX)
for i in range(input_length):
inputs_man = distribute_func(input_vals[i], glimpses_man)
states_man = wrapped_apply_func(states_man, inputs_man,
input_mask_vals[i])
glimpses_man, weights_man = attention_func(states_man, attended_vals,
attended_mask_vals)
assert_allclose(states_man, states_vals[i], rtol=1e-5)
assert_allclose(glimpses_man, glimpses_vals[i], rtol=1e-5)
assert_allclose(weights_man, weight_vals[i], rtol=1e-5)
# weights for not masked position must be zero
assert numpy.all(weight_vals * (1 - attended_mask_vals.T) == 0)
# weights for masked positions must be non-zero
assert numpy.all(abs(weight_vals + (1 - attended_mask_vals.T)) > 1e-5)
# weights from different steps should be noticeably different
assert (abs(weight_vals[0] - weight_vals[1])).sum() > 1e-2
# weights for all state after the last masked position should be same
for i in range(batch_size):
last = int(input_mask_vals[:, i].sum())
for j in range(last, input_length):
assert_allclose(weight_vals[last, i], weight_vals[j, i], 1e-5)
brick.initialize() lstm.weights_init = IsotropicGaussian(0.01) #lstm.weights_init = Orthogonal() lstm.biases_init = Constant(0.) lstm.initialize() #ComputationGraph(encode.apply(x)).get_theano_function()(features_test)[0].shape #ComputationGraph(lstm.apply(encoded)).get_theano_function()(features_test) #ComputationGraph(decode.apply(hiddens[-1])).get_theano_function()(features_test)[0].shape #ComputationGraph(SquaredError().apply(y, y_hat.flatten())).get_theano_function()(features_test, targets_test)[0].shape encoded = encode.apply(x) #hiddens = lstm.apply(encoded, gates.apply(x)) hiddens = lstm.apply(encoded) y_hat = decode.apply(hiddens[-1]) cost = SquaredError().apply(y, y_hat) cost.name = 'cost' #ipdb.set_trace() #ComputationGraph(y_hat).get_theano_function()(features_test)[0].shape #ComputationGraph(cost).get_theano_function()(features_test, targets_test)[0].shape cg = ComputationGraph(cost) #cg = ComputationGraph(hiddens).get_theano_function() #ipdb.set_trace() algorithm = GradientDescent(cost=cost,
brick.initialize() lstm.weights_init = IsotropicGaussian(0.01) #lstm.weights_init = Orthogonal() lstm.biases_init = Constant(0.) lstm.initialize() #ComputationGraph(encode.apply(x)).get_theano_function()(features_test)[0].shape #ComputationGraph(lstm.apply(encoded)).get_theano_function()(features_test) #ComputationGraph(decode.apply(hiddens[-1])).get_theano_function()(features_test)[0].shape #ComputationGraph(SquaredError().apply(y, y_hat.flatten())).get_theano_function()(features_test, targets_test)[0].shape encoded = encode.apply(x) #hiddens = lstm.apply(encoded, gates.apply(x)) hiddens = lstm.apply(encoded) y_hat = decode.apply(hiddens[-1]) cost = SquaredError().apply(y, y_hat) cost.name = 'cost' #ipdb.set_trace() #ComputationGraph(y_hat).get_theano_function()(features_test)[0].shape #ComputationGraph(cost).get_theano_function()(features_test, targets_test)[0].shape cg = ComputationGraph(cost) #cg = ComputationGraph(hiddens).get_theano_function() #ipdb.set_trace() algorithm = GradientDescent(cost=cost,
class Rnn(Initializable, BaseRecurrent):
def __init__(self, dims=(88, 100, 100), **kwargs):
super(Rnn, self).__init__(**kwargs)
self.dims = dims
self.input_transform = Linear(
input_dim=dims[0],
output_dim=dims[1],
weights_init=IsotropicGaussian(0.01),
# biases_init=Constant(0.0),
use_bias=False,
name="input_transfrom")
self.gru_layer = SimpleRecurrent(dim=dims[1],
activation=Tanh(),
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0.0),
use_bias=True,
name="gru_rnn_layer")
# TODO: find a way to automatically set the output dim in case of lstm vs normal rnn
self.linear_trans = Linear(input_dim=dims[1],
output_dim=dims[2] * 4,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0.0),
use_bias=False,
name="h2h_transform")
self.lstm_layer = LSTM(dim=dims[2],
activation=Tanh(),
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0.0),
use_bias=True,
name="lstm_rnn_layer")
self.out_transform = MLP(activations=[Sigmoid()],
dims=[dims[2], dims[0]],
weights_init=IsotropicGaussian(0.01),
use_bias=True,
biases_init=Constant(0.0),
name="out_layer")
self.children = [
self.input_transform, self.gru_layer, self.linear_trans,
self.lstm_layer, self.out_transform
]
# @recurrent(sequences=['inputs', 'input_mask'], contexts=[],
# states=['gru_state', 'lstm_state', 'lstm_cells'],
# outputs=['gru_state', 'lstm_state', 'lstm_cells'])
def rnn_apply(self,
inputs,
mask=None,
gru_state=None,
lstm_state=None,
lstm_cells=None):
input_transform = self.input_transform.apply(inputs)
gru_state = self.gru_layer.apply(
inputs=input_transform,
# update_inputs=input_transform,
# reset_inputs=input_transform,
states=gru_state,
mask=mask,
iterate=False)
lstm_transform = self.linear_trans.apply(gru_state)
lstm_state, lstm_cells = self.lstm_layer.apply(inputs=lstm_transform,
states=lstm_state,
cells=lstm_cells,
mask=mask,
iterate=False)
return gru_state, lstm_state, lstm_cells
@recurrent(sequences=[],
contexts=[],
states=['inputs', 'gru_state', 'lstm_state', 'lstm_cells'],
