def jobman(state, channel):
# load dataset
rng = numpy.random.RandomState(state['seed'])
# declare the dimensionalies of the input and output
if state['chunks'] == 'words':
state['n_in'] = 10000
state['n_out'] = 10000
else:
state['n_in'] = 50
state['n_out'] = 50
train_data, valid_data, test_data = get_text_data(state)
## BEGIN Tutorial
### Define Theano Input Variables
x = TT.lvector('x')
y = TT.lvector('y')
h0 = theano.shared(
numpy.zeros((eval(state['nhids'])[-1], ), dtype='float32'))
### Neural Implementation of the Operators: \oplus
#### Word Embedding
emb_words = MultiLayer(rng,
n_in=state['n_in'],
n_hids=eval(state['inp_nhids']),
activation=eval(state['inp_activ']),
init_fn='sample_weights_classic',
weight_noise=state['weight_noise'],
rank_n_approx=state['rank_n_approx'],
scale=state['inp_scale'],
sparsity=state['inp_sparse'],
learn_bias=True,
bias_scale=eval(state['inp_bias']),
name='emb_words')
#### Deep Transition Recurrent Layer
rec = eval(state['rec_layer'])(
rng,
eval(state['nhids']),
activation=eval(state['rec_activ']),
#activation = 'TT.nnet.sigmoid',
bias_scale=eval(state['rec_bias']),
scale=eval(state['rec_scale']),
sparsity=eval(state['rec_sparse']),
init_fn=eval(state['rec_init']),
weight_noise=state['weight_noise'],
name='rec')
#### Stiching them together
##### (1) Get the embedding of a word
x_emb = emb_words(x, no_noise_bias=state['no_noise_bias'])
##### (2) Embedding + Hidden State via DT Recurrent Layer
reset = TT.scalar('reset')
rec_layer = rec(x_emb,
n_steps=x.shape[0],
init_state=h0 * reset,
no_noise_bias=state['no_noise_bias'],
truncate_gradient=state['truncate_gradient'],
batch_size=1)
## BEGIN Exercise: DOT-RNN
### Neural Implementation of the Operators: \lhd
#### Exercise (1)
#### TODO: Define a layer from the hidden state to the intermediate layer
emb_layer = MultiLayer(rng, )
#### Exercise (1)
#### TODO: Define a layer from the input to the intermediate Layer
#### Hidden State: Combine emb_state and emb_words_out
#### Exercise (1)
#### TODO: Define an activation layer
#### Exercise (2)
#### TODO: Define a dropout layer
#### Softmax Layer
output_layer = SoftmaxLayer(rng,
eval(state['dout_nhid']),
state['n_out'],
scale=state['out_scale'],
bias_scale=state['out_bias_scale'],
init_fn="sample_weights_classic",
weight_noise=state['weight_noise'],
sparsity=state['out_sparse'],
sum_over_time=True,
name='out')
### Few Optional Things
#### Direct shortcut from x to y
if state['shortcut_inpout']:
shortcut = MultiLayer(rng,
n_in=state['n_in'],
n_hids=eval(state['inpout_nhids']),
activations=eval(state['inpout_activ']),
init_fn='sample_weights_classic',
weight_noise=state['weight_noise'],
scale=eval(state['inpout_scale']),
sparsity=eval(state['inpout_sparse']),
learn_bias=eval(state['inpout_learn_bias']),
bias_scale=eval(state['inpout_bias']),
name='shortcut')
#### Learning rate scheduling (1/(1+n/beta))
state['clr'] = state['lr']
def update_lr(obj, cost):
stp = obj.step
if isinstance(obj.state['lr_start'],
int) and stp > obj.state['lr_start']:
time = float(stp - obj.state['lr_start'])
new_lr = obj.state['clr'] / (1 + time / obj.state['lr_beta'])
obj.lr = new_lr
if state['lr_adapt']:
rec.add_schedule(update_lr)
### Neural Implementations of the Language Model
#### Training
if state['shortcut_inpout']:
additional_inputs = [rec_layer, shortcut(x)]
else:
additional_inputs = [rec_layer]
##### Exercise (1): Compute the output intermediate layer
##### TODO: Compute the output intermediate layer
##### Exercise (2): Apply Dropout
##### TODO: Apply the dropout layer
train_model = output_layer(outhid,
no_noise_bias=state['no_noise_bias'],
additional_inputs=additional_inputs).train(
target=y,
