def create_model(self):
input_dim = self.input_dim
x = self.x
x_to_h = Linear(input_dim,
input_dim * 4,
name='x_to_h',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
lstm = LSTM(input_dim,
name='lstm',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
h_to_o = Linear(input_dim,
1,
name='h_to_o',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
x_transform = x_to_h.apply(x)
self.x_to_h = x_to_h
self.lstm = lstm
self.h_to_o = h_to_o
h, c = lstm.apply(x_transform)
# only values of hidden units of the last timeframe are used for
# the classification
probs = h_to_o.apply(h[-1])
return probs
def __init__(self, feature_dim, memory_dim, fc1_dim, fc2_dim):
self.W = Linear(input_dim=feature_dim,
output_dim=memory_dim * 4,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
use_bias=False,
name='seqDecoder_W')
self.GRU_A = LSTM(feature_dim,
name='seqDecoder_A',
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
self.GRU_B = LSTM(memory_dim,
name='seqDecoder_B',
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
self.W.initialize()
self.GRU_A.initialize()
self.GRU_B.initialize()
self.fc1 = Linear(input_dim=memory_dim,
output_dim=fc1_dim,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
name='fc1')
self.fc2 = Linear(input_dim=fc1_dim,
output_dim=fc2_dim,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
name='fc2')
self.fc1.initialize()
self.fc2.initialize()
def __init__(self, emb_dim, dim, num_input_words,
num_output_words, vocab,
**kwargs):
if emb_dim == 0:
emb_dim = dim
if num_input_words == 0:
num_input_words = vocab.size()
if num_output_words == 0:
num_output_words = vocab.size()
self._num_input_words = num_input_words
self._num_output_words = num_output_words
self._vocab = vocab
self._word_to_id = WordToIdOp(self._vocab)
children = []
self._main_lookup = LookupTable(self._num_input_words, emb_dim, name='main_lookup')
self._encoder_fork = Linear(emb_dim, 4 * dim, name='encoder_fork')
self._encoder_rnn = LSTM(dim, name='encoder_rnn')
self._decoder_fork = Linear(emb_dim, 4 * dim, name='decoder_fork')
self._decoder_rnn = LSTM(dim, name='decoder_rnn')
children.extend([self._main_lookup,
self._encoder_fork, self._encoder_rnn,
self._decoder_fork, self._decoder_rnn])
self._pre_softmax = Linear(dim, self._num_output_words)
self._softmax = NDimensionalSoftmax()
children.extend([self._pre_softmax, self._softmax])
super(LanguageModel, self).__init__(children=children, **kwargs)
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
x = tensor.tensor3('x', dtype=floatX)
y = tensor.tensor3('y', dtype=floatX)
x_to_lstm = Linear(name="x_to_lstm", input_dim=input_size, output_dim=4 * hidden_size,
weights_init=IsotropicGaussian(), biases_init=Constant(0))
lstm = LSTM(dim=hidden_size, name="lstm", weights_init=IsotropicGaussian(), biases_init=Constant(0))
lstm_to_output = Linear(name="lstm_to_output", input_dim=hidden_size, output_dim=output_size,
weights_init=IsotropicGaussian(), biases_init=Constant(0))
x_transform = x_to_lstm.apply(x)
h, c = lstm.apply(x_transform)
y_hat = lstm_to_output.apply(h)
y_hat = Logistic(name="y_hat").apply(y_hat)
self.cost = BinaryCrossEntropy(name="cost").apply(y, y_hat)
x_to_lstm.initialize()
lstm.initialize()
lstm_to_output.initialize()
self.computation_graph = ComputationGraph(self.cost)
def apply(self, input_, target):
x_to_h = Linear(name='x_to_h',
input_dim=self.dims[0],
output_dim=self.dims[1] * 4)
pre_rnn = x_to_h.apply(input_)
pre_rnn.name = 'pre_rnn'
rnn = LSTM(activation=Tanh(), dim=self.dims[1], name=self.name)
h, _ = rnn.apply(pre_rnn)
h.name = 'h'
h_to_y = Linear(name='h_to_y',
input_dim=self.dims[1],
output_dim=self.dims[2])
y_hat = h_to_y.apply(h)
y_hat.name = 'y_hat'
cost = SquaredError().apply(target, y_hat)
cost.name = 'MSE'
self.outputs = {}
self.outputs['y_hat'] = y_hat
self.outputs['cost'] = cost
self.outputs['pre_rnn'] = pre_rnn
self.outputs['h'] = h
# Initialization
for brick in (rnn, x_to_h, h_to_y):
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0)
brick.initialize()
def __init__(self,
input_dim,
output_dim,
lstm_dim,
print_intermediate=False,
print_attrs=['__str__'],
**kwargs):
super(LinearLSTM, self).__init__(**kwargs)
self.x_to_h = Linear(input_dim,
lstm_dim * 4,
name='x_to_h',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.lstm = LSTM(lstm_dim,
name='lstm',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.h_to_o = Linear(lstm_dim,
output_dim,
name='h_to_o',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.children = [self.x_to_h, self.lstm, self.h_to_o]
self.print_intermediate = print_intermediate
self.print_attrs = print_attrs
def make_bidir_lstm_stack(seq, seq_dim, mask, sizes, skip=True, name=''):
bricks = []
curr_dim = [seq_dim]
curr_hidden = [seq]
hidden_list = []
for k, dim in enumerate(sizes):
fwd_lstm_ins = [Linear(input_dim=d, output_dim=4*dim, name='%s_fwd_lstm_in_%d_%d'%(name,k,l)) for l, d in enumerate(curr_dim)]
fwd_lstm = LSTM(dim=dim, activation=Tanh(), name='%s_fwd_lstm_%d'%(name,k))
bwd_lstm_ins = [Linear(input_dim=d, output_dim=4*dim, name='%s_bwd_lstm_in_%d_%d'%(name,k,l)) for l, d in enumerate(curr_dim)]
bwd_lstm = LSTM(dim=dim, activation=Tanh(), name='%s_bwd_lstm_%d'%(name,k))
bricks = bricks + [fwd_lstm, bwd_lstm] + fwd_lstm_ins + bwd_lstm_ins
fwd_tmp = sum(x.apply(v) for x, v in zip(fwd_lstm_ins, curr_hidden))
bwd_tmp = sum(x.apply(v) for x, v in zip(bwd_lstm_ins, curr_hidden))
fwd_hidden, _ = fwd_lstm.apply(fwd_tmp, mask=mask)
bwd_hidden, _ = bwd_lstm.apply(bwd_tmp[::-1], mask=mask[::-1])
hidden_list = hidden_list + [fwd_hidden, bwd_hidden]
if skip:
curr_hidden = [seq, fwd_hidden, bwd_hidden[::-1]]
curr_dim = [seq_dim, dim, dim]
else:
curr_hidden = [fwd_hidden, bwd_hidden[::-1]]
curr_dim = [dim, dim]
return bricks, hidden_list
def __init__(self, config, **kwargs):
super(Model, self).__init__(**kwargs)
self.config = config
self.pre_context_embedder = ContextEmbedder(
config.pre_embedder, name='pre_context_embedder')
self.post_context_embedder = ContextEmbedder(
config.post_embedder, name='post_context_embedder')
in1 = 2 + sum(x[2] for x in config.pre_embedder.dim_embeddings)
self.input_to_rec = MLP(activations=[Tanh()],
dims=[in1, config.hidden_state_dim],
name='input_to_rec')
self.rec = LSTM(dim=config.hidden_state_dim, name='recurrent')
in2 = config.hidden_state_dim + sum(
x[2] for x in config.post_embedder.dim_embeddings)
self.rec_to_output = MLP(activations=[Tanh()],
dims=[in2, 2],
name='rec_to_output')
self.sequences = ['latitude', 'latitude_mask', 'longitude']
self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs
self.inputs = self.sequences + self.context
self.children = [
self.pre_context_embedder, self.post_context_embedder,
self.input_to_rec, self.rec, self.rec_to_output
]
self.initial_state_ = shared_floatx_zeros((config.hidden_state_dim, ),
name="initial_state")
self.initial_cells = shared_floatx_zeros((config.hidden_state_dim, ),
name="initial_cells")
def __init__(self, dim, mini_dim, summary_dim, **kwargs):
super(LSTMwMini, self).__init__(**kwargs)
self.dim = dim
self.mini_dim = mini_dim
self.summary_dim = summary_dim
self.recurrent_layer = LSTM(dim=self.summary_dim,
activation=Rectifier(),
name='recurrent_layer',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.mini_recurrent_layer = LSTM(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.mini_to_main2 = Linear(self.summary_dim,
self.summary_dim * 4,
name='mini_to_main2',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.children = [
self.recurrent_layer, self.mini_recurrent_layer, self.mini_to_main,
self.mini_to_main2
]
def lstm_layer(in_dim, h, h_dim, n, pref=""):
linear = Linear(input_dim=in_dim,
output_dim=h_dim * 4,
name='linear' + str(n) + pref)
lstm = LSTM(dim=h_dim, name='lstm' + str(n) + pref)
initialize([linear, lstm])
return lstm.apply(linear.apply(h))[0]
def apply(self, input_, target):
