def _create_model(with_dropout):
cg = ComputationGraph(gan.compute_losses(x, z))
if with_dropout:
inputs = VariableFilter(bricks=gan.discriminator.children[1:],
roles=[INPUT])(cg.variables)
cg = apply_dropout(cg, inputs, 0.5)
inputs = VariableFilter(bricks=[gan.discriminator],
roles=[INPUT])(cg.variables)
cg = apply_dropout(cg, inputs, 0.2)
return Model(cg.outputs)
def _create_model(with_dropout):
cg = ComputationGraph(gan.compute_losses(x, z))
if with_dropout:
inputs = VariableFilter(
bricks=gan.discriminator.children[1:],
roles=[INPUT])(cg.variables)
cg = apply_dropout(cg, inputs, 0.5)
inputs = VariableFilter(
bricks=[gan.discriminator],
roles=[INPUT])(cg.variables)
cg = apply_dropout(cg, inputs, 0.2)
return Model(cg.outputs)
def _create_model(with_dropout):
cg = ComputationGraph(ali.compute_losses(x, z))
if with_dropout:
inputs = VariableFilter(
bricks=([ali.discriminator.x_discriminator.layers[0],
ali.discriminator.z_discriminator.layers[0]]),
roles=[INPUT])(cg.variables)
cg = apply_dropout(cg, inputs, 0.2)
inputs = VariableFilter(
bricks=(ali.discriminator.x_discriminator.layers[2::3] +
ali.discriminator.z_discriminator.layers[2::2] +
ali.discriminator.joint_discriminator.layers[::2]),
roles=[INPUT])(cg.variables)
cg = apply_dropout(cg, inputs, 0.5)
return Model(cg.outputs)
def train_base_model(self, train_data, test_data, input_dim):
x = T.matrix('features')
y = T.matrix('targets')
mlp, cost, mis_cost = self.create_base_model(x, y, input_dim)
cg = ComputationGraph([cost])
inputs = VariableFilter(roles=[INPUT])(cg.variables)
cg = apply_dropout(cg, inputs, 0.2)
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=Adam(learning_rate=0.001))
data_stream = train_data
data_stream_test = test_data
monitor = DataStreamMonitoring(variables=[mis_cost],
data_stream=data_stream_test,
prefix="test")
plot_ext = Plot('F1-measure',
channels=[['test_MisclassificationRate']],
after_batch=True)
main_loop = MainLoop(data_stream=data_stream,
algorithm=algorithm,
extensions=[
monitor,
FinishAfter(after_n_epochs=50),
Printing(), plot_ext
])
main_loop.run()
return mlp
def build_model(images, labels):
vgg = VGG(layer='conv4_4')
vgg.push_initialization_config()
vgg.initialize()
tdb = top_direction_block()
tdb.push_initialization_config()
tdb.initialize()
# Construct feedforward sequence
ss_seq = FeedforwardSequence([vgg.apply, tdb.apply])
ss_seq.push_initialization_config()
ss_seq.initialize()
prediction = ss_seq.apply(images)
cost = StructuredCost().apply(labels, theano.tensor.clip(prediction, 1e-5, 1 - 1e-5))
cg = ComputationGraph(cost)
cg_dropout = apply_dropout(cg, [VariableFilter(roles=[OUTPUT])(cg.variables)[0]], .5)
cost_dropout = cg_dropout.outputs[0]
# define learned parameters
selector = Selector([ss_seq])
W = selector.get_parameters()
parameters = []
parameters += [v for k, v in W.items()]
return cost_dropout, parameters
def build_mlp(features_cat, features_int, labels):
mlp_int = MLP(activations=[Rectifier(), Rectifier()],
dims=[19, 50, 50],
weights_init=IsotropicGaussian(),
biases_init=Constant(0),
name='mlp_interval')
mlp_int.initialize()
mlp_cat = MLP(activations=[Logistic()],
dims=[320, 50],
weights_init=IsotropicGaussian(),
biases_init=Constant(0),
name='mlp_categorical')
mlp_cat.initialize()
mlp = MLP(activations=[Rectifier(), None],
dims=[50, 50, 1],
weights_init=IsotropicGaussian(),
biases_init=Constant(0))
mlp.initialize()
gated = mlp_cat.apply(features_cat) * mlp_int.apply(features_int)
prediction = mlp.apply(gated)
cost = MAPECost().apply(prediction, labels)
cg = ComputationGraph(cost)
print cg.variables
cg_dropout1 = apply_dropout(cg, [VariableFilter(roles=[OUTPUT])(cg.variables)[1], VariableFilter(roles=[OUTPUT])(cg.variables)[3]], .2)
cost_dropout1 = cg_dropout1.outputs[0]
return cost_dropout1, cg_dropout1.parameters, cost
def apply_dropout(self, dropout, variables=None):
if dropout and dropout > 0:
if variables == None:
var_filter = VariableFilter(theano_name_regex='linear.*input_')
variables = var_filter(self.cg.variables)
self.cg = apply_dropout(self.cg, variables, dropout)
self._cost = self.cg.outputs[0]
def build_mlp(features_int, features_cat, labels, labels_mean):
inputs = tensor.concatenate([features_int, features_cat], axis=1)
mlp = MLP(activations=[Rectifier(),
Rectifier(),
Rectifier(), None],
dims=[337, 800, 1200, 1],
weights_init=IsotropicGaussian(),
biases_init=Constant(1))
mlp.initialize()
prediction = mlp.apply(inputs)
cost = MAPECost().apply(prediction, labels, labels_mean)
cg = ComputationGraph(cost)
#cg_dropout0 = apply_dropout(cg, [VariableFilter(roles=[INPUT])(cg.variables)[1]], .2)
cg_dropout1 = apply_dropout(cg, [
VariableFilter(roles=[OUTPUT])(cg.variables)[1],
VariableFilter(roles=[OUTPUT])(cg.variables)[3],
VariableFilter(roles=[OUTPUT])(cg.variables)[5]
], .2)
cost_dropout1 = cg_dropout1.outputs[0]
return cost_dropout1, cg_dropout1.parameters, cost #cost, cg.parameters, cost #
def build_mlp(features_car_cat, features_car_int, features_nocar_cat,
features_nocar_int, features_cp, features_hascar, means, labels):
features = tensor.concatenate([
features_hascar, means['cp'][features_cp[:, 0]],
means['dep'][features_cp[:, 1]]
],
axis=1)
mlp = MLP(activations=[Rectifier(), Rectifier(), None],
dims=[5, 50, 50, 1],
weights_init=IsotropicGaussian(.1),
biases_init=Constant(0),
name='mlp')
mlp.initialize()
prediction = mlp.apply(features)
cost = MAPECost().apply(labels, prediction)
cg = ComputationGraph(cost)
input_var = VariableFilter(roles=[INPUT])(cg.variables)
print input_var
cg_dropout1 = apply_dropout(cg, [input_var[3], input_var[5]], .4)
cost_dropout1 = cg_dropout1.outputs[0]
return prediction, cost_dropout1, cg_dropout1.parameters, cost
def build_mlp(features_car_cat, features_car_int, features_nocar_cat,
features_nocar_int, features_cp, features_hascar, means, labels):
prediction, _, _, _, = \
build_mlp_onlyloc(features_car_cat, features_car_int,
features_nocar_cat, features_nocar_int, features_cp, features_hascar,
means, labels)
mlp_crm = MLP(activations=[None],
dims=[1, 1],
weights_init=IsotropicGaussian(.1),
biases_init=Constant(0),
name='mlp_crm')
mlp_crm.initialize()
crm = features_nocar_int[:, 0][:, None]
prediction = prediction * mlp_crm.apply(crm)
cost = MAPECost().apply(labels, prediction)
cg = ComputationGraph(cost)
input_var = VariableFilter(roles=[INPUT])(cg.variables)
print input_var
cg_dropout = apply_dropout(cg, [input_var[7], input_var[5]], .4)
cost_dropout = cg_dropout.outputs[0]
return prediction, cost_dropout, cg_dropout.parameters, cost
def _apply_dropout(self, outputs, *args, **kwargs):
variables = [self.word_embed.W, self.hashtag_embed.W]
cgs = ComputationGraph(outputs)
cg_dropouts = apply_dropout(cgs,
variables,
drop_prob=self.config.dropout_prob,
seed=123).outputs
return cg_dropouts
def build_mlp(features_car_cat, features_car_int, features_nocar_cat,
features_nocar_int, features_cp, features_hascar, means, labels):
mlp_car = MLP(activations=[Rectifier(), Rectifier(), None],
dims=[8 + 185, 200, 200, 1],
weights_init=IsotropicGaussian(.1),
biases_init=Constant(0),
name='mlp_interval_car')
mlp_car.initialize()
mlp_nocar = MLP(activations=[Rectifier(), Rectifier(), None],
dims=[5 + 135, 200, 200, 1],
weights_init=IsotropicGaussian(.1),
biases_init=Constant(0),
name='mlp_interval_nocar')
mlp_nocar.initialize()
feature_car = tensor.concatenate((features_car_cat, features_car_int),
axis=1)
feature_nocar = tensor.concatenate(
(features_nocar_cat, features_nocar_int), axis=1)
prediction = mlp_nocar.apply(feature_nocar)
# gating with the last feature : does the dude own a car
prediction += tensor.addbroadcast(features_hascar,
1) * mlp_car.apply(feature_car)
prediction_loc, _, _, _, = \
build_mlp_onlyloc(features_car_cat, features_car_int,
features_nocar_cat, features_nocar_int,
features_cp, features_hascar,
means, labels)
prediction += prediction_loc
# add crm
mlp_crm = MLP(activations=[None],
dims=[1, 1],
weights_init=IsotropicGaussian(.1),
biases_init=Constant(0),
name='mlp_crm')
mlp_crm.initialize()
crm = features_nocar_int[:, 0][:, None]
prediction = prediction * mlp_crm.apply(crm)
cost = MAPECost().apply(labels, prediction)
cg = ComputationGraph(cost)
input_var = VariableFilter(roles=[INPUT])(cg.variables)
print input_var
cg_dropout1 = apply_dropout(cg, [input_var[6], input_var[7]], .4)
cost_dropout1 = cg_dropout1.outputs[0]
return prediction, cost_dropout1, cg_dropout1.parameters, cost
def test_apply_dropout_custom_divisor():
x = tensor.vector()
y = tensor.vector()
z = x - y
cg = ComputationGraph([z])
scaled_dropped_cg = apply_dropout(cg, [y], 0.8, seed=2, custom_divisor=2.5)
x_ = numpy.array([9., 8., 9.], dtype=theano.config.floatX)
y_ = numpy.array([4., 5., 6.], dtype=theano.config.floatX)
assert_allclose(
scaled_dropped_cg.outputs[0].eval({x: x_, y: y_}),
x_ - (y_ * MRG_RandomStreams(2).binomial((3,), p=0.2).eval() / 2.5))
def test_apply_dropout():
x = tensor.vector()
y = tensor.vector()
z = x * y
cg = ComputationGraph([z])
dropped_cg = apply_dropout(cg, [x], 0.4, seed=1)
x_ = numpy.array([5., 6., 7.], dtype=theano.config.floatX)
y_ = numpy.array([1., 2., 3.], dtype=theano.config.floatX)
assert_allclose(
dropped_cg.outputs[0].eval({x: x_, y: y_}),
x_ * y_ * MRG_RandomStreams(1).binomial((3,), p=0.6).eval() / 0.6)
def test_apply_dropout():
x = tensor.vector()
y = tensor.vector()
z = x * y
cg = ComputationGraph([z])
dropped_cg = apply_dropout(cg, [x], 0.4, seed=1)
x_ = numpy.array([5., 6., 7.], dtype=theano.config.floatX)
y_ = numpy.array([1., 2., 3.], dtype=theano.config.floatX)
assert_allclose(
dropped_cg.outputs[0].eval({x: x_, y: y_}),
x_ * y_ * MRG_RandomStreams(1).binomial((3,), p=0.6).eval() / 0.6)
def test_apply_dropout_custom_divisor():
x = tensor.vector()
y = tensor.vector()
z = x - y
cg = ComputationGraph([z])
scaled_dropped_cg = apply_dropout(cg, [y], 0.8, seed=2, custom_divisor=2.5)
x_ = numpy.array([9., 8., 9.], dtype=theano.config.floatX)
y_ = numpy.array([4., 5., 6.], dtype=theano.config.floatX)
assert_allclose(
scaled_dropped_cg.outputs[0].eval({x: x_, y: y_}),
x_ - (y_ * MRG_RandomStreams(2).binomial((3,), p=0.2).eval() / 2.5))
def dropout(cg):
"""Create dropout computation graph.
Parameters
----------
cg : ComputationGraph
origin computation graph
Returns
-------
dropout_cg : ComputationGraph
dropped out computation graph
"""
inputs = VariableFilter(roles=[INPUT])(cg.variables)
dropout_cg = apply_dropout(cg, inputs, 0.5)
return dropout_cg
def create_training_computation_graphs():
x = tensor.tensor4('features')
y = tensor.imatrix('targets')
convnet, mlp = create_model_bricks()
y_hat = mlp.apply(convnet.apply(x).flatten(ndim=2))
cost = BinaryCrossEntropy().apply(y, y_hat)
accuracy = 1 - tensor.neq(y > 0.5, y_hat > 0.5).mean()
cg = ComputationGraph([cost, accuracy])
# Create a graph which uses batch statistics for batch normalization
# as well as dropout on selected variables
bn_cg = apply_batch_normalization(cg)
bricks_to_drop = ([convnet.layers[i] for i in (5, 11, 17)] +
[mlp.application_methods[1].brick])
variables_to_drop = VariableFilter(
roles=[OUTPUT], bricks=bricks_to_drop)(bn_cg.variables)
bn_dropout_cg = apply_dropout(bn_cg, variables_to_drop, 0.5)
return cg, bn_dropout_cg
def __init__(self, config, vocab_size):
question = tensor.imatrix('question')
question_mask = tensor.imatrix('question_mask')
context = tensor.imatrix('context')
context_mask = tensor.imatrix('context_mask')
answer = tensor.imatrix('answer')
answer_mask = tensor.imatrix('answer_mask')
bricks = []
question = question.dimshuffle(1, 0)
question_mask = question_mask.dimshuffle(1, 0)
context = context.dimshuffle(1, 0)
context_mask = context_mask.dimshuffle(1, 0)
answer = answer.dimshuffle(1, 0)
answer_mask = answer_mask.dimshuffle(1, 0)
# Embed questions and context
embed = LookupTable(vocab_size,
config.embed_size,
name='question_embed')
embed.weights_init = IsotropicGaussian(0.01)
# Calculate question encoding (concatenate layer1)
qembed = embed.apply(question)
qlstms, qhidden_list = make_bidir_lstm_stack(
qembed, config.embed_size,
question_mask.astype(theano.config.floatX),
config.question_lstm_size, config.question_skip_connections, 'q')
bricks = bricks + qlstms
if config.question_skip_connections:
qenc_dim = 2 * sum(config.question_lstm_size)
qenc = tensor.concatenate([h[-1, :, :] for h in qhidden_list],
axis=1)
else:
qenc_dim = 2 * config.question_lstm_size[-1]
qenc = tensor.concatenate([h[-1, :, :] for h in qhidden_list[-2:]],
axis=1)
qenc.name = 'qenc'
# Calculate context encoding (concatenate layer1)
cembed = embed.apply(context)
clstms, chidden_list = make_bidir_lstm_stack(
cembed, config.embed_size,
context_mask.astype(theano.config.floatX), config.ctx_lstm_size,
config.ctx_skip_connections, 'ctx')
bricks = bricks + clstms
if config.ctx_skip_connections:
cenc_dim = 2 * sum(config.ctx_lstm_size) #2 : fw & bw
cenc = tensor.concatenate(chidden_list, axis=2)
else:
cenc_dim = 2 * config.question_lstm_size[-1]
cenc = tensor.concatenate(chidden_list[-2:], axis=2)
cenc.name = 'cenc'
# Attention mechanism MLP
attention_mlp = MLP(dims=config.attention_mlp_hidden + [1],
activations=config.attention_mlp_activations[1:] +
[Identity()],
name='attention_mlp')
attention_qlinear = Linear(input_dim=qenc_dim,
output_dim=config.attention_mlp_hidden[0],
name='attq')
attention_clinear = Linear(input_dim=cenc_dim,
output_dim=config.attention_mlp_hidden[0],
use_bias=False,
name='attc')
bricks += [attention_mlp, attention_qlinear, attention_clinear]
layer1 = Tanh().apply(
attention_clinear.apply(
cenc.reshape((cenc.shape[0] * cenc.shape[1], cenc.shape[2]
))).reshape((cenc.shape[0], cenc.shape[1],
config.attention_mlp_hidden[0])) +
attention_qlinear.apply(qenc)[None, :, :])
layer1.name = 'layer1'
att_weights = attention_mlp.apply(
layer1.reshape(
(layer1.shape[0] * layer1.shape[1], layer1.shape[2])))
att_weights = att_weights.reshape((layer1.shape[0], layer1.shape[1]))
att_weights = tensor.nnet.sigmoid(att_weights.T).T
att_weights.name = 'att_weights'
att_target = tensor.eq(
tensor.tile(answer[None, :, :], (context.shape[0], 1, 1)),
tensor.tile(context[:, None, :],
(1, answer.shape[0], 1))).sum(axis=1).clip(0, 1)
cost = (tensor.nnet.binary_crossentropy(att_weights, att_target) *
context_mask).sum() / context_mask.sum()
self.predictions = tensor.gt(att_weights, 0.1) * context
# Apply dropout
cg = ComputationGraph([cost])
if config.w_noise > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, config.w_noise)
if config.dropout > 0:
cg = apply_dropout(cg, qhidden_list + chidden_list, config.dropout)
[cost_reg] = cg.outputs
# Other stuff
cost.name = 'cost'
cost_reg.name = 'cost_reg'
self.sgd_cost = cost_reg
self.monitor_vars = [[cost_reg]]
self.monitor_vars_valid = [[cost_reg]]
# Initialize bricks
embed.initialize()
for brick in bricks:
brick.weights_init = config.weights_init
brick.biases_init = config.biases_init
brick.initialize()
def __init__(self, config, vocab_size):
question = tensor.imatrix('question')
question_mask = tensor.imatrix('question_mask')
context = tensor.imatrix('context')
context_mask = tensor.imatrix('context_mask')
answer = tensor.imatrix('answer')
answer_mask = tensor.imatrix('answer_mask')
ans_indices = tensor.imatrix('ans_indices') # n_steps * n_samples
ans_indices_mask = tensor.imatrix('ans_indices_mask')
context_bag = tensor.eq(context[:, :, None],
tensor.arange(vocab_size)).sum(axis=1).clip(
0, 1)
bricks = []
question = question.dimshuffle(1, 0)
question_mask = question_mask.dimshuffle(1, 0)
context = context.dimshuffle(1, 0)
context_mask = context_mask.dimshuffle(1, 0)
answer = answer.dimshuffle(1, 0)
answer_mask = answer_mask.dimshuffle(1, 0)
ans_indices = ans_indices.dimshuffle(1, 0)
ans_indices_mask = ans_indices_mask.dimshuffle(1, 0)
# Embed questions and context
embed = LookupTable(vocab_size,
config.embed_size,
name='question_embed')
embed.weights_init = IsotropicGaussian(0.01)
# embeddings_initial_value = init_embedding_table(filename='embeddings/vocab_embeddings.txt')
# embed.weights_init = Constant(embeddings_initial_value)
# Calculate question encoding (concatenate layer1)
qembed = embed.apply(question)
qlstms, qhidden_list = make_bidir_lstm_stack(
qembed, config.embed_size,
question_mask.astype(theano.config.floatX),
config.question_lstm_size, config.question_skip_connections, 'q')
bricks = bricks + qlstms
if config.question_skip_connections:
qenc_dim = 2 * sum(config.question_lstm_size)
qenc = tensor.concatenate([h[-1, :, :] for h in qhidden_list],
axis=1)
else:
qenc_dim = 2 * config.question_lstm_size[-1]
qenc = tensor.concatenate([h[-1, :, :] for h in qhidden_list[-2:]],
axis=1)
qenc.name = 'qenc'
#embed size: 200, lstm_size = 256
#qenc: length * batch_size * (2*lstm_size)
# Calculate context encoding (concatenate layer1)
cembed = embed.apply(context)
cqembed = tensor.concatenate(
[
cembed,
tensor.extra_ops.repeat(
qenc[None, :, :], cembed.shape[0], axis=0)
],
axis=2
) #length * batch_size * (embed+2*lstm_size) this is what goes into encoder
clstms, chidden_list = make_bidir_lstm_stack(
cqembed, config.embed_size + qenc_dim,
context_mask.astype(theano.config.floatX), config.ctx_lstm_size,
config.ctx_skip_connections, 'ctx')
bricks = bricks + clstms
if config.ctx_skip_connections:
cenc_dim = 2 * sum(config.ctx_lstm_size) #2 : fw & bw
cenc = tensor.concatenate(chidden_list, axis=2)
else:
cenc_dim = 2 * config.question_lstm_size[-1]