scale=numpy.float32(1. / state['seqlen']))
nw_h0 = rec_layer.out[rec_layer.out.shape[0] - 1]
if state['carry_h0']:
train_model.updates += [(h0, nw_h0)]
#### Validation
h0val = theano.shared(
numpy.zeros((eval(state['nhids'])[-1], ), dtype='float32'))
rec_layer = rec(emb_words(x, use_noise=False),
n_steps=x.shape[0],
batch_size=1,
init_state=h0val * reset,
use_noise=False)
nw_h0 = rec_layer.out[rec_layer.out.shape[0] - 1]
##### Exercise (1):
##### TODO: Compute the output intermediate layer
##### Exercise (2): Apply Dropout
##### TODO: Apply the dropout layer without noise
if state['shortcut_inpout']:
additional_inputs = [rec_layer, shortcut(x, use_noise=False)]
else:
additional_inputs = [rec_layer]
valid_model = output_layer(outhid,
additional_inputs=additional_inputs,
use_noise=False).validate(target=y,
sum_over_time=True)
valid_updates = []
if state['carry_h0']:
valid_updates = [(h0val, nw_h0)]
valid_fn = theano.function([x, y, reset],
valid_model.cost,
name='valid_fn',
updates=valid_updates)
#### Sampling
##### single-step sampling
def sample_fn(word_tm1, h_tm1):
x_emb = emb_words(word_tm1, use_noise=False, one_step=True)
h0 = rec(x_emb, state_before=h_tm1, one_step=True, use_noise=False)[-1]
outhid = outhid_dropout(outhid_activ(
emb_state(h0, use_noise=False, one_step=True) +
emb_words_out(word_tm1, use_noise=False, one_step=True),
one_step=True),
use_noise=False,
one_step=True)
word = output_layer.get_sample(state_below=outhid,
additional_inputs=[h0],
temp=1.)
return word, h0
##### scan for iterating the single-step sampling multiple times
[samples, summaries], updates = scan(sample_fn,
states=[
TT.alloc(numpy.int64(0),
state['sample_steps']),
TT.alloc(numpy.float32(0), 1,
eval(state['nhids'])[-1])
],
n_steps=state['sample_steps'],
name='sampler_scan')
##### build a Theano function for sampling
sample_fn = theano.function([], [samples],
updates=updates,
profile=False,
name='sample_fn')
##### Load a dictionary
dictionary = numpy.load(state['dictionary'])
if state['chunks'] == 'chars':
dictionary = dictionary['unique_chars']
else:
dictionary = dictionary['unique_words']
def hook_fn():
sample = sample_fn()[0]
print 'Sample:',
if state['chunks'] == 'chars':
print "".join(dictionary[sample])
else:
for si in sample:
print dictionary[si],
print
### Build and Train a Model
#### Define a model
model = LM_Model(cost_layer=train_model,
weight_noise_amount=state['weight_noise_amount'],
valid_fn=valid_fn,
clean_before_noise_fn=False,
noise_fn=None,
rng=rng)
if state['reload']:
model.load(state['prefix'] + 'model.npz')
#### Define a trainer
##### Training algorithm (SGD)
if state['moment'] < 0:
algo = SGD(model, state, train_data)
else:
algo = SGD_m(model, state, train_data)
##### Main loop of the trainer
main = MainLoop(train_data,
valid_data,
test_data,
model,
algo,
state,
channel,
train_cost=False,
hooks=hook_fn,
validate_postprocess=eval(state['validate_postprocess']))
## Run!
main.main()
def main():
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
parser = argparse.ArgumentParser(
"Case study of generating simple 1d sequences with RNN.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"mode",
choices=["train", "plot"],
help="The mode to run. Use `train` to train a new model"
" and `plot` to plot a sequence generated by an"
" existing one.")
parser.add_argument("prefix",
default="sine",
help="The prefix for model, timing and state files")
parser.add_argument("--input-noise",
type=float,
default=0.0,
help="Adds Gaussian noise of given intensity to the "
" training sequences.")
parser.add_argument(
"--function",
default="lambda a, x: numpy.sin(a * x)",
help="An analytical description of the sequence family to learn."