outputs=['inputs', 'gru_state', 'lstm_state', 'lstm_cells'])
def rnn_generate(self,
inputs=None,
gru_state=None,
lstm_state=None,
lstm_cells=None):
output = self.apply(inputs=inputs,
gru_state=gru_state,
lstm_state=lstm_state,
lstm_cells=lstm_cells,
iterate=False)
return output, gru_state, lstm_state, lstm_cells
@recurrent(sequences=['inputs', 'mask'],
contexts=[],
states=['gru_state', 'lstm_state', 'lstm_cells'],
outputs=['output', 'gru_state', 'lstm_state', 'lstm_cells'])
def apply(self,
inputs,
mask,
gru_state=None,
lstm_state=None,
lstm_cells=None):
# input_transform = self.input_transform.apply(inputs)
# gru_state = self.gru_layer.apply(
# inputs=input_transform,
# mask=mask,
# states=gru_state,
# iterate=False)
# lstm_transform = self.linear_trans.apply(gru_state)
# lstm_state, lstm_cells = self.lstm_layer.apply(inputs=lstm_transform, states=lstm_state,
# cells=lstm_cells,
# mask=mask, iterate=False)
gru_state, lstm_state, lstm_cells = self.rnn_apply(
inputs=inputs,
mask=mask,
gru_state=gru_state,
lstm_state=lstm_state,
lstm_cells=lstm_cells)
output = 1.17 * self.out_transform.apply(lstm_state) * mask[:, None]
return output, gru_state, lstm_state, lstm_cells
def get_dim(self, name):
dims = dict(zip(['outputs', 'gru_state', 'lstm_state'], self.dims))
dims['lstm_cells'] = dims['lstm_state']
return dims.get(name, None) or super(Rnn, self).get_dim(name)
def test_attention_recurrent():
rng = numpy.random.RandomState(1234)
dim = 5
batch_size = 4
input_length = 20
attended_dim = 10
attended_length = 15
wrapped = SimpleRecurrent(dim, Identity())
attention = SequenceContentAttention(
state_names=wrapped.apply.states,
attended_dim=attended_dim, match_dim=attended_dim)
recurrent = AttentionRecurrent(wrapped, attention, seed=1234)
recurrent.weights_init = IsotropicGaussian(0.5)
recurrent.biases_init = Constant(0)
recurrent.initialize()
attended = tensor.tensor3("attended")
attended_mask = tensor.matrix("attended_mask")
inputs = tensor.tensor3("inputs")
inputs_mask = tensor.matrix("inputs_mask")
outputs = recurrent.apply(
inputs=inputs, mask=inputs_mask,
attended=attended, attended_mask=attended_mask)
states, glimpses, weights = outputs
assert states.ndim == 3
assert glimpses.ndim == 3
assert weights.ndim == 3
# For values.
def rand(size):
return rng.uniform(size=size).astype(theano.config.floatX)
# For masks.
def generate_mask(length, batch_size):
mask = numpy.ones((length, batch_size), dtype=theano.config.floatX)
# To make it look like read data
for i in range(batch_size):
mask[1 + rng.randint(0, length - 1):, i] = 0.0
return mask
input_vals = rand((input_length, batch_size, dim))
input_mask_vals = generate_mask(input_length, batch_size)
attended_vals = rand((attended_length, batch_size, attended_dim))
attended_mask_vals = generate_mask(attended_length, batch_size)
func = theano.function([inputs, inputs_mask, attended, attended_mask],
[states, glimpses, weights])
states_vals, glimpses_vals, weight_vals = func(
input_vals, input_mask_vals,
attended_vals, attended_mask_vals)
assert states_vals.shape == (input_length, batch_size, dim)
assert glimpses_vals.shape == (input_length, batch_size, attended_dim)
assert (len(ComputationGraph(outputs).shared_variables) ==
len(Selector(recurrent).get_parameters()))
# Manual reimplementation
inputs2d = tensor.matrix()
states2d = tensor.matrix()
mask1d = tensor.vector()
weighted_averages = tensor.matrix()
distribute_func = theano.function(
[inputs2d, weighted_averages],
recurrent.distribute.apply(
inputs=inputs2d,
weighted_averages=weighted_averages))
wrapped_apply_func = theano.function(
[states2d, inputs2d, mask1d], wrapped.apply(
states=states2d, inputs=inputs2d, mask=mask1d, iterate=False))
attention_func = theano.function(
[states2d, attended, attended_mask],
attention.take_glimpses(
attended=attended, attended_mask=attended_mask,
states=states2d))
states_man = wrapped.initial_states(batch_size).eval()
glimpses_man = numpy.zeros((batch_size, attended_dim),
dtype=theano.config.floatX)
for i in range(input_length):
inputs_man = distribute_func(input_vals[i], glimpses_man)
states_man = wrapped_apply_func(states_man, inputs_man,
input_mask_vals[i])
glimpses_man, weights_man = attention_func(
states_man, attended_vals, attended_mask_vals)
assert_allclose(states_man, states_vals[i], rtol=1e-5)
assert_allclose(glimpses_man, glimpses_vals[i], rtol=1e-5)
assert_allclose(weights_man, weight_vals[i], rtol=1e-5)
# weights for not masked position must be zero
assert numpy.all(weight_vals * (1 - attended_mask_vals.T) == 0)
# weights for masked positions must be non-zero
assert numpy.all(abs(weight_vals + (1 - attended_mask_vals.T)) > 1e-5)
# weights from different steps should be noticeably different
assert (abs(weight_vals[0] - weight_vals[1])).sum() > 1e-2
# weights for all state after the last masked position should be same
for i in range(batch_size):
last = int(input_mask_vals[:, i].sum())
for j in range(last, input_length):
assert_allclose(weight_vals[last, i], weight_vals[j, i], 1e-5)
h_dim = 100
o_dim = 10
batch_size = 50
print 'Building model ...'
