def test_lstm():
from layers import LSTM
lstm = LSTM(input_size, layer_size)
lstm.set_state(batch_size)
x = T.tensor3()
f = theano.function([x], lstm(x), updates=lstm.updates)
X = np.ones((batch_size, time_steps, input_size), dtype=np.float32)
assert f(X).shape == (batch_size, time_steps, layer_size)
def test_lstm():
from layers import LSTM
lstm = LSTM(input_size, layer_size)
lstm.set_state(batch_size)
x = T.tensor3()
f = theano.function([x], lstm(x), updates=lstm.updates)
X = np.ones((batch_size, time_steps, input_size), dtype=np.float32)
assert f(X).shape == (batch_size, time_steps, layer_size)
def __init__(self, vocab_size, embedding_size, hidden_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.embedding = Embedding(vocab_size, embedding_size)
self.lstm = LSTM(embedding_size, hidden_size)
self.layers = [self.embedding, self.lstm]
self.params = list(
itertools.chain(*[
layer.params for layer in self.layers
if hasattr(layer, 'params')
]))
class Decoder(Sequential):
def __init__(self, vocab_size, embedding_size, hidden_size, output_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.output_size = output_size
self.lstm = LSTM(embedding_size, hidden_size)
self.lstm_output = TimeDistributed(hidden_size,
output_size,
activation='tanh')
self.softmax = TimeDistributed(output_size,
vocab_size,
activation='softmax')
self.embedding = Embedding(vocab_size, embedding_size)
self.layers = [
self.lstm, self.lstm_output, self.softmax, self.embedding
]
self.params = list(
itertools.chain(*[
layer.params for layer in self.layers
if hasattr(layer, 'params')
]))
def forward(self, ec_H, ec_C, mask):
(sens_size, batch_size) = T.shape(mask)
def step(m, prev_Y, prev_H, prev_C):
"""Forward a time step of the decoder."""
# LSTM forward time step
(H, C) = self.lstm.step(prev_Y, m, prev_H, prev_C)
# LSTM output
O = self.lstm_output.forward(H)
# Apply softmax to LSTM output
P = self.softmax.forward(O)
# Make prediction
one_hot_Y = T.argmax(P, axis=1)
# Feed the output to the next time step
Y = self.embedding.forward(one_hot_Y)
# FIXME: Deal with differ length ?
return (P, Y, H, C)
results, updates = theano.scan(fn=step,
sequences=[mask],
outputs_info=[
None,
dict(initial=T.zeros(
(batch_size,
self.embedding_size)),
taps=[-1]),
dict(initial=ec_H, taps=[-1]),
dict(initial=ec_C, taps=[-1])
])
# return np.swapaxes(results[0], 0, 1) # returns the softmax probabilities
return results[0]
def __init__(self, vocab_size, embedding_size, hidden_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.embedding = Embedding(vocab_size, embedding_size)
self.lstm = LSTM(embedding_size, hidden_size)
self.layers = [self.embedding, self.lstm]
self.params = list(itertools.chain(*[layer.params for layer in self.layers if hasattr(layer, 'params')]))
def build_test_model(self, data):
rng = np.random.RandomState(3435)
lstm_params, hidden_params, hidden_relu_params, full_connect_params = self.load_trained_params()
data_x, data_y, maxlen = data
test_len = len(data_x)
n_test_batches = test_len // self.batch_size
x = T.matrix('x')
y = T.ivector('y')
index = T.lscalar()
Words = theano.shared(value=self.word_vectors, name="Words", borrow=True)
input_width = self.hidden_sizes[0]
layer0_input = T.cast(Words[T.cast(x.flatten(), dtype="int32")], dtype=floatX).reshape((self.batch_size, maxlen, input_width))
lstm = LSTM(dim=input_width, batch_size=self.batch_size, number_step=maxlen, params=lstm_params)
layer0_input = lstm.feed_foward(layer0_input)
lstm.mean_pooling_input(layer0_input)
hidden_sizes = [self.hidden_sizes[0], self.hidden_sizes[0]]
hidden_layer = HiddenLayer(rng, hidden_sizes=hidden_sizes, input_vectors=lstm.output, activation=utils.Tanh, name="Hidden_Tanh", W=hidden_params[0], b=hidden_params[1])
hidden_layer.predict()
hidden_layer_relu = HiddenLayer(rng, hidden_sizes=hidden_sizes, input_vectors=hidden_layer.output, W=hidden_relu_params[0], b=hidden_relu_params[1])
hidden_layer_relu.predict()
# hidden_layer_dropout = HiddenLayerDropout(rng, hidden_sizes=self.hidden_sizes[:2], input_vectors=lstm.output, W=hidden_layer.W, b=hidden_layer.b)
full_connect = FullConnectLayer(rng, layers_size=[self.hidden_sizes[0], self.hidden_sizes[-1]],
input_vector=hidden_layer_relu.output, W=full_connect_params[0], b=full_connect_params[1])
full_connect.predict()
test_data_x = theano.shared(np.asarray(data_x, dtype=floatX), borrow=True)
test_data_y = theano.shared(np.asarray(data_y, dtype='int32'), borrow=True)
errors = 0.