" The arguments before the last one are considered parameters.")
parser.add_argument("--steps",
type=int,
default=100,
help="Number of steps to plot")
parser.add_argument("--params", help="Parameter values for plotting")
args = parser.parse_args()
function = eval(args.function)
num_params = len(inspect.getargspec(function).args) - 1
class Emitter(TrivialEmitter):
@application
def cost(self, readouts, outputs):
"""Compute MSE."""
return ((readouts - outputs)**2).sum(axis=readouts.ndim - 1)
transition = GatedRecurrent(name="transition",
activation=Tanh(),
dim=10,
weights_init=Orthogonal())
with_params = AddParameters(transition,
num_params,
"params",
name="with_params")
generator = SequenceGenerator(LinearReadout(
readout_dim=1,
source_names=["states"],
emitter=Emitter(name="emitter"),
name="readout"),
with_params,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
name="generator")
generator.allocate()
logger.debug("Parameters:\n" +
pprint.pformat(Selector(generator).get_params().keys()))
if args.mode == "train":
seed = 1
rng = numpy.random.RandomState(seed)
batch_size = 10
generator.initialize()
cost = Cost(
generator.cost(tensor.tensor3('x'),
params=tensor.matrix("params")).sum())
if args.input_noise:
cost.apply_noise(cost.inputs, args.input_noise)
gh_model = GroundhogModel(generator, cost)
state = GroundhogState(args.prefix, batch_size,
learning_rate=0.0001).as_dict()
data = SeriesIterator(rng, function, 100, batch_size)
trainer = SGD(gh_model, state, data)
main_loop = MainLoop(data, None, None, gh_model, trainer, state, None)
main_loop.load()
main_loop.main()
elif args.mode == "plot":
load_params(generator, args.prefix + "model.npz")
params = tensor.matrix("params")
sample = theano.function([params],
generator.generate(params=params,
n_steps=args.steps,
batch_size=1))
param_values = numpy.array(map(float, args.params.split()),
dtype=floatX)
states, outputs, _ = sample(param_values[None, :])
actual = outputs[:, 0, 0]
desired = numpy.array(
[function(*(list(param_values) + [T])) for T in range(args.steps)])
print("MSE: {}".format(((actual - desired)**2).sum()))
pyplot.plot(numpy.hstack([actual[:, None], desired[:, None]]))
pyplot.show()
else:
assert False
def main():
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
parser = argparse.ArgumentParser(
"Case study of generating a Markov chain with RNN.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"mode",
choices=["train", "sample"],
help="The mode to run. Use `train` to train a new model"
" and `sample` to sample a sequence generated by an"
" existing one.")
parser.add_argument("prefix",
default="sine",
help="The prefix for model, timing and state files")
parser.add_argument("--steps",
type=int,
default=100,
help="Number of steps to plot")
args = parser.parse_args()
dim = 10
num_states = ChainIterator.num_states
feedback_dim = 8
transition = GatedRecurrent(name="transition", activation=Tanh(), dim=dim)
generator = SequenceGenerator(LinearReadout(
readout_dim=num_states,
source_names=["states"],
emitter=SoftmaxEmitter(name="emitter"),
feedbacker=LookupFeedback(num_states, feedback_dim, name='feedback'),
name="readout"),
transition,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
name="generator")
generator.allocate()
logger.debug("Parameters:\n" + pprint.pformat(
[(key, value.get_value().shape)
for key, value in Selector(generator).get_params().items()],
width=120))
if args.mode == "train":
rng = numpy.random.RandomState(1)
batch_size = 50
generator.push_initialization_config()
transition.weights_init = Orthogonal()
generator.initialize()
logger.debug("transition.weights_init={}".format(
transition.weights_init))
cost = generator.cost(tensor.lmatrix('x')).sum()
gh_model = GroundhogModel(generator, cost)
state = GroundhogState(args.prefix, batch_size,
learning_rate=0.0001).as_dict()
data = ChainIterator(rng, 100, batch_size)
trainer = SGD(gh_model, state, data)
main_loop = MainLoop(data, None, None, gh_model, trainer, state, None)
main_loop.main()
elif args.mode == "sample":
load_params(generator, args.prefix + "model.npz")
sample = ComputationGraph(
generator.generate(n_steps=args.steps, batch_size=1,
iterate=True)).function()
states, outputs, costs = [data[:, 0] for data in sample()]
numpy.set_printoptions(precision=3, suppress=True)
print("Generation cost:\n{}".format(costs.sum()))
freqs = numpy.bincount(outputs).astype(floatX)
freqs /= freqs.sum()
print("Frequencies:\n {} vs {}".format(freqs,
ChainIterator.equilibrium))
trans_freqs = numpy.zeros((num_states, num_states), dtype=floatX)
for a, b in zip(outputs, outputs[1:]):
trans_freqs[a, b] += 1
trans_freqs /= trans_freqs.sum(axis=1)[:, None]
print("Transition frequencies:\n{}\nvs\n{}".format(
trans_freqs, ChainIterator.trans_prob))
else:
assert False
def main():
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
parser = argparse.ArgumentParser(
"Case study of language modeling with RNN",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"mode",
choices=["train", "sample"],
help="The mode to run. Use `train` to train a new model"
" and `sample` to sample a sequence generated by an"
" existing one.")