cenc = tensor.concatenate(chidden_list[-2:], axis=2)
cenc.name = 'cenc'
#cenc: length * batch_size * (2*lstm_size)
#pointer networks decoder LSTM and Attention parameters
params = init_params(data_dim=config.decoder_data_dim,
lstm_dim=config.decoder_lstm_output_dim)
tparams = init_tparams(params)
self.theano_params = []
add_role(tparams['lstm_de_W'], WEIGHT)
add_role(tparams['lstm_de_U'], WEIGHT)
add_role(tparams['lstm_de_b'], BIAS)
add_role(tparams['ptr_v'], WEIGHT)
add_role(tparams['ptr_W1'], WEIGHT)
add_role(tparams['ptr_W2'], WEIGHT)
self.theano_params = tparams.values()
# for p in tparams.values():
# add_role(p, WEIGHT)
# self.theano_params.append(p)
#n_steps = length , n_samples = batch_size
n_steps = ans_indices.shape[0]
n_samples = ans_indices.shape[1]
preds, generations = ptr_network(
tparams, cqembed, context_mask.astype(theano.config.floatX),
ans_indices, ans_indices_mask.astype(theano.config.floatX),
config.decoder_lstm_output_dim, cenc)
self.generations = generations
idx_steps = tensor.outer(tensor.arange(n_steps, dtype='int64'),
tensor.ones((n_samples, ), dtype='int64'))
idx_samples = tensor.outer(tensor.ones((n_steps, ), dtype='int64'),
tensor.arange(n_samples, dtype='int64'))
probs = preds[idx_steps, ans_indices, idx_samples]
# probs *= y_mask
off = 1e-8
if probs.dtype == 'float16':
off = 1e-6
# probs += (1 - y_mask) # change unmasked position to 1, since log(1) = 0
probs += off
# probs_printed = theano.printing.Print('this is probs')(probs)
cost = -tensor.log(probs)
cost *= ans_indices_mask
cost = cost.sum(axis=0) / ans_indices_mask.sum(axis=0)
cost = cost.mean()
# Apply dropout
cg = ComputationGraph([cost])
if config.w_noise > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, config.w_noise)
if config.dropout > 0:
cg = apply_dropout(cg, qhidden_list + chidden_list, config.dropout)
[cost_reg] = cg.outputs
# Other stuff
cost.name = 'cost'
cost_reg.name = 'cost_reg'
self.sgd_cost = cost_reg
self.monitor_vars = [[cost_reg]]
self.monitor_vars_valid = [[cost_reg]]
# Initialize bricks
embed.initialize()
for brick in bricks:
brick.weights_init = config.weights_init
brick.biases_init = config.biases_init
brick.initialize()
def main(name, epochs, batch_size, learning_rate,
dim, mix_dim, old_model_name, max_length, bokeh, GRU, dropout,
depth, max_grad, step_method, epsilon, sample, skip, uniform, top):
#----------------------------------------------------------------------
datasource = name
def shnum(x):
""" Convert a positive float into a short tag-usable string
E.g.: 0 -> 0, 0.005 -> 53, 100 -> 1-2
"""
return '0' if x <= 0 else '%s%d' % (("%e"%x)[0], -np.floor(np.log10(x)))
jobname = "%s-%dX%dm%dd%dr%sb%de%s" % (datasource, depth, dim, mix_dim,
int(dropout*10),
shnum(learning_rate), batch_size,
shnum(epsilon))
if max_length != 600:
jobname += '-L%d'%max_length
if GRU:
jobname += 'g'
if max_grad != 5.:
jobname += 'G%g'%max_grad
if step_method != 'adam':
jobname += step_method
if skip:
jobname += 'D'
assert depth > 1
if top:
jobname += 'T'
assert depth > 1
if uniform > 0.:
jobname += 'u%d'%int(uniform*100)
if debug:
jobname += ".debug"
if sample:
print("Sampling")
else:
print("\nRunning experiment %s" % jobname)
if old_model_name:
print("starting from model %s"%old_model_name)
#----------------------------------------------------------------------
transitions = [GatedRecurrent(dim=dim) if GRU else LSTM(dim=dim)
for _ in range(depth)]
if depth > 1:
transition = RecurrentStack(transitions, name="transition",
fast=True, skip_connections=skip or top)
if skip:
source_names=['states'] + ['states_%d'%d for d in range(1,depth)]
else:
source_names=['states_%d'%(depth-1)]
else:
transition = transitions[0]
transition.name = "transition"
source_names=['states']
emitter = SketchEmitter(mix_dim=mix_dim,
epsilon=epsilon,
name="emitter")
readout = Readout(
readout_dim=emitter.get_dim('inputs'),
source_names=source_names,
emitter=emitter,
name="readout")
normal_inputs = [name for name in transition.apply.sequences
if 'mask' not in name]
fork = Fork(normal_inputs, prototype=Linear(use_bias=True))
generator = SequenceGenerator(readout=readout, transition=transition,
fork=fork)
# Initialization settings
if uniform > 0.:
generator.weights_init = Uniform(width=uniform*2.)
else:
generator.weights_init = OrthogonalGlorot()
generator.biases_init = Constant(0)
# Build the cost computation graph [steps, batch_size, 3]
x = T.tensor3('features', dtype=floatX)
if debug:
x.tag.test_value = np.ones((max_length,batch_size,3)).astype(floatX)
x = x[:max_length,:,:] # has to be after setting test_value
cost = generator.cost(x)
cost.name = "sequence_log_likelihood"
# Give an idea of what's going on
model = Model(cost)
params = model.get_params()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, value.get_value().shape) for key, value
in params.items()],
width=120))
model_size = 0
for v in params.itervalues():
s = v.get_value().shape
model_size += s[0] * (s[1] if len(s) > 1 else 1)
logger.info("Total number of parameters %d"%model_size)
#------------------------------------------------------------
extensions = []
if old_model_name == 'continue':
extensions.append(LoadFromDump(jobname))
elif old_model_name:
# or you can just load the weights without state using:
old_params = LoadFromDump(old_model_name).manager.load_parameters()
model.set_param_values(old_params)
else:
# Initialize parameters
for brick in model.get_top_bricks():
brick.initialize()
if sample:
assert old_model_name and old_model_name != 'continue'
Sample(generator, steps=max_length, path=old_model_name).do(None)
exit(0)
#------------------------------------------------------------
# Define the training algorithm.
cg = ComputationGraph(cost)
if dropout > 0.:
from blocks.roles import INPUT, OUTPUT
dropout_target = VariableFilter(roles=[OUTPUT],
bricks=transitions,
name_regex='states')(cg.variables)
print('# dropout %d' % len(dropout_target))
cg = apply_dropout(cg, dropout_target, dropout)
opt_cost = cg.outputs[0]
else:
opt_cost = cost
if step_method == 'adam':
step_rule = Adam(learning_rate)
elif step_method == 'rmsprop':
step_rule = RMSProp(learning_rate, decay_rate=0.95)
elif step_method == 'adagrad':
step_rule = AdaGrad(learning_rate)
elif step_method == 'adadelta':
step_rule = AdaDelta()
elif step_method == 'scale':
step_rule = Scale(learning_rate)
else:
raise Exception('Unknown sttep method %s'%step_method)
step_rule = CompositeRule([StepClipping(max_grad), step_rule])
algorithm = GradientDescent(
cost=opt_cost, params=cg.parameters,
step_rule=step_rule)
#------------------------------------------------------------
observables = [cost]
# Fetch variables useful for debugging
(energies,) = VariableFilter(
applications=[generator.readout.readout],
name_regex="output")(cg.variables)
min_energy = named_copy(energies.min(), "min_energy")
max_energy = named_copy(energies.max(), "max_energy")
observables += [min_energy, max_energy]
# (activations,) = VariableFilter(
# applications=[generator.transition.apply],
# name=generator.transition.apply.states[0])(cg.variables)
# mean_activation = named_copy(abs(activations).mean(),
# "mean_activation")
# observables.append(mean_activation)
observables += [algorithm.total_step_norm, algorithm.total_gradient_norm]
for name, param in params.items():
observables.append(named_copy(
param.norm(2), name + "_norm"))
observables.append(named_copy(
algorithm.gradients[param].norm(2), name + "_grad_norm"))
#------------------------------------------------------------
datasource_fname = os.path.join(fuel.config.data_path, datasource,
datasource+'.hdf5')
train_ds = H5PYDataset(datasource_fname, #max_length=max_length,
which_set='train', sources=('features',),
load_in_memory=True)
train_stream = DataStream(train_ds,
iteration_scheme=ShuffledScheme(
train_ds.num_examples, batch_size))
test_ds = H5PYDataset(datasource_fname, #max_length=max_length,
which_set='test', sources=('features',),
load_in_memory=True)
test_stream = DataStream(test_ds,
iteration_scheme=SequentialScheme(
test_ds.num_examples, batch_size))
train_stream = Mapping(train_stream, _transpose)
test_stream = Mapping(test_stream, _transpose)
def stream_stats(ds, label):
itr = ds.get_epoch_iterator(as_dict=True)
batch_count = 0
examples_count = 0
for batch in itr:
batch_count += 1
examples_count += batch['features'].shape[1]
print('%s #batch %d #examples %d' %
(label, batch_count, examples_count))
stream_stats(train_stream, 'train')
stream_stats(test_stream, 'test')
extensions += [Timing(every_n_batches=10),
TrainingDataMonitoring(
observables, prefix="train",
every_n_batches=10),
DataStreamMonitoring(
[cost], # without dropout
test_stream,
prefix="test",
on_resumption=True,
after_epoch=False, # by default this is True
every_n_batches=100),
# all monitored data is ready so print it...
# (next steps may take more time and we want to see the
# results as soon as possible so print as soon as you can)
Printing(every_n_batches=10),
# perform multiple dumps at different intervals
# so if one of them breaks (has nan) we can hopefully
# find a model from few batches ago in the other
Dump(jobname, every_n_batches=11),
Dump(jobname+'.test', every_n_batches=100),
Sample(generator, steps=max_length,
path=jobname+'.test',
every_n_batches=100),
ProgressBar(),
FinishAfter(after_n_epochs=epochs)
# This shows a way to handle NaN emerging during
# training: simply finish it.
.add_condition("after_batch", _is_nan),
]
if bokeh:
from blocks.extensions.plot import Plot
extensions.append(Plot(
'sketch',
channels=[
['cost'],]))
# Construct the main loop and start training!
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions
)
main_loop.run()
## initialize the model
dpm = model.DiffusionModel(spatial_width, n_colors, uniform_noise=uniform_noise, **model_args)
dpm.initialize()
## set up optimization
features = T.matrix('features', dtype=theano.config.floatX)
cost = dpm.cost(features)
blocks_model = blocks.model.Model(cost)
cg_nodropout = ComputationGraph(cost)
if args.dropout_rate > 0:
# DEBUG this triggers an error on my machine
# apply dropout to all the input variables
inputs = VariableFilter(roles=[INPUT])(cg_nodropout.variables)
# dropconnect
# inputs = VariableFilter(roles=[PARAMETER])(cg_nodropout.variables)
cg = apply_dropout(cg_nodropout, inputs, args.dropout_rate)
else:
cg = cg_nodropout
step_compute = RMSProp(learning_rate=args.lr, max_scaling=1e10)
algorithm = GradientDescent(step_rule=CompositeRule([RemoveNotFinite(),
step_compute]),
parameters=cg.parameters, cost=cost)
extension_list = []
extension_list.append(
SharedVariableModifier(step_compute.learning_rate,
extensions.decay_learning_rate,
after_batch=False,
every_n_batches=batches_per_epoch, ))
extension_list.append(FinishAfter(after_n_epochs=100001))
## logging of test set performance
predict = top_mlp.apply(conv_out)
# ---------------------------------------------------------------
# Building computational graph
# ---------------------------------------------------------------
cost = CategoricalCrossEntropy().apply(y.flatten(), predict).copy(name='cost')
error = MisclassificationRate().apply(y.flatten(), predict)
error_rate = error.copy(name='error_rate')
error_rate2 = error.copy(name='error_rate2')
cg = ComputationGraph([cost, error_rate])
inputs = VariableFilter(roles=[INPUT])(cg.variables)
linear_inputs_index = [-10,-8,6]
linear_inputs = list(itemgetter(*linear_inputs_index)(inputs))
cg_dropout = apply_dropout(cg,linear_inputs, 0.5)
# ---------------------------------------------------------------
# Set ports listeners for Fuel data servers
# ---------------------------------------------------------------
data_valid_stream = ServerDataStream(('image_features','targets'), False, port=3040)
data_train_stream = ServerDataStream(('image_features','targets'), False, port=3041)
# ---------------------------------------------------------------
# Training settings
# ---------------------------------------------------------------
#algorithm = GradientDescent(cost=cost, parameters=cg_dropout.parameters, step_rule=Adam())
algorithm = GradientDescent(cost=cost, parameters=cg_dropout.parameters, step_rule=Scale(learning_rate=learning_rate))
save_to = 'Glorot__overfeat_4conv_1full_bn.pkl'
def run_training(config, tr_stream, dev_stream=None, use_bokeh=True):
# Monitoring extensions
try:
from blocks_extras.extensions.plot import Plot
BOKEH_AVAILABLE = True
except ImportError:
BOKEH_AVAILABLE = False
print('Bokeh avalablity: ' + str(BOKEH_AVAILABLE))
logger = logging.getLogger(__name__)
# Create Theano variables
logger.info('Creating theano variables')
x = T.tensor3('features', dtype=config.params['data_dtype'])
x_mask = T.tensor3('features_mask', dtype=config.params['mask_dtype'])
y = T.matrix('targets', dtype=config.params['data_dtype'])
y_mask = T.matrix('targets_mask', dtype=config.params['mask_dtype'])
# Construct model
logger.info('Building baseline model')
baseline_model = BaselineModel(config.params)
baseline_model.initialize()
cost = baseline_model.cost(subword_id_input_=x, subword_id_input_mask_=x_mask,
subword_id_target_=y, subword_id_target_mask_=y_mask)
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# apply dropout for regularization
if config.params['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
logger.info('Applying dropout')
dropout_inputs = [x for x in cg.intermediary_variables
if x.name == 'maxout_apply_output']
print(cg.intermediary_variables)
print(cg.variables)
print(cg.inputs)
print(cg.parameters)
print(dropout_inputs)
cg = apply_dropout(cg, dropout_inputs, config.params['dropout'])
logger.info("Initializing extensions")
extensions = [
FinishAfter(after_n_batches=config.params['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True)
#CheckpointNMT(config['saveto'], every_n_batches=config['save_freq'])]
]
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot('Baseline model', channels=[['baselinemodel_cost_cost']],
after_batch=True))
# Set up training algorithm
logger.info("Initializing training algorithm")
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=CompositeRule([StepClipping(config.params['step_clipping']), eval(config.params['step_rule'])()])
)
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(
model=baseline_model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions
)
# Train
main_loop.run()
print('DONE TRAINING')
def main(job_id, params, config_file='params.ec'):
config = ConfigParser.ConfigParser()
config.readfp(open('./configs/{}'.format(config_file)))
pr = pprint.PrettyPrinter(indent=4)
pr.pprint(config)
net_name = config.get('hyperparams', 'net_name', 'adni')
struct_name = net_name.split('_')[0]
max_epoch = int(config.get('hyperparams', 'max_iter', 100))
base_lr = float(config.get('hyperparams', 'base_lr', 0.01))
train_batch = int(config.get('hyperparams', 'train_batch', 256))
valid_batch = int(config.get('hyperparams', 'valid_batch', 512))
test_batch = int(config.get('hyperparams', 'valid_batch', 512))
W_sd = float(config.get('hyperparams', 'W_sd', 0.01))
W_mu = float(config.get('hyperparams', 'W_mu', 0.0))
b_sd = float(config.get('hyperparams', 'b_sd', 0.01))
b_mu = float(config.get('hyperparams', 'b_mu', 0.0))
hidden_units = int(config.get('hyperparams', 'hidden_units', 32))
input_dropout_ratio = float(config.get('hyperparams', 'input_dropout_ratio', 0.2))
dropout_ratio = float(config.get('hyperparams', 'dropout_ratio', 0.2))
weight_decay = float(config.get('hyperparams', 'weight_decay', 0.001))
max_norm = float(config.get('hyperparams', 'max_norm', 100.0))
solver = config.get('hyperparams', 'solver_type', 'rmsprop')
data_file = config.get('hyperparams', 'data_file')
side = config.get('hyperparams', 'side', 'b')
input_dim = input_dims[struct_name]
# Spearmint optimization parameters:
if params:
base_lr = float(params['base_lr'][0])
dropout_ratio = float(params['dropout_ratio'][0])
hidden_units = params['hidden_units'][0]
weight_decay = params['weight_decay'][0]
if 'adagrad' in solver:
solver_type = CompositeRule([AdaGrad(learning_rate=base_lr), VariableClipping(threshold=max_norm)])
else:
solver_type = CompositeRule([RMSProp(learning_rate=base_lr), VariableClipping(threshold=max_norm)])
data_file = config.get('hyperparams', 'data_file')
if 'b' in side:
train = H5PYDataset(data_file, which_set='train')
valid = H5PYDataset(data_file, which_set='valid')
test = H5PYDataset(data_file, which_set='test')
x_l = tensor.matrix('l_features')
x_r = tensor.matrix('r_features')
x = tensor.concatenate([x_l, x_r], axis=1)
else:
train = H5PYDataset(data_file, which_set='train', sources=['{}_features'.format(side), 'targets'])
valid = H5PYDataset(data_file, which_set='valid', sources=['{}_features'.format(side), 'targets'])
test = H5PYDataset(data_file, which_set='test', sources=['{}_features'.format(side), 'targets'])
x = tensor.matrix('{}_features'.format(side))
y = tensor.lmatrix('targets')
# Define a feed-forward net with an input, two hidden layers, and a softmax output:
model = MLP(activations=[
Rectifier(name='h1'),
Rectifier(name='h2'),
Softmax(name='output'),
],
dims=[
input_dim[side],
hidden_units,
hidden_units,
2],
weights_init=IsotropicGaussian(std=W_sd, mean=W_mu),
biases_init=IsotropicGaussian(b_sd, b_mu))
# Don't forget to initialize params:
model.initialize()
# y_hat is the output of the neural net with x as its inputs
y_hat = model.apply(x)