if test_len == 1:
test_model = theano.function([index],outputs=full_connect.get_predict(), on_unused_input='ignore', givens={
x: test_data_x[index * self.batch_size: (index + 1) * self.batch_size],
y: test_data_y[index * self.batch_size: (index + 1) * self.batch_size]
})
index = 0
avg_errors = test_model(index)
else:
test_model = theano.function([index], outputs=full_connect.errors(y), givens={
x: test_data_x[index * self.batch_size: (index + 1) * self.batch_size],
y: test_data_y[index * self.batch_size: (index + 1) * self.batch_size]
})
for i in xrange(n_test_batches):
errors += test_model(i)
avg_errors = errors / n_test_batches
return avg_errors
def __init__(self, dmemory, daddress, nstates, dinput, doutput):
self.layers = {}
self.layers['INPUT'] = Dense(dinput, dmemory)
self.layers['PREVIOUS_READ'] = Dense(dmemory, dmemory)
self.layers['CONTROL_KEY'] = LSTM(dmemory + dmemory, nstates)
self.layers['OUTPUT'] = Dense(doutput, doutput)
self.daddress = daddress
self.dmemory = dmemory
self.doutput = doutput
def __init__(self, vocabulary_size: int, hidden_size: int, n_layers: int,
embedding_size: int, dropout: float):
super().__init__()
self.vocabulary_size = vocabulary_size
self.dropout = dropout
self.embedding = torch.nn.Embedding(vocabulary_size + 1,
embedding_size)
self.lstm = LSTM(embedding_size,
hidden_size,
n_layers,
dropout=dropout)
def run_lstm_alt(self, context_out, question_pool, context_len, is_train):
# tile pooled question rep and concat with context
q_rep = tf.expand_dims(question_pool, 1) # (batch_size, 1, D)
context_shape = tf.shape(context_out)[1]
q_rep = tf.tile(q_rep, [1, context_shape, 1])
q_c_rep = tf.concat([context_out, q_rep], axis=-1)
with tf.variable_scope('lstm_') as scope:
lstm_out_fw = LSTM(q_c_rep,
context_len,
self.hidden_units,
tf.cond(is_train, lambda: self.output_keep_prob,
lambda: 1.0),
tf.cond(is_train, lambda: self.input_keep_prob,
lambda: 1.0),
tf.cond(is_train, lambda: self.state_keep_prob,
lambda: 1.0),
use_last=False,
seed=self.seed,
reuse=False)
q_c_rep_rev = _reverse(q_c_rep, context_len, 1, 0)
lstm_out_rev = LSTM(q_c_rep_rev,
context_len,
self.hidden_units,
tf.cond(is_train,
lambda: self.output_keep_prob,
lambda: 1.0),
tf.cond(is_train, lambda: self.input_keep_prob,
lambda: 1.0),
tf.cond(is_train, lambda: self.state_keep_prob,
lambda: 1.0),
use_last=False,
seed=self.seed,
reuse=True)
lstm_out_bw = _reverse(lstm_out_rev, context_len, 1, 0)
lstm_out = tf.concat([lstm_out_fw, lstm_out_bw],
2,
name='lstm_out')
return lstm_out
def __init__(self, vocab_size, embedding_size, hidden_size, output_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.output_size = output_size
self.lstm = LSTM(embedding_size, hidden_size)
self.lstm_output = TimeDistributed(hidden_size, output_size, activation='tanh')
self.softmax = TimeDistributed(output_size, vocab_size, activation='softmax')
self.embedding = Embedding(vocab_size, embedding_size)
self.layers = [self.lstm, self.lstm_output, self.softmax, self.embedding]
self.params = list(itertools.chain(*[layer.params for layer in self.layers if hasattr(layer, 'params')]))
def __init__(self, vocab_size, embedding_size, hidden_size, output_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.output_size = output_size
self.lstm = LSTM(embedding_size, hidden_size)
self.lstm_output = TimeDistributed(hidden_size,
output_size,
activation='tanh')
self.softmax = TimeDistributed(output_size,
vocab_size,
activation='softmax')
self.embedding = Embedding(vocab_size, embedding_size)
self.layers = [
self.lstm, self.lstm_output, self.softmax, self.embedding
]
self.params = list(
itertools.chain(*[
layer.params for layer in self.layers
if hasattr(layer, 'params')
]))
def main():
input_size, output_size = 3, 3
rnn = RNN()
rnn.add_layer(LSTM(input_size, output_size))
X_train = [[[1, 0, 0]], [[0, 1, 0]], [[0, 0, 1]]]
Y_train = [[[0, 1, 0]], [[0, 0, 1]], [[1, 0, 0]]]
epochs = 1000
rnn.train(X_train, Y_train, epochs=epochs)
for p, y in zip(rnn.predict(X_train), Y_train):