# T x B x F
x = tensor.tensor3('x', dtype=floatX)
y = tensor.tensor3('y', dtype='int32')
x_to_h1 = Linear(name='x_to_h1',
input_dim=x_dim,
output_dim=h_dim)
pre_rnn = x_to_h1.apply(x)
rnn = SimpleRecurrent(activation=Rectifier(),
dim=h_dim, name="rnn")
h1 = rnn.apply(pre_rnn)
h1_to_o = Linear(name='h1_to_o',
input_dim=h_dim,
output_dim=o_dim)
pre_softmax = h1_to_o.apply(h1)
softmax = Softmax()
shape = pre_softmax.shape
softmax_out = softmax.apply(pre_softmax.reshape((-1, o_dim)))
softmax_out = softmax_out.reshape(shape)
softmax_out.name = 'softmax_out'
# comparing only last time-step
cost = CategoricalCrossEntropy().apply(y[-1, :, 0], softmax_out[-1])
cost.name = 'CrossEntropy'
error_rate = MisclassificationRate().apply(y[-1, :, 0], softmax_out[-1])
error_rate.name = 'error_rate'
class SimpleRecurrentLayer(Initializable, Feedforward):
""" Blocks implementation of SimpleRecurrent is general and only
handles the "recursive part". This class wraps the SimpleRecurrent
class and adds linear input transformation. It can be used for most basic
cases as a layer in a sequence of layers.
Parameters
----------
input_dim : int
state_dim : int
activation : Brick
state_weights_init : NdarrayInitialization
Initialization of weights in LSTM (including gates).
input_weights_init : NdarrayInitialization
Initialization of weights in linear transformation of input.
biases_init : NdarrayInitialization
Initialization of biases in linear transformation of input.
"""
@lazy()
def __init__(
self,
input_dim,
state_dim,
activation=Tanh(),
state_weights_init=None,
input_weights_init=None,
biases_init=None,
**kwargs
):
super(SimpleRecurrentLayer, self).__init__(biases_init=biases_init, **kwargs)
if state_weights_init is None:
state_weights_init = init.IsotropicGaussian(0.01)
if input_weights_init is None:
input_weights_init = init.IsotropicGaussian(0.01)
if biases_init is None:
biases_init = init.Constant(0)
self.input_transformation = Linear(
input_dim=input_dim, output_dim=state_dim, weights_init=input_weights_init, biases_init=biases_init
)
self.rnn = SimpleRecurrent(dim=state_dim, activation=activation, weights_init=state_weights_init)
self.children = [self.input_transformation, self.rnn]
@application
def apply(self, inputs, *args, **kwargs):
""" Transforms input, sends to BasicRecurrent and returns output.
Parameters
----------
inputs : tensor.TensorVariable
The 3 dimensional tensor of inputs in the shape (timesteps,
batch_size, features).
Returns
-------
outputs : tensor.TensorVariable
The 3 dimensional tensor of outputs in the shape (timesteps,
batch_size, features).
"""
rnn_inputs = self.input_transformation.apply(inputs)
outputs = self.rnn.apply(inputs=rnn_inputs, *args, **kwargs)
return outputs
@apply.delegate
def apply_delegate(self):
return self.children[0].apply
@property
def input_dim(self):
return self.input_transformation.input_dim
@input_dim.setter
def input_dim(self, value):
self.input_transformation.input_dim = value
@property
def output_dim(self):
return self.rnn.dim
@output_dim.setter
def output_dim(self, value):
self.rnn.dim = value
class EUTHM(UTHM):
'''
UTH model with extend information
'''
def __init__(self, config, dataset, *args, **kwargs):
super(EUTHM, self).__init__(config, dataset)
def _define_inputs(self, *args, **kwargs):
super(EUTHM, self)._define_inputs()
self.user_word = tensor.ivector('user_word')
self.user_word_sparse_mask = tensor.vector('user_word_sparse_mask',
dtype=theano.config.floatX)
self.user_word_left_idx = tensor.ivector('user_word_idx_left_idx')
self.user_word_right_idx = tensor.ivector('user_word_idx_right_idx')
self.hashtag_word = tensor.ivector('hashtag_word')
self.hashtag_sparse_mask = tensor.vector('hashtag_word_sparse_mask',
dtype=theano.config.floatX)
self.hashtag_word_left_idx = tensor.ivector(
'hashtag_word_idx_left_idx')
self.hashtag_word_right_idx = tensor.ivector(
'hashtag_word_idx_right_idx')
self.sparse_word = tensor.imatrix('sparse_word')
self.sparse_word_sparse_mask = tensor.vector(
'sparse_word_sparse_mask', dtype=theano.config.floatX)
self.sparse_word_mask = tensor.matrix('sparse_word_mask',
dtype=theano.config.floatX)
self.sparse_word_left_idx = tensor.ivector('sparse_word_idx_left_idx')
self.sparse_word_right_idx = tensor.ivector(
'sparse_word_idx_right_idx')
def _build_bricks(self, *args, **kwargs):