# Define a cost function to optimize, and a classification error rate.
# Also apply the outputs from the net and corresponding targets:
cost = CategoricalCrossEntropy().apply(y.flatten(), y_hat)
error = MisclassificationRate().apply(y.flatten(), y_hat)
error.name = 'error'
# This is the model: before applying dropout
model = Model(cost)
# Need to define the computation graph for the cost func:
cost_graph = ComputationGraph([cost])
# This returns a list of weight vectors for each layer
W = VariableFilter(roles=[WEIGHT])(cost_graph.variables)
# Add some regularization to this model:
cost += weight_decay * l2_norm(W)
cost.name = 'entropy'
# computational graph with l2 reg
cost_graph = ComputationGraph([cost])
# Apply dropout to inputs:
inputs = VariableFilter([INPUT])(cost_graph.variables)
dropout_inputs = [input for input in inputs if input.name.startswith('linear_')]
dropout_graph = apply_dropout(cost_graph, [dropout_inputs[0]], input_dropout_ratio)
dropout_graph = apply_dropout(dropout_graph, dropout_inputs[1:], dropout_ratio)
dropout_cost = dropout_graph.outputs[0]
dropout_cost.name = 'dropout_entropy'
# Learning Algorithm (notice: we use the dropout cost for learning):
algo = GradientDescent(
step_rule=solver_type,
params=dropout_graph.parameters,
cost=dropout_cost)
# algo.step_rule.learning_rate.name = 'learning_rate'
# Data stream used for training model:
training_stream = Flatten(
DataStream.default_stream(
dataset=train,
iteration_scheme=ShuffledScheme(
train.num_examples,
batch_size=train_batch)))
training_monitor = TrainingDataMonitoring([dropout_cost,
aggregation.mean(error),
aggregation.mean(algo.total_gradient_norm)],
after_batch=True)
# Use the 'valid' set for validation during training:
validation_stream = Flatten(
DataStream.default_stream(
dataset=valid,
iteration_scheme=ShuffledScheme(
valid.num_examples,
batch_size=valid_batch)))
validation_monitor = DataStreamMonitoring(
variables=[cost, error],
data_stream=validation_stream,
prefix='validation',
after_epoch=True)
test_stream = Flatten(
DataStream.default_stream(
dataset=test,
iteration_scheme=ShuffledScheme(
test.num_examples,
batch_size=test_batch)))
test_monitor = DataStreamMonitoring(
variables=[error],
data_stream=test_stream,
prefix='test',
after_training=True)
plotting = Plot('{}_{}'.format(net_name, side),
channels=[
['dropout_entropy'],
['error', 'validation_error'],
],
after_batch=False)
# Checkpoint class used to save model and log:
stamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d-%H:%M')
checkpoint = Checkpoint('./models/{}/{}/{}'.format(struct_name, side, stamp),
save_separately=['model', 'log'],
every_n_epochs=1)
# Home-brewed class for early stopping when we detect we have started to overfit:
# And by that I mean if the means of the val error and training error over the
# previous 'epochs' is greater than the 'threshold', we are overfitting.
early_stopper = FinishIfOverfitting(error_name='error',
validation_name='validation_error',
threshold=0.05,
epochs=5,
burn_in=100)
# The main loop will train the network and output reports, etc
main_loop = MainLoop(
data_stream=training_stream,
model=model,
algorithm=algo,
extensions=[
validation_monitor,
training_monitor,
plotting,
FinishAfter(after_n_epochs=max_epoch),
early_stopper,
Printing(),
ProgressBar(),
checkpoint,
test_monitor,
])
main_loop.run()
ve = float(main_loop.log.last_epoch_row['validation_error'])
te = float(main_loop.log.last_epoch_row['error'])
spearmint_loss = ve + abs(te - ve)
print 'Spearmint Loss: {}'.format(spearmint_loss)
return spearmint_loss
def main(mode, config, use_bokeh=False):
# Construct model
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(
config['src_vocab_size'], config['enc_embed'], config['enc_nhids'])
decoder = Decoder(
config['trg_vocab_size'], config['dec_embed'], config['dec_nhids'],
config['enc_nhids'] * 2)
if mode == "train":
# Create Theano variables
logger.info('Creating theano variables')
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence = tensor.lmatrix('target')
target_sentence_mask = tensor.matrix('target_mask')
sampling_input = tensor.lmatrix('input')
# Get training and development set streams
tr_stream = get_tr_stream(**config)
dev_stream = get_dev_stream(**config)
# Get cost of the model
cost = decoder.cost(
encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence, target_sentence_mask)
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# Initialize model
logger.info('Initializing model')
encoder.weights_init = decoder.weights_init = IsotropicGaussian(
config['weight_scale'])
encoder.biases_init = decoder.biases_init = Constant(0)
encoder.push_initialization_config()
decoder.push_initialization_config()
encoder.bidir.prototype.weights_init = Orthogonal()
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
# apply dropout for regularization
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
logger.info('Applying dropout')
dropout_inputs = [x for x in cg.intermediary_variables
if x.name == 'maxout_apply_output']
cg = apply_dropout(cg, dropout_inputs, config['dropout'])
# Apply weight noise for regularization
if config['weight_noise_ff'] > 0.0:
logger.info('Applying weight noise to ff layers')
enc_params = Selector(encoder.lookup).get_params().values()
enc_params += Selector(encoder.fwd_fork).get_params().values()
enc_params += Selector(encoder.back_fork).get_params().values()
dec_params = Selector(
decoder.sequence_generator.readout).get_params().values()
dec_params += Selector(
decoder.sequence_generator.fork).get_params().values()
dec_params += Selector(decoder.state_init).get_params().values()
cg = apply_noise(
cg, enc_params+dec_params, config['weight_noise_ff'])
# Print shapes
shapes = [param.get_value().shape for param in cg.parameters]
logger.info("Parameter shapes: ")
for shape, count in Counter(shapes).most_common():
logger.info(' {:15}: {}'.format(shape, count))
logger.info("Total number of parameters: {}".format(len(shapes)))
# Print parameter names
enc_dec_param_dict = merge(Selector(encoder).get_parameters(),
Selector(decoder).get_parameters())
logger.info("Parameter names: ")
for name, value in enc_dec_param_dict.items():
logger.info(' {:15}: {}'.format(value.get_value().shape, name))
logger.info("Total number of parameters: {}"
.format(len(enc_dec_param_dict)))
# Set up training model
logger.info("Building model")
training_model = Model(cost)
# Set extensions
logger.info("Initializing extensions")
extensions = [
FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'],
every_n_batches=config['save_freq'])
]
# Set up beam search and sampling computation graphs if necessary
if config['hook_samples'] >= 1 or config['bleu_script'] is not None:
logger.info("Building sampling model")
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
generated = decoder.generate(
sampling_input, sampling_representation)
search_model = Model(generated)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1]))
# Add sampling
if config['hook_samples'] >= 1:
logger.info("Building sampler")
extensions.append(
Sampler(model=search_model, data_stream=tr_stream,
hook_samples=config['hook_samples'],
every_n_batches=config['sampling_freq'],
src_vocab_size=config['src_vocab_size']))
# Add early stopping based on bleu
if config['bleu_script'] is not None:
logger.info("Building bleu validator")
extensions.append(
BleuValidator(sampling_input, samples=samples, config=config,
model=search_model, data_stream=dev_stream,
normalize=config['normalized_bleu'],
every_n_batches=config['bleu_val_freq']))
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot('Cs-En', channels=[['decoder_cost_cost']],
after_batch=True))
# Set up training algorithm
logger.info("Initializing training algorithm")
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=CompositeRule([StepClipping(config['step_clipping']),
eval(config['step_rule'])()])
)
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(
model=training_model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions
)
# Train!
main_loop.run()
elif mode == 'translate':
# Create Theano variables
logger.info('Creating theano variables')
sampling_input = tensor.lmatrix('source')
# Get test set stream
test_stream = get_dev_stream(
config['test_set'], config['src_vocab'],
config['src_vocab_size'], config['unk_id'])
ftrans = open(config['test_set'] + '.trans.out', 'w')
# Helper utilities
sutils = SamplingBase()
unk_idx = config['unk_id']
src_eos_idx = config['src_vocab_size'] - 1
trg_eos_idx = config['trg_vocab_size'] - 1
# Get beam search
logger.info("Building sampling model")
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
generated = decoder.generate(sampling_input, sampling_representation)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
beam_search = BeamSearch(samples=samples)
logger.info("Loading the model..")
model = Model(generated)
loader = LoadNMT(config['saveto'])
loader.set_model_parameters(model, loader.load_parameters())
# Get target vocabulary
trg_vocab = _ensure_special_tokens(
pickle.load(open(config['trg_vocab'], 'rb')), bos_idx=0,
eos_idx=trg_eos_idx, unk_idx=unk_idx)
trg_ivocab = {v: k for k, v in trg_vocab.items()}
logger.info("Started translation: ")
total_cost = 0.0
for i, line in enumerate(test_stream.get_epoch_iterator()):
seq = sutils._oov_to_unk(
line[0], config['src_vocab_size'], unk_idx)
input_ = numpy.tile(seq, (config['beam_size'], 1))
# draw sample, checking to ensure we don't get an empty string back
trans, costs = \
beam_search.search(
input_values={sampling_input: input_},
max_length=3*len(seq), eol_symbol=src_eos_idx,
ignore_first_eol=True)
# normalize costs according to the sequence lengths
if config['normalized_bleu']:
lengths = numpy.array([len(s) for s in trans])
costs = costs / lengths
best = numpy.argsort(costs)[0]
try:
total_cost += costs[best]
trans_out = trans[best]
# convert idx to words
trans_out = sutils._idx_to_word(trans_out, trg_ivocab)
except ValueError:
logger.info(
"Can NOT find a translation for line: {}".format(i+1))
trans_out = '<UNK>'
print(trans_out, file=ftrans)
if i != 0 and i % 100 == 0:
logger.info(
"Translated {} lines of test set...".format(i))
logger.info("Total cost of the test: {}".format(total_cost))
ftrans.close()
train_stream = stream.train(req_vars)
valid_stream = stream.valid(req_vars)
cost = model.cost(**inputs)
cg = ComputationGraph(cost)
monitored = set([cost] + VariableFilter(roles=[roles.COST])(cg.variables))
valid_monitored = monitored
if hasattr(model, 'valid_cost'):
valid_cost = model.valid_cost(**inputs)
valid_cg = ComputationGraph(valid_cost)
valid_monitored = set([valid_cost] + VariableFilter(roles=[roles.COST])(valid_cg.variables))
if hasattr(config, 'dropout') and config.dropout < 1.0:
cg = apply_dropout(cg, config.dropout_inputs(cg), config.dropout)
if hasattr(config, 'noise') and config.noise > 0.0:
cg = apply_noise(cg, config.noise_inputs(cg), config.noise)
cost = cg.outputs[0]
cg = Model(cost)
logger.info('# Parameter shapes:')
parameters_size = 0
for value in cg.parameters:
logger.info(' %20s %s' % (value.get_value().shape, value.name))
parameters_size += reduce(operator.mul, value.get_value().shape, 1)
logger.info('Total number of parameters: %d in %d matrices' % (parameters_size, len(cg.parameters)))
if hasattr(config, 'step_rule'):
step_rule = config.step_rule
else:
cost_graph = ComputationGraph([cost])
# This returns a list of weight vectors for each layer
W = VariableFilter(roles=[WEIGHT])(cost_graph.variables)
# Add some regularization to this model:
cost += weight_decay * l2_norm(W)
cost.name = 'entropy'
# computational graph with l2 reg
cost_graph = ComputationGraph([cost])
# Apply dropout to inputs:
inputs = VariableFilter([INPUT])(cost_graph.variables)
dropout_inputs = [input for input in inputs if input.name.startswith('linear_')]
dropout_graph = apply_dropout(cost_graph, dropout_inputs, dropout_ratio)
dropout_cost = dropout_graph.outputs[0]
dropout_cost.name = 'dropout_entropy'
# Learning Algorithm:
algo = GradientDescent(
step_rule=solver_type,
params=dropout_graph.parameters,
cost=dropout_cost)
# Data stream used for training model:
training_stream = Flatten(
DataStream.default_stream(
dataset=train,
iteration_scheme=ShuffledScheme(
train.num_examples,
def main(save_to, num_epochs):
mlp = MLP([Tanh(), Tanh(), Softmax()], [784, 100, 100, 10],
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
mlp.initialize()
x = tensor.matrix('features')
y = tensor.lmatrix('targets')
probs = mlp.apply(tensor.flatten(x, outdim=2))
cost = CategoricalCrossEntropy().apply(y.flatten(), probs)
error_rate = MisclassificationRate().apply(y.flatten(), probs)
cg = ComputationGraph([cost, error_rate])
cost.name = 'final_cost'
test_cost = cost
for_dropout = VariableFilter(roles=[INPUT],
bricks=mlp.linear_transformations[1:])(cg.variables)
dropout_graph = apply_dropout(cg, for_dropout, 0.5)
dropout_graph = apply_dropout(dropout_graph, [x], 0.1)
dropout_cost, dropout_error_rate = dropout_graph.outputs
mnist_train = MNIST(("train",))
mnist_test = MNIST(("test",))
algorithm = GradientDescent(
cost=dropout_cost, parameters=cg.parameters,
step_rule=Scale(learning_rate=0.1))
extensions = [Timing(),
FinishAfter(after_n_epochs=num_epochs),
DataStreamMonitoring(
[cost, error_rate],
Flatten(
DataStream.default_stream(
mnist_test,
iteration_scheme=SequentialScheme(
mnist_test.num_examples, 500)),
which_sources=('features',)),
prefix="test"),
TrainingDataMonitoring(
[dropout_cost, dropout_error_rate,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
Checkpoint(save_to),
Printing()]
if BLOCKS_EXTRAS_AVAILABLE:
extensions.append(Plot(
'MNIST example',
channels=[
['test_final_cost',
'test_misclassificationrate_apply_error_rate'],
['train_total_gradient_norm']]))
main_loop = MainLoop(
algorithm,
Flatten(
DataStream.default_stream(
mnist_train,
iteration_scheme=SequentialScheme(
mnist_train.num_examples, 50)),
which_sources=('features',)),
model=Model(dropout_cost),
extensions=extensions)
main_loop.run()
def main_run(_config, _log):
from collections import namedtuple
c = namedtuple("Config", _config.keys())(*_config.values())
_log.info("Running with" + str(_config))
import theano
from theano import tensor as T
import numpy as np
from dataset import IMDBText, GloveTransformer
from blocks.initialization import Uniform, Constant, IsotropicGaussian, NdarrayInitialization, Identity, Orthogonal
from blocks.bricks.recurrent import LSTM, SimpleRecurrent, GatedRecurrent
from blocks.bricks.parallel import Fork
from blocks.bricks import Linear, Sigmoid, Tanh, Rectifier
from blocks import bricks
from blocks.extensions import Printing, Timing
from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
from blocks.extensions.plot import Plot
from plot import PlotHistogram
from blocks.algorithms import GradientDescent, Adam, Scale, StepClipping, CompositeRule, AdaDelta
from blocks.graph import ComputationGraph, apply_dropout
from blocks.main_loop import MainLoop
from blocks.model import Model
from cuboid.algorithms import AdaM, NAG
from cuboid.extensions import EpochProgress
from fuel.streams import DataStream, ServerDataStream
from fuel.transformers import Padding
from fuel.schemes import ShuffledScheme
from Conv1D import Conv1D, MaxPooling1D
from schemes import BatchwiseShuffledScheme
from bricks import WeightedSigmoid, GatedRecurrentFull
from multiprocessing import Process
import fuel
import logging
from initialization import SumInitialization
from transformers import DropSources
global train_p
global test_p
x = T.tensor3("features")
# m = T.matrix('features_mask')
y = T.imatrix("targets")
# x = x+m.mean()*0
dropout_variables = []
embedding_size = 300
glove_version = "glove.6B.300d.txt"
# embedding_size = 50
# glove_version = "vectors.6B.50d.txt"
gloveMapping = Linear(
input_dim=embedding_size,
output_dim=c.rnn_input_dim,