x_to_h = Linear(name='x_to_h',
input_dim=self.dims[0],
output_dim=self.dims[1] * 4)
pre_rnn = x_to_h.apply(input_)
pre_rnn.name = 'pre_rnn'
rnn = LSTM(activation=Tanh(),
dim=self.dims[1], name=self.name)
h, _ = rnn.apply(pre_rnn)
h.name = 'h'
h_to_y = Linear(name='h_to_y',
input_dim=self.dims[1],
output_dim=self.dims[2])
y_hat = h_to_y.apply(h)
y_hat.name = 'y_hat'
cost = SquaredError().apply(target, y_hat)
cost.name = 'MSE'
self.outputs = {}
self.outputs['y_hat'] = y_hat
self.outputs['cost'] = cost
self.outputs['pre_rnn'] = pre_rnn
self.outputs['h'] = h
# Initialization
for brick in (rnn, x_to_h, h_to_y):
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0)
brick.initialize()
def __init__(self,
num_input_words,
emb_dim,
dim,
vocab,
lookup=None,
fork_and_rnn=None,
**kwargs):
if num_input_words > 0:
logger.info("Restricting def vocab to " + str(num_input_words))
self._num_input_words = num_input_words
else:
self._num_input_words = vocab.size()
self._vocab = vocab
children = []
if lookup is None:
self._def_lookup = LookupTable(self._num_input_words,
emb_dim,
name='def_lookup')
else:
self._def_lookup = lookup
if fork_and_rnn is None:
self._def_fork = Linear(emb_dim, 4 * dim, name='def_fork')
self._def_rnn = LSTM(dim, name='def_rnn')
else:
self._def_fork, self._def_rnn = fork_and_rnn
children.extend([self._def_lookup, self._def_fork, self._def_rnn])
super(LSTMReadDefinitions, self).__init__(children=children, **kwargs)
def main(max_seq_length, lstm_dim, batch_size, num_batches, num_epochs):
dataset_train = IterableDataset(generate_data(max_seq_length, batch_size,
num_batches))
dataset_test = IterableDataset(generate_data(max_seq_length, batch_size,
100))
stream_train = DataStream(dataset=dataset_train)
stream_test = DataStream(dataset=dataset_test)
x = T.tensor3('x')
y = T.matrix('y')
# we need to provide data for the LSTM layer of size 4 * ltsm_dim, see
# LSTM layer documentation for the explanation
x_to_h = Linear(1, lstm_dim * 4, name='x_to_h',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
lstm = LSTM(lstm_dim, name='lstm',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
h_to_o = Linear(lstm_dim, 1, name='h_to_o',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
x_transform = x_to_h.apply(x)
h, c = lstm.apply(x_transform)
# only values of hidden units of the last timeframe are used for
# the classification
y_hat = h_to_o.apply(h[-1])
y_hat = Logistic().apply(y_hat)
cost = BinaryCrossEntropy().apply(y, y_hat)
cost.name = 'cost'
lstm.initialize()
x_to_h.initialize()
h_to_o.initialize()
cg = ComputationGraph(cost)
algorithm = GradientDescent(cost=cost, parameters=cg.parameters,
step_rule=Adam())
test_monitor = DataStreamMonitoring(variables=[cost],
data_stream=stream_test, prefix="test")
train_monitor = TrainingDataMonitoring(variables=[cost], prefix="train",
after_epoch=True)
main_loop = MainLoop(algorithm, stream_train,
extensions=[test_monitor, train_monitor,
FinishAfter(after_n_epochs=num_epochs),
Printing(), ProgressBar()])
main_loop.run()
print 'Learned weights:'
for layer in (x_to_h, lstm, h_to_o):
print "Layer '%s':" % layer.name
for param in layer.parameters:
print param.name, ': ', param.get_value()
print
def __init__(self, feature_dim, hidden_dim, output_dim):
self.image_embed = Linear(input_dim=feature_dim,
output_dim=hidden_dim,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
use_bias=False,
name='image_embed')
self.word_embed = Linear(input_dim=feature_dim,
output_dim=hidden_dim,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
use_bias=False,
name='word_embed')
self.r_embed = Linear(input_dim=feature_dim,
output_dim=hidden_dim,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
use_bias=False,
name='r_embed')
self.m_to_s = Linear(input_dim=hidden_dim,
output_dim=1,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
use_bias=False,
name='m_to_s')
self.attention_dist = Softmax(name='attention_dist_softmax')
self.r_to_r = Linear(input_dim=feature_dim,
output_dim=feature_dim,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
use_bias=False,
name='r_to_r')
# self.r_to_g = Linear(input_dim=feature_dim,
# output_dim=output_dim,
# weights_init=IsotropicGaussian(0.01),
# biases_init=Constant(0),
# use_bias=False,
# name='r_to_g')
self.image_embed.initialize()
self.word_embed.initialize()
self.r_embed.initialize()
self.m_to_s.initialize()
self.r_to_r.initialize()
# self.r_to_g.initialize()
# the sequence to sequence LSTM
self.seq = LSTM(output_dim,
name='rewatcher_seq',
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
self.seq_embed = Linear(feature_dim,
output_dim * 4,
name='rewatcher_seq_embed',
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
use_bias=False)
self.seq.initialize()
self.seq_embed.initialize()
def lstm_layer(in_size, dim, x, h, n, task, first_layer=False):
if connect_h_to_h == 'all-previous':
if first_layer:
lstm_input = x
linear = Linear(input_dim=in_size,
output_dim=dim * 4,
name='linear' + str(n) + '-' + str(task))
elif connect_x_to_h:
lstm_input = T.concatenate([x] + [hidden for hidden in h], axis=2)
linear = Linear(input_dim=in_size + dim * (n),
output_dim=dim * 4,
name='linear' + str(n) + '-' + str(task))
else:
lstm_input = T.concatenate([hidden for hidden in h], axis=2)
linear = Linear(input_dim=dim * (n + 1),
output_dim=dim * 4,
name='linear' + str(n) + '-' + str(task))
elif connect_h_to_h == 'two-previous':
if first_layer:
lstm_input = x
linear = Linear(input_dim=in_size,
output_dim=dim * 4,
name='linear' + str(n) + '-' + str(task))
elif connect_x_to_h:
lstm_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 * 4,
name='linear' + str(n) + '-' + str(task))
else:
lstm_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 * 4,
name='linear' + str(n) + '-' + str(task))
elif connect_h_to_h == 'one-previous':
if first_layer:
lstm_input = x
linear = Linear(input_dim=in_size,
output_dim=dim * 4,
name='linear' + str(n) + '-' + str(task))
elif connect_x_to_h:
lstm_input = T.concatenate([x] + [h[n - 1]], axis=2)
linear = Linear(input_dim=in_size + dim,
output_dim=dim * 4,
name='linear' + str(n) + '-' + str(task))
else:
lstm_input = h[n - 1]
linear = Linear(input_dim=dim,
output_dim=dim * 4,
name='linear' + str(n) + '-' + str(task))
lstm = LSTM(dim=dim, name=layer_models[n] + str(n) + '-' + str(task))
initialize([linear, lstm])
if layer_models[n] == 'lstm':
return lstm.apply(linear.apply(lstm_input))
elif layer_models[n] == 'mt_lstm':
return lstm.apply(linear.apply(lstm_input),
time_scale=layer_resolutions[n],
time_offset=layer_execution_time_offset[n])
def construct_model(activation_function, r_dim, hidden_dim, out_dim):
# Construct the model
r = tensor.fmatrix('r')
x = tensor.fmatrix('x')
y = tensor.ivector('y')
nx = x.shape[0]
nj = x.shape[1] # also is r.shape[0]
nr = r.shape[1]
# r is nj x nr
# x is nx x nj
# y is nx
# Get a representation of r of size r_dim
r = DAE(r)
# r is now nj x r_dim
# r_rep is nx x nj x r_dim
r_rep = r[None, :, :].repeat(axis=0, repeats=nx)
# x3 is nx x nj x 1
x3 = x[:, :, None]
# concat is nx x nj x (r_dim + 1)
concat = tensor.concatenate([r_rep, x3], axis=2)
# Change concat from Batch x Time x Features to T X B x F
rnn_input = concat.dimshuffle(1, 0, 2)
linear = Linear(input_dim=r_dim + 1,
output_dim=4 * hidden_dim,
name="input_linear")
lstm = LSTM(dim=hidden_dim,
activation=activation_function,
name="hidden_recurrent")
top_linear = Linear(input_dim=hidden_dim,
output_dim=out_dim,
name="out_linear")
pre_rnn = linear.apply(rnn_input)
states = lstm.apply(pre_rnn)[0]
activations = top_linear.apply(states)
activations = tensor.mean(activations, axis=0)
cost = Softmax().categorical_cross_entropy(y, activations)
pred = activations.argmax(axis=1)
error_rate = tensor.neq(y, pred).mean()
# Initialize parameters
for brick in (linear, lstm, top_linear):
brick.weights_init = IsotropicGaussian(0.1)
brick.biases_init = Constant(0.)