_p = np.zeros_like(p).astype(int)
_p[:, np.argmax(p)] = 1
print('%30s %10s %10s' % (p.reshape(1, -1), _p, np.array(y)))
class Encoder(Sequential):
def __init__(self, vocab_size, embedding_size, hidden_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.embedding = Embedding(vocab_size, embedding_size)
self.lstm = LSTM(embedding_size, hidden_size)
self.layers = [self.embedding, self.lstm]
self.params = list(itertools.chain(*[layer.params for layer in self.layers if hasattr(layer, 'params')]))
def forward(self, batch, mask):
# ``batch`` is a matrix whose row ``x`` is a sentence, e.g. x = [1, 4, 5, 2, 0]
# ``emb`` is a list of embedding matrix, e[i].shape = (sene_size, embedding_size)
emb = self.embedding.forward(batch)
(H, C) = self.lstm.forward(emb, mask)
return (H[-1], C[-1])
class Decoder(Sequential):
def __init__(self, vocab_size, embedding_size, hidden_size, output_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.output_size = output_size
self.lstm = LSTM(embedding_size, hidden_size)
self.lstm_output = TimeDistributed(hidden_size, output_size, activation='tanh')
self.softmax = TimeDistributed(output_size, vocab_size, activation='softmax')
self.embedding = Embedding(vocab_size, embedding_size)
self.layers = [self.lstm, self.lstm_output, self.softmax, self.embedding]
self.params = list(itertools.chain(*[layer.params for layer in self.layers if hasattr(layer, 'params')]))
def forward(self, ec_H, ec_C, mask):
(sens_size, batch_size) = T.shape(mask)
def step(m, prev_Y, prev_H, prev_C):
"""Forward a time step of the decoder."""
# LSTM forward time step
(H, C) = self.lstm.step(prev_Y, m, prev_H, prev_C)
# LSTM output
O = self.lstm_output.forward(H)
# Apply softmax to LSTM output
P = self.softmax.forward(O)
# Make prediction
one_hot_Y = T.argmax(P, axis=1)
# Feed the output to the next time step
Y = self.embedding.forward(one_hot_Y)
# FIXME: Deal with differ length ?
return (P, Y, H, C)
results, updates = theano.scan(
fn=step,
sequences=[mask],
outputs_info=[
None,
dict(initial=T.zeros((batch_size, self.embedding_size)), taps=[-1]),
dict(initial=ec_H, taps=[-1]),
dict(initial=ec_C, taps=[-1])
]
)
# return np.swapaxes(results[0], 0, 1) # returns the softmax probabilities
return results[0]
def __init__(self, vocabulary_size: int, hidden_size: int, n_layers: int,
embedding_size: int, dropout: float, max_out_len: int):
super().__init__()
self.dropout = dropout
self.vocabulary_size = vocabulary_size
self.embedding = torch.nn.Embedding(vocabulary_size + 1,
embedding_size)
self.lstm = LSTM(embedding_size,
hidden_size,
n_layers,
dropout=dropout)
self.output_projection = Linear(hidden_size, vocabulary_size + 1)
self.max_out_len = max_out_len
self.sos_token = self.vocabulary_size
self.eos_token = self.vocabulary_size
def main():
x = tensor.tensor3()
t = tensor.matrix()
task = TempOrder(args.low,
args.high,
args.length,
batch_size=config.batch_size,
long_sequences=False)
train_db = SyntheticDatabase(task,
number_of_batches=config.number_of_batches)
valid_db = SyntheticDatabase(task,
number_of_batches=config.test_sequences //
config.batch_size,
phase='valid')
model = TempOrderModel(x, t, [
LSTM(task.input_size,
config.layer_size,
config.batch_size,
args.hid_dropout_rate,
args.drop_candidates,
args.per_step,
weight_init=Uniform(config.scale),
persistent=False),
LastStepPooling(),
Linear(config.layer_size, task.output_size)
])
if args.finetune:
model.load('exp/temp_order/opt.pkl')
opt = SGDOptimizer(model,
x,
t,
train_db,
test_db=valid_db,
name="temp_order",
clip_gradients=True,
clip_threshold=5,
print_norms=True)
opt.train(train_db, test_db=valid_db, learning_rate=args.lr, epochs=1000)
class Encoder(Sequential):
def __init__(self, vocab_size, embedding_size, hidden_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.embedding = Embedding(vocab_size, embedding_size)
self.lstm = LSTM(embedding_size, hidden_size)
self.layers = [self.embedding, self.lstm]
self.params = list(
itertools.chain(*[