# Build lookup tables
super(EUTHM, self)._build_bricks()
self.user2word = MLP(
activations=[Tanh('user2word_tanh')],
dims=[self.config.user_embed_dim, self.config.word_embed_dim],
name='user2word_mlp')
self.user2word.weights_init = IsotropicGaussian(
std=1 / numpy.sqrt(self.config.word_embed_dim))
self.user2word.biases_init = Constant(0)
self.user2word.initialize()
self.hashtag2word = MLP(
activations=[Tanh('hashtag2word_tanh')],
dims=[
self.config.user_embed_dim + self.config.word_embed_dim,
self.config.word_embed_dim
],
name='hashtag2word_mlp')
self.hashtag2word.weights_init = IsotropicGaussian(
std=1 / numpy.sqrt(self.config.word_embed_dim))
self.hashtag2word.biases_init = Constant(0)
self.hashtag2word.initialize()
self.user2word_bias = Bias(dim=1, name='user2word_bias')
self.user2word_bias.biases_init = Constant(0)
self.user2word_bias.initialize()
self.hashtag2word_bias = Bias(dim=1, name='hashtag2word_bias')
self.hashtag2word_bias.biases_init = Constant(0)
self.hashtag2word_bias.initialize()
#Build character embedding
self.char_embed = self._embed(len(self.dataset.char2index),
self.config.char_embed_dim,
name='char_embed')
# Build sparse word encoder
self.rnn_ins = Linear(input_dim=self.config.char_embed_dim,
output_dim=self.config.word_embed_dim,
name='rnn_in')
self.rnn_ins.weights_init = IsotropicGaussian(
std=numpy.sqrt(2) / numpy.sqrt(self.config.char_embed_dim +
self.config.word_embed_dim))
self.rnn_ins.biases_init = Constant(0)
self.rnn_ins.initialize()
self.rnn = SimpleRecurrent(dim=self.config.word_embed_dim,
activation=Tanh())
self.rnn.weights_init = IsotropicGaussian(
std=1 / numpy.sqrt(self.config.word_embed_dim))
self.rnn.initialize()
def _set_OV_value(self, *args, **kwargs):
'''Train a <unk> representation'''
tensor.set_subtensor(
self.char_embed.W[self.dataset.char2index['<unk>']],
numpy.zeros(self.config.char_embed_dim,
dtype=theano.config.floatX))
def _get_text_vec(self, *args, **kwargs):
# Transpose text
self.text = self.text.dimshuffle(1, 0)
self.text_mask = self.text_mask.dimshuffle(1, 0)
self.sparse_word = self.sparse_word.dimshuffle(1, 0)
self.sparse_word_mask = self.sparse_word_mask.dimshuffle(1, 0)
# Turn word, user and hashtag into vector representation
text_vec = self.word_embed.apply(self.text)
# Apply user word, hashtag word and url
text_vec = self._apply_user_word(text_vec)
text_vec = self._apply_hashtag_word(text_vec)
text_vec = self._apply_sparse_word(text_vec)
return text_vec
@abstractmethod
def _apply_user_word(self, text_vec, *args, **kwargs):
'''
Replace @a with transformed author vector
:param text_vec:
:param args:
:param kwargs:
:return:
'''
user_word_vec = self.user2word.apply(self.user_embed.apply(self.user_word)) + \
self.user2word_bias.parameters[0][0]
text_vec = tensor.set_subtensor(
text_vec[self.user_word_right_idx, self.user_word_left_idx],
text_vec[self.user_word_right_idx, self.user_word_left_idx] *
(1 - self.user_word_sparse_mask[:, None]) +
user_word_vec * self.user_word_sparse_mask[:, None])
return text_vec
@abstractmethod
def _apply_hashtag_word(self, text_vec, *args, **kwargs):
'''
Replace #h with transformed hashtag vector
:param text_vec:
:param args:
:param kwargs:
:return:
'''
hashtag_word_vec = self.hashtag2word.apply(self.hashtag_embed.apply(self.hashtag_word)) +\
self.hashtag2word_bias.parameters[0][0]
text_vec = tensor.set_subtensor(
text_vec[self.hashtag_word_right_idx, self.hashtag_word_left_idx],
text_vec[self.hashtag_word_right_idx, self.hashtag_word_left_idx] *
(1 - self.hashtag_sparse_mask[:, None]) +
hashtag_word_vec * self.hashtag_sparse_mask[:, None])
return text_vec
@abstractmethod
def _apply_sparse_word(self, text_vec, *args, **kwargs):
'''
Replace sparse word encoding with character embedding. (maybe lstm)
:param text_vec:
:param args:
:param kwargs:
:return:
'''
sparse_word_vec = self.char_embed.apply(self.sparse_word)
sparse_word_hiddens = self.rnn.apply(
inputs=self.rnn_ins.apply(sparse_word_vec),
mask=self.sparse_word_mask)
tmp = sparse_word_hiddens[-1]
text_vec = tensor.set_subtensor(
text_vec[self.sparse_word_right_idx, self.sparse_word_left_idx],
text_vec[self.sparse_word_right_idx, self.sparse_word_left_idx] *
(1 - self.sparse_word_sparse_mask[:, None]) +
tmp * self.sparse_word_sparse_mask[:, None])
return text_vec
n_epochs = 30
x_dim = 1
h_dim = 100
o_dim = 10
batch_size = 50
print 'Building model ...'