brick.initialize()
return cost, error_rate
def create_rnn(hidden_dim, vocab_dim,mode="rnn"):
# input
x = tensor.imatrix('inchar')
y = tensor.imatrix('outchar')
#
W = LookupTable(
name = "W1",
#dim = hidden_dim*4,
dim = hidden_dim,
length = vocab_dim,
weights_init = initialization.IsotropicGaussian(0.01),
biases_init = initialization.Constant(0)
)
if mode == "lstm":
# Long Short Term Memory
H = LSTM(
hidden_dim,
name = 'H',
weights_init = initialization.IsotropicGaussian(0.01),
biases_init = initialization.Constant(0.0)
)
else:
# recurrent history weight
H = SimpleRecurrent(
name = "H",
dim = hidden_dim,
activation = Tanh(),
weights_init = initialization.IsotropicGaussian(0.01)
)
#
S = Linear(
name = "W2",
input_dim = hidden_dim,
output_dim = vocab_dim,
weights_init = initialization.IsotropicGaussian(0.01),
biases_init = initialization.Constant(0)
)
A = NDimensionalSoftmax(
name = "softmax"
)
initLayers([W,H,S])
activations = W.apply(x)
hiddens = H.apply(activations)#[0]
activations2 = S.apply(hiddens)
y_hat = A.apply(activations2, extra_ndim=1)
cost = A.categorical_cross_entropy(y, activations2, extra_ndim=1).mean()
cg = ComputationGraph(cost)
#print VariableFilter(roles=[WEIGHT])(cg.variables)
#W1,H,W2 = VariableFilter(roles=[WEIGHT])(cg.variables)
layers = (x, W, H, S, A, y)
return cg, layers, y_hat, cost
def add_lstm(input_dim, input_var):
linear = Linear(input_dim=input_dim,output_dim=input_dim*4,name="linear_layer")
lstm = LSTM(dim=input_dim, name="lstm_layer")
testing_init(linear)
#linear.initialize()
default_init(lstm)
h = linear.apply(input_var)
return lstm.apply(h)
def construct_model(activation_function, r_dim, hidden_dim, out_dim):
# Construct the model
r = tensor.fmatrix('r')
x = tensor.fmatrix('x')
y = tensor.ivector('y')
nx = x.shape[0]
nj = x.shape[1] # also is r.shape[0]
nr = r.shape[1]
# r is nj x nr
# x is nx x nj
# y is nx
# Get a representation of r of size r_dim
r = DAE(r)
# r is now nj x r_dim
# r_rep is nx x nj x r_dim
r_rep = r[None, :, :].repeat(axis=0, repeats=nx)
# x3 is nx x nj x 1
x3 = x[:, :, None]
# concat is nx x nj x (r_dim + 1)
concat = tensor.concatenate([r_rep, x3], axis=2)
# Change concat from Batch x Time x Features to T X B x F
rnn_input = concat.dimshuffle(1, 0, 2)
linear = Linear(input_dim=r_dim + 1, output_dim=4 * hidden_dim,
name="input_linear")
lstm = LSTM(dim=hidden_dim, activation=activation_function,
name="hidden_recurrent")
top_linear = Linear(input_dim=hidden_dim, output_dim=out_dim,
name="out_linear")
pre_rnn = linear.apply(rnn_input)
states = lstm.apply(pre_rnn)[0]
activations = top_linear.apply(states)
activations = tensor.mean(activations, axis=0)
cost = Softmax().categorical_cross_entropy(y, activations)
pred = activations.argmax(axis=1)
error_rate = tensor.neq(y, pred).mean()
# Initialize parameters
for brick in (linear, lstm, top_linear):
brick.weights_init = IsotropicGaussian(0.1)
brick.biases_init = Constant(0.)
brick.initialize()
return cost, error_rate
def create_model(self):
input_dim = self.input_dim
x = self.x
y = self.y
p = self.p
mask = self.mask
hidden_dim = self.hidden_dim
embedding_dim = self.embedding_dim
lookup = LookupTable(self.dict_size,
embedding_dim,
weights_init=IsotropicGaussian(0.001),
name='LookupTable')
x_to_h = Linear(embedding_dim,
hidden_dim * 4,
name='x_to_h',
weights_init=IsotropicGaussian(0.001),
biases_init=Constant(0.0))
lstm = LSTM(hidden_dim,
name='lstm',
weights_init=IsotropicGaussian(0.001),
biases_init=Constant(0.0))
h_to_o = MLP([Logistic()], [hidden_dim, 1],
weights_init=IsotropicGaussian(0.001),
biases_init=Constant(0),
name='h_to_o')
lookup.initialize()
x_to_h.initialize()
lstm.initialize()
h_to_o.initialize()
embed = lookup.apply(x).reshape(
(x.shape[0], x.shape[1], self.embedding_dim))
embed.name = "embed_vec"
x_transform = x_to_h.apply(embed.transpose(1, 0, 2))
x_transform.name = "Transformed X"
self.lookup = lookup
self.x_to_h = x_to_h
self.lstm = lstm
self.h_to_o = h_to_o
#if mask is None:
h, c = lstm.apply(x_transform)
#else:
#h, c = lstm.apply(x_transform, mask=mask)
h.name = "hidden_state"
c.name = "cell state"
# only values of hidden units of the last timeframe are used for
# the classification
indices = T.sum(mask, axis=0) - 1
rel_hid = h[indices, T.arange(h.shape[1])]
out = self.h_to_o.apply(rel_hid)
probs = out
return probs
def lstm_layer(dim, h, n, x_mask, first, **kwargs):
linear = Linear(input_dim=dim, output_dim=dim * 4, name='linear' + str(n))
lstm = LSTM(dim=dim, activation=Rectifier(), name='lstm' + str(n))
initialize([linear, lstm])
applyLin = linear.apply(h)
if first:
lstmApply = lstm.apply(applyLin, mask=x_mask, **kwargs)[0]
else:
lstmApply = lstm.apply(applyLin, **kwargs)[0]
return lstmApply
def lstm_layer(self, h, n):
"""
Performs the LSTM update for a batch of word sequences
:param h The word embeddings for this update
:param n The number of layers of the LSTM
"""
# Maps the word embedding to a dimensionality to be used in the LSTM
linear = Linear(input_dim=self.hidden_size, output_dim=self.hidden_size * 4, name='linear_lstm' + str(n))
initialize(linear, sqrt(6.0 / (5 * self.hidden_size)))
lstm = LSTM(dim=self.hidden_size, name='lstm' + str(n))
initialize(lstm, 0.08)
return lstm.apply(linear.apply(h))
def create_rnn(hidden_dim, vocab_dim, mode="rnn"):
# input
x = tensor.imatrix('inchar')
y = tensor.imatrix('outchar')
#
W = LookupTable(
name="W1",
#dim = hidden_dim*4,
dim=hidden_dim,
length=vocab_dim,
weights_init=initialization.IsotropicGaussian(0.01),
biases_init=initialization.Constant(0))
if mode == "lstm":
# Long Short Term Memory
H = LSTM(hidden_dim,
name='H',
weights_init=initialization.IsotropicGaussian(0.01),
biases_init=initialization.Constant(0.0))
else:
# recurrent history weight
H = SimpleRecurrent(
name="H",
dim=hidden_dim,
activation=Tanh(),
weights_init=initialization.IsotropicGaussian(0.01))
#
S = Linear(name="W2",
input_dim=hidden_dim,
output_dim=vocab_dim,
weights_init=initialization.IsotropicGaussian(0.01),
biases_init=initialization.Constant(0))
A = NDimensionalSoftmax(name="softmax")
initLayers([W, H, S])
activations = W.apply(x)
hiddens = H.apply(activations) #[0]
activations2 = S.apply(hiddens)
y_hat = A.apply(activations2, extra_ndim=1)
cost = A.categorical_cross_entropy(y, activations2, extra_ndim=1).mean()
cg = ComputationGraph(cost)
#print VariableFilter(roles=[WEIGHT])(cg.variables)
#W1,H,W2 = VariableFilter(roles=[WEIGHT])(cg.variables)
layers = (x, W, H, S, A, y)
return cg, layers, y_hat, cost
class CoreNetwork(BaseRecurrent, Initializable):
def __init__(self, input_dim, dim, **kwargs):
super(CoreNetwork, self).__init__(**kwargs)
self.input_dim = input_dim
self.dim = dim
self.lstm = LSTM(dim=dim, name=self.name + '_lstm',
weights_init=self.weights_init,
biases_init=self.biases_init)
self.proj = Linear(input_dim=input_dim, output_dim=dim*4,
name=self.name + '_proj',
weights_init=self.weights_init,
biases_init=self.biases_init)
self.children = [self.lstm, self.proj]
def get_dim(self, name):
if name == 'inputs':
return self.input_dim
elif name in ['state', 'cell']:
return self.dim
else:
raise ValueError
@recurrent(sequences=['inputs'], states=['state', 'cell'], contexts=[],
outputs=['state', 'cell'])
def apply(self, inputs, state, cell):
state, cell = self.lstm.apply(self.proj.apply(inputs), state, cell,
iterate=False)
return state, cell
def __init__(self, batch_size, num_subwords, num_words, subword_embedding_size, input_vocab_size,
subword_RNN_hidden_state_size, table_width=0.08, init_type='xavier', **kwargs):
super(LSTMCompositionalLayer, 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.table_width = table_width
# 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=table_width)
self.lookup.biases_init = Constant(0)
if init_type == 'xavier':
linear_init = XavierInitializationOriginal(self.subword_embedding_size, self.subword_RNN_hidden_state_size)
lstm_init = XavierInitializationOriginal(self.subword_embedding_size, self.subword_RNN_hidden_state_size)
else: # default is gaussian
linear_init = IsotropicGaussian()
lstm_init = IsotropicGaussian()
# The `inputs` are then split in this order: Input gates, forget gates, cells and output gates
self.linear_forward = Linear(input_dim=self.subword_embedding_size, output_dim=self.subword_RNN_hidden_state_size * 4,