weights_init=Orthogonal(),
# weights_init = IsotropicGaussian(c.wstd),
biases_init=Constant(0.0),
name="gloveMapping",
)
gloveMapping.initialize()
o = gloveMapping.apply(x)
o = Rectifier(name="gloveRec").apply(o)
dropout_variables.append(o)
summed_mapped_glove = o.sum(axis=1) # take out the sequence
glove_out = Linear(
input_dim=c.rnn_input_dim,
output_dim=1.0,
weights_init=IsotropicGaussian(c.wstd),
biases_init=Constant(0.0),
name="mapping_to_output",
)
glove_out.initialize()
deeply_sup_0 = glove_out.apply(summed_mapped_glove)
deeply_sup_probs = Sigmoid(name="deeply_sup_softmax").apply(deeply_sup_0)
input_dim = c.rnn_input_dim
hidden_dim = c.rnn_dim
gru = GatedRecurrentFull(
hidden_dim=hidden_dim,
activation=Tanh(),
# activation=bricks.Identity(),
gate_activation=Sigmoid(),
state_to_state_init=SumInitialization([Identity(1.0), IsotropicGaussian(c.wstd)]),
state_to_reset_init=IsotropicGaussian(c.wstd),
state_to_update_init=IsotropicGaussian(c.wstd),
input_to_state_transform=Linear(
input_dim=input_dim,
output_dim=hidden_dim,
weights_init=IsotropicGaussian(c.wstd),
biases_init=Constant(0.0),
),
input_to_update_transform=Linear(
input_dim=input_dim,
output_dim=hidden_dim,
weights_init=IsotropicGaussian(c.wstd),
# biases_init=Constant(-2.0)),
biases_init=Constant(-1.0),
),
input_to_reset_transform=Linear(
input_dim=input_dim,
output_dim=hidden_dim,
weights_init=IsotropicGaussian(c.wstd),
# biases_init=Constant(-3.0))
biases_init=Constant(-2.0),
),
)
gru.initialize()
rnn_in = o.dimshuffle(1, 0, 2)
# rnn_in = o
# rnn_out = gru.apply(rnn_in, mask=m.T)
rnn_out = gru.apply(rnn_in)
state_to_state = gru.rnn.state_to_state
state_to_state.name = "state_to_state"
# o = rnn_out[-1, :, :]
o = rnn_out[-1]
# o = rnn_out[:, -1, :]
# o = rnn_out.mean(axis=1)
# print rnn_last_out.eval({
# x: np.ones((3, 101, 300), dtype=theano.config.floatX),
# m: np.ones((3, 101), dtype=theano.config.floatX)})
# raw_input()
# o = rnn_out.mean(axis=1)
dropout_variables.append(o)
score_layer = Linear(
input_dim=hidden_dim,
output_dim=1,
weights_init=IsotropicGaussian(std=c.wstd),
biases_init=Constant(0.0),
name="linear2",
)
score_layer.initialize()
o = score_layer.apply(o)
probs = Sigmoid().apply(o)
# probs = deeply_sup_probs
cost = -(y * T.log(probs) + (1 - y) * T.log(1 - probs)).mean()
# cost_deeply_sup0 = - (y * T.log(deeply_sup_probs) + (1-y) * T.log(1 - deeply_sup_probs)).mean()
# cost += cost_deeply_sup0 * c.deeply_factor
cost.name = "cost"
misclassification = (y * (probs < 0.5) + (1 - y) * (probs > 0.5)).mean()
misclassification.name = "misclassification"
# print rnn_in.shape.eval(
# {x : np.ones((45, 111, embedding_size), dtype=theano.config.floatX),
# })
# print rnn_out.shape.eval(
# {x : np.ones((45, 111, embedding_size), dtype=theano.config.floatX),
# m : np.ones((45, 111), dtype=theano.config.floatX)})
# print (m).sum(axis=1).shape.eval({
# m : np.ones((45, 111), dtype=theano.config.floatX)})
# print (m).shape.eval({
# m : np.ones((45, 111), dtype=theano.config.floatX)})
# raw_input()
# =================
cg = ComputationGraph([cost])
cg = apply_dropout(cg, variables=dropout_variables, drop_prob=0.5)
params = cg.parameters
algorithm = GradientDescent(
cost=cg.outputs[0],
params=params,
step_rule=CompositeRule(
[
StepClipping(threshold=4),
Adam(learning_rate=0.002, beta1=0.1, beta2=0.001),
# NAG(lr=0.1, momentum=0.9),
# AdaDelta(),
]
),
)
# ========
print "setting up data"
ports = {
"gpu0_train": 5557,
"gpu0_test": 5558,
"cuda0_train": 5557,
"cuda0_test": 5558,
"opencl0:0_train": 5557,
"opencl0:0_test": 5558,
"gpu1_train": 5559,
"gpu1_test": 5560,
}
# batch_size = 16
# batch_size = 32
batch_size = 40
def start_server(port, which_set):
fuel.server.logger.setLevel("WARN")
dataset = IMDBText(which_set, sorted=True)
n_train = dataset.num_examples
# scheme = ShuffledScheme(examples=n_train, batch_size=batch_size)
scheme = BatchwiseShuffledScheme(examples=n_train, batch_size=batch_size)
stream = DataStream(dataset=dataset, iteration_scheme=scheme)
print "loading glove"
glove = GloveTransformer(glove_version, data_stream=stream)
padded = Padding(
data_stream=glove,
# mask_sources=('features',)
mask_sources=("features",),
)
padded = DropSources(padded, ["features_mask"])
fuel.server.start_server(padded, port=port, hwm=20)
train_port = ports[theano.config.device + "_train"]
train_p = Process(target=start_server, args=(train_port, "train"))
train_p.start()
test_port = ports[theano.config.device + "_test"]
test_p = Process(target=start_server, args=(test_port, "test"))
test_p.start()
# train_stream = ServerDataStream(('features', 'features_mask', 'targets'), port=train_port)
# test_stream = ServerDataStream(('features', 'features_mask', 'targets'), port=test_port)
train_stream = ServerDataStream(("features", "targets"), port=train_port)
test_stream = ServerDataStream(("features", "targets"), port=test_port)
print "setting up model"
# ipdb.set_trace()
n_examples = 25000
print "Batches per epoch", n_examples // (batch_size + 1)
batches_extensions = 100
monitor_rate = 50
# ======
model = Model(cg.outputs[0])
extensions = []
extensions.append(EpochProgress(batch_per_epoch=n_examples // batch_size + 1))
extensions.append(TrainingDataMonitoring([cost, misclassification], prefix="train", every_n_batches=monitor_rate))
extensions.append(
DataStreamMonitoring(
[cost, misclassification],
data_stream=test_stream,
prefix="test",
after_epoch=True,
before_first_epoch=False,
)
)
extensions.append(Timing())
extensions.append(Printing())
# extensions.append(Plot("norms", channels=[['train_lstm_norm', 'train_pre_norm']], after_epoch=True))
# extensions.append(Plot(theano.config.device+"_result", channels=[['test_misclassification', 'train_misclassification']], after_epoch=True))
# extensions.append(PlotHistogram(
# channels=['train_state_to_state'],
# bins=50,
# every_n_batches=30))
extensions.append(
Plot(
theano.config.device + "_result",
channels=[["train_cost"], ["train_misclassification"]],
every_n_batches=monitor_rate,
)
)
main_loop = MainLoop(model=model, data_stream=train_stream, algorithm=algorithm, extensions=extensions)
main_loop.run()
def build_submodel(input_shape,
output_dim,
L_dim_conv_layers,
L_filter_size,
L_pool_size,
L_activation_conv,
L_dim_full_layers,
L_activation_full,
L_exo_dropout_conv_layers,
L_exo_dropout_full_layers,
L_endo_dropout_conv_layers,
L_endo_dropout_full_layers,
L_border_mode=None,
L_filter_step=None,
L_pool_step=None):
# TO DO : target size and name of the features
x = T.tensor4('features')
y = T.imatrix('targets')
assert len(input_shape) == 3, "input_shape must be a 3d tensor"
num_channels = input_shape[0]
image_size = tuple(input_shape[1:])
print image_size
print num_channels
prediction = output_dim
# CONVOLUTION
output_conv = x
output_dim = num_channels*np.prod(image_size)
conv_layers = []
assert len(L_dim_conv_layers) == len(L_filter_size)
if L_filter_step is None:
L_filter_step = [None] * len(L_dim_conv_layers)
assert len(L_dim_conv_layers) == len(L_pool_size)
if L_pool_step is None:
L_pool_step = [None] * len(L_dim_conv_layers)
assert len(L_dim_conv_layers) == len(L_pool_step)
assert len(L_dim_conv_layers) == len(L_activation_conv)
if L_border_mode is None:
L_border_mode = ["valid"] * len(L_dim_conv_layers)
assert len(L_dim_conv_layers) == len(L_border_mode)
assert len(L_dim_conv_layers) == len(L_endo_dropout_conv_layers)
assert len(L_dim_conv_layers) == len(L_exo_dropout_conv_layers)
# regarding the batch dropout : the dropout is applied on the filter
# which is equivalent to the output dimension
# you have to look at the dropout_rate of the next layer
# that is why we need to have the first dropout value of L_exo_dropout_full_layers
# the first value has to be 0.0 in this context, and we'll
# assume that it is, but let's have an assert
assert L_exo_dropout_conv_layers[0] == 0.0, "L_exo_dropout_conv_layers[0] has to be 0.0 in this context. There are ways to make it work, of course, but we don't support this with this scripts."
# here modifitication of L_exo_dropout_conv_layers
L_exo_dropout_conv_layers = L_exo_dropout_conv_layers[1:] + [L_exo_dropout_full_layers[0]]
if len(L_dim_conv_layers):
for (num_filters, filter_size, filter_step,
pool_size, pool_step, activation_str, border_mode,
dropout, index) in zip(L_dim_conv_layers,
L_filter_size,
L_filter_step,
L_pool_size,
L_pool_step,
L_activation_conv,
L_border_mode,
L_exo_dropout_conv_layers,
xrange(len(L_dim_conv_layers))
):
# convert filter_size and pool_size in tuple
filter_size = tuple(filter_size)
if filter_step is None:
filter_step = (1, 1)
else:
filter_step = tuple(filter_step)
if pool_size is None:
pool_size = (0,0)
else:
pool_size = tuple(pool_size)
# TO DO : leaky relu
if activation_str.lower() == 'rectifier':
activation = Rectifier().apply
elif activation_str.lower() == 'tanh':
activation = Tanh().apply
elif activation_str.lower() in ['sigmoid', 'logistic']:
activation = Logistic().apply
elif activation_str.lower() in ['id', 'identity']:
activation = Identity().apply
else:
raise Exception("unknown activation function : %s", activation_str)
assert 0.0 <= dropout and dropout < 1.0
num_filters = num_filters - int(num_filters*dropout)
print "border_mode : %s" % border_mode
# filter_step
# http://blocks.readthedocs.org/en/latest/api/bricks.html#module-blocks.bricks.conv
kwargs = {}
if filter_step is None or filter_step == (1,1):
pass
else:
# there's a bit of a mix of names because `Convolutional` takes
# a "step" argument, but `ConvolutionActivation` takes "conv_step" argument
kwargs['conv_step'] = filter_step
if (pool_size[0] == 0 and pool_size[1] == 0):
layer_conv = ConvolutionalActivation(activation=activation,
filter_size=filter_size,
num_filters=num_filters,
border_mode=border_mode,
name="layer_%d" % index,
**kwargs)
else:
if pool_step is None:
pass
else:
kwargs['pooling_step'] = tuple(pool_step)
layer_conv = ConvolutionalLayer(activation=activation,
filter_size=filter_size,
num_filters=num_filters,
border_mode=border_mode,
pooling_size=pool_size,
name="layer_%d" % index,
**kwargs)
conv_layers.append(layer_conv)
convnet = ConvolutionalSequence(conv_layers, num_channels=num_channels,
image_size=image_size,
weights_init=Uniform(width=0.1),
biases_init=Constant(0.0),
name="conv_section")
convnet.push_allocation_config()
convnet.initialize()
output_dim = np.prod(convnet.get_dim('output'))
output_conv = convnet.apply(output_conv)
output_conv = Flattener().apply(output_conv)
# FULLY CONNECTED
output_mlp = output_conv
full_layers = []
assert len(L_dim_full_layers) == len(L_activation_full)
assert len(L_dim_full_layers) + 1 == len(L_endo_dropout_full_layers)
assert len(L_dim_full_layers) + 1 == len(L_exo_dropout_full_layers)
# reguarding the batch dropout : the dropout is applied on the filter
# which is equivalent to the output dimension
# you have to look at the dropout_rate of the next layer
# that is why we throw away the first value of L_exo_dropout_full_layers
L_exo_dropout_full_layers = L_exo_dropout_full_layers[1:]
pre_dim = output_dim
print "When constructing the model, the output_dim of the conv section is %d." % output_dim
if len(L_dim_full_layers):
for (dim, activation_str,
dropout, index) in zip(L_dim_full_layers,
L_activation_full,
L_exo_dropout_full_layers,
range(len(L_dim_conv_layers),
len(L_dim_conv_layers)+
len(L_dim_full_layers))
):
# TO DO : leaky relu
if activation_str.lower() == 'rectifier':
activation = Rectifier().apply
elif activation_str.lower() == 'tanh':
activation = Tanh().apply
elif activation_str.lower() in ['sigmoid', 'logistic']:
activation = Logistic().apply
elif activation_str.lower() in ['id', 'identity']:
activation = Identity().apply
else:
raise Exception("unknown activation function : %s", activation_str)
assert 0.0 <= dropout and dropout < 1.0
dim = dim - int(dim*dropout)
print "When constructing the fully-connected section, we apply dropout %f to add an MLP going from pre_dim %d to dim %d." % (dropout, pre_dim, dim)
layer_full = MLP(activations=[activation], dims=[pre_dim, dim],
weights_init=Uniform(width=0.1),
biases_init=Constant(0.0),
name="layer_%d" % index)
layer_full.initialize()
full_layers.append(layer_full)
pre_dim = dim
for layer in full_layers:
output_mlp = layer.apply(output_mlp)
output_dim = L_dim_full_layers[-1] - int(L_dim_full_layers[-1]*L_exo_dropout_full_layers[-1])
# COST FUNCTION
output_layer = Linear(output_dim, prediction,
weights_init=Uniform(width=0.1),
biases_init=Constant(0.0),
name="layer_"+str(len(L_dim_conv_layers)+
len(L_dim_full_layers))
)
output_layer.initialize()
full_layers.append(output_layer)
y_pred = output_layer.apply(output_mlp)
y_hat = Softmax().apply(y_pred)
# SOFTMAX and log likelihood
y_pred = Softmax().apply(y_pred)
# be careful. one version expects the output of a softmax; the other expects just the
# output of the network
cost = CategoricalCrossEntropy().apply(y.flatten(), y_pred)
#cost = Softmax().categorical_cross_entropy(y.flatten(), y_pred)
cost.name = "cost"
# Misclassification
error_rate_brick = MisclassificationRate()
error_rate = error_rate_brick.apply(y.flatten(), y_hat)
error_rate.name = "error_rate"
# put names
D_params, D_kind = build_params(x, T.matrix(), conv_layers, full_layers)
# test computation graph
cg = ComputationGraph(cost)
# DROPOUT
L_endo_dropout = L_endo_dropout_conv_layers + L_endo_dropout_full_layers
cg_dropout = cg
inputs = VariableFilter(roles=[INPUT])(cg.variables)
for (index, drop_rate) in enumerate(L_endo_dropout):
for input_ in inputs:
m = re.match(r"layer_(\d+)_apply.*", input_.name)
if m and index == int(m.group(1)):
if drop_rate < 0.0001:
print "Skipped applying dropout on %s because the dropout rate was under 0.0001." % input_.name
break
else:
cg_dropout = apply_dropout(cg, [input_], drop_rate)
print "Applied dropout %f on %s." % (drop_rate, input_.name)
break
cg = cg_dropout
return (cg, error_rate, cost, D_params, D_kind)
def __init__(self, ref_data, output_dim):
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name='ref_data')
# Construct the model
j = tensor.lvector('j')
x = tensor.fmatrix('x')
y = tensor.ivector('y')
last_outputs = []
s_dropout_vars = []
r_dropout_vars = []
i_dropout_vars = []
penalties = []
for i in range(nparts):
fs = numpy.random.binomial(1, part_r_proba, size=(ref_data.shape[1],))
input_dim = int(fs.sum())
fs_sh = theano.shared(fs)
r = ref_data_sh[j, :][:, fs_sh.nonzero()[0]]
mlp0 = MLP(activations=activation_functions_0,
dims=[input_dim] + hidden_dims_0, name='enc%d'%i)
mlp0r = MLP(activations=[None], dims=[hidden_dims_0[-1], input_dim], name='dec%d'%i)
mlp1 = MLP(activations=activation_functions_1,
dims=[hidden_dims_0[-1]] + hidden_dims_1 + [n_inter], name='inter_gen_%d'%i)
mlp2 = MLP(activations=activation_functions_2 + [None],
dims=[n_inter] + hidden_dims_2 + [output_dim],
name='end_mlp_%d'%i)
encod = mlp0.apply(r)
rprime = mlp0r.apply(encod)
inter_weights = mlp1.apply(encod)
ibias = Bias(n_inter, name='inter_bias_%d'%i)
inter = ibias.apply(tensor.dot(x, inter_weights))
inter = inter_act_fun.apply(inter)
out = mlp2.apply(inter)
penalties.append(tensor.sqrt(((rprime - r)**2).sum(axis=1)).mean()[None])
last_outputs.append(out)
r_dropout_vars.append(r)
s_dropout_vars = s_dropout_vars + (
VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([inter_weights]))
)
i_dropout_vars.append(inter)
# Initialize parameters
for brick in [mlp0, mlp0r, mlp1, mlp2, ibias]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
final = tensor.concatenate([x[:, :, None] for x in last_outputs], axis=2).mean(axis=2)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
error_rate = tensor.neq(y, pred).mean()
# apply regularization
cg = ComputationGraph([cost, error_rate])
if w_noise_std != 0:
# - apply noise on weight variables
weight_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, weight_vars, w_noise_std)