# T x B x F
x = tensor.tensor3('x', dtype=floatX)
y = tensor.tensor3('y', dtype='int32')
x_to_h1 = Linear(name='x_to_h1', input_dim=x_dim, output_dim=h_dim)
pre_rnn = x_to_h1.apply(x)
rnn = SimpleRecurrent(activation=Rectifier(), dim=h_dim, name="rnn")
h1 = rnn.apply(pre_rnn)
h1_to_o = Linear(name='h1_to_o', input_dim=h_dim, output_dim=o_dim)
pre_softmax = h1_to_o.apply(h1)
softmax = Softmax()
shape = pre_softmax.shape
softmax_out = softmax.apply(pre_softmax.reshape((-1, o_dim)))
softmax_out = softmax_out.reshape(shape)
softmax_out.name = 'softmax_out'
# comparing only last time-step
cost = CategoricalCrossEntropy().apply(y[-1, :, 0], softmax_out[-1])
cost.name = 'CrossEntropy'
error_rate = MisclassificationRate().apply(y[-1, :, 0], softmax_out[-1])
error_rate.name = 'error_rate'
# Initialization
def main(num_epochs=100):
x = tensor.matrix('features')
m = tensor.matrix('features_mask')
x_int = x.astype(dtype='int32').T
train_dataset = TextFile('inspirational.txt')
train_dataset.indexables[0] = numpy.array(sorted(
train_dataset.indexables[0], key=len
))
n_voc = len(train_dataset.dict.keys())
init_probs = numpy.array(
[sum(filter(lambda idx:idx == w,
[s[0] for s in train_dataset.indexables[
train_dataset.sources.index('features')]]
)) for w in xrange(n_voc)],
dtype=theano.config.floatX
)
init_probs = init_probs / init_probs.sum()
n_h = 100
linear_embedding = LookupTable(
length=n_voc,
dim=n_h,
weights_init=Uniform(std=0.01),
biases_init=Constant(0.)
)
linear_embedding.initialize()
lstm_biases = numpy.zeros(4 * n_h).astype(dtype=theano.config.floatX)
lstm_biases[n_h:(2 * n_h)] = 4.
rnn = SimpleRecurrent(
dim=n_h,
activation=Tanh(),
weights_init=Uniform(std=0.01),
biases_init=Constant(0.)
)
rnn.initialize()
score_layer = Linear(
input_dim=n_h,
output_dim=n_voc,
weights_init=Uniform(std=0.01),
biases_init=Constant(0.)
)
score_layer.initialize()
embedding = (linear_embedding.apply(x_int[:-1])
* tensor.shape_padright(m.T[1:]))
rnn_out = rnn.apply(inputs=embedding, mask=m.T[1:])
probs = softmax(
sequence_map(score_layer.apply, rnn_out, mask=m.T[1:])[0]
)
idx_mask = m.T[1:].nonzero()
cost = CategoricalCrossEntropy().apply(
x_int[1:][idx_mask[0], idx_mask[1]],
probs[idx_mask[0], idx_mask[1]]
)
cost.name = 'cost'
misclassification = MisclassificationRate().apply(
x_int[1:][idx_mask[0], idx_mask[1]],
probs[idx_mask[0], idx_mask[1]]
)
misclassification.name = 'misclassification'
cg = ComputationGraph([cost])
params = cg.parameters
algorithm = GradientDescent(
cost=cost,
params=params,
step_rule=Adam()
)
train_data_stream = Padding(
data_stream=DataStream(
dataset=train_dataset,
iteration_scheme=BatchwiseShuffledScheme(
examples=train_dataset.num_examples,
batch_size=10,
)
),
mask_sources=('features',)
)
model = Model(cost)
extensions = []
extensions.append(Timing())
extensions.append(FinishAfter(after_n_epochs=num_epochs))
extensions.append(TrainingDataMonitoring(
[cost, misclassification],
prefix='train',
after_epoch=True))
batch_size = 10
length = 30
trng = MRG_RandomStreams(18032015)
u = trng.uniform(size=(length, batch_size, n_voc))
gumbel_noise = -tensor.log(-tensor.log(u))
init_samples = (tensor.log(init_probs).dimshuffle(('x', 0))
+ gumbel_noise[0]).argmax(axis=-1)
init_states = rnn.initial_state('states', batch_size)
def sampling_step(g_noise, states, samples_step):
embedding_step = linear_embedding.apply(samples_step)
next_states = rnn.apply(inputs=embedding_step,
states=states,
iterate=False)
probs_step = softmax(score_layer.apply(next_states))
next_samples = (tensor.log(probs_step)
+ g_noise).argmax(axis=-1)
return next_states, next_samples
[_, samples], _ = theano.scan(
fn=sampling_step,
sequences=[gumbel_noise[1:]],
outputs_info=[init_states, init_samples]
)
sampling = theano.function([], samples.owner.inputs[0].T)
plotters = []
plotters.append(Plotter(
channels=[['train_cost', 'train_misclassification']],
titles=['Costs']))
extensions.append(PlotManager('Language modelling example',
plotters=plotters,
after_epoch=True,
after_training=True))
extensions.append(Printing())
extensions.append(PrintSamples(sampler=sampling,
voc=train_dataset.inv_dict))
main_loop = MainLoop(model=model,
data_stream=train_data_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
labels_mask = data[b'mask_labels']
print('Building model ...')