name='linear_forward', weights_init=linear_init, biases_init=Constant(0.0))
self.compositional_subword_to_word_RNN_forward = LSTM(
dim=self.subword_RNN_hidden_state_size, activation=Tanh(), name='subword_RNN_forward',
weights_init=lstm_init, biases_init=Constant(0.0))
self.children = [self.lookup, self.linear_forward, self.compositional_subword_to_word_RNN_forward]
def __init__(self, image_shape, patch_shape, hidden_dim,
n_spatial_dims, whatwhere_interaction, prefork_area_transform,
postmerge_area_transform, patch_transform, batch_normalize,
response_transform, location_std, scale_std, cutoff,
batched_window, initargs, emitter, **kwargs):
self.rnn = LSTM(activation=Tanh(),
dim=hidden_dim,
name="recurrent",
weights_init=IsotropicGaussian(1e-4),
biases_init=Constant(0))
self.locator = masonry.Locator(hidden_dim, n_spatial_dims,
area_transform=prefork_area_transform,
location_std=location_std,
scale_std=scale_std,
**initargs)
self.cropper = crop.LocallySoftRectangularCropper(
n_spatial_dims=n_spatial_dims,
image_shape=image_shape, patch_shape=patch_shape,
kernel=crop.Gaussian(), cutoff=cutoff,
batched_window=batched_window)
self.merger = masonry.Merger(
patch_transform=patch_transform,
area_transform=postmerge_area_transform,
response_transform=response_transform,
n_spatial_dims=n_spatial_dims,
whatwhere_interaction=whatwhere_interaction,
batch_normalize=batch_normalize,
**initargs)
self.attention = masonry.SpatialAttention(
self.locator, self.cropper, self.merger,
name="sa")
self.emitter = emitter
self.model = masonry.RecurrentAttentionModel(
self.rnn, self.attention, self.emitter,
name="ram")
def bilstm_layer(in_dim, inp, h_dim, n):
linear = Linear(input_dim=in_dim, output_dim=h_dim * 4, name='linear' + str(n)+inp.name)
lstm = LSTM(dim=h_dim, name='lstm' + str(n)+inp.name)
bilstm = Bidirectional(prototype=lstm)
bilstm.name = 'bilstm' + str(n) + inp.name
initialize([linear, bilstm])
return bilstm.apply(linear.apply(inp))[0]
def __init__(self, config, **kwargs):
super(Model, self).__init__(**kwargs)
self.config = config
self.pre_context_embedder = ContextEmbedder(config.pre_embedder, name='pre_context_embedder')
self.post_context_embedder = ContextEmbedder(config.post_embedder, name='post_context_embedder')
in1 = 2 + sum(x[2] for x in config.pre_embedder.dim_embeddings)
self.input_to_rec = MLP(activations=[Tanh()], dims=[in1, config.hidden_state_dim], name='input_to_rec')
self.rec = LSTM(
dim = config.hidden_state_dim,
name = 'recurrent'
)
in2 = config.hidden_state_dim + sum(x[2] for x in config.post_embedder.dim_embeddings)
self.rec_to_output = MLP(activations=[Tanh()], dims=[in2, 2], name='rec_to_output')
self.sequences = ['latitude', 'latitude_mask', 'longitude']
self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs
self.inputs = self.sequences + self.context
self.children = [ self.pre_context_embedder, self.post_context_embedder, self.input_to_rec, self.rec, self.rec_to_output ]
self.initial_state_ = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_state")
self.initial_cells = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_cells")
def __init__(self, image_feature_dim, embedding_dim, **kwargs):
super(Encoder, self).__init__(**kwargs)
self.image_embedding = Linear(
input_dim=image_feature_dim
, output_dim=embedding_dim
# , weights_init=IsotropicGaussian(0.02)
# , biases_init=Constant(0.)
, name="image_embedding"
)
self.to_inputs = Linear(
input_dim=embedding_dim
, output_dim=embedding_dim*4 # gate_inputs = vstack(input, forget, cell, hidden)
# , weights_init=IsotropicGaussian(0.02)
# , biases_init=Constant(0.)
, name="to_inputs"
)
# Don't think this dim has to also be dimension, more arbitrary
self.transition = LSTM(
dim=embedding_dim, name="transition")
self.children = [ self.image_embedding
, self.to_inputs
, self.transition
]
class LinearLSTM(Initializable):
def __init__(self,
input_dim,
output_dim,
lstm_dim,
print_intermediate=False,
print_attrs=['__str__'],
**kwargs):
super(LinearLSTM, self).__init__(**kwargs)
self.x_to_h = Linear(input_dim,
lstm_dim * 4,
name='x_to_h',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.lstm = LSTM(lstm_dim,
name='lstm',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.h_to_o = Linear(lstm_dim,
output_dim,
name='h_to_o',
weights_init=IsotropicGaussian(),
biases_init=Constant(0.0))
self.children = [self.x_to_h, self.lstm, self.h_to_o]
self.print_intermediate = print_intermediate
self.print_attrs = print_attrs
@application
def apply(self, source):
x_linear = self.x_to_h.apply(
source.reshape(
(source.shape[1], source.shape[0], source.shape[2])))
x_linear.name = 'x_linear'
if self.print_intermediate:
x_linear = Print(message='x_linear info',
attrs=self.print_attrs)(x_linear)
h, c = self.lstm.apply(x_linear)
if self.print_intermediate:
h = Print(message="hidden states info", attrs=self.print_attrs)(h)
y_hat = self.h_to_o.apply(h)
y_hat.name = 'y_hat'
if self.print_intermediate:
y_hat = Print(message="y_hat info", attrs=self.print_attrs)(y_hat)
return y_hat
def initialize(self):
for child in self.children:
child.initialize()
def reset_allocation(self):
for child in self.children:
child.allocated = False
def __init__(self, input1_size, input2_size, lookup1_dim=200, lookup2_dim=200, hidden_size=512):
self.hidden_size = hidden_size
self.input1_size = input1_size
self.input2_size = input2_size
self.lookup1_dim = lookup1_dim
self.lookup2_dim = lookup2_dim
x1 = tensor.lmatrix('durations')
x2 = tensor.lmatrix('syllables')
y = tensor.lmatrix('pitches')
lookup1 = LookupTable(dim=self.lookup1_dim, length=self.input1_size, name='lookup1',
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
lookup1.initialize()
lookup2 = LookupTable(dim=self.lookup2_dim, length=self.input2_size, name='lookup2',
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
lookup2.initialize()
merge = Merge(['lookup1', 'lookup2'], [self.lookup1_dim, self.lookup2_dim], self.hidden_size,
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
merge.initialize()
recurrent_block = LSTM(dim=self.hidden_size, activation=Tanh(),
weights_init=initialization.Uniform(width=0.01)) #RecurrentStack([LSTM(dim=self.hidden_size, activation=Tanh())] * 3)
recurrent_block.initialize()
linear = Linear(input_dim=self.hidden_size, output_dim=self.input1_size,
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
linear.initialize()
softmax = NDimensionalSoftmax()
l1 = lookup1.apply(x1)
l2 = lookup2.apply(x2)
m = merge.apply(l1, l2)
h = recurrent_block.apply(m)
a = linear.apply(h)
y_hat = softmax.apply(a, extra_ndim=1)
# ValueError: x must be 1-d or 2-d tensor of floats. Got TensorType(float64, 3D)
self.Cost = softmax.categorical_cross_entropy(y, a, extra_ndim=1).mean()
self.ComputationGraph = ComputationGraph(self.Cost)
self.Model = Model(y_hat)
def __init__(self, image_feature_dim, embedding_dim, **kwargs):
super(Encoder, self).__init__(**kwargs)
self.image_embedding = Linear(input_dim=image_feature_dim,
output_dim=embedding_dim,
name="image_embedding")
self.to_inputs = Linear(
input_dim=embedding_dim,
output_dim=embedding_dim *
4 # times 4 cuz vstack(input, forget, cell, hidden)
,
name="to_inputs")
self.transition = LSTM(dim=embedding_dim, name="transition")
self.children = [self.image_embedding, self.to_inputs, self.transition]
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
]
def example4():
"""LSTM -> Plante lors de l'initialisation du lstm."""
x = tensor.tensor3('x')
dim=3
# gate_inputs = theano.function([x],x*4)
gate_inputs = Linear(input_dim=dim,output_dim=dim*4, name="linear",weights_init=initialization.Identity(), biases_init=Constant(2))
lstm = LSTM(dim=dim,activation=Tanh(), weights_init=IsotropicGaussian(), biases_init=Constant(0))
gate_inputs.initialize()
hg = gate_inputs.apply(x)
#print(gate_inputs.parameters)
#print(gate_inputs.parameters[1].get_value())
lstm.initialize()
h, cells = lstm.apply(hg)
print(lstm.parameters)
f = theano.function([x], h)
print(f(np.ones((dim, 1, dim), dtype=theano.config.floatX)))
print(f(np.ones((dim, 1, dim), dtype=theano.config.floatX)))
print(f(4*np.ones((dim, 1, dim), dtype=theano.config.floatX)))
print("Good Job!")