layer.params for layer in self.layers
if hasattr(layer, 'params')
]))
def forward(self, batch, mask):
# ``batch`` is a matrix whose row ``x`` is a sentence, e.g. x = [1, 4, 5, 2, 0]
# ``emb`` is a list of embedding matrix, e[i].shape = (sene_size, embedding_size)
emb = self.embedding.forward(batch)
(H, C) = self.lstm.forward(emb, mask)
return (H[-1], C[-1])
def train(self, train_data, dev_data, test_data, maxlen):
# tr = tracker.SummaryTracker()
rng = np.random.RandomState(3435)
train_x, train_y = self.shared_dataset(train_data)
dev_x, dev_y = self.shared_dataset(dev_data)
test_x, test_y = self.shared_dataset(test_data)
test_len = len(test_data[0])
n_train_batches = len(train_data[0]) // self.batch_size
n_val_batches = len(dev_data[0]) // self.batch_size
n_test_batches = test_len // self.batch_size
input_width = self.hidden_sizes[0]
x = T.matrix('x')
y = T.ivector('y')
index = T.lscalar()
Words = theano.shared(value=self.word_vectors, name="Words", borrow=True)
layer0_input = Words[T.cast(x.flatten(), dtype="int32")].reshape((self.batch_size, maxlen, input_width))
lstm = LSTM(dim=input_width, batch_size=self.batch_size, number_step=maxlen)
leyer0_output = lstm.feed_foward(layer0_input)
lstm.mean_pooling_input(leyer0_output)
hidden_sizes = [self.hidden_sizes[0], self.hidden_sizes[0]]
hidden_layer = HiddenLayer(rng, hidden_sizes=hidden_sizes, input_vectors=lstm.output, activation=utils.Tanh, name="Hidden_Tanh")
hidden_layer.predict()
hidden_layer_relu = HiddenLayer(rng, hidden_sizes=hidden_sizes, input_vectors=hidden_layer.output)
hidden_layer_relu.predict()
# hidden_layer_dropout = HiddenLayerDropout(rng, hidden_sizes=self.hidden_sizes[:2], input_vectors=lstm.output, W=hidden_layer.W, b=hidden_layer.b)
full_connect = FullConnectLayer(rng, layers_size=[self.hidden_sizes[0], self.hidden_sizes[-1]], input_vector=hidden_layer_relu.output)
full_connect.predict()
cost = full_connect.negative_log_likelihood(y)
params = lstm.params + hidden_layer.params + hidden_layer_relu.params + full_connect.params
# params = hidden_layer.params + hidden_layer_relu.params + full_connect.params
params_length = len(params)
#init value for e_grad time 0, e_delta time 0 and delta at time 0
e_grad, e_delta_prev, delta = self.init_hyper_values(params_length)
# e_grad_d, e_delta_prev_d, delta_d = self.init_hyper_values(params_length, name="D")
#apply gradient
grads = T.grad(cost, params)
#dropout hidden layer
# hidden_layer_dropout.dropout()
# hidden_layer_dropout.predict()
# full_connect.setInput(hidden_layer_dropout.output)
# full_connect.predict()
# cost_d = full_connect.negative_log_likelihood(y)
#apply gradient to cost_d
# grads_d = T.grad(cost_d, params)
e_grad, e_delta_prev, delta = self.adadelta(grads, e_grad, e_delta_prev)
# e_grad_d, e_delta_prev_d, delta_d = self.adadelta(grads_d, e_grad_d, e_delta_prev_d, delta_d)
grads = delta
# grad_d = delta_d
updates = [(p, p - d) for p, d in zip(params, grads)]
# updates = [(p, p - self.learning_rate * d) for p, d in zip(params, grads)]
train_model = theano.function([index], cost, updates=updates, givens={
x: train_x[(index * self.batch_size):((index + 1) * self.batch_size)],
y: train_y[(index * self.batch_size):((index + 1) * self.batch_size)]
})
val_model = theano.function([index], full_connect.errors(y), givens={
x: dev_x[index * self.batch_size: (index + 1) * self.batch_size],
y: dev_y[index * self.batch_size: (index + 1) * self.batch_size],
})
test_model = theano.function(inputs=[index], outputs=full_connect.errors(y), givens={
x: test_x[index * self.batch_size: (index + 1) * self.batch_size],
y: test_y[index * self.batch_size: (index + 1) * self.batch_size]
})
validation_frequency = min(n_train_batches, self.patience // 2)
val_batch_lost = 1.
best_batch_lost = 1.
best_test_lost = 1.
stop_count = 0
epoch = 0
done_loop = False
current_time_step = 0
improve_threshold = 0.995
iter_list = range(n_train_batches)
while(epoch < self.epochs and done_loop is not True):
epoch_cost_train = 0.