parser.add_argument("prefix",
default="sine",
help="The prefix for model, timing and state files")
parser.add_argument("state",
nargs="?",
default="",
help="Changes to Groundhog state")
parser.add_argument("--path", help="Path to a language dataset")
parser.add_argument("--dict", help="Path to the dataset dictionary")
parser.add_argument("--restart", help="Start anew")
parser.add_argument("--reset",
action="store_true",
default=False,
help="Reset the hidden state between batches")
parser.add_argument("--steps",
type=int,
default=100,
help="Number of steps to plot for the 'sample' mode"
" OR training sequence length for the 'train' mode.")
args = parser.parse_args()
logger.debug("Args:\n" + str(args))
dim = 200
num_chars = 50
transition = GatedRecurrent(name="transition",
activation=Tanh(),
dim=dim,
weights_init=Orthogonal())
generator = SequenceGenerator(LinearReadout(
readout_dim=num_chars,
source_names=["states"],
emitter=SoftmaxEmitter(name="emitter"),
feedbacker=LookupFeedback(num_chars, dim, name='feedback'),
name="readout"),
transition,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
name="generator")
generator.allocate()
logger.debug("Parameters:\n" + pprint.pformat(
[(key, value.get_value().shape)
for key, value in Selector(generator).get_params().items()],
width=120))
if args.mode == "train":
batch_size = 1
seq_len = args.steps
generator.initialize()
# Build cost computation graph that uses the saved hidden states.
# An issue: for Groundhog this is completely transparent, that's
# why it does not carry the hidden state over the period when
# validation in done. We should find a way to fix in the future.
x = tensor.lmatrix('x')
init_states = shared_floatx_zeros((batch_size, dim),
name='init_states')
reset = tensor.scalar('reset')
cost = ComputationGraph(
generator.cost(x, states=init_states * reset).sum())
# TODO: better search routine
states = [
v for v in cost.variables if hasattr(v.tag, 'application_call')
and v.tag.application_call.brick == generator.transition and
(v.tag.application_call.application == generator.transition.apply)
and v.tag.role == VariableRole.OUTPUT and v.tag.name == 'states'
]
assert len(states) == 1
states = states[0]
gh_model = GroundhogModel(generator, cost)
gh_model.properties.append(
('bpc', cost.outputs[0] * numpy.log(2) / seq_len))
gh_model.properties.append(('mean_init_state', init_states.mean()))
gh_model.properties.append(('reset', reset))
if not args.reset:
gh_model.updates.append((init_states, states[-1]))
state = GroundhogState(args.prefix, batch_size,
learning_rate=0.0001).as_dict()
changes = eval("dict({})".format(args.state))
state.update(changes)
def output_format(x, y, reset):
return dict(x=x[:, None], reset=reset)
train, valid, test = [
LMIterator(batch_size=batch_size,
use_infinite_loop=mode == 'train',
path=args.path,
seq_len=seq_len,
mode=mode,
chunks='chars',
output_format=output_format,
can_fit=True) for mode in ['train', 'valid', 'test']
]
trainer = SGD(gh_model, state, train)
state['on_nan'] = 'warn'
state['cutoff'] = 1.
main_loop = MainLoop(train, valid, None, gh_model, trainer, state,
None)
if not args.restart:
main_loop.load()
main_loop.main()
elif args.mode == "sample":
load_params(generator, args.prefix + "model.npz")
chars = numpy.load(args.dict)['unique_chars']
sample = ComputationGraph(
generator.generate(n_steps=args.steps, batch_size=10,
iterate=True)).function()
states, outputs, costs = sample()
for i in range(10):
print("Generation cost: {}".format(costs[:, i].sum()))
print("".join([chars[o] for o in outputs[:, i]]))
else:
assert False