# lstm_output =
#Initial State
h0 = tensor.matrix('h0')
c = tensor.matrix('cells')
h,c1 = lstm.apply(inputs=x, states=h0, cells=c) # lstm.apply(states=h0,cells=cells,inputs=gate_inputs)
f = theano.function([x, h0, c], h)
print("a")
print(f(np.ones((3, 1, 3), dtype=theano.config.floatX),
np.ones((1, 3), dtype=theano.config.floatX),
np.ones((1, 3), dtype=theano.config.floatX)))
def getBidir2(input_dim,input_var):
""" LSTM-based bidirectionnal """
bidir = Bidirectional(weights_init=Orthogonal(),
prototype=LSTM(dim=input_dim, name='lstm'))
#bidir.allocate()
bidir.initialize()
h = bidir.apply(input_var)
net = add_softmax_layer(h, input_dim, 2)
return net
def __init__(self, dim, activation=None, depth=2, name=None,
lstm_name=None, **kwargs):
super(LSTMstack, self).__init__(name=name, **kwargs)
# use the name allready processed by superclass
name = self.name
self.dim = dim
self.children = []
self.depth = depth
for d in range(self.depth):
layer_node = LSTM(dim, activation, name=lstm_name)
layer_node.name = '%s_%s_%d'%(name, layer_node.name, d)
if d > 0:
# convert states of previous layer to inputs of new layer
layer_name = '%s_%d_%d'%(name, d-1, d)
input_dim = layer_node.get_dim('inputs')
self.children.append(Linear(dim, input_dim,
use_bias=True,
name=layer_name))
self.children.append(layer_node)
def __init__(self, layers_no, dim, alphabet_size, batch_size):
# characters -> 1-of-N embedder -> N-to-dim -> LSTM#0 -> ... -> LSTM#(layers_no-1) -> dim-to-N -> softmax
# TODO zdefiniowac blad
# TODO first_resizer
# LSTM stack
self.stack = []
lstms = map(lambda _: LSTM(dim=dim), range(layers_no))
for lstm in lstms:
state, cell = lstm.initial_states(batch_size)
self.stack.append(lstm, state, cell)
def make_bidir_lstm_stack(seq, seq_dim, mask, sizes, skip=True, name=''):
bricks = []
curr_dim = [seq_dim]
curr_hidden = [seq]
hidden_list = []
for k, dim in enumerate(sizes):
fwd_lstm_ins = [Linear(input_dim=d, output_dim=4*dim, name='%s_fwd_lstm_in_%d_%d'%(name,k,l)) for l, d in enumerate(curr_dim)]
fwd_lstm = LSTM(dim=dim, activation=Tanh(), name='%s_fwd_lstm_%d'%(name,k))
bwd_lstm_ins = [Linear(input_dim=d, output_dim=4*dim, name='%s_bwd_lstm_in_%d_%d'%(name,k,l)) for l, d in enumerate(curr_dim)]
bwd_lstm = LSTM(dim=dim, activation=Tanh(), name='%s_bwd_lstm_%d'%(name,k))
bricks = bricks + [fwd_lstm, bwd_lstm] + fwd_lstm_ins + bwd_lstm_ins
fwd_tmp = sum(x.apply(v) for x, v in zip(fwd_lstm_ins, curr_hidden))
bwd_tmp = sum(x.apply(v) for x, v in zip(bwd_lstm_ins, curr_hidden))
fwd_hidden, _ = fwd_lstm.apply(fwd_tmp, mask=mask)
bwd_hidden, _ = bwd_lstm.apply(bwd_tmp[::-1], mask=mask[::-1])
hidden_list = hidden_list + [fwd_hidden, bwd_hidden]
if skip:
curr_hidden = [seq, fwd_hidden, bwd_hidden[::-1]]
curr_dim = [seq_dim, dim, dim]
else:
curr_hidden = [fwd_hidden, bwd_hidden[::-1]]
curr_dim = [dim, dim]
return bricks, hidden_list
def __init__(self, input_dim, dim, **kwargs):
super(CoreNetwork, self).__init__(**kwargs)
self.input_dim = input_dim
self.dim = dim
self.lstm = LSTM(dim=dim, name=self.name + '_lstm',
weights_init=self.weights_init,
biases_init=self.biases_init)
self.proj = Linear(input_dim=input_dim, output_dim=dim*4,
name=self.name + '_proj',
weights_init=self.weights_init,
biases_init=self.biases_init)
self.children = [self.lstm, self.proj]
def __init__(self, dimension, input_size, embed_input=False, **kwargs):
super(LSTMEncoder, self).__init__(**kwargs)
if embed_input:
self.embedder = LookupTable(input_size, dimension)
else:
self.embedder = Linear(input_size, dimension)
self.fork = Fork(['inputs'],
dimension,
output_dims=[dimension],
prototype=Linear(dimension, 4 * dimension))
encoder = Bidirectional(LSTM(dim=dimension, activation=Tanh()))
self.encoder = encoder
self.children = [encoder, self.embedder, self.fork]
def setUp(self):
n_iter = 2
x_dim = 8
z_dim = 10
dec_dim = 12
enc_dim = 16
read_dim = 2 * x_dim
rnninits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
reader = Reader(x_dim=x_dim, dec_dim=dec_dim, **inits)
writer = Writer(input_dim=dec_dim, output_dim=x_dim, **inits)
encoder_rnn = LSTM(dim=enc_dim, name="RNN_enc", **rnninits)
decoder_rnn = LSTM(dim=dec_dim, name="RNN_dec", **rnninits)
encoder_mlp = MLP([Identity()], [(read_dim + dec_dim), 4 * enc_dim],
name="MLP_enc",
**inits)
decoder_mlp = MLP([Identity()], [z_dim, 4 * dec_dim],
name="MLP_dec",
**inits)
q_sampler = Qsampler(input_dim=enc_dim, output_dim=z_dim, **inits)
self.draw = DrawModel(n_iter, reader, encoder_mlp, encoder_rnn,
q_sampler, decoder_mlp, decoder_rnn, writer)
self.draw.initialize()
def __init__(self, word_dim, hidden_dim):
self.forward_lstm= LSTM(hidden_dim,
name='question_forward_lstm',
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
self.backward_lstm= LSTM(hidden_dim,
name='question_backward_lstm',
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
self.x_to_h_forward = Linear(word_dim,
hidden_dim * 4,
name='word_x_to_h_forward',
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
self.x_to_h_backward = Linear(word_dim,
hidden_dim * 4,
name='word_x_to_h_backward',
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
self.forward_lstm.initialize()
self.backward_lstm.initialize()
self.x_to_h_forward.initialize()
self.x_to_h_backward.initialize()
def setUp(self):
depth = 4
self.depth = depth
dim = 3 # don't change, hardwired in the code
transitions = [LSTM(dim=dim) for _ in range(depth)]
self.stack0 = RecurrentStack(transitions,
weights_init=Constant(2),
biases_init=Constant(0))
self.stack0.initialize()
self.stack2 = RecurrentStack(transitions,
weights_init=Constant(2),
biases_init=Constant(0),
skip_connections=True)
self.stack2.initialize()
def build_theano_functions(self) :
#import pdb ; pdb.set_trace()
x = T.fmatrix('x')
s = T.fvector('s')
mu = T.fvector('mu')
mu = T.reshape(mu,(self.number_of_mix,1))
pi = T.fvector('pi')
lstm = LSTM(
dim=self.input_dim/4,
weights_init=IsotropicGaussian(0.5),
biases_init=Constant(1))
lstm.initialize()
h, c = lstm.apply(x)
h = h[0][0][-1]
LL = T.sum(pi*(1./(T.sqrt(2.*np.pi)*s))*T.exp(\
-0.5*(h-mu)**2/T.reshape(s,(self.number_of_mix,1))**2.).sum(axis=1))
cost = -T.log(LL)
#cg = ComputationGraph(cost)
#self.cg = cg
#parameters = cg.parameters
model = Model(cost)
self.model = model
parameters = model.parameters
grads = T.grad(cost, parameters)
updates = []
for i in range(len(grads)) :
updates.append(tuple([parameters[i], parameters[i] - self.lr*grads[i]]))
gradf = theano.function([x,s,mu,pi],[cost],updates=updates)
f = theano.function([x],[h])
return gradf, f
class Encoder(Initializable):
def __init__(self, image_feature_dim, embedding_dim, **kwargs):
super(Encoder, self).__init__(**kwargs)
self.image_embedding = Linear(
input_dim=image_feature_dim
, output_dim=embedding_dim
# , weights_init=IsotropicGaussian(0.02)
# , biases_init=Constant(0.)
, name="image_embedding"
)
self.to_inputs = Linear(
input_dim=embedding_dim
, output_dim=embedding_dim*4 # gate_inputs = vstack(input, forget, cell, hidden)