epoch += 1
batch_train = 0
print("Start epoch: %i" % epoch)
start = time.time()
random.shuffle(iter_list)
for mini_batch, m_b_i in zip(iter_list, xrange(n_train_batches)):
current_time_step = (epoch - 1) * n_train_batches + m_b_i
epoch_cost_train += train_model(mini_batch)
batch_train += 1
if (current_time_step + 1) % validation_frequency == 0:
val_losses = [val_model(i) for i in xrange(n_val_batches)]
val_losses = np.array(val_losses)
val_batch_lost = np.mean(val_losses)
if val_batch_lost < best_batch_lost:
if best_batch_lost * improve_threshold > val_batch_lost:
self.patience = max(self.patience, current_time_step * self.patience_frq)
best_batch_lost = val_batch_lost
# test it on the test set
test_losses = [
test_model(i)
for i in range(n_test_batches)
]
current_test_lost = np.mean(test_losses)
print(('epoch %i minibatch %i test accuracy of %i example is: %.5f') % (epoch, m_b_i, test_len, (1 - current_test_lost) * 100.))
if best_test_lost > current_test_lost:
best_test_lost = current_test_lost
if self.patience <= current_time_step:
print(self.patience)
done_loop = True
break
print('epoch: %i, training time: %.2f secs; with avg cost: %.5f' % (epoch, time.time() - start, epoch_cost_train / batch_train))
print('Best test accuracy is: %.5f' % (1 - best_test_lost))
utils.save_layer_params(lstm, 'lstm')
utils.save_layer_params(hidden_layer, 'hidden_lstm')
utils.save_layer_params(hidden_layer_relu, 'hidden_relu_lstm')
utils.save_layer_params(full_connect, 'full_connect_lstm')
return lstm.params
def _build_layers(self):
self.emb = HRRWordEmbedding(self.vocab_size, self.cell_dim, self.num_roles, self.num_fillers)
self.rnn = LSTM(self.num_layers, self.cell_dim, keep_prob=self.keep_prob)
char_to_int[chars[i]] = i
int_to_char[i] = chars[i]
vec = np.zeros((len(chars), 1))
vec[i] = 1.
char_to_vec[chars[i]] = vec
source = np.array(list(source))[:(len(source) // BATCH_SIZE) * BATCH_SIZE]
source = np.array(np.split(source, BATCH_SIZE))
EMBEDDING_LENGTH = len(chars)
# Creating the model.
model = Network(
LSTM(size=512,
input_size=EMBEDDING_LENGTH,
batch_size=BATCH_SIZE,
backprop_depth=SEQUENCE_LENGTH,
stateful=True),
LSTM(size=512,
input_size=512,
batch_size=BATCH_SIZE,
backprop_depth=SEQUENCE_LENGTH,
stateful=True),
TimeDistributed(
Dense(size=EMBEDDING_LENGTH,
input_size=512,
activation=SparseSoftmax())))
if RESTORE_MODEL_PATH:
model.loadParams(RESTORE_MODEL_PATH)
def _build(self): """ Build model Input's form is BatchSize x Num_Time_Steps x Num_Channels Params: None Returns: None """ self.layers.append( Conv1D(num_in_channels=self.num_input_channels, num_out_channels=16, filter_size=3, strides=1, padding="SAME", dropout=0.0, bias=True, act=leak_relu ) ) self.layers.append( Conv1D(num_in_channels=16, num_out_channels=32, filter_size=3, strides=1, padding="SAME", dropout=0.0, bias=True, act=leak_relu ) ) self.layers.append( LSTM(input_dim=32, num_units=128, length=self.num_time_steps, batch_size=32, return_sequece=False, bias=True ) ) self.layers.append( Dense(input_dim=128, output_dim=64, dropout=0.0, sparse_inputs=False, act=leak_relu, bias=True ) ) self.layers.append(CenterLoss(num_classes=self.num_classes, num_feas=64, learning_rate=0.5)) self.layers.append( Dense(input_dim=64, output_dim=self.num_classes, dropout=0.0, sparse_inputs=False, act=leak_relu, bias=True ) )
char_to_int[chars[i]] = i
int_to_char[i] = chars[i]
vec = np.zeros((len(chars), 1))
vec[i] = 1.
char_to_vec[chars[i]] = vec
# The length of the vector that represents a character
# is equivalent to the number of different characters
# in the text.
EMBEDDING_LENGTH = len(chars)
# Creating the model.
model = Network(
LSTM(size=512,
input_size=EMBEDDING_LENGTH,
batch_size=1,
backprop_depth=1,
stateful=True),
LSTM(size=512,
input_size=512,
batch_size=1,
backprop_depth=1,
stateful=True),
TimeDistributed(
Dense(size=EMBEDDING_LENGTH,
input_size=512,
activation=SparseSoftmax(TEMPERATURE))))
model.loadParams(MODEL)
# optimizer = Adam(learning_rate=lambda n: 0.001, beta_1=0.9, beta_2=0.999)
train_data_iter = data_iterator_simple(load_train_func,
len(x_train),
batch_size,
shuffle=True,
with_file_cache=False)
valid_data_iter = data_iterator_simple(load_valid_func,
len(x_valid),
batch_size,
shuffle=True,
with_file_cache=False)
x = nn.Variable((batch_size, sentence_length))
t = nn.Variable((batch_size, sentence_length, 1))
h = PF.embed(x, vocab_size, embedding_size)
h = LSTM(h, hidden, return_sequences=True)
h = TimeDistributed(PF.affine)(h, hidden, name='hidden')
y = TimeDistributed(PF.affine)(h, vocab_size, name='output')
mask = F.sum(F.sign(t), axis=2) # do not predict 'pad'.