if s_dropout != 0:
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if r_dropout != 0:
cg = apply_dropout(cg, r_dropout_vars, r_dropout)
if i_dropout != 0:
cg = apply_dropout(cg, i_dropout_vars, i_dropout)
[cost_reg, error_rate_reg] = cg.outputs
cost_reg = cost_reg + reconstruction_penalty * tensor.concatenate(penalties, axis=0).sum()
self.cost = cost
self.cost_reg = cost_reg
self.error_rate = error_rate
self.error_rate_reg = error_rate_reg
self.pred = pred
self.confidence = confidence
def __init__(self, ref_data, output_dim):
input_dim = ref_data.shape[1]
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name="ref_data")
# Construct the model
j = tensor.lvector("j")
r = ref_data_sh[j, :]
x = tensor.fmatrix("x")
y = tensor.ivector("y")
# input_dim must be nr
mlp0 = MLP(activations=activation_functions_0, dims=[input_dim] + hidden_dims_0, name="e0")
mlp0vs = MLP(activations=[None], dims=[hidden_dims_0[-1], input_dim], name="de0")
mlp1 = MLP(
activations=activation_functions_1, dims=[hidden_dims_0[-1]] + hidden_dims_1 + [n_inter], name="inter_gen"
)
mlp2 = MLP(
activations=activation_functions_2 + [None], dims=[n_inter] + hidden_dims_2 + [output_dim], name="end_mlp"
)
encod = mlp0.apply(r)
rprime = mlp0vs.apply(encod)
inter_weights = mlp1.apply(encod)
ibias = Bias(n_inter)
ibias.biases_init = Constant(0)
ibias.initialize()
inter = inter_act_fun.apply(ibias.apply(tensor.dot(x, inter_weights)))
final = mlp2.apply(inter)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
error_rate = tensor.neq(y, pred).mean()
# Initialize parameters
for brick in [mlp0, mlp0vs, mlp1, mlp2]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
# apply regularization
cg = ComputationGraph([cost, error_rate])
if r_dropout != 0:
# - dropout on input vector r : r_dropout
cg = apply_dropout(cg, [r], r_dropout)
if s_dropout != 0:
# - dropout on intermediate layers of first mlp : s_dropout
s_dropout_vars = list(
set(VariableFilter(bricks=[Tanh], name="output")(ComputationGraph([inter_weights])))
- set([inter_weights])
)
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if i_dropout != 0:
# - dropout on input to second mlp : i_dropout
cg = apply_dropout(cg, [inter], i_dropout)
if a_dropout != 0:
# - dropout on hidden layers of second mlp : a_dropout
a_dropout_vars = list(
set(VariableFilter(bricks=[Tanh], name="output")(ComputationGraph([final])))
- set([inter_weights])
- set(s_dropout_vars)
)
cg = apply_dropout(cg, a_dropout_vars, a_dropout)
if w_noise_std != 0:
# - apply noise on weight variables
weight_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, weight_vars, w_noise_std)
[cost_reg, error_rate_reg] = cg.outputs
# add reconstruction penalty for AE part
penalty_val = tensor.sqrt(((r - rprime) ** 2).sum(axis=1)).mean()
cost_reg = cost_reg + reconstruction_penalty * penalty_val
self.cost = cost
self.cost_reg = cost_reg
self.error_rate = error_rate
self.error_rate_reg = error_rate_reg
self.pred = pred
self.confidence = confidence
def construct_model(input_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
# r_rep is nx x nj x nr
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 (nr + 1)
concat = tensor.concatenate([r_rep, x3], axis=2)
# Change concat from Batch x Time x Features to T X B x F
mlp_input = concat.dimshuffle(1, 0, 2)
if use_ensembling:
# Split time dimension into batches of size num_feats
# Join that dimension with the B dimension
ens_shape = (num_feats,
mlp_input.shape[0]/num_feats,
mlp_input.shape[1])
mlp_input = mlp_input.reshape(ens_shape + (input_dim+1,))
mlp_input = mlp_input.reshape((ens_shape[0], ens_shape[1] * ens_shape[2], input_dim+1))
mlp = MLP(dims=[input_dim+1] + mlp_hidden_dims,
activations=[activation_function for _ in mlp_hidden_dims],
name='mlp')
lstm_bot_linear = Linear(input_dim=mlp_hidden_dims[-1], output_dim=4 * lstm_hidden_dim,
name="lstm_input_linear")
lstm = LSTM(dim=lstm_hidden_dim, activation=activation_function,
name="hidden_recurrent")
lstm_top_linear = Linear(input_dim=lstm_hidden_dim, output_dim=out_dim,
name="out_linear")
rnn_input = mlp.apply(mlp_input)
pre_rnn = lstm_bot_linear.apply(rnn_input)
states = lstm.apply(pre_rnn)[0]
activations = lstm_top_linear.apply(states)
if use_ensembling:
activations = activations.reshape(ens_shape + (out_dim,))
# Unsplit batches (ensembling)
activations = tensor.mean(activations, axis=1)
# Mean over time
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 (mlp, lstm_bot_linear, lstm, lstm_top_linear):
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.)
brick.initialize()
# apply noise
cg = ComputationGraph([cost, error_rate])
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
apply_noise(cg, noise_vars, noise_std)
apply_dropout(cg, [rnn_input], dropout)
[cost_reg, error_rate_reg] = cg.outputs
return cost_reg, error_rate_reg, cost, error_rate
def start(self):
x = T.matrix('features', config.floatX)
y = T.imatrix('targets')
self.x = x
DIMS = [108*5, 1000, 1000, 1000, 1000, 1943]
NUMS = [1, 1, 1, 1, 1, 1]
FUNCS = [
Rectifier,
Rectifier,
Rectifier,
Rectifier,
# Rectifier,
# Maxout(num_pieces=5),
# Maxout(num_pieces=5),
# Maxout(num_pieces=5),
# SimpleRecurrent,
# SimpleRecurrent,
# SimpleRecurrent,
Softmax,
]
def lllistool(i, inp, func):
l = Linear(input_dim=DIMS[i], output_dim=DIMS[i+1] * NUMS[i+1],
weights_init=IsotropicGaussian(std=DIMS[i]**(-0.5)),
biases_init=IsotropicGaussian(std=DIMS[i]**(-0.5)),
name='Lin{}'.format(i))
l.initialize()
func.name='Fun{}'.format(i)
if func == SimpleRecurrent:
gong = func(dim=DIMS[i+1], activation=Rectifier(), weights_init=IsotropicGaussian(std=(DIMS[i]+DIMS[i+1])**(-0.5)))
else:
gong = func()
ret = gong.apply(l.apply(inp))
return ret
oup = x
for i in range(len(DIMS)-1):
oup = lllistool(i, oup, FUNCS[i])
y_hat = oup
self.y_hat_prob = y_hat
cost = CategoricalCrossEntropy().apply(y.flatten(), y_hat).astype(config.floatX)
cg = ComputationGraph(cost)
orig_cg = cg
ips = VariableFilter(roles=[INPUT])(cg.variables)
ops = VariableFilter(roles=[OUTPUT])(cg.variables)
cg = apply_dropout(cg, ips[0:2:1], 0.2)
cg = apply_dropout(cg, ips[2:-2:1], 0.5)
cost = cg.outputs[0]
cost.name = 'cost'
# mps = theano.shared(np.array([ph2id(ph48239(id2ph(t))) for t in range(48)]))
mps = theano.shared(np.array([ph2id(state239(t)) for t in range(1943)]))
z_hat = T.argmax(y_hat, axis=1)
y39,_ = scan(fn=lambda t: mps[t], outputs_info=None, sequences=[y.flatten()])
y_hat39,_ = scan(fn=lambda t: mps[t], outputs_info=None, sequences=[z_hat])
self.y_hat39 = y_hat39
lost01 = (T.sum(T.neq(y_hat39, y39)) / y39.shape[0]).astype(config.floatX)
lost01.name = '0/1 loss'
lost23 = (T.sum(T.neq(y_hat39, y39)) / y39.shape[0]).astype(config.floatX)
#lost23 = MisclassificationRate().apply(y39, y_hat39).astype(config.floatX)
lost23.name = '2/3 loss'
Ws = VariableFilter(roles=[WEIGHT])(cg.variables)
norms = sum(w.norm(2) for w in Ws)
norms.name = 'norms'
path = pjoin(PATH['fuel'], pfx+'_train.hdf5')
data = H5PYDataset(path, which_set='train', load_in_memory=True, subset=slice(0, 100000))
# data = H5PYDataset(path, which_set='train', load_in_memory=True)
data_v = H5PYDataset(pjoin(PATH['fuel'], pfx+'_validate.hdf5'), which_set='validate', load_in_memory=True)
num = data.num_examples
data_stream = DataStream(data, iteration_scheme=ShuffledScheme(
num, batch_size=128))
data_stream_v = DataStream(data_v, iteration_scheme=SequentialScheme(
data_v.num_examples, batch_size=128))
algo = GradientDescent(cost=cost, params=cg.parameters, step_rule=CompositeRule([Momentum(0.002, 0.9)]))
monitor = DataStreamMonitoring( variables=[cost, lost01, norms],
data_stream=data_stream)
monitor_v = DataStreamMonitoring( variables=[lost23],
data_stream=data_stream_v)
plt = Plot('AlpAlpAlp', channels=[['0/1 loss', '2/3 loss']], after_epoch=True)
main_loop = MainLoop(data_stream = data_stream,
algorithm=algo,
extensions=[monitor, monitor_v, FinishAfter(after_n_epochs=2000), Printing(), plt])
main_loop.run()
def train(args, trial=11, no_valid=False):
# Creating unique strings to save for experiments.
data_valid = "data/"+args.data_name+"_trial_"+str(trial)+"_valid_size_"+str(args.train_size)+\
"_transitions_"+str(args.transitions)
data_test = data_valid.replace("_valid_size", "_test_size")
# If we want validation set to match modData of test set
if modDataValid == 1:
data_valid = data_valid.replace("_trial_", "_" + modData + "_trial_")
data_test = data_test.replace("_trial_", "_" + modData + "_trial_")
# By default, it is m0
data_train = "data/"+args.data_name+"_trial_"+str(trial)+"_train_size_"+str(args.train_size)+\
"_transitions_"+str(args.transitions)
subStr = "rnn_type_"+args.rnn_type + "_trial_"+str(trial) + "_hiddenSize_"+str(args.hidden_size)+\
"_numLayers_"+str(args.num_layers)+ \
"_dropout_"+str(args.dropout)+"_train_size_"+str(args.train_size) + "_transitions_"+str(args.transitions)+\
"_novalid_"+str(args.no_valid)
if modData == "m1":
data_train = data_train.replace("_trial_", "_m1_trial_")
subStr = subStr.replace("_trial_", "_m1_trial_")
elif modData == "m3":
data_train = data_train.replace("_trial_", "_m3_trial_")
subStr = subStr.replace("_trial_", "_m3_trial_")
data_valid = "data/"+args.data_name+"_m3_trial_"+str(trial)+"_valid_size_"+str(args.train_size)+\
"_transitions_"+str(args.transitions)
data_test = "data/"+args.data_name+"_m3_trial_"+str(trial)+"_test_size_"+str(args.train_size)+\
"_transitions_"+str(args.transitions)
print("on test: " + subStr)
# Perform folder prefixing
prefix_path = models_folder + args.data_name + "/" + subStr +"_tgrad_"+str(args.truncate_gradient)+\
"_boost_"+bStr(args.boosting)
load_path2 = prefix + load_path
save_path2 = prefix + save_path
last_path2 = prefix + last_path
plots_output2 = plots_output + args.data_name + "/" + subStr +"_tgrad_"+str(args.truncate_gradient)+\
"_boost_"+bStr(args.boosting)
# obtain vocabulary size
ix_to_char, char_to_ix, vocab_size = get_metadata(
data_test.replace("_test", ""))
print("vocab_size: " + str(vocab_size))
# Get train, valid, test streams
sharedDataTrain, train_stream = get_stream_inGPU(data_train,
sharedName='sharedData')
train_streamCopy = copy.deepcopy(train_stream)
sharedDataValid, dev_stream = get_stream_inGPU(data_valid,
sharedName='sharedData')
valid_streamCopy = copy.deepcopy(dev_stream)
sharedDataTest, test_stream = get_stream_inGPU(data_test,
sharedName='sharedData')
test_streamCopy = copy.deepcopy(test_stream)
# Create dummy sums
sharedMRRSUM = shared(np.array(0.0, dtype=theano.config.floatX))
sharedTOTSUM = shared(np.array(0.0, dtype=theano.config.floatX))
sharedSUMVARs = {
'sharedMRRSUM': sharedMRRSUM,
'sharedTOTSUM': sharedTOTSUM
}
# Initialize batches
batch_index_From = T.scalar('int_stream_From', dtype='int32')
batch_index_To = T.scalar('int_stream_To', dtype='int32')
# Index theano variables
x = sharedDataTrain['x'][:, batch_index_From:batch_index_To]
x.name = 'x'
x_mask = sharedDataTrain['x_mask'][:, batch_index_From:batch_index_To]
x_mask.name = 'x_mask'
x_mask_o = sharedDataTrain['x_mask_o'][:, batch_index_From:batch_index_To]
x_mask_o.name = 'x_mask_o'
x_mask_o_mask = sharedDataTrain[
'x_mask_o_mask'][:, batch_index_From:batch_index_To]
x_mask_o_mask.name = 'x_mask_o_mask'
y = sharedDataTrain['y'][:, batch_index_From:batch_index_To]
y.name = 'y'
y_mask = sharedDataTrain['y_mask'][:, batch_index_From:batch_index_To]
y_mask.name = 'y_mask'
y_mask_o = sharedDataTrain['y_mask_o'][:, batch_index_From:batch_index_To]
y_mask_o.name = 'y_mask_o'
y_mask_o_mask = sharedDataTrain[
'y_mask_o_mask'][:, batch_index_From:batch_index_To]
y_mask_o_mask.name = 'y_mask_o_mask'
lens = sharedDataTrain['lens'][:, batch_index_From:batch_index_To]
lens.name = 'lens'
# Generate temp shared vars
tempSharedData = {}
tempSharedData[theano.config.floatX] = [
shared(np.array([[0], [0]], dtype=theano.config.floatX)),
shared(np.array([[0], [0]], dtype=theano.config.floatX)),
shared(np.array([[0], [0]], dtype=theano.config.floatX)),
shared(np.array([[0], [0]], dtype=theano.config.floatX)),
shared(np.array([[0], [0]], dtype=theano.config.floatX)),
shared(np.array([[0], [0]], dtype=theano.config.floatX))
]
tempSharedData['uint8'] = [
shared(np.array([[0], [0]], dtype='uint8')),
shared(np.array([[0], [0]], dtype='uint8')),
shared(np.array([[0], [0]], dtype='uint8'))
]
# Final mask is due to the generated mask and the input mask
x_mask_final = x_mask * x_mask_o * x_mask_o_mask
y_mask_final = y_mask * y_mask_o * y_mask_o_mask
# Build neural network
linear_output, cost = nn_fprop(
x,
x_mask_final,
y,
y_mask_final,
lens,
vocab_size,
hidden_size,
num_layers,
rnn_type,
boosting=boosting,
scan_kwargs={'truncate_gradient': truncate_gradient})
# Keep a constant in gpu memory
constant1 = shared(np.float32(1.0))
cost_int, ymasksum = RR_cost(y, linear_output, y_mask_final, constant1)
# Validation calculations
fRR = function(inputs=[
theano.In(batch_index_From, borrow=True),
theano.In(batch_index_To, borrow=True)
],
updates=[(sharedMRRSUM, sharedMRRSUM + cost_int),
(sharedTOTSUM, sharedTOTSUM + ymasksum)])
# COST
cg = ComputationGraph(cost)
if dropout > 0:
# Apply dropout only to the non-recurrent inputs (Zaremba et al. 2015)
inputs = VariableFilter(theano_name_regex=r'.*apply_input.*')(
cg.variables)
cg = apply_dropout(cg, inputs, dropout)
cost = cg.outputs[0]
# Learning algorithm
step_rules = [
RMSProp(learning_rate=rmsPropLearnRate, decay_rate=decay_rate),
StepClipping(step_clipping)
]
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule(step_rules))
# Extensions
# This is for tracking our best result
trackbest = track_best('valid_MRR', save_path2, last_path2, num_epochs,
nepochs, maxIterations, epsilon, tempSharedData)
if onlyPlots:
prefixes = ["train_cross", "valid_cross", "test_cross"]
gradient_norm = aggregation.mean(algorithm.total_gradient_norm)
step_norm = aggregation.mean(algorithm.total_step_norm)
monitored_vars = [cost, gradient_norm, step_norm]
#this is faster
train_monitor = myTrainingDataMonitoring(
variables=monitored_vars,
prefix=prefixes[0],
after_batch=True,
saveEveryXIteration=saveEveryXIteration)
#train_monitor = DataStreamMonitoringPlot(variables=[cost],
# data_stream=train_streamCopy, prefix=prefixes[0], sharedDataTrain=sharedDataTrain, sharedDataActualTest=sharedDataTrain, after_batch=True, saveEveryXIteration = saveEveryXIteration)
valid_monitor = DataStreamMonitoringPlot(
variables=[cost],
data_stream=valid_streamCopy,
prefix=prefixes[1],
sharedDataTrain=sharedDataTrain,
sharedDataActualTest=sharedDataValid,
after_batch=True,
saveEveryXIteration=saveEveryXIteration)
test_monitor = DataStreamMonitoringPlot(
variables=[cost],
data_stream=test_streamCopy,
prefix=prefixes[2],
sharedDataTrain=sharedDataTrain,
sharedDataActualTest=sharedDataTest,
after_batch=True,
saveEveryXIteration=saveEveryXIteration)
trackbest = [trackbest[0], trackbest[2], trackbest[3], trackbest[4]]
plot = Plot('Live Plotting',
saveFolder=plots_output2,
channels=[
'train_cross_cost', 'valid_cross_cost',
'test_cross_cost'
],
numProcesses=numProcesses,
saveEveryXIteration=saveEveryXIteration,
after_batch=True)
extensions = [
train_monitor,
valid_monitor,
test_monitor,
plot,
Printing(),
ProgressBar(),
] + trackbest
else:
dev_monitor = myDataStreamMonitoring(after_epoch=True,
before_epoch=False,
data_stream=dev_stream,
prefix="valid",
fRR=fRR,
sharedVars=sharedSUMVARs,
sharedDataTrain=sharedDataTrain,
sharedDataValid=sharedDataValid)
extensions = [
dev_monitor,
Printing(),
ProgressBar(),
] + trackbest
if learning_rate_decay not in (0, 1):
extensions.append(
SharedVariableModifier(step_rules[0].learning_rate,
lambda n, lr: np.cast[theano.config.floatX]
(learning_rate_decay * lr),
after_epoch=True,
after_batch=False))
print 'number of parameters in the model: ' + str(
T.sum([p.size for p in cg.parameters]).eval())
# Finally build the main loop and train the model
main_loop = MainLoop(data_stream=train_stream,