# , weights_init=IsotropicGaussian(0.02)
# , biases_init=Constant(0.)
, name="to_inputs"
)
# Don't think this dim has to also be dimension, more arbitrary
self.transition = LSTM(
dim=embedding_dim, name="transition")
self.children = [ self.image_embedding
, self.to_inputs
, self.transition
]
@application(inputs=['image_vects', 'word_vects'], outputs=['image_embedding', 'sentence_embedding'])
def apply(self, image_vects, word_vects):
image_embedding = self.image_embedding.apply(image_vects)
# inputs = word_vects
inputs = self.to_inputs.apply(word_vects)
inputs = inputs.dimshuffle(1, 0, 2)
hidden, cells = self.transition.apply(inputs=inputs, mask=None)
# the last hidden state represents the accumulation of all the words (i.e. the sentence)
# grab all batches, grab the last value representing accumulation of the sequence, grab all features
sentence_embedding = hidden[-1]
# sentence_embedding = inputs.mean(axis=0)
return image_embedding, sentence_embedding
class Encoder(Initializable):
def __init__(self, image_feature_dim, embedding_dim, **kwargs):
super(Encoder, self).__init__(**kwargs)
self.image_embedding = Linear(
input_dim=image_feature_dim
, output_dim=embedding_dim
, name="image_embedding"
)
self.to_inputs = Linear(
input_dim=embedding_dim
, output_dim=embedding_dim*4 # times 4 cuz vstack(input, forget, cell, hidden)
, name="to_inputs"
)
self.transition = LSTM(
dim=embedding_dim, name="transition")
self.children = [ self.image_embedding
, self.to_inputs
, self.transition
]
@application(
inputs=['image_vects', 'word_vects']
, outputs=['image_embedding', 'sentence_embedding']
)
def apply(self, image_vects, word_vects):
image_embedding = self.image_embedding.apply(image_vects)
inputs = self.to_inputs.apply(word_vects)
# shuffle dimensions to correspond to (sequence, batch, features)
inputs = inputs.dimshuffle(1, 0, 2)
hidden, cells = self.transition.apply(inputs=inputs, mask=None)
# last hidden state represents the accumulation of word embeddings
# (i.e. the sentence embedding)
sentence_embedding = hidden[-1]
return image_embedding, sentence_embedding
def __init__(self, image_feature_dim, embedding_dim, **kwargs):
super(Encoder, self).__init__(**kwargs)
self.image_embedding = Linear(
input_dim=image_feature_dim
, output_dim=embedding_dim
, name="image_embedding"
)
self.to_inputs = Linear(
input_dim=embedding_dim
, output_dim=embedding_dim*4 # times 4 cuz vstack(input, forget, cell, hidden)
, name="to_inputs"
)
self.transition = LSTM(
dim=embedding_dim, name="transition")
self.children = [ self.image_embedding
, self.to_inputs
, self.transition
]
def __init__(
self,
input_dim,
state_dim,
activation=Tanh(),
state_weights_init=None,
input_weights_init=None,
biases_init=init.Constant(0),
**kwargs
):
super(LSTMLayer, 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 * 4, weights_init=input_weights_init, biases_init=biases_init
)
self.lstm = LSTM(dim=state_dim, activation=activation, weights_init=state_weights_init)
self.children = [self.input_transformation, self.lstm]
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]
def setUp(self):
self.lstm = LSTM(dim=3, weights_init=Constant(2),
biases_init=Constant(0))
self.lstm.initialize()
class TestLSTM(unittest.TestCase):
def setUp(self):
self.lstm = LSTM(dim=3, weights_init=Constant(2),
biases_init=Constant(0))
self.lstm.initialize()
def test_one_step(self):
h0 = tensor.matrix('h0')
c0 = tensor.matrix('c0')
x = tensor.matrix('x')
h1, c1 = self.lstm.apply(x, h0, c0, iterate=False)
next_h = theano.function(inputs=[x, h0, c0], outputs=[h1])
h0_val = 0.1 * numpy.array([[1, 1, 0], [0, 1, 1]],
dtype=theano.config.floatX)
c0_val = 0.1 * numpy.array([[1, 1, 0], [0, 1, 1]],
dtype=theano.config.floatX)
x_val = 0.1 * numpy.array([range(12), range(12, 24)],
dtype=theano.config.floatX)
W_state_val = 2 * numpy.ones((3, 12), dtype=theano.config.floatX)
W_cell_to_in = 2 * numpy.ones((3,), dtype=theano.config.floatX)
W_cell_to_out = 2 * numpy.ones((3,), dtype=theano.config.floatX)
W_cell_to_forget = 2 * numpy.ones((3,), dtype=theano.config.floatX)
# omitting biases because they are zero
activation = numpy.dot(h0_val, W_state_val) + x_val
def sigmoid(x):
return 1. / (1. + numpy.exp(-x))
i_t = sigmoid(activation[:, :3] + c0_val * W_cell_to_in)
f_t = sigmoid(activation[:, 3:6] + c0_val * W_cell_to_forget)
next_cells = f_t * c0_val + i_t * numpy.tanh(activation[:, 6:9])
o_t = sigmoid(activation[:, 9:12] +
next_cells * W_cell_to_out)
h1_val = o_t * numpy.tanh(next_cells)
assert_allclose(h1_val, next_h(x_val, h0_val, c0_val)[0],
rtol=1e-6)
def test_many_steps(self):
x = tensor.tensor3('x')
mask = tensor.matrix('mask')
h, c = self.lstm.apply(x, mask=mask, iterate=True)
calc_h = theano.function(inputs=[x, mask], outputs=[h])
x_val = (0.1 * numpy.asarray(
list(itertools.islice(itertools.permutations(range(12)), 0, 24)),
dtype=theano.config.floatX))
x_val = numpy.ones((24, 4, 12),
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)
c_val = numpy.zeros((25, 4, 3), dtype=theano.config.floatX)
W_state_val = 2 * numpy.ones((3, 12), dtype=theano.config.floatX)
W_cell_to_in = 2 * numpy.ones((3,), dtype=theano.config.floatX)
W_cell_to_out = 2 * numpy.ones((3,), dtype=theano.config.floatX)
W_cell_to_forget = 2 * numpy.ones((3,), dtype=theano.config.floatX)
def sigmoid(x):
return 1. / (1. + numpy.exp(-x))
for i in range(1, 25):
activation = numpy.dot(h_val[i-1], W_state_val) + x_val[i-1]
i_t = sigmoid(activation[:, :3] + c_val[i-1] * W_cell_to_in)
f_t = sigmoid(activation[:, 3:6] + c_val[i-1] * W_cell_to_forget)
c_val[i] = f_t * c_val[i-1] + i_t * numpy.tanh(activation[:, 6:9])
o_t = sigmoid(activation[:, 9:12] +
c_val[i] * W_cell_to_out)
h_val[i] = o_t * numpy.tanh(c_val[i])
h_val[i] = (mask_val[i - 1, :, None] * h_val[i] +
(1 - mask_val[i - 1, :, None]) * h_val[i - 1])
c_val[i] = (mask_val[i - 1, :, None] * c_val[i] +
(1 - mask_val[i - 1, :, None]) * c_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
initial1, initial2 = VariableFilter(roles=[INITIAL_STATE])(
ComputationGraph(h))
assert is_shared_variable(initial1)
assert is_shared_variable(initial2)
assert {initial1.name, initial2.name} == {
'initial_state', 'initial_cells'}
def __init__(self):
inp = tensor.tensor3('input')
inp = inp.dimshuffle(1,0,2)
target = tensor.matrix('target')
target = target.reshape((target.shape[0],))
product = tensor.lvector('product')
missing = tensor.eq(inp, 0)
train_input_mean = 1470614.1
train_input_std = 3256577.0
trans_1 = tensor.concatenate((inp[1:,:,:],tensor.zeros((1,inp.shape[1],inp.shape[2]))), axis=0)
trans_2 = tensor.concatenate((tensor.zeros((1,inp.shape[1],inp.shape[2])), inp[:-1,:,:]), axis=0)
inp = tensor.switch(missing,(trans_1+trans_2)/2, inp)
lookup = LookupTable(length = 352, dim=4*hidden_dim)
product_embed= lookup.apply(product)
salut = tensor.concatenate((inp, missing),axis =2)
linear = Linear(input_dim=input_dim+1, output_dim=4*hidden_dim,
name="lstm_in")
inter = linear.apply(salut)
inter = inter + product_embed[None,:,:]
lstm = LSTM(dim=hidden_dim, activation=activation_function,
name="lstm")
hidden, cells = lstm.apply(inter)
linear2= Linear(input_dim = hidden_dim, output_dim = out_dim,
name="ouput_linear")
pred = linear2.apply(hidden[-1])*train_input_std + train_input_mean
pred = pred.reshape((product.shape[0],))
cost = tensor.mean(abs((pred-target)/target))
# Initialize all bricks
for brick in [linear, linear2, lstm, lookup]:
brick.weights_init = IsotropicGaussian(0.1)
brick.biases_init = Constant(0.)