entropy = TimeDistributedSoftmaxCrossEntropy(y, t) * mask
count = F.sum(mask, axis=1)
loss = F.mean(F.div2(F.sum(entropy, axis=1), count))
# Create solver.
solver = S.Momentum(1e-2, momentum=0.9)
solver.set_parameters(nn.get_parameters())
# Create monitor.
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor('./tmp-lstmlm')
def _build_layers(self):
self.emb = HRRWordEmbedding(self.vocab_size, self.cell_dim, self.num_roles, self.num_fillers)
self.rnn = LSTM(self.num_layers, self.cell_dim, keep_prob=self.keep_prob, proj_dim=self.cell_dim * 2)
self.chunk_layer = HRRChunkLayer(self.cell_dim, self.num_roles)
def main():
with open(LOOKUP_FILE, 'r') as file:
chars = json.load(file)
# Here we make dictionaries that can be used to convert
# between characters, integer id-s of characters, and one-hot
# vectors that will be used to represent the characters.
char_to_int = dict()
int_to_char = dict()
char_to_vec = dict()
for i in range(len(chars)):
char_to_int[chars[i]] = i
int_to_char[i] = chars[i]
vec = np.zeros((len(chars), 1))
vec[i] = 1.
char_to_vec[chars[i]] = vec
# The length of the vector that represents a character
# is equivalent to the number of different characters
# in the text.
EMBEDDING_LENGTH = len(chars)
# Create the LSTM layers only. We don't use the Network class,
# since we are only interested in the activations of the recurrent
# layers.
first_layer = LSTM(size=512,
input_size=EMBEDDING_LENGTH,
batch_size=1,
backprop_depth=1,
stateful=True)
second_layer = LSTM(size=512,
input_size=512,
batch_size=1,
backprop_depth=1,
stateful=True)
# Load the weights.
with open(MODEL, 'r') as file:
weights = json.load(file)
first_layer.loadParams(weights[0])
second_layer.loadParams(weights[1])
# Loading in the file.
with open(TEXT_FILE, 'r', encoding='utf8') as file:
text = file.read()
source = list(text)
for i in range(len(source)):
source[i] = char_to_vec[source[i]]
# Feed the text to the network.
# Here we look at the activation of the neurons of the
# hidden state at the 2nd LSTM layer.
# We take the first element of the output as there is only one
# batch.
out = second_layer.forward(first_layer.forward(np.array([source])))[0]
# ###############---TKINTER---#############################################
class Wrap:
NEURON_INDEX = 0
def showNeuron():
for j in range(out.shape[0]):
# We will leave the background of the newline characters white,
# regardless of its activation. The reason for that is that the color
# would fill the entire remainder of the line, which is very disturbing to look at.
intensity = 255 if text[j] == '\n' else 255 - int(
(out[j, Wrap.NEURON_INDEX, 0] + 1) * 127.5)
text_box.tag_config(str(j),
background="#%02x%02x%02x" %
(255, intensity, intensity))
def inputFromEntry(evt):
Wrap.NEURON_INDEX = int(entry.get())
entry.delete(0, "end")
showNeuron()
def nextButtonClicked():
Wrap.NEURON_INDEX += 1
entry.delete(0, "end")
entry.insert(tk.INSERT, str(Wrap.NEURON_INDEX))
showNeuron()
# Making the tkinter window.
root = tk.Tk()
text_box = tk.Text(root, height=35)
text_box.insert(tk.INSERT, text)
text_box.pack()
current_line = 1
current_char = 0
for i in range(out.shape[0]):
text_box.tag_add(str(i), f"{current_line}.{current_char}")
current_char += 1
if text[i] == '\n':
current_line += 1
current_char = 0
# Making the entry box.
entry = tk.Entry(root, width=5)
entry.pack()
entry.bind("<Return>", inputFromEntry)
# Buttons
up = tk.Button(text="Next", command=nextButtonClicked)
up.pack()