# T x B x F
x = tensor.tensor3('x', dtype=floatX)
# T x B
x_mask = tensor.matrix('x_mask', dtype=floatX)
# L x B
y = tensor.matrix('y', dtype=floatX)
# L x B
y_mask = tensor.matrix('y_mask', dtype=floatX)
x_to_h = Linear(name='x_to_h', input_dim=x_dim, output_dim=h_dim)
x_transform = x_to_h.apply(x)
rnn = SimpleRecurrent(activation=Tanh(), dim=h_dim, name="rnn")
h = rnn.apply(x_transform)
h_to_o = Linear(name='h_to_o', input_dim=h_dim, output_dim=num_classes + 1)
h_transform = h_to_o.apply(h)
# T x B x C+1
y_hat = tensor.nnet.softmax(h_transform.reshape(
(-1, num_classes + 1))).reshape((h.shape[0], h.shape[1], -1))
y_hat.name = 'y_hat'
y_hat_mask = x_mask
cost = CTC().apply(y, y_hat, y_mask, y_hat_mask, 'normal_scale')
cost.name = 'CTC'
# Initialization
for brick in (rnn, x_to_h, h_to_o):
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0)
brick.initialize()
def construct_model(vocab_size, embedding_dim, hidden_dim, activation):
# Construct the model
x = tensor.lmatrix('features')
x_mask = tensor.fmatrix('features_mask')
y = tensor.lmatrix('targets')
# Batch X Time
y_mask = tensor.fmatrix('targets_mask')
# Batch X Time
frequency_mask = tensor.fmatrix('frequency_mask')
frequency_mask_mask = tensor.fmatrix('frequency_mask_mask')
# Only for the validation
last_word = tensor.lvector('last_word')
lookup = LookupTable(length=vocab_size, dim=embedding_dim, name='lookup')
linear = Linear(input_dim=embedding_dim,
output_dim=hidden_dim,
name="linear")
hidden = SimpleRecurrent(dim=hidden_dim,
activation=activation,
name='hidden_recurrent')
top_linear = Linear(input_dim=hidden_dim,
output_dim=vocab_size,
name="top_linear")
# Return 3D Tensor: Batch X Time X embedding_dim
embeddings = lookup.apply(x)
# Give time as the first index: Time X Batch X embedding_dim
embeddings = embeddings.dimshuffle(1, 0, 2)
pre_recurrent = linear.apply(embeddings)
after_recurrent = hidden.apply(inputs=pre_recurrent, mask=x_mask.T)[:-1]
after_recurrent_last = after_recurrent[-1]
presoft = top_linear.apply(after_recurrent)
# Define the cost
# Give y as a vector and reshape presoft to 2D tensor
y = y.flatten()
shape = presoft.shape
presoft = presoft.dimshuffle(1, 0, 2)
presoft = presoft.reshape((shape[0] * shape[1], shape[2]))
# Build cost_matrix
presoft = presoft - presoft.max(axis=1).dimshuffle(0, 'x')
log_prob = presoft - \
tensor.log(tensor.exp(presoft).sum(axis=1).dimshuffle(0, 'x'))
flat_log_prob = log_prob.flatten()
range_ = tensor.arange(y.shape[0])
flat_indices = y + range_ * presoft.shape[1]
cost_matrix = flat_log_prob[flat_indices]
# Mask useless values from the cost_matrix
cost_matrix = - cost_matrix * \
y_mask.flatten() * frequency_mask.flatten() * \
frequency_mask_mask.flatten()
# Average the cost
cost = cost_matrix.sum()
cost = cost / (y_mask * frequency_mask).sum()
# Initialize parameters
for brick in (lookup, linear, hidden, top_linear):
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.)
brick.initialize()
return cost
class CompositionalLayerToyBidirectional(Initializable):
def __init__(self, batch_size, num_subwords, num_words, subword_embedding_size, input_vocab_size,
subword_RNN_hidden_state_size, add_one = True, **kwargs):
super(CompositionalLayerToyBidirectional, self).__init__(**kwargs)
self.batch_size = batch_size
self.num_subwords = num_subwords # number of subwords which make up a word
self.num_words = num_words # number of words in the sentence
self.subword_embedding_size = subword_embedding_size
self.input_vocab_size = input_vocab_size
self.subword_RNN_hidden_state_size = subword_RNN_hidden_state_size
self.add_one = add_one #adds 1 to the backwards embeddings
# create the look up table
self.lookup = LookupTable(length=self.input_vocab_size, dim=self.subword_embedding_size, name='input_lookup')
self.lookup.weights_init = Uniform(width=0.08)
self.lookup.biases_init = Constant(0)
# has one RNN which reads the subwords into a word embedding
self.compositional_subword_to_word_RNN_forward = SimpleRecurrent(
dim=self.subword_RNN_hidden_state_size, activation=Identity(), name='subword_RNN_forward',
weights_init=Identity_init())
self.compositional_subword_to_word_RNN_backward = SimpleRecurrent(
dim=self.subword_RNN_hidden_state_size, activation=Identity(), name='subword_RNN_backward',
weights_init=Identity_init())
self.children = [self.lookup, self.compositional_subword_to_word_RNN_forward,
self.compositional_subword_to_word_RNN_backward]
'''
The RNN will eat up the subwords dimension, resulting in a
3d tensor of shape = (num_words, batch_size, RNN_hidden_value_size * 2), which is returned as 'word_embeddings'
The backwords embbedding elements are +1, to show them as different from the forward ones.