brick.initialize()
# Apply noise and dropout
cg = ComputationGraph([cost])
if w_noise_std > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, w_noise_std)
if i_dropout > 0:
cg = apply_dropout(cg, [hidden], i_dropout)
[cost_reg] = cg.outputs
cost_reg += 1e-20
if cost_reg is not cost:
self.cost = cost
self.cost_reg = cost_reg
cost_reg.name = 'cost_reg'
cost.name = 'cost'
self.sgd_cost = cost_reg
self.monitor_vars = [[cost, cost_reg]]
else:
self.cost = cost
cost.name = 'cost'
self.sgd_cost = cost
self.monitor_vars = [[cost]]
self.pred = pred
pred.name = 'pred'
def main(nvis, nhid, encoding_lstm_dim, decoding_lstm_dim, T=1):
x = tensor.matrix('features')
# Construct and initialize model
encoding_mlp = MLP([Tanh()], [None, None])
decoding_mlp = MLP([Tanh()], [None, None])
encoding_lstm = LSTM(dim=encoding_lstm_dim)
decoding_lstm = LSTM(dim=decoding_lstm_dim)
draw = DRAW(nvis=nvis, nhid=nhid, T=T, encoding_mlp=encoding_mlp,
decoding_mlp=decoding_mlp, encoding_lstm=encoding_lstm,
decoding_lstm=decoding_lstm, biases_init=Constant(0),
weights_init=Orthogonal())
draw.push_initialization_config()
encoding_lstm.weights_init = IsotropicGaussian(std=0.001)
decoding_lstm.weights_init = IsotropicGaussian(std=0.001)
draw.initialize()
# Compute cost
cost = -draw.log_likelihood_lower_bound(x).mean()
cost.name = 'nll_upper_bound'
model = Model(cost)
# Datasets and data streams
mnist_train = BinarizedMNIST('train')
train_loop_stream = ForceFloatX(DataStream(
dataset=mnist_train,
iteration_scheme=SequentialScheme(mnist_train.num_examples, 100)))
train_monitor_stream = ForceFloatX(DataStream(
dataset=mnist_train,
iteration_scheme=SequentialScheme(mnist_train.num_examples, 500)))
mnist_valid = BinarizedMNIST('valid')
valid_monitor_stream = ForceFloatX(DataStream(
dataset=mnist_valid,
iteration_scheme=SequentialScheme(mnist_valid.num_examples, 500)))
mnist_test = BinarizedMNIST('test')
test_monitor_stream = ForceFloatX(DataStream(
dataset=mnist_test,
iteration_scheme=SequentialScheme(mnist_test.num_examples, 500)))
# Get parameters and monitoring channels
computation_graph = ComputationGraph([cost])
params = VariableFilter(roles=[PARAMETER])(computation_graph.variables)
monitoring_channels = dict([
('avg_' + channel.tag.name, channel.mean()) for channel in
VariableFilter(name='.*term$')(computation_graph.auxiliary_variables)])
for name, channel in monitoring_channels.items():
channel.name = name
monitored_quantities = monitoring_channels.values() + [cost]
# Training loop
step_rule = RMSProp(learning_rate=1e-3, decay_rate=0.95)
algorithm = GradientDescent(cost=cost, params=params, step_rule=step_rule)
algorithm.add_updates(computation_graph.updates)
main_loop = MainLoop(
model=model, data_stream=train_loop_stream, algorithm=algorithm,
extensions=[
Timing(),
SerializeMainLoop('vae.pkl', save_separately=['model']),
FinishAfter(after_n_epochs=200),
DataStreamMonitoring(
monitored_quantities, train_monitor_stream, prefix="train",
updates=computation_graph.updates),
DataStreamMonitoring(
monitored_quantities, valid_monitor_stream, prefix="valid",
updates=computation_graph.updates),
DataStreamMonitoring(
monitored_quantities, test_monitor_stream, prefix="test",
updates=computation_graph.updates),
ProgressBar(),
Printing()])
main_loop.run()
def build_theano_functions(self):
x = T.fmatrix('time_sequence')
x = x.reshape((self.batch_dim, self.sequence_dim, self.time_dim))
y = x[:,1:self.sequence_dim,:]
x = x[:,:self.sequence_dim-1,:]
# if we try to include the spectrogram features
spec_dims = 0
if self.image_size is not None :
print "Convolution activated"
self.init_conv()
spec = T.ftensor4('spectrogram')
spec_features, spec_dims = self.conv.build_conv_layers(spec)
print "Conv final dims =", spec_dims
spec_dims = np.prod(spec_dims)
spec_features = spec_features.reshape(
(self.batch_dim, self.sequence_dim-1, spec_dims))
x = T.concatenate([x, spec_features], axis=2)
layers_input = [x]
dims =np.array([self.time_dim + spec_dims])
for dim in self.lstm_layers_dim :
dims = np.append(dims, dim)
print "Dimensions =", dims
# layer is just an index of the layer
for layer in range(len(self.lstm_layers_dim)) :
# before the cell, input, forget and output gates, x needs to
# be transformed
linear = Linear(dims[layer],
dims[layer+1]*4,
weights_init=Orthogonal(self.orth_scale),
biases_init=Constant(0),
name="linear"+str(layer))
linear.initialize()
lstm_input = linear.apply(layers_input[layer])
# the lstm wants batch X sequence X time
lstm = LSTM(
dim=dims[layer+1],
weights_init=IsotropicGaussian(mean=0.,std=0.5),
biases_init=Constant(1),
name="lstm"+str(layer))
lstm.initialize()
# hack to use Orthogonal on lstm w_state
lstm.W_state.set_value(
self.orth_scale*Orthogonal().generate(np.random, lstm.W_state.get_value().shape))
h, _dummy = lstm.apply(lstm_input)
layers_input.append(h)
# this is where Alex Graves' paper starts
print "Last linear transform dim :", dims[1:].sum()
output_transform = Linear(dims[1:].sum(),
self.output_dim,
weights_init=Orthogonal(self.orth_scale),
use_bias=False,
name="output_transform")
output_transform.initialize()
if len(self.lstm_layers_dim) == 1 :
print "hallo there, only one layer speaking"
y_hat = output_transform.apply(layers_input[-1])
else :
y_hat = output_transform.apply(T.concatenate(layers_input[1:], axis=2))
# transforms to find each gmm params (mu, pi, sig)
# small hack to softmax a 3D tensor
pis = T.reshape(
T.nnet.softmax(
T.reshape(y_hat[:,:,:self.gmm_dim], ((self.sequence_dim-1)*self.batch_dim, self.gmm_dim))),
(self.batch_dim, (self.sequence_dim-1), self.gmm_dim))
sig = T.exp(y_hat[:,:,self.gmm_dim:self.gmm_dim*2])+1e-6
mus = y_hat[:,:,self.gmm_dim*2:]
pis = pis[:,:,:,np.newaxis]
mus = mus[:,:,:,np.newaxis]
sig = sig[:,:,:,np.newaxis]
y = y[:,:,np.newaxis,:]
y = T.patternbroadcast(y, (False, False, True, False))
mus = T.patternbroadcast(mus, (False, False, False, True))
sig = T.patternbroadcast(sig, (False, False, False, True))
# sum likelihood with targets
# see blog for this crazy Pr() = sum log sum prod
# axes :: (batch, sequence, mixture, time)
expo_term = -0.5*((y-mus)**2)/sig**2
coeff = T.log(T.maximum(1./(T.sqrt(2.*np.pi)*sig), EPS))
#coeff = T.log(1./(T.sqrt(2.*np.pi)*sig))
sequences = coeff + expo_term
log_sequences = T.log(pis + EPS) + T.sum(sequences, axis=3, keepdims=True)
log_sequences_max = T.max(log_sequences, axis=2, keepdims=True)
LL = -(log_sequences_max + T.log(EPS + T.sum(T.exp(log_sequences - log_sequences_max), axis=2, keepdims=True))).mean()
LL.name = "summed_likelihood"
model = Model(LL)
self.model = model
parameters = model.parameters
algorithm = GradientDescent(
cost=LL,
parameters=model.parameters,
step_rule=Adam())
f = theano.function([x],[pis, sig, mus])
return algorithm, f
class LanguageModel(Initializable):
"""
This takes the word embeddings from LSTMCompositionalLayer and creates sentence embeddings using a LSTM
compositional_layer_type can be:
1) 'BidirectionalLSTMCompositionalLayer'
2) 'UnidirectionalLSTMCompositionalLayer'
3) 'BaselineLSTMCompositionalLayer'
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 dimensions of
(num_words = num RNN states, batch_size, sentence embedding size = LM_RNN_hidden_state_size = subword_RNN_hidden_state_size * 2)
"""
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, table_width=0.08,
compositional_layer_type='BidirectionalLSTMCompositionalLayer', init_type='xavier', **kwargs):
super(LanguageModel, 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 #i.e. word embedding size
self.LM_RNN_hidden_state_size = LM_RNN_hidden_state_size #i.e sentence embedding size
self.table_width = table_width
self.name = 'Language_Model'
if init_type == 'xavier':
linear_init = XavierInitializationOriginal(self.subword_RNN_hidden_state_size, self.LM_RNN_hidden_state_size)
lstm_init = XavierInitializationOriginal(self.subword_RNN_hidden_state_size, self.LM_RNN_hidden_state_size)
else: # default is gaussian
linear_init = IsotropicGaussian()
lstm_init = IsotropicGaussian()
self.compositional_layer = None
self.linear = None
if compositional_layer_type == 'BidirectionalLSTMCompositionalLayer':
self.compositional_layer = BidirectionalLSTMCompositionalLayer(self.batch_size, self.num_subwords, self.num_words,
self.subword_embedding_size, self.input_vocab_size,
self.subword_RNN_hidden_state_size, self.table_width,
init_type=init_type, name='compositional_layer')
if init_type == 'xavier':
linear_init = XavierInitializationOriginal(self.subword_RNN_hidden_state_size * 2, self.LM_RNN_hidden_state_size)
lstm_init = XavierInitializationOriginal(self.subword_RNN_hidden_state_size * 2, self.LM_RNN_hidden_state_size)
else: # default is gaussian
linear_init = IsotropicGaussian()
lstm_init = IsotropicGaussian()
self.linear = Linear(input_dim=self.subword_RNN_hidden_state_size * 2, # 2 * for the bidirectional
output_dim=self.LM_RNN_hidden_state_size * 4,
name='linear', weights_init=IsotropicGaussian(), biases_init=Constant(0.0))
elif compositional_layer_type == 'UnidirectionalLSTMCompositionalLayer':