# Show the first neuron by default.
showNeuron()
root.mainloop()
from loss import CrossEntropyLoss
from optimizer import SGDOptimizer, RMSpropOptimizer
from network import Network
from data_preparation import load_data
from solve_rnn import solve_rnn
import theano.tensor as T
X_train, y_train, X_test, y_test = load_data()
HIDDEN_DIM = 32
INPUT_DIM = 20
OUTPUT_DIM = 10
model = Network()
model.add(LSTM('rnn1', HIDDEN_DIM, INPUT_DIM,
0.1)) # output shape: 4 x HIDDEN_DIM
model.add(Linear('fc', HIDDEN_DIM, OUTPUT_DIM,
0.1)) # output shape: 4 x OUTPUT_DIM
model.add(Softmax('softmax'))
loss = CrossEntropyLoss('xent')
optim = RMSpropOptimizer(learning_rate=0.01, rho=0.9)
input_placeholder = T.fmatrix('input')
label_placeholder = T.fmatrix('label')
model.compile(input_placeholder, label_placeholder, loss, optim)
MAX_EPOCH = 6
DISP_FREQ = 1000
TEST_FREQ = 10000
output = []
for f, f_size in zip(filters, filster_sizes):
_h = PF.convolution(h,
f,
kernel=(1, f_size),
pad=(0, f_size // 2),
name='conv_{}'.format(f_size))
_h = F.max_pooling(_h, kernel=(1, word_length))
output.append(_h)
h = F.concatenate(*output, axis=1)
h = F.transpose(h, (0, 2, 1, 3))
h = F.reshape(h, (batch_size, sentence_length, sum(filters)))
# h = PF.batch_normalization(h, axes=[2])
h = TimeDistributed(Highway)(h, name='highway1')
h = TimeDistributed(Highway)(h, name='highway2')
h = LSTM(h, lstm_size, return_sequences=True, name='lstm1')
h = LSTM(h, lstm_size, return_sequences=True, name='lstm2')
h = TimeDistributed(PF.affine)(h, lstm_size, name='hidden')
y = TimeDistributed(PF.affine)(h, word_vocab_size, name='output')
t = nn.Variable((batch_size, sentence_length, 1))
mask = F.sum(F.sign(t), axis=2) # do not predict 'pad'.
entropy = TimeDistributedSoftmaxCrossEntropy(y, t) * mask
count = F.sum(mask, axis=1)
loss = F.mean(F.div2(F.sum(entropy, axis=1), count))
# Create solver.
solver = S.Momentum(1e-2, momentum=0.9)
solver.set_parameters(nn.get_parameters())
# Create monitor.
def _build_layers(self):
emb_cls = TiedIOEmbedding if self.tied_io else EmbeddingLayer
self.emb = emb_cls(self.vocab_size, self.cell_dim)
self.rnn = LSTM(self.num_layers, self.cell_dim, keep_prob=self.keep_prob)
def __init__(self,
word_V,
dep_V,
word_d=100,
pos_d=25,
mlp_d=100,
mlp_label_d=100,
num_lstm_layers=2,
lstm_d=125,
embeddings_init=None,
pos_V=None,
seed=0,
verbose=False):
'''
word_V - size of word vocab
dep_V - size of relation label vocab
word_d - dimension of word embeddings
pos_d - dimension of POS embeddings
mlp_d - dimension of hidden layer for arc prediction MLP
mlp_label_d - dimension of hidden layer for label prediction MLP
num_lstm_layers - number of bi-directional LSTM layers to stack
lstm_d - dimension of hidden state in the LSTM
embeddings_init - use pre-trained embeddings
pos_V - size of POS vocab
seed - random seed for initialization
verbose - whether to print information about these parameters
'''
if verbose:
print('Word vocabulary size: {}'.format(word_V))
print('Dependency relation vocabulary size: {}'.format(dep_V))
print('POS vocabulary size: {}'.format(pos_V))
self.word_V = word_V
self.dep_V = dep_V
self.pos_V = pos_V
self.word_d = word_d
self.pos_d = pos_d
self.mlp_d = mlp_d
self.mlp_label_d = mlp_label_d
self.lstm_layers = num_lstm_layers
self.lstm_d = lstm_d
np.random.seed(seed)
self.model = dynet.Model()
#embedding layers for words and POS
self.embeddings = self.model.add_lookup_parameters(
(self.word_V, self.word_d))
if pos_V is not None:
self.pos_embeddings = self.model.add_lookup_parameters(
(self.pos_V, self.pos_d))
#bi-directional LSTM layers
#embeddings -> layer1 -> layer2
lstm_layers = []
for i in range(num_lstm_layers):
input_d = word_d
if i:
input_d = 2 * lstm_d
elif pos_V is not None:
input_d += pos_d
fwd_lstm_layer = LSTM(self.model, input_d, lstm_d)
rev_lstm_layer = LSTM(self.model, input_d, lstm_d, reverse=True)
lstm_layers.append((fwd_lstm_layer, rev_lstm_layer))
#arc prediction MLP
#layer2(i), layer2(j) -> concatenate -> score
mlp_layer = MLP(self.model, lstm_d * 4, mlp_d, 1)
#label prediction MLP
if mlp_label_d:
mlp_label_layer = MLP(self.model, lstm_d * 4, mlp_label_d, dep_V)
else:
mlp_label_layer = None
#train the model using Adam optimizer
self.trainer = dynet.AdamTrainer(self.model)
#take in word and pos_indices, return the output of the 2nd layer