'''
@application(inputs=['subword_id_input_', 'subword_id_input_mask_'], outputs=['word_embeddings', 'word_embeddings_mask'])
def apply(self, subword_id_input_, subword_id_input_mask_):
##shape = (num_words, num_subwords, batch_size, embedding size)
subword_embeddings = self.lookup.apply(subword_id_input_)
forward_result, updates = theano.scan( #loop over each word and have the rnn eat up the subwords
fn=lambda subword_embeddings, subword_id_input_mask_: self.compositional_subword_to_word_RNN_forward.apply(subword_embeddings, mask=subword_id_input_mask_),
sequences= [subword_embeddings, subword_id_input_mask_])
forward_word_embeddings = forward_result.dimshuffle(1,0,2,3) #put the states as the last dimension
forward_word_embeddings = forward_word_embeddings[-1] #take only the last state, since we dont need the others
backward_result, updates = theano.scan( #loop over each word and have the rnn eat up the subwords
fn=lambda subword_embeddings, subword_id_input_mask_: self.compositional_subword_to_word_RNN_backward.apply(subword_embeddings, mask=subword_id_input_mask_),
sequences= [subword_embeddings[:,::-1,:], subword_id_input_mask_[:,::-1,:]])
backward_word_embeddings = backward_result.dimshuffle(1,0,2,3) #put the states as the last dimension
backward_word_embeddings = backward_word_embeddings[-1] #take only the last state, since we dont need the others
# NOTE! added + 1 to backword embeddings to show them as different from forward embeddings
backward_word_embeddings = backward_word_embeddings + 1.0
word_embeddings = T.concatenate([forward_word_embeddings, backward_word_embeddings], axis=2)
#remove subword dim from mask
#if subword is empty then word is emptry the word is emptry, if not then the word is used
word_embeddings_mask = subword_id_input_mask_.max(axis=1)
return word_embeddings, word_embeddings_mask
def construct_model(vocab_size, embedding_dim, hidden_dim,
activation):
# Construct the model
x = tensor.lmatrix('features')
x_mask = tensor.fmatrix('features_mask')
y = tensor.lmatrix('targets')
# Batch X Time
y_mask = tensor.fmatrix('targets_mask')
# Batch X Time
frequency_mask = tensor.fmatrix('frequency_mask')
frequency_mask_mask = tensor.fmatrix('frequency_mask_mask')
# Only for the validation
last_word = tensor.lvector('last_word')
lookup = LookupTable(length=vocab_size, dim=embedding_dim, name='lookup')
linear = Linear(input_dim=embedding_dim, output_dim=hidden_dim,
name="linear")
hidden = SimpleRecurrent(dim=hidden_dim, activation=activation,
name='hidden_recurrent')
top_linear = Linear(input_dim=hidden_dim, output_dim=vocab_size,
name="top_linear")
# Return 3D Tensor: Batch X Time X embedding_dim
embeddings = lookup.apply(x)
# Give time as the first index: Time X Batch X embedding_dim
embeddings = embeddings.dimshuffle(1, 0, 2)
pre_recurrent = linear.apply(embeddings)
after_recurrent = hidden.apply(inputs=pre_recurrent,
mask=x_mask.T)[:-1]
after_recurrent_last = after_recurrent[-1]
presoft = top_linear.apply(after_recurrent)
# Define the cost
# Give y as a vector and reshape presoft to 2D tensor
y = y.flatten()
shape = presoft.shape
presoft = presoft.dimshuffle(1, 0, 2)
presoft = presoft.reshape((shape[0] * shape[1], shape[2]))
# Build cost_matrix
presoft = presoft - presoft.max(axis=1).dimshuffle(0, 'x')
log_prob = presoft - \
tensor.log(tensor.exp(presoft).sum(axis=1).dimshuffle(0, 'x'))
flat_log_prob = log_prob.flatten()
range_ = tensor.arange(y.shape[0])
flat_indices = y + range_ * presoft.shape[1]
cost_matrix = flat_log_prob[flat_indices]
# Mask useless values from the cost_matrix
cost_matrix = - cost_matrix * \
y_mask.flatten() * frequency_mask.flatten() * \
frequency_mask_mask.flatten()
# Average the cost
cost = cost_matrix.sum()
cost = cost / (y_mask * frequency_mask).sum()
# Initialize parameters
for brick in (lookup, linear, hidden, top_linear):
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.)
brick.initialize()
return cost
iteration = 300 # number of epochs of gradient descent
print "Building Model"
# Symbolic variables
x = tensor.tensor3('x', dtype=floatX)
target = tensor.tensor3('target', dtype=floatX)
# Build the model
linear = Linear(input_dim=n_u, output_dim=n_h, name="first_layer")
rnn = SimpleRecurrent(dim=n_h, activation=Tanh())
linear2 = Linear(input_dim=n_h, output_dim=n_y, name="output_layer")
sigm = Sigmoid()
x_transform = linear.apply(x)
h = rnn.apply(x_transform)
predict = sigm.apply(linear2.apply(h))
# only for generation B x h_dim
h_initial = tensor.tensor3('h_initial', dtype=floatX)
h_testing = rnn.apply(x_transform, h_initial, iterate=False)
y_hat_testing = linear2.apply(h_testing)
y_hat_testing = sigm.apply(y_hat_testing)
y_hat_testing.name = 'y_hat_testing'
# Cost function
cost = SquaredError().apply(predict, target)
# Initialization
class Rnn(Initializable, BaseRecurrent):
def __init__(self, dims=(88, 100, 100), **kwargs):
super(Rnn, self).__init__(**kwargs)
self.dims = dims
self.input_transform = Linear(input_dim=dims[0], output_dim=dims[1],
weights_init=IsotropicGaussian(0.01),
# biases_init=Constant(0.0),
use_bias=False,
name="input_transfrom")
self.gru_layer = SimpleRecurrent(dim=dims[1], activation=Tanh(),
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0.0),
use_bias=True,
name="gru_rnn_layer")
# TODO: find a way to automatically set the output dim in case of lstm vs normal rnn
self.linear_trans = Linear(input_dim=dims[1], output_dim=dims[2] * 4,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0.0),