self.compositional_layer = LSTMCompositionalLayer(self.batch_size, self.num_subwords, self.num_words,
self.subword_embedding_size, self.input_vocab_size,
self.subword_RNN_hidden_state_size, self.table_width,
init_type=init_type, name='compositional_layer')
self.linear = Linear(input_dim=self.subword_RNN_hidden_state_size,
output_dim=self.LM_RNN_hidden_state_size * 4,
name='linear', weights_init=IsotropicGaussian(), biases_init=Constant(0.0))
elif compositional_layer_type == 'BaselineLSTMCompositionalLayer':
self.compositional_layer = BaselineLSTMCompositionalLayer(self.batch_size, self.num_subwords, self.num_words,
self.subword_embedding_size, self.input_vocab_size,
self.subword_RNN_hidden_state_size, self.table_width,
init_type=init_type, name='compositional_layer')
self.linear = Linear(input_dim=self.subword_RNN_hidden_state_size,
output_dim=self.LM_RNN_hidden_state_size * 4,
name='linear', weights_init=IsotropicGaussian(), biases_init=Constant(0.0))
else:
print('ERROR: compositional_layer_type = ' + compositional_layer_type + ' is invalid')
sys.exit()
# has one RNN which reads the word embeddings into a sentence embedding, or partial sentence embeddings
self.language_model_RNN = LSTM(
dim=self.LM_RNN_hidden_state_size, activation=Identity(), name='language_model_RNN',
weights_init=IsotropicGaussian(), biases_init=Constant(0.0))
self.children = [self.compositional_layer, self.linear, self.language_model_RNN]
@application(inputs=['subword_id_input_', 'subword_id_input_mask_'], outputs=['sentence_embeddings', 'word_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(
self.linear.apply(word_embeddings), mask=word_embeddings_mask)[0] #[0] = hidden states, [1] = cells
# sentence_embeddings_mask = word_embeddings_mask.max(axis=0).T
return sentence_embeddings, word_embeddings_mask
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, table_width=0.08,
compositional_layer_type='BidirectionalLSTMCompositionalLayer', init_type='xavier', **kwargs):
super(LanguageModel, 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 #i.e. word embedding size
self.LM_RNN_hidden_state_size = LM_RNN_hidden_state_size #i.e sentence embedding size
self.table_width = table_width
self.name = 'Language_Model'
if init_type == 'xavier':
linear_init = XavierInitializationOriginal(self.subword_RNN_hidden_state_size, self.LM_RNN_hidden_state_size)
lstm_init = XavierInitializationOriginal(self.subword_RNN_hidden_state_size, self.LM_RNN_hidden_state_size)
else: # default is gaussian
linear_init = IsotropicGaussian()
lstm_init = IsotropicGaussian()
self.compositional_layer = None
self.linear = None
if compositional_layer_type == 'BidirectionalLSTMCompositionalLayer':
self.compositional_layer = BidirectionalLSTMCompositionalLayer(self.batch_size, self.num_subwords, self.num_words,
self.subword_embedding_size, self.input_vocab_size,
self.subword_RNN_hidden_state_size, self.table_width,
init_type=init_type, name='compositional_layer')
if init_type == 'xavier':
linear_init = XavierInitializationOriginal(self.subword_RNN_hidden_state_size * 2, self.LM_RNN_hidden_state_size)
lstm_init = XavierInitializationOriginal(self.subword_RNN_hidden_state_size * 2, self.LM_RNN_hidden_state_size)
else: # default is gaussian
linear_init = IsotropicGaussian()
lstm_init = IsotropicGaussian()
self.linear = Linear(input_dim=self.subword_RNN_hidden_state_size * 2, # 2 * for the bidirectional
output_dim=self.LM_RNN_hidden_state_size * 4,
name='linear', weights_init=IsotropicGaussian(), biases_init=Constant(0.0))
elif compositional_layer_type == 'UnidirectionalLSTMCompositionalLayer':
self.compositional_layer = LSTMCompositionalLayer(self.batch_size, self.num_subwords, self.num_words,
self.subword_embedding_size, self.input_vocab_size,
self.subword_RNN_hidden_state_size, self.table_width,
init_type=init_type, name='compositional_layer')
self.linear = Linear(input_dim=self.subword_RNN_hidden_state_size,
output_dim=self.LM_RNN_hidden_state_size * 4,
name='linear', weights_init=IsotropicGaussian(), biases_init=Constant(0.0))
elif compositional_layer_type == 'BaselineLSTMCompositionalLayer':
self.compositional_layer = BaselineLSTMCompositionalLayer(self.batch_size, self.num_subwords, self.num_words,
self.subword_embedding_size, self.input_vocab_size,
self.subword_RNN_hidden_state_size, self.table_width,
init_type=init_type, name='compositional_layer')
self.linear = Linear(input_dim=self.subword_RNN_hidden_state_size,
output_dim=self.LM_RNN_hidden_state_size * 4,
name='linear', weights_init=IsotropicGaussian(), biases_init=Constant(0.0))
else:
print('ERROR: compositional_layer_type = ' + compositional_layer_type + ' is invalid')
sys.exit()
# has one RNN which reads the word embeddings into a sentence embedding, or partial sentence embeddings
self.language_model_RNN = LSTM(
dim=self.LM_RNN_hidden_state_size, activation=Identity(), name='language_model_RNN',
weights_init=IsotropicGaussian(), biases_init=Constant(0.0))
self.children = [self.compositional_layer, self.linear, self.language_model_RNN]
class Model(Initializable):
@lazy()
def __init__(self, config, **kwargs):
super(Model, self).__init__(**kwargs)
self.config = config
self.pre_context_embedder = ContextEmbedder(config.pre_embedder, name='pre_context_embedder')
self.post_context_embedder = ContextEmbedder(config.post_embedder, name='post_context_embedder')
in1 = 2 + sum(x[2] for x in config.pre_embedder.dim_embeddings)
self.input_to_rec = MLP(activations=[Tanh()], dims=[in1, config.hidden_state_dim], name='input_to_rec')
self.rec = LSTM(
dim = config.hidden_state_dim,
name = 'recurrent'
)
in2 = config.hidden_state_dim + sum(x[2] for x in config.post_embedder.dim_embeddings)
self.rec_to_output = MLP(activations=[Tanh()], dims=[in2, 2], name='rec_to_output')
self.sequences = ['latitude', 'latitude_mask', 'longitude']
self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs
self.inputs = self.sequences + self.context
self.children = [ self.pre_context_embedder, self.post_context_embedder, self.input_to_rec, self.rec, self.rec_to_output ]
self.initial_state_ = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_state")
self.initial_cells = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_cells")
def _push_initialization_config(self):
for mlp in [self.input_to_rec, self.rec_to_output]:
mlp.weights_init = self.config.weights_init
mlp.biases_init = self.config.biases_init
self.rec.weights_init = self.config.weights_init
def get_dim(self, name):
return self.rec.get_dim(name)
@application
def initial_state(self, *args, **kwargs):
return self.rec.initial_state(*args, **kwargs)
@recurrent(states=['states', 'cells'], outputs=['destination', 'states', 'cells'], sequences=['latitude', 'longitude', 'latitude_mask'])
def predict_all(self, latitude, longitude, latitude_mask, **kwargs):
latitude = (latitude - data.train_gps_mean[0]) / data.train_gps_std[0]
longitude = (longitude - data.train_gps_mean[1]) / data.train_gps_std[1]
pre_emb = tuple(self.pre_context_embedder.apply(**kwargs))
latitude = tensor.shape_padright(latitude)
longitude = tensor.shape_padright(longitude)
itr = self.input_to_rec.apply(tensor.concatenate(pre_emb + (latitude, longitude), axis=1))
itr = itr.repeat(4, axis=1)
(next_states, next_cells) = self.rec.apply(itr, kwargs['states'], kwargs['cells'], mask=latitude_mask, iterate=False)
post_emb = tuple(self.post_context_embedder.apply(**kwargs))
rto = self.rec_to_output.apply(tensor.concatenate(post_emb + (next_states,), axis=1))
rto = (rto * data.train_gps_std) + data.train_gps_mean
return (rto, next_states, next_cells)
@predict_all.property('contexts')
def predict_all_inputs(self):
return self.context
@application(outputs=['destination'])
def predict(self, latitude, longitude, latitude_mask, **kwargs):
latitude = latitude.T
longitude = longitude.T
latitude_mask = latitude_mask.T
res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0]
return res[-1]
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@application(outputs=['cost_matrix'])
def cost_matrix(self, latitude, longitude, latitude_mask, **kwargs):
latitude = latitude.T
longitude = longitude.T
latitude_mask = latitude_mask.T
res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0]
target = tensor.concatenate(
(kwargs['destination_latitude'].dimshuffle('x', 0, 'x'),
kwargs['destination_longitude'].dimshuffle('x', 0, 'x')),
axis=2)
target = target.repeat(latitude.shape[0], axis=0)
ce = error.erdist(target.reshape((-1, 2)), res.reshape((-1, 2)))
ce = ce.reshape(latitude.shape)
return ce * latitude_mask
@cost_matrix.property('inputs')
def cost_matrix_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def cost(self, latitude_mask, **kwargs):
return self.cost_matrix(latitude_mask=latitude_mask, **kwargs).sum() / latitude_mask.sum()
@cost.property('inputs')
def cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def valid_cost(self, **kwargs):
# Only works when batch_size is 1.
return self.cost_matrix(**kwargs)[-1,0]
@valid_cost.property('inputs')
def valid_cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