algorithm=algorithm,
model=Model(cost),
extensions=extensions)
main_loop.run()
train_stream = get_stream(hdf5_file, 'train', batch_size)
dev_stream = get_stream(hdf5_file, 'dev', batch_size)
# MODEL
x = tensor.matrix('features', dtype='uint8')
y = tensor.matrix('targets', dtype='uint8')
y_hat, cost = nn_fprop(x, y, vocab_size, hidden_size, num_layers, model)
# COST
cg = ComputationGraph(cost)
if dropout > 0:
# Apply dropout only to the non-recurrent inputs (Zaremba et al. 2015)
inputs = VariableFilter(theano_name_regex=r'.*apply_input.*')(cg.variables)
cg = apply_dropout(cg, inputs, dropout)
cost = cg.outputs[0]
# Learning algorithm
step_rules = [RMSProp(learning_rate=learning_rate, decay_rate=decay_rate),
StepClipping(step_clipping)]
algorithm = GradientDescent(cost=cost, parameters=cg.parameters,
step_rule=CompositeRule(step_rules))
# Extensions
gradient_norm = aggregation.mean(algorithm.total_gradient_norm)
step_norm = aggregation.mean(algorithm.total_step_norm)
monitored_vars = [cost, gradient_norm, step_norm]
dev_monitor = DataStreamMonitoring(variables=[cost], after_epoch=True,
before_first_epoch=True, data_stream=dev_stream, prefix="dev")
def main():
# set para
config = getattr(configurations, "get_config_cs2en")()
logger.info("Model options:\n{}".format(pprint.pformat(config)))
tr_stream = get_tr_stream(**config)
# Create Theano variables
logger.info("Creating theano variables")
source_sentence0 = tensor.lmatrix("source0")
source_sentence_mask0 = tensor.matrix("source0_mask")
target_sentence0 = tensor.lmatrix("target0")
target_sentence_mask0 = tensor.matrix("target0_mask")
source_sentence1 = tensor.lmatrix("source1")
source_sentence_mask1 = tensor.matrix("source1_mask")
target_sentence1 = tensor.lmatrix("target1")
target_sentence_mask1 = tensor.matrix("target1_mask")
source_sentence2 = tensor.lmatrix("source2")
source_sentence_mask2 = tensor.matrix("source2_mask")
target_sentence2 = tensor.lmatrix("target2")
target_sentence_mask2 = tensor.matrix("target2_mask")
sampling_input0 = tensor.lmatrix("input0")
sampling_input1 = tensor.lmatrix("input1")
sampling_input2 = tensor.lmatrix("input2")
sampling_hstates0 = tensor.fmatrix("hstates0")
sampling_hstates1 = tensor.fmatrix("hstates1")
sampling_hstates2 = tensor.fmatrix("hstates2")
sampling_lastrep0 = tensor.tensor3("lastrep0")
sampling_lastrep1 = tensor.tensor3("lastrep1")
hstates = theano.shared(value=numpy.zeros((config["enc_nhids"]), dtype=theano.config.floatX), name="hstates")
# Get vocab
sources = get_attr_rec(tr_stream, "data_stream")
src_vocab = sources.data_streams[0].dataset.dictionary
trg_vocab = sources.data_streams[1].dataset.dictionary
# Construct model
logger.info("Building PoemModel")
block0 = PoemBlock(config=config, blockid="block0", name="poemblock0")
block1 = PoemBlock(config=config, blockid="block1", name="poemblock1")
block2 = PoemBlock(config=config, blockid="block2", name="poemblock2")
cost0, hsta0, rep0 = block0.cost(
source_sentence0,
source_sentence_mask0,
source_sentence_mask1,
source_sentence_mask0,
target_sentence0,
target_sentence_mask0,
hstates,
lastrep0=None,
lastrep1=None,
)
cost1, hsta1, rep1 = block1.cost(
source_sentence1,
source_sentence_mask0,
source_sentence_mask1,
source_sentence_mask1,
target_sentence1,
target_sentence_mask1,
hsta0,
lastrep0=rep0,
lastrep1=None,
)
cost2, hsta2, rep2 = block2.cost(
source_sentence2,
source_sentence_mask0,
source_sentence_mask1,
source_sentence_mask2,
target_sentence2,
target_sentence_mask2,
hsta1,
lastrep0=rep0,
lastrep1=rep1,
)
cost = cost0 + cost1 + cost2
cost.name = "total_cost"
logger.info("Creating computational graph")
cg = ComputationGraph(cost)
# Initialize model
logger.info("Initializing model")
block0.set_initw(IsotropicGaussian(config["weight_scale"]))
block0.set_initb(Constant(0))
block0.push_initialization_config()
block0.set_specialinit(Orthogonal(), Orthogonal())
block0.initialize()
block1.set_initw(IsotropicGaussian(config["weight_scale"]))
block1.set_initb(Constant(0))
block1.push_initialization_config()
block1.set_specialinit(Orthogonal(), Orthogonal())
block1.initialize()
block2.set_initw(IsotropicGaussian(config["weight_scale"]))
block2.set_initb(Constant(0))
block2.push_initialization_config()
block2.set_specialinit(Orthogonal(), Orthogonal())
block2.initialize()
# apply dropout for regularization
if config["dropout"] < 1.0:
# dropout is applied to the output of maxout in ghog
logger.info("Applying dropout")
dropout_inputs = [x for x in cg.intermediary_variables if x.name == "maxout_apply_output"]
cg = apply_dropout(cg, dropout_inputs, config["dropout"])
# Print shapes
shapes = [param.get_value().shape for param in cg.parameters]
logger.info("Parameter shapes: ")
for shape, count in Counter(shapes).most_common():
logger.info(" {:15}: {}".format(shape, count))
logger.info("Total number of parameters: {}".format(len(shapes)))
# Print parameter names
param_dict = Selector(block0).get_parameters()
logger.info("Parameter names: ")
for name, value in param_dict.items():
logger.info(" {:15}: {}".format(value.get_value().shape, name))
logger.info("Total number of parameters: {}".format(len(param_dict)))
# Set up training model
logger.info("Building model")
training_model = Model(cost)
# logger.info(cg.auxiliary_variables)
# logger.info("______________________________")
"""
weights = ""
for va in cg.auxiliary_variables:
if va.name == "sequence_generator_block0_cost_matrix_weighted_averages":
weights = va
weightsize = weights.shape
weightsize.name = "weightsize"
states = ""
for va in cg.auxiliary_variables:
if va.name == "sequence_generator_block0_cost_matrix_states":
states = va
statesize = states.shape
statesize.name = "statesize"
rep = ""
for va in cg.auxiliary_variables:
if va.name == "poemblock0_cost_block0hstatesRepeat":
rep = va
repsize = rep.shape
repsize.name = "repsize"
"""
# Set extensions
logger.info("Initializing extensions")
extensions = [
FinishAfter(after_n_batches=config["finish_after"]),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config["saveto"], every_n_batches=config["save_freq"]),
]
# Set up training algorithm
logger.info("Initializing training algorithm")
algorithm = GradientDescent(
cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule([StepClipping(config["step_clipping"]), eval(config["step_rule"])()]),
)
# Reload model if necessary
if config["reload"]:
extensions.append(LoadNMT(config["saveto"]))
# Add sampling
if config["hook_samples"] >= 1:
logger.info("Building sampler")
generated0 = block0.mygenerate(sampling_input0, sampling_hstates0)
search_model0 = Model(generated0)
generated1 = block1.mygenerate(sampling_input1, sampling_hstates1, sampling_lastrep0)
search_model1 = Model(generated1)
generated2 = block2.mygenerate(sampling_input2, sampling_hstates2, sampling_lastrep0, sampling_lastrep1)
search_model2 = Model(generated2)
extensions.append(
Sampler(
config=config,
model0=search_model0,
model1=search_model1,
model2=search_model2,
data_stream=tr_stream,
hook_samples=config["hook_samples"],
every_n_batches=config["sampling_freq"],
src_vocab_size=config["src_vocab_size"],
)
)
logger.info("End of building sampler")
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(model=training_model, algorithm=algorithm, data_stream=tr_stream, extensions=extensions)
# Train!
main_loop.run()
train_stream = stream.train(req_vars)
valid_stream = stream.valid(req_vars)
cost = model.cost(**inputs)
cg = ComputationGraph(cost)
monitored = set([cost] + VariableFilter(roles=[roles.COST])(cg.variables))
valid_monitored = monitored
if hasattr(model, 'valid_cost'):
valid_cost = model.valid_cost(**inputs)
valid_cg = ComputationGraph(valid_cost)
valid_monitored = set([valid_cost] + VariableFilter(
roles=[roles.COST])(valid_cg.variables))
if hasattr(config, 'dropout') and config.dropout < 1.0:
cg = apply_dropout(cg, config.dropout_inputs(cg), config.dropout)
if hasattr(config, 'noise') and config.noise > 0.0:
cg = apply_noise(cg, config.noise_inputs(cg), config.noise)
cost = cg.outputs[0]
cg = Model(cost)
logger.info('# Parameter shapes:')
parameters_size = 0
for value in cg.parameters:
logger.info(' %20s %s' % (value.get_value().shape, value.name))
parameters_size += reduce(operator.mul, value.get_value().shape, 1)
logger.info('Total number of parameters: %d in %d matrices' %
(parameters_size, len(cg.parameters)))
if hasattr(config, 'step_rule'):
step_rule = config.step_rule
def main(exp_config, source_vocab, target_vocab, dev_stream, use_bokeh=True):
# def setup_model_and_stream(exp_config, source_vocab, target_vocab):
# def setup_model_and_stream(exp_config, source_vocab, target_vocab):
train_encoder, train_decoder, theano_sampling_source_input, theano_sampling_context_input, generated, masked_stream = setup_model_and_stream(
exp_config, source_vocab, target_vocab)
cost = create_model(train_encoder, train_decoder,
exp_config.get('imt_smoothing_constant', 0.005))
# Set up training model
logger.info("Building model")
train_model = Model(cost)
# Set the parameters from a trained models (.npz file)
logger.info("Loading parameters from model: {}".format(
exp_config['saved_parameters']))
# Note the brick delimeter='-' is here for legacy reasons because blocks changed the serialization API
param_values = LoadNMT.load_parameter_values(
exp_config['saved_parameters'],
brick_delimiter=exp_config.get('brick_delimiter', None))
LoadNMT.set_model_parameters(train_model, param_values)
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# GRAPH TRANSFORMATIONS FOR BETTER TRAINING
if exp_config.get('l2_regularization', False) is True:
l2_reg_alpha = exp_config['l2_regularization_alpha']
logger.info(
'Applying l2 regularization with alpha={}'.format(l2_reg_alpha))
model_weights = VariableFilter(roles=[WEIGHT])(cg.variables)
for W in model_weights:
cost = cost + (l2_reg_alpha * (W**2).sum())
# why do we need to rename the cost variable? Where did the original name come from?
cost.name = 'decoder_cost_cost'
cg = ComputationGraph(cost)
# apply dropout for regularization
# Note dropout variables are hard-coded here
if exp_config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
# this is the probability of dropping out, so you probably want to make it <=0.5
logger.info('Applying dropout')
dropout_inputs = [
x for x in cg.intermediary_variables
if x.name == 'maxout_apply_output'
]
cg = apply_dropout(cg, dropout_inputs, exp_config['dropout'])
# create the training directory, and copy this config there if directory doesn't exist
if not os.path.isdir(exp_config['saveto']):
os.makedirs(exp_config['saveto'])
# TODO: mv the actual config file once we switch to .yaml for min-risk
shutil.copy(exp_config['config_file'], exp_config['saveto'])
# Set extensions
logger.info("Initializing extensions")
extensions = [
FinishAfter(after_n_batches=exp_config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(exp_config['saveto'],
every_n_batches=exp_config['save_freq'])
]
# Set up beam search and sampling computation graphs if necessary
# TODO: change the if statement here
if exp_config['hook_samples'] >= 1 or exp_config['bleu_script'] is not None:
logger.info("Building sampling model")
search_model = Model(generated)
_, samples = VariableFilter(
bricks=[train_decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
# Add sampling -- TODO: sampling is broken for min-risk
#if config['hook_samples'] >= 1:
# logger.info("Building sampler")
# extensions.append(
# Sampler(model=search_model, data_stream=tr_stream,
# hook_samples=config['hook_samples'],
# every_n_batches=config['sampling_freq'],
# src_vocab_size=config['src_vocab_size']))
# Add early stopping based on bleu
# TODO: use multimodal meteor and BLEU validator
# TODO: add 'validator' key to IMT config
# Add early stopping based on bleu
if exp_config.get('bleu_script', None) is not None:
logger.info("Building bleu validator")
extensions.append(
BleuValidator(theano_sampling_source_input,
theano_sampling_context_input,
samples=samples,
config=exp_config,
model=search_model,
data_stream=dev_stream,
src_vocab=source_vocab,
trg_vocab=target_vocab,
normalize=exp_config['normalized_bleu'],
every_n_batches=exp_config['bleu_val_freq']))
if exp_config.get('imt_f1_validation', False) is not False:
logger.info("Building imt F1 validator")
extensions.append(
IMT_F1_Validator(theano_sampling_source_input,
theano_sampling_context_input,
samples=samples,
config=exp_config,
model=search_model,
data_stream=dev_stream,
src_vocab=source_vocab,
trg_vocab=target_vocab,
normalize=exp_config['normalized_bleu'],
every_n_batches=exp_config['bleu_val_freq']))
# Add early stopping based on Meteor
# if exp_config.get('meteor_directory', None) is not None:
# logger.info("Building meteor validator")
# extensions.append(
# MeteorValidator(theano_sampling_source_input, theano_sampling_context_input,
# samples=samples,
# config=config,
# model=search_model, data_stream=dev_stream,
# src_vocab=src_vocab,
# trg_vocab=trg_vocab,
# normalize=config['normalized_bleu'],
# every_n_batches=config['bleu_val_freq']))
# Reload model if necessary
if exp_config['reload']:
extensions.append(LoadNMT(exp_config['saveto']))
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot(exp_config['model_save_directory'],
channels=[[
'decoder_cost_cost', 'validation_set_imt_f1_score',
'validation_set_bleu_score', 'validation_set_meteor_score'
]],
every_n_batches=10))
# Set up training algorithm
logger.info("Initializing training algorithm")
# if there is l2_regularization, dropout or random noise, we need to use the output of the modified graph
# WORKING: try to catch and fix nan
if exp_config['dropout'] < 1.0:
if exp_config.get('nan_guard', False):
from theano.compile.nanguardmode import NanGuardMode
algorithm = GradientDescent(cost=cg.outputs[0],
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(
exp_config['step_clipping']),
eval(exp_config['step_rule'])()
]),
on_unused_sources='warn',
theano_func_kwargs={
'mode':
NanGuardMode(nan_is_error=True,
inf_is_error=True)
})
else:
algorithm = GradientDescent(cost=cg.outputs[0],
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(
exp_config['step_clipping']),
eval(exp_config['step_rule'])()
]),
on_unused_sources='warn')
else:
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(
exp_config['step_clipping']),
eval(exp_config['step_rule'])()
]),
on_unused_sources='warn')
# enrich the logged information
extensions.append(Timing(every_n_batches=100))
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(model=train_model,
algorithm=algorithm,
data_stream=masked_stream,
extensions=extensions)
# Train!
main_loop.run()
def train(config, save_path, bokeh_name,
params, bokeh_server, test_tag, use_load_ext,
load_log, fast_start, validation_epochs, validation_batches,
per_epochs, per_batches):
root_path, extension = os.path.splitext(save_path)
data = Data(**config['data'])
# Build the main brick and initialize all parameters.
recognizer = SpeechRecognizer(
data.recordings_source, data.labels_source,
data.eos_label,
data.num_features, data.num_labels,
name="recognizer",
data_prepend_eos=data.prepend_eos,
character_map=data.character_map,
**config["net"])
for brick_path, attribute_dict in sorted(
config['initialization'].items(),
key=lambda (k, v): -k.count('/')):
for attribute, value in attribute_dict.items():
brick, = Selector(recognizer).select(brick_path).bricks
setattr(brick, attribute, value)
brick.push_initialization_config()
recognizer.initialize()
# Separate attention_params to be handled differently
# when regularization is applied
attention = recognizer.generator.transition.attention
attention_params = Selector(attention).get_parameters().values()
logger.info(
"Initialization schemes for all bricks.\n"
"Works well only in my branch with __repr__ added to all them,\n"
"there is an issue #463 in Blocks to do that properly.")