use_bias=False,
name="h2h_transform")
self.lstm_layer = LSTM(dim=dims[2], activation=Tanh(),
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0.0),
use_bias=True,
name="lstm_rnn_layer")
self.out_transform = MLP(activations=[Sigmoid()], dims=[dims[2], dims[0]],
weights_init=IsotropicGaussian(0.01),
use_bias=True,
biases_init=Constant(0.0),
name="out_layer")
self.children = [self.input_transform, self.gru_layer, self.linear_trans,
self.lstm_layer, self.out_transform]
# @recurrent(sequences=['inputs', 'input_mask'], contexts=[],
# states=['gru_state', 'lstm_state', 'lstm_cells'],
# outputs=['gru_state', 'lstm_state', 'lstm_cells'])
def rnn_apply(self, inputs, mask=None, gru_state=None, lstm_state=None, lstm_cells=None):
input_transform = self.input_transform.apply(inputs)
gru_state = self.gru_layer.apply(
inputs=input_transform,
# update_inputs=input_transform,
# reset_inputs=input_transform,
states=gru_state,
mask=mask,
iterate=False)
lstm_transform = self.linear_trans.apply(gru_state)
lstm_state, lstm_cells = self.lstm_layer.apply(inputs=lstm_transform, states=lstm_state,
cells=lstm_cells,
mask=mask, iterate=False)
return gru_state, lstm_state, lstm_cells
@recurrent(sequences=[], contexts=[],
states=['inputs', 'gru_state', 'lstm_state', 'lstm_cells'],
outputs=['inputs', 'gru_state', 'lstm_state', 'lstm_cells'])
def rnn_generate(self, inputs=None, gru_state=None, lstm_state=None, lstm_cells=None):
output = self.apply(inputs=inputs,
gru_state=gru_state,
lstm_state=lstm_state,
lstm_cells=lstm_cells,
iterate=False)
return output, gru_state, lstm_state, lstm_cells
@recurrent(sequences=['inputs', 'mask'], contexts=[],
states=['gru_state', 'lstm_state', 'lstm_cells'],
outputs=['output', 'gru_state', 'lstm_state', 'lstm_cells'])
def apply(self, inputs, mask, gru_state=None, lstm_state=None, lstm_cells=None):
# input_transform = self.input_transform.apply(inputs)
# gru_state = self.gru_layer.apply(
# inputs=input_transform,
# mask=mask,
# states=gru_state,
# iterate=False)
# lstm_transform = self.linear_trans.apply(gru_state)
# lstm_state, lstm_cells = self.lstm_layer.apply(inputs=lstm_transform, states=lstm_state,
# cells=lstm_cells,
# mask=mask, iterate=False)
gru_state, lstm_state, lstm_cells = self.rnn_apply(inputs=inputs,
mask=mask,
gru_state=gru_state,
lstm_state=lstm_state,
lstm_cells=lstm_cells)
output = 1.17 * self.out_transform.apply(lstm_state) * mask[:, None]
return output, gru_state, lstm_state, lstm_cells
def get_dim(self, name):
dims = dict(zip(['outputs', 'gru_state', 'lstm_state'], self.dims))
dims['lstm_cells'] = dims['lstm_state']
return dims.get(name, None) or super(Rnn, self).get_dim(name)
def main(save_to, num_epochs):
batch_size = 128
dim = 100
n_steps = 20
i2h1 = MLP([Identity()], [784, dim], biases_init=Constant(0.), weights_init=IsotropicGaussian(.001))
h2o1 = MLP([Rectifier(), Logistic()], [dim, dim, 784],
biases_init=Constant(0.), weights_init=IsotropicGaussian(.001))
rec1 = SimpleRecurrent(dim=dim, activation=Tanh(), weights_init=Orthogonal())
i2h1.initialize()
h2o1.initialize()
rec1.initialize()
x = tensor.tensor3('features')
x1 = x[1:, :, :]
x2 = x[:-1, :, :]
preproc = i2h1.apply(x1)
h1 = rec1.apply(preproc)
x_hat = h2o1.apply(h1)
cost = tensor.nnet.binary_crossentropy(x_hat, x2).mean()
# cost = CategoricalCrossEntropy().apply(y.flatten(), probs)
cost.name = 'final_cost'
cg = ComputationGraph([cost, ])
mnist_train = MNIST("train", subset=slice(0, 50000), sources=('features', ))
mnist_valid = MNIST("train", subset=slice(50000, 60000), sources=('features',))
mnist_test = MNIST("test")
trainstream = Mapping(Flatten(DataStream(mnist_train,
iteration_scheme=SequentialScheme(50000, batch_size))),
_meanize(n_steps))
validstream = Mapping(Flatten(DataStream(mnist_valid,
iteration_scheme=SequentialScheme(10000,
batch_size))),
_meanize(n_steps))
teststream = Mapping(Flatten(DataStream(mnist_test,
iteration_scheme=SequentialScheme(10000,
batch_size))),
_meanize(n_steps))
algorithm = GradientDescent(
cost=cost, params=cg.parameters,
step_rule=CompositeRule([Adam(), StepClipping(100)]))
main_loop = MainLoop(
algorithm,
trainstream,
extensions=[Timing(),
FinishAfter(after_n_epochs=num_epochs),
# DataStreamMonitoring(
# [cost, ],
# teststream,
# prefix="test"),
DataStreamMonitoringAndSaving(
[cost, ],
validstream,
[i2h1, h2o1, rec1],
'best_'+save_to+'.pkl',
cost_name=cost.name,
after_epoch=True,
prefix='valid'),
TrainingDataMonitoring(
[cost,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
# Plot(
# save_to,
# channels=[
# ['test_final_cost',
# 'test_misclassificationrate_apply_error_rate'],
# ['train_total_gradient_norm']]),
Printing()])
main_loop.run()
# T x B x F
x = tensor.tensor3('x', dtype=floatX)
# T x B
x_mask = tensor.matrix('x_mask', dtype=floatX)
# L x B
y = tensor.matrix('y', dtype=floatX)
# L x B
y_mask = tensor.matrix('y_mask', dtype=floatX)
x_to_h = Linear(name='x_to_h',
input_dim=x_dim,
output_dim=h_dim)
x_transform = x_to_h.apply(x)
rnn = SimpleRecurrent(activation=Tanh(),
dim=h_dim, name="rnn")
h = rnn.apply(x_transform)
h_to_o = Linear(name='h_to_o',
input_dim=h_dim,
output_dim=num_classes + 1)
h_transform = h_to_o.apply(h)
# T x B x C+1
y_hat = tensor.nnet.softmax(
h_transform.reshape((-1, num_classes + 1))
).reshape((h.shape[0], h.shape[1], -1))
y_hat.name = 'y_hat'
y_hat_mask = x_mask
cost = CTC().apply(y, y_hat, y_mask, y_hat_mask, 'normal_scale')
cost.name = 'CTC'
# Initialization
for brick in (rnn, x_to_h, h_to_o):