def get_lstm_output(indices, pos_indices=None):
embeddings_out = [self.embeddings[w] for w in indices]
x = embeddings_out
if pos_V is not None and pos_indices is not None:
x = []
for i, input in enumerate(embeddings_out):
x.append(
dynet.concatenate(
[input, self.pos_embeddings[pos_indices[i]]]))
for i in range(num_lstm_layers):
x_1 = lstm_layers[i][0].get_output(x)[0]
x_2 = lstm_layers[i][1].get_output(x)[0]
x = [
dynet.concatenate([x_1[i], x_2[i]])
for i in range(len(indices))
]
return x
self.states = get_lstm_output
#score all arcs from i to j using the arc prediction MLP
def score_arcs(states, value=True):
length = len(states)
scores = [[None for i in range(length)] for j in range(length)]
for i in range(length):
for j in range(length):
score = mlp_layer.get_output(
dynet.concatenate([states[i], states[j]]))
if value:
scores[i][j] = score.scalar_value()
else:
scores[i][j] = score
return scores
self.score_arcs = score_arcs
#score all labels at i using the label prediction MLP
def score_labels(states, arcs, value=True):
scores = []
for i in range(len(states)):
score = mlp_label_layer.get_output(
dynet.concatenate([states[i], states[arcs[i]]]))
if value:
scores.append(score.value())
else:
scores.append(score)
return scores
self.score_labels = score_labels
def build_test_model(self, data):
rng = np.random.RandomState(3435)
lstm_params, hidden_params, hidden_relu_params, full_connect_params, convs = self.load_trained_params(
)
data_x, data_y, maxlen = data
test_len = len(data_x)
n_test_batches = test_len // self.batch_size
x = T.matrix('x')
y = T.ivector('y')
index = T.lscalar()
Words = theano.shared(value=self.word_vectors,
name="Words",
borrow=True)
input_width = self.hidden_sizes[0]
layer0_input = Words[T.cast(x.flatten(), dtype="int32")].reshape(
(self.batch_size, maxlen, input_width))
lstm = LSTM(dim=input_width,
batch_size=self.batch_size,
number_step=maxlen,
params=lstm_params)
leyer0_output = lstm.feed_foward(layer0_input)
conv_outputs = list()
conv_nnets = list()
params = list()
output = T.cast(layer0_input.flatten(), dtype=floatX)
conv_input = output.reshape((self.batch_size, 1, maxlen, input_width))
for it, p_conv in enumerate(convs):
pheight = maxlen - self.filter_sizes[it] + 1
conv = ConvolutionLayer(rng=rng,
filter_shape=(self.kernel, 1,
self.filter_sizes[it],
input_width),
input_shape=(self.batch_size, 1, maxlen,
input_width),
poolsize=(pheight, 1),
name="conv" + str(self.filter_sizes[it]),
W=p_conv[0],
b=p_conv[1])
#=>batch size * 1 * 100 * width
output = conv.predict(conv_input)
layer1_input = output.flatten(2)
params += conv.params
conv_outputs.append(layer1_input)
conv_nnets.append(conv)
conv_nnets_output = T.concatenate(conv_outputs, axis=1)
hidden_layer = HiddenLayer(
rng,
hidden_sizes=[self.kernel * 3, self.hidden_sizes[0]],
input_vectors=conv_nnets_output,
activation=utils.Tanh,
name="Hidden_Tanh",
W=hidden_params[0],
b=hidden_params[1])
hidden_layer.predict()
hidden_layer_relu = HiddenLayer(
rng,
hidden_sizes=[self.hidden_sizes[0], self.hidden_sizes[0]],
input_vectors=hidden_layer.output,
W=hidden_relu_params[0],
b=hidden_relu_params[1])
hidden_layer_relu.predict()
# hidden_layer_dropout = HiddenLayerDropout(rng, hidden_sizes=self.hidden_sizes[:2], input_vectors=lstm.output, W=hidden_layer.W, b=hidden_layer.b)
full_connect = FullConnectLayer(
rng,
layers_size=[self.hidden_sizes[0], self.hidden_sizes[-1]],
input_vector=hidden_layer_relu.output,
W=full_connect_params[0],
b=full_connect_params[1])
full_connect.predict()
test_data_x = theano.shared(np.asarray(data_x, dtype=floatX),
borrow=True)
test_data_y = theano.shared(np.asarray(data_y, dtype='int32'),
borrow=True)
errors = 0.
if test_len == 1:
test_model = theano.function(
[index],
outputs=full_connect.get_predict(),
on_unused_input='ignore',
givens={
x:
test_data_x[index * self.batch_size:(index + 1) *
self.batch_size],
y:
test_data_y[index * self.batch_size:(index + 1) *
self.batch_size]
})
index = 0
avg_errors = test_model(index)
else:
test_model = theano.function(
[index],
outputs=full_connect.errors(y),
givens={
x:
test_data_x[index * self.batch_size:(index + 1) *
self.batch_size],
y:
test_data_y[index * self.batch_size:(index + 1) *
self.batch_size]
})
for i in xrange(n_test_batches):
errors += test_model(i)
avg_errors = errors / n_test_batches
return avg_errors