def show_init_scheme(cur):
result = dict()
for attr in dir(cur):
if attr.endswith('_init'):
result[attr] = getattr(cur, attr)
for child in cur.children:
result[child.name] = show_init_scheme(child)
return result
logger.info(pprint.pformat(show_init_scheme(recognizer)))
if params:
logger.info("Load parameters from " + params)
recognizer.load_params(params)
if test_tag:
tensor.TensorVariable.__str__ = tensor.TensorVariable.__repr__
__stream = data.get_stream("train")
__data = next(__stream.get_epoch_iterator(as_dict=True))
recognizer.recordings.tag.test_value = __data[data.recordings_source]
recognizer.recordings_mask.tag.test_value = __data[data.recordings_source + '_mask']
recognizer.labels.tag.test_value = __data[data.labels_source]
recognizer.labels_mask.tag.test_value = __data[data.labels_source + '_mask']
theano.config.compute_test_value = 'warn'
batch_cost = recognizer.get_cost_graph().sum()
batch_size = named_copy(recognizer.recordings.shape[1], "batch_size")
# Assumes constant batch size. `aggregation.mean` is not used because
# of Blocks #514.
cost = batch_cost / batch_size
cost.name = "sequence_log_likelihood"
logger.info("Cost graph is built")
# Fetch variables useful for debugging.
# It is important not to use any aggregation schemes here,
# as it's currently impossible to spread the effect of
# regularization on their variables, see Blocks #514.
cost_cg = ComputationGraph(cost)
r = recognizer
energies, = VariableFilter(
applications=[r.generator.readout.readout], name="output_0")(
cost_cg)
bottom_output, = VariableFilter(
applications=[r.bottom.apply], name="output")(
cost_cg)
attended, = VariableFilter(
applications=[r.generator.transition.apply], name="attended")(
cost_cg)
attended_mask, = VariableFilter(
applications=[r.generator.transition.apply], name="attended_mask")(
cost_cg)
weights, = VariableFilter(
applications=[r.generator.evaluate], name="weights")(
cost_cg)
max_recording_length = named_copy(r.recordings.shape[0],
"max_recording_length")
# To exclude subsampling related bugs
max_attended_mask_length = named_copy(attended_mask.shape[0],
"max_attended_mask_length")
max_attended_length = named_copy(attended.shape[0],
"max_attended_length")
max_num_phonemes = named_copy(r.labels.shape[0],
"max_num_phonemes")
min_energy = named_copy(energies.min(), "min_energy")
max_energy = named_copy(energies.max(), "max_energy")
mean_attended = named_copy(abs(attended).mean(),
"mean_attended")
mean_bottom_output = named_copy(abs(bottom_output).mean(),
"mean_bottom_output")
weights_penalty = named_copy(monotonicity_penalty(weights, r.labels_mask),
"weights_penalty")
weights_entropy = named_copy(entropy(weights, r.labels_mask),
"weights_entropy")
mask_density = named_copy(r.labels_mask.mean(),
"mask_density")
cg = ComputationGraph([
cost, weights_penalty, weights_entropy,
min_energy, max_energy,
mean_attended, mean_bottom_output,
batch_size, max_num_phonemes,
mask_density])
# Regularization. It is applied explicitly to all variables
# of interest, it could not be applied to the cost only as it
# would not have effect on auxiliary variables, see Blocks #514.
reg_config = config['regularization']
regularized_cg = cg
if reg_config.get('dropout'):
logger.info('apply dropout')
regularized_cg = apply_dropout(cg, [bottom_output], 0.5)
if reg_config.get('noise'):
logger.info('apply noise')
noise_subjects = [p for p in cg.parameters if p not in attention_params]
regularized_cg = apply_noise(cg, noise_subjects, reg_config['noise'])
regularized_cost = regularized_cg.outputs[0]
regularized_weights_penalty = regularized_cg.outputs[1]
# Model is weird class, we spend lots of time arguing with Bart
# what it should be. However it can already nice things, e.g.
# one extract all the parameters from the computation graphs
# and give them hierahical names. This help to notice when a
# because of some bug a parameter is not in the computation
# graph.
model = SpeechModel(regularized_cost)
params = model.get_parameter_dict()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, params[key].get_value().shape) for key
in sorted(params.keys())],
width=120))
# Define the training algorithm.
train_conf = config['training']
clipping = StepClipping(train_conf['gradient_threshold'])
clipping.threshold.name = "gradient_norm_threshold"
rule_names = train_conf.get('rules', ['momentum'])
core_rules = []
if 'momentum' in rule_names:
logger.info("Using scaling and momentum for training")
core_rules.append(Momentum(train_conf['scale'], train_conf['momentum']))
if 'adadelta' in rule_names:
logger.info("Using AdaDelta for training")
core_rules.append(AdaDelta(train_conf['decay_rate'], train_conf['epsilon']))
max_norm_rules = []
if reg_config.get('max_norm', False):
logger.info("Apply MaxNorm")
maxnorm_subjects = VariableFilter(roles=[WEIGHT])(cg.parameters)
if reg_config.get('max_norm_exclude_lookup', False):
maxnorm_subjects = [v for v in maxnorm_subjects
if not isinstance(get_brick(v), LookupTable)]
logger.info("Parameters covered by MaxNorm:\n"
+ pprint.pformat([name for name, p in params.items()
if p in maxnorm_subjects]))
logger.info("Parameters NOT covered by MaxNorm:\n"
+ pprint.pformat([name for name, p in params.items()
if not p in maxnorm_subjects]))
max_norm_rules = [
Restrict(VariableClipping(reg_config['max_norm'], axis=0),
maxnorm_subjects)]
algorithm = GradientDescent(
cost=regularized_cost +
reg_config.get("penalty_coof", .0) * regularized_weights_penalty / batch_size +
reg_config.get("decay", .0) *
l2_norm(VariableFilter(roles=[WEIGHT])(cg.parameters)) ** 2,
parameters=params.values(),
step_rule=CompositeRule(
[clipping] + core_rules + max_norm_rules +
# Parameters are not changed at all
# when nans are encountered.
[RemoveNotFinite(0.0)]))
# More variables for debugging: some of them can be added only
# after the `algorithm` object is created.
observables = regularized_cg.outputs
observables += [
algorithm.total_step_norm, algorithm.total_gradient_norm,
clipping.threshold]
for name, param in params.items():
num_elements = numpy.product(param.get_value().shape)
norm = param.norm(2) / num_elements ** 0.5
grad_norm = algorithm.gradients[param].norm(2) / num_elements ** 0.5
step_norm = algorithm.steps[param].norm(2) / num_elements ** 0.5
stats = tensor.stack(norm, grad_norm, step_norm, step_norm / grad_norm)
stats.name = name + '_stats'
observables.append(stats)
def attach_aggregation_schemes(variables):
# Aggregation specification has to be factored out as a separate
# function as it has to be applied at the very last stage
# separately to training and validation observables.
result = []
for var in variables:
if var.name == 'weights_penalty':
result.append(named_copy(aggregation.mean(var, batch_size),
'weights_penalty_per_recording'))
elif var.name == 'weights_entropy':
result.append(named_copy(aggregation.mean(
var, recognizer.labels_mask.sum()), 'weights_entropy_per_label'))
else:
result.append(var)
return result
# Build main loop.
logger.info("Initialize extensions")
extensions = []
if use_load_ext and params:
extensions.append(Load(params, load_iteration_state=True, load_log=True))
if load_log and params:
extensions.append(LoadLog(params))
extensions += [
Timing(after_batch=True),
CGStatistics(),
#CodeVersion(['lvsr']),
]
extensions.append(TrainingDataMonitoring(
[observables[0], algorithm.total_gradient_norm,
algorithm.total_step_norm, clipping.threshold,
max_recording_length,
max_attended_length, max_attended_mask_length], after_batch=True))
average_monitoring = TrainingDataMonitoring(
attach_aggregation_schemes(observables),
prefix="average", every_n_batches=10)
extensions.append(average_monitoring)
validation = DataStreamMonitoring(
attach_aggregation_schemes([cost, weights_entropy, weights_penalty]),
data.get_stream("valid"), prefix="valid").set_conditions(
before_first_epoch=not fast_start,
every_n_epochs=validation_epochs,
every_n_batches=validation_batches,
after_training=False)
extensions.append(validation)
recognizer.init_beam_search(10)
per = PhonemeErrorRate(recognizer, data.get_dataset("valid"))
per_monitoring = DataStreamMonitoring(
[per], data.get_stream("valid", batches=False, shuffle=False),
prefix="valid").set_conditions(
before_first_epoch=not fast_start,
every_n_epochs=per_epochs,
every_n_batches=per_batches,
after_training=False)
extensions.append(per_monitoring)
track_the_best_per = TrackTheBest(
per_monitoring.record_name(per)).set_conditions(
before_first_epoch=True, after_epoch=True)
track_the_best_likelihood = TrackTheBest(
validation.record_name(cost)).set_conditions(
before_first_epoch=True, after_epoch=True)
extensions += [track_the_best_likelihood, track_the_best_per]
extensions.append(AdaptiveClipping(
algorithm.total_gradient_norm.name,
clipping, train_conf['gradient_threshold'],
decay_rate=0.998, burnin_period=500))
extensions += [
SwitchOffLengthFilter(data.length_filter,
after_n_batches=train_conf.get('stop_filtering')),
FinishAfter(after_n_batches=train_conf['num_batches'],
after_n_epochs=train_conf['num_epochs'])
.add_condition(["after_batch"], _gradient_norm_is_none),
# Live plotting: requires launching `bokeh-server`
# and allows to see what happens online.
Plot(bokeh_name
if bokeh_name
else os.path.basename(save_path),
[# Plot 1: training and validation costs
[average_monitoring.record_name(regularized_cost),
validation.record_name(cost)],
# Plot 2: gradient norm,
[average_monitoring.record_name(algorithm.total_gradient_norm),
average_monitoring.record_name(clipping.threshold)],
# Plot 3: phoneme error rate
[per_monitoring.record_name(per)],
# Plot 4: training and validation mean weight entropy
[average_monitoring._record_name('weights_entropy_per_label'),
validation._record_name('weights_entropy_per_label')],
# Plot 5: training and validation monotonicity penalty
[average_monitoring._record_name('weights_penalty_per_recording'),
validation._record_name('weights_penalty_per_recording')]],
every_n_batches=10,
server_url=bokeh_server),
Checkpoint(save_path,
before_first_epoch=not fast_start, after_epoch=True,
every_n_batches=train_conf.get('save_every_n_batches'),
save_separately=["model", "log"],
use_cpickle=True)
.add_condition(
['after_epoch'],
OnLogRecord(track_the_best_per.notification_name),
(root_path + "_best" + extension,))
.add_condition(
['after_epoch'],
OnLogRecord(track_the_best_likelihood.notification_name),
(root_path + "_best_ll" + extension,)),
ProgressBar(),
Printing(every_n_batches=1,
attribute_filter=PrintingFilterList()
)]
# Save the config into the status
log = TrainingLog()
log.status['_config'] = repr(config)
main_loop = MainLoop(
model=model, log=log, algorithm=algorithm,
data_stream=data.get_stream("train"),
extensions=extensions)
main_loop.run()
# COST AND ERROR MEASURE
cost = Softmax().categorical_cross_entropy(label, output).mean()
cost.name = 'cost'
error_rate = tensor.neq(tensor.argmax(output, axis=1), label).mean()
error_rate.name = 'error_rate'
# REGULARIZATION
cg = ComputationGraph([cost, error_rate])
if weight_noise > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, weight_noise)
if dropout > 0:
cg = apply_dropout(cg, [eeg1, eeg2, data1, data2] + VariableFilter(name='output', bricks=fc.linear_transformations[:-1])(cg), dropout)
# for vfilter, p in dropout_locs:
# cg = apply_dropout(cg, vfilter(cg), p)
[cost_reg, error_rate_reg] = cg.outputs
# INITIALIZATION
for brick in [conv_eeg, maxpool_eeg, conv_eeg2, maxpool_eeg2, conv, maxpool, conv2, maxpool2, fc]:
brick.weights_init = weights_init
brick.biases_init = biases_init
brick.initialize()
# ==========================================================================================
# THE INFRASTRUCTURE
# ==========================================================================================
def main(mode, save_to, num_epochs, load_params=None,
feature_maps=None, mlp_hiddens=None,
conv_sizes=None, pool_sizes=None, stride=None, repeat_times=None,
batch_size=None, num_batches=None, algo=None,
test_set=None, valid_examples=None,
dropout=None, max_norm=None, weight_decay=None,
batch_norm=None):
if feature_maps is None:
feature_maps = [20, 50, 50]
if mlp_hiddens is None:
mlp_hiddens = [500]
if conv_sizes is None:
conv_sizes = [5, 5, 5]
if pool_sizes is None:
pool_sizes = [2, 2, 2]
if repeat_times is None:
repeat_times = [1, 1, 1]
if batch_size is None:
batch_size = 500
if valid_examples is None:
valid_examples = 2500
if stride is None:
stride = 1
if test_set is None:
test_set = 'test'
if algo is None:
algo = 'rmsprop'
if batch_norm is None:
batch_norm = False
image_size = (128, 128)
output_size = 2
if (len(feature_maps) != len(conv_sizes) or
len(feature_maps) != len(pool_sizes) or
len(feature_maps) != len(repeat_times)):
raise ValueError("OMG, inconsistent arguments")
# Use ReLUs everywhere and softmax for the final prediction
conv_activations = [Rectifier() for _ in feature_maps]
mlp_activations = [Rectifier() for _ in mlp_hiddens] + [Softmax()]
convnet = LeNet(conv_activations, 3, image_size,
stride=stride,
filter_sizes=zip(conv_sizes, conv_sizes),
feature_maps=feature_maps,
pooling_sizes=zip(pool_sizes, pool_sizes),
repeat_times=repeat_times,
top_mlp_activations=mlp_activations,
top_mlp_dims=mlp_hiddens + [output_size],
border_mode='full',
batch_norm=batch_norm,
weights_init=Glorot(),
biases_init=Constant(0))
# We push initialization config to set different initialization schemes
# for convolutional layers.
convnet.initialize()
logging.info("Input dim: {} {} {}".format(
*convnet.children[0].get_dim('input_')))
for i, layer in enumerate(convnet.layers):
if isinstance(layer, Activation):
logging.info("Layer {} ({})".format(
i, layer.__class__.__name__))
else:
logging.info("Layer {} ({}) dim: {} {} {}".format(
i, layer.__class__.__name__, *layer.get_dim('output')))
single_x = tensor.tensor3('image_features')
x = tensor.tensor4('image_features')
single_y = tensor.lvector('targets')
y = tensor.lmatrix('targets')
# Training
with batch_normalization(convnet):
probs = convnet.apply(x)
cost = (CategoricalCrossEntropy().apply(y.flatten(), probs)
.copy(name='cost'))
error_rate = (MisclassificationRate().apply(y.flatten(), probs)
.copy(name='error_rate'))
cg = ComputationGraph([cost, error_rate])
extra_updates = []
if batch_norm: # batch norm:
logger.debug("Apply batch norm")
pop_updates = get_batch_normalization_updates(cg)
# p stands for population mean
# m stands for minibatch
alpha = 0.005
extra_updates = [(p, m * alpha + p * (1 - alpha))
for p, m in pop_updates]
population_statistics = [p for p, m in extra_updates]
if dropout:
relu_outputs = VariableFilter(bricks=[Rectifier], roles=[OUTPUT])(cg)
cg = apply_dropout(cg, relu_outputs, dropout)
cost, error_rate = cg.outputs
if weight_decay:
logger.debug("Apply weight decay {}".format(weight_decay))
cost += weight_decay * l2_norm(cg.parameters)
cost.name = 'cost'
# Validation
valid_probs = convnet.apply_5windows(single_x)
valid_cost = (CategoricalCrossEntropy().apply(single_y, valid_probs)
.copy(name='cost'))
valid_error_rate = (MisclassificationRate().apply(
single_y, valid_probs).copy(name='error_rate'))
model = Model([cost, error_rate])
if load_params:
logger.info("Loaded params from {}".format(load_params))
with open(load_params, 'r') as src:
model.set_parameter_values(load_parameters(src))
# Training stream with random cropping
train = DogsVsCats(("train",), subset=slice(None, 25000 - valid_examples, None))
train_str = DataStream(
train, iteration_scheme=ShuffledScheme(train.num_examples, batch_size))
train_str = add_transformers(train_str, random_crop=True)
# Validation stream without cropping
valid = DogsVsCats(("train",), subset=slice(25000 - valid_examples, None, None))
valid_str = DataStream(
valid, iteration_scheme=SequentialExampleScheme(valid.num_examples))
valid_str = add_transformers(valid_str)
if mode == 'train':
directory, _ = os.path.split(sys.argv[0])
env = dict(os.environ)
env['THEANO_FLAGS'] = 'floatX=float32'
port = numpy.random.randint(1025, 10000)
server = subprocess.Popen(
[directory + '/server.py',
str(25000 - valid_examples), str(batch_size), str(port)],
env=env, stderr=subprocess.STDOUT)
train_str = ServerDataStream(
('image_features', 'targets'), produces_examples=False,
port=port)
save_to_base, save_to_extension = os.path.splitext(save_to)
# Train with simple SGD
if algo == 'rmsprop':
step_rule = RMSProp(decay_rate=0.999, learning_rate=0.0003)
elif algo == 'adam':
step_rule = Adam()
else:
assert False
if max_norm:
conv_params = VariableFilter(bricks=[Convolutional], roles=[WEIGHT])(cg)
linear_params = VariableFilter(bricks=[Linear], roles=[WEIGHT])(cg)
step_rule = CompositeRule(
[step_rule,
Restrict(VariableClipping(max_norm, axis=0), linear_params),
Restrict(VariableClipping(max_norm, axis=(1, 2, 3)), conv_params)])
algorithm = GradientDescent(
cost=cost, parameters=model.parameters,
step_rule=step_rule)
algorithm.add_updates(extra_updates)
# `Timing` extension reports time for reading data, aggregating a batch
# and monitoring;
# `ProgressBar` displays a nice progress bar during training.
extensions = [Timing(every_n_batches=100),
FinishAfter(after_n_epochs=num_epochs,
after_n_batches=num_batches),
DataStreamMonitoring(
[valid_cost, valid_error_rate],
valid_str,
prefix="valid"),
TrainingDataMonitoring(
[cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
TrackTheBest("valid_error_rate"),
Checkpoint(save_to, save_separately=['log'],
parameters=cg.parameters +
(population_statistics if batch_norm else []),
before_training=True, after_epoch=True)
.add_condition(
['after_epoch'],
OnLogRecord("valid_error_rate_best_so_far"),
(save_to_base + '_best' + save_to_extension,)),
Printing(every_n_batches=100)]
model = Model(cost)
main_loop = MainLoop(
algorithm,
train_str,
model=model,
extensions=extensions)
try:
main_loop.run()
finally:
server.terminate()
elif mode == 'test':
classify = theano.function([single_x], valid_probs.argmax())
test = DogsVsCats((test_set,))
test_str = DataStream(
test, iteration_scheme=SequentialExampleScheme(test.num_examples))
test_str = add_transformers(test_str)
correct = 0
with open(save_to, 'w') as dst:
print("id", "label", sep=',', file=dst)
for index, example in enumerate(test_str.get_epoch_iterator()):
image = example[0]
prediction = classify(image)
print(index + 1, classify(image), sep=',', file=dst)
if len(example) > 1 and prediction == example[1]:
correct += 1
print(correct / float(test.num_examples))
else:
assert False
def __init__(self, ref_data, output_dim):
input_dim = ref_data.shape[1]
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32), name='ref_data')
rng = RandomStreams()
ae_bricks = []
ae_input = ref_data_sh
ae_costs = []
for i, (idim, odim) in enumerate(zip([input_dim] + ae_dims[:-1], ae_dims)):
ae_mlp = MLP(activations=[ae_activations[i]],
dims=[idim, odim],
name='enc%i'%i)
enc = ae_mlp.apply(ae_input)
enc_n = ae_mlp.apply(ae_input + rng.normal(size=ae_input.shape, std=ae_f_noise_std))
ae_mlp_dec = MLP(activations=[ae_activations[i]],
dims=[odim, idim],
name='dec%i'%i)
dec = ae_mlp_dec.apply(enc_n)
cost = tensor.sqrt(((ae_input - dec) ** 2).sum(axis=1)).mean() + \
ae_l1_pen * abs(enc).sum(axis=1).mean()
ae_costs.append(cost)
ae_input = enc
ae_bricks = ae_bricks + [ae_mlp, ae_mlp_dec]
self.ae_costs = ae_costs
ref_data_enc = ae_input
# Construct the model
j = tensor.lvector('j')
r = ref_data_enc[j, :]
x = tensor.fmatrix('x')
y = tensor.ivector('y')
# input_dim must be nr
mlp = MLP(activations=activation_functions,
dims=[ae_dims[-1]] + hidden_dims + [n_inter], name='inter_gen')
mlp2 = MLP(activations=activation_functions_2 + [None],
dims=[n_inter] + hidden_dims_2 + [output_dim],
name='end_mlp')
inter_weights = mlp.apply(r)
if inter_bias == None:
ibias = Bias(n_inter)
ibias.biases_init = Constant(0)
ibias.initialize()
inter = ibias.apply(tensor.dot(x, inter_weights))
else:
inter = tensor.dot(x, inter_weights) - inter_bias
inter = inter_act_fun.apply(inter)
final = mlp2.apply(inter)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
# error_rate = tensor.neq(y, pred).mean()
ber = balanced_error_rate.ber(y, pred)
# Initialize parameters
for brick in ae_bricks + [mlp, mlp2]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
# apply regularization
cg = ComputationGraph([cost, ber])
if r_dropout != 0:
# - dropout on input vector r : r_dropout
cg = apply_dropout(cg, [r], r_dropout)
if x_dropout != 0:
cg = apply_dropout(cg, [x], x_dropout)
if s_dropout != 0:
# - dropout on intermediate layers of first mlp : s_dropout
s_dropout_vars = list(set(VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([inter_weights])))
- set([inter_weights]))
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if i_dropout != 0:
# - dropout on input to second mlp : i_dropout
cg = apply_dropout(cg, [inter], i_dropout)
if a_dropout != 0:
# - dropout on hidden layers of second mlp : a_dropout
a_dropout_vars = list(set(VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([final])))
- set([inter_weights]) - set(s_dropout_vars))
cg = apply_dropout(cg, a_dropout_vars, a_dropout)
if r_noise_std != 0:
cg = apply_noise(cg, [r], r_noise_std)
if w_noise_std != 0:
# - apply noise on weight variables
weight_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, weight_vars, w_noise_std)
[cost_reg, ber_reg] = cg.outputs
if s_l1pen != 0:
s_weights = VariableFilter(bricks=mlp.linear_transformations, roles=[WEIGHT])(cg)
cost_reg = cost_reg + s_l1pen * sum(abs(w).sum() for w in s_weights)
if i_l1pen != 0:
cost_reg = cost_reg + i_l1pen * abs(inter).sum()
if a_l1pen != 0:
a_weights = VariableFilter(bricks=mlp2.linear_transformations, roles=[WEIGHT])(cg)
cost_reg = cost_reg + a_l1pen * sum(abs(w).sum() for w in a_weights)
self.cost = cost
self.cost_reg = cost_reg
self.ber = ber
self.ber_reg = ber_reg
self.pred = pred
self.confidence = confidence
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 set_up_predictor(self, nmt_model_path):
"""Initializes the predictor with the given NMT model. Code
following ``blocks.machine_translation.main``.
"""
self.src_vocab_size = self.config['src_vocab_size']
self.trgt_vocab_size = self.config['trg_vocab_size']
# Create Theano variables
logging.info('Creating theano variables')
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence = tensor.lmatrix('target')
target_sentence_mask = tensor.matrix('target_mask')
sampling_input = tensor.lmatrix('input')
# Construct model
logging.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(self.config['src_vocab_size'],
self.config['enc_embed'],
self.config['enc_nhids'])
decoder = Decoder(self.config['trg_vocab_size'],
self.config['dec_embed'],
self.config['dec_nhids'],
self.config['enc_nhids'] * 2)
cost = decoder.cost(
encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence, target_sentence_mask)
logging.info('Creating computational graph')
cg = ComputationGraph(cost)
# Initialize model (TODO: really necessary?)
logging.info('Initializing model')
encoder.weights_init = decoder.weights_init = IsotropicGaussian(
self.config['weight_scale'])
encoder.biases_init = decoder.biases_init = Constant(0)
encoder.push_initialization_config()
decoder.push_initialization_config()
encoder.bidir.prototype.weights_init = Orthogonal()
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
# Apply dropout for regularization (TODO: remove?)
if self.config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
logging.info('Applying dropout')
dropout_inputs = [x for x in cg.intermediary_variables
if x.name == 'maxout_apply_output']
cg = apply_dropout(cg, dropout_inputs, self.config['dropout'])
# Apply weight noise for regularization (TODO: remove?)
if self.config['weight_noise_ff'] > 0.0:
logging.info('Applying weight noise to ff layers')
enc_params = Selector(encoder.lookup).get_params().values()
enc_params += Selector(encoder.fwd_fork).get_params().values()
enc_params += Selector(encoder.back_fork).get_params().values()
dec_params = Selector(
decoder.sequence_generator.readout).get_params().values()
dec_params += Selector(
decoder.sequence_generator.fork).get_params().values()
dec_params += Selector(decoder.state_init).get_params().values()
cg = apply_noise(cg,
enc_params+dec_params,
self.config['weight_noise_ff'])
# Print shapes
shapes = [param.get_value().shape for param in cg.parameters]
logging.debug("Parameter shapes: ")
for shape, count in Counter(shapes).most_common():
logging.debug(' {:15}: {}'.format(shape, count))
logging.info("Total number of parameters: {}".format(len(shapes)))
# Print parameter names
enc_dec_param_dict = merge(Selector(encoder).get_parameters(),
Selector(decoder).get_parameters())
logging.debug("Parameter names: ")
for name, value in enc_dec_param_dict.items():
logging.debug(' {:15}: {}'.format(value.get_value().shape,
name))
logging.info("Total number of parameters: {}"
.format(len(enc_dec_param_dict)))
# Set up training model
logging.info("Building model")
# Set extensions
logging.info("Initializing extensions")
# Set up beam search and sampling computation graphs if necessary
logging.info("Building sampling model")
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
generated = decoder.generate(sampling_input, sampling_representation)
search_model = Model(generated)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
# Follows blocks.machine_translation.BleuValidator.__init__
self.source_sentence = sampling_input
self.samples = samples
self.model = search_model
self.normalize = True
self.verbose = self.config.get('val_set_out', None)
# Reload model if necessary
if self.config['reload']:
loader = LoadNMT(nmt_model_path,
self.config['saveto'],
search_model)
loader.load_weights()
self.best_models = []
self.val_bleu_curve = []
self.search_algorithm = MyopticSearch(samples=samples)
self.search_algorithm.compile()
def main(num_epochs,
feature_maps=None,
mlp_hiddens=None,
conv_sizes=None,
pool_sizes=None,
batch_size=500,
num_batches=None):
############# Architecture #############
if feature_maps is None:
feature_maps = [20, 50]
if mlp_hiddens is None:
mlp_hiddens = [500]
if conv_sizes is None:
conv_sizes = [5, 5]
if pool_sizes is None:
pool_sizes = [2, 2]
image_size = (32, 32)
batch_size = 50
output_size = 2
learningRate = 0.1
num_epochs = 10
num_batches = None
delta = 0.01
drop_prob = 0.5
weight_noise = 0.75
# Use ReLUs everywhere and softmax for the final prediction
conv_activations = [Rectifier() for _ in feature_maps]
mlp_activations = [Rectifier() for _ in mlp_hiddens] + [Softmax()]
convnet = LeNet(conv_activations,
3,
image_size,
filter_sizes=zip(conv_sizes, conv_sizes),
feature_maps=feature_maps,
pooling_sizes=zip(pool_sizes, pool_sizes),
top_mlp_activations=mlp_activations,
top_mlp_dims=mlp_hiddens + [output_size],
border_mode='full',
weights_init=Uniform(width=.2),
biases_init=Constant(0))
# We push initialization config to set different initialization schemes
# for convolutional layers.
convnet.push_initialization_config()
convnet.layers[0].weights_init = Uniform(width=.2)
convnet.layers[1].weights_init = Uniform(width=.09)
convnet.top_mlp.linear_transformations[0].weights_init = Uniform(width=.08)
convnet.top_mlp.linear_transformations[1].weights_init = Uniform(width=.11)
convnet.initialize()
logging.info(
"Input dim: {} {} {}".format(*convnet.children[0].get_dim('input_')))
for i, layer in enumerate(convnet.layers):
if isinstance(layer, Activation):
logging.info("Layer {} ({})".format(i, layer.__class__.__name__))
else:
logging.info("Layer {} ({}) dim: {} {} {}".format(
i, layer.__class__.__name__, *layer.get_dim('output')))
x = tensor.tensor4('image_features')
y = tensor.lmatrix('targets')
probs = (convnet.apply(x)).copy(name='probs')
# Computational Graph just for cost for drop_out and noise application
cg_probs = ComputationGraph([probs])
inputs = VariableFilter(roles=[INPUT])(cg_probs.variables)
weights = VariableFilter(roles=[FILTER, WEIGHT])(cg_probs.variables)
############# Regularization #############
#regularization = 0
logger.info('Applying regularization')
regularization = delta * sum([(W**2).mean() for W in weights])
probs.name = "reg_probs"
############# Guaussian Noise #############
logger.info('Applying Gaussian noise')
cg_train = apply_noise(cg_probs, weights, weight_noise)
############# Dropout #############
logger.info('Applying dropout')
cg_probs = apply_dropout(cg_probs, inputs, drop_prob)
dropped_out = VariableFilter(roles=[DROPOUT])(cg_probs.variables)
inputs_referenced = [var.tag.replacement_of for var in dropped_out]
set(inputs) == set(inputs_referenced)
############# Batch normalization #############
# recalculate probs after dropout and noise and regularization:
probs = cg_probs.outputs[0] + regularization
cost = (CategoricalCrossEntropy().apply(y.flatten(),
probs).copy(name='cost'))
error_rate = (MisclassificationRate().apply(y.flatten(),
probs).copy(name='error_rate'))
cg = ComputationGraph([probs, cost, error_rate])
cg = apply_batch_normalization(cg)
########### Loading images #####################
from fuel.datasets.dogs_vs_cats import DogsVsCats
from fuel.streams import DataStream, ServerDataStream
from fuel.schemes import ShuffledScheme
from fuel.transformers.image import RandomFixedSizeCrop, MinimumImageDimensions, Random2DRotation
from fuel.transformers import Flatten, Cast, ScaleAndShift
def create_data(data):
stream = DataStream(data,
iteration_scheme=ShuffledScheme(
data.num_examples, batch_size))
stream_downscale = MinimumImageDimensions(
stream, image_size, which_sources=('image_features', ))
stream_rotate = Random2DRotation(stream_downscale,
which_sources=('image_features', ))
stream_max = ScikitResize(stream_rotate,
image_size,
which_sources=('image_features', ))
stream_scale = ScaleAndShift(stream_max,
1. / 255,
0,
which_sources=('image_features', ))
stream_cast = Cast(stream_scale,
dtype='float32',
which_sources=('image_features', ))
#stream_flat = Flatten(stream_scale, which_sources=('image_features',))
return stream_cast
stream_data_train = create_data(
DogsVsCats(('train', ), subset=slice(0, 20)))
stream_data_test = create_data(
DogsVsCats(('train', ), subset=slice(20, 30)))
# Train with simple SGD
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=Scale(learning_rate=learningRate))
#algorithm = GradientDescent(cost=cost, parameters=cg.parameters,step_rule=Adam(0.001))
#algorithm.add_updates(extra_updates)
# `Timing` extension reports time for reading data, aggregating a batch and monitoring;
# `ProgressBar` displays a nice progress bar during training.
extensions = []
extensions.append(Timing())
extensions.append(
FinishAfter(after_n_epochs=num_epochs, after_n_batches=num_batches))
extensions.append(
DataStreamMonitoring([cost, error_rate],
stream_data_test,
prefix="valid"))
extensions.append(
TrainingDataMonitoring([
cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)
],
prefix="train",
after_epoch=True))
#extensions.append(Checkpoint(save_to))
extensions.append(ProgressBar())
extensions.append(Printing())
logger.info("Building the model")
model = Model(cost)
main_loop = MainLoop(algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()
def main(config, tr_stream, dev_stream, use_bokeh=False, the_task=None, the_track=None):
config['the_task'] = the_task
# Create Theano variables
logger.info('Creating theano variables')
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence = tensor.lmatrix('target')
target_sentence_mask = tensor.matrix('target_mask')
sampling_input = tensor.lmatrix('input')
# Construct model
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(
# end_embed is dimension of word embedding matrix in encoder; enc_nhids number of hidden units in encoder GRU
config['src_vocab_size'], config['enc_embed'], config['enc_nhids'])
decoder = Decoder(
config['trg_vocab_size'], config['dec_embed'], config['dec_nhids'],
config['enc_nhids'] * 2, config['use_attention'], cost_type=config['error_fct'])
cost = decoder.cost(
encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence, target_sentence_mask)
testVar = decoder.getTestVar(
encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence, target_sentence_mask)
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# Initialize model
logger.info('Initializing model')
my_rng = numpy.random.RandomState(config['rng_value'])
if config['identity_init']:
encoder.weights_init = decoder.weights_init = Identity()
else:
encoder.weights_init = decoder.weights_init = IsotropicGaussian(
config['weight_scale'])
encoder.rng = decoder.rng = my_rng
encoder.biases_init = decoder.biases_init = Constant(0)
encoder.push_initialization_config()
decoder.push_initialization_config()
encoder.bidir.prototype.weights_init = Orthogonal()
encoder.bidir.prototype.rng = my_rng
decoder.transition.weights_init = Orthogonal()
decoder.transition.rng = my_rng
encoder.initialize()
decoder.initialize()
# apply dropout for regularization
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
logger.info('Applying dropout')
dropout_inputs = [x for x in cg.intermediary_variables
if x.name == 'maxout_apply_output']
cg = apply_dropout(cg, dropout_inputs, config['dropout'])
# Apply weight noise for regularization
if config['weight_noise_ff'] > 0.0:
logger.info('Applying weight noise to ff layers')
enc_params = Selector(encoder.lookup).get_params().values()
enc_params += Selector(encoder.fwd_fork).get_params().values()
enc_params += Selector(encoder.back_fork).get_params().values()
dec_params = Selector(
decoder.sequence_generator.readout).get_params().values()
dec_params += Selector(
decoder.sequence_generator.fork).get_params().values()
dec_params += Selector(decoder.state_init).get_params().values()
cg = apply_noise(cg, enc_params+dec_params, config['weight_noise_ff'], seed=my_rng)
cost = cg.outputs[0]
# Print shapes
shapes = [param.get_value().shape for param in cg.parameters]
logger.info("Parameter shapes: ")
for shape, count in Counter(shapes).most_common():
logger.info(' {:15}: {}'.format(shape, count))
logger.info("Total number of parameters: {}".format(len(shapes)))
# Print parameter names
enc_dec_param_dict = merge(Selector(encoder).get_parameters(),
Selector(decoder).get_parameters())
logger.info("Parameter names: ")
for name, value in enc_dec_param_dict.items():
logger.info(' {:15}: {}'.format(value.get_value().shape, name))
logger.info("Total number of parameters: {}"
.format(len(enc_dec_param_dict)))
# Set up training model
logger.info("Building model")
training_model = Model(cost)
# Set extensions
logger.info("Initializing extensions")
# this is ugly code and done, because I am not sure if the order of the extensions is important
if 'track2' in config['saveto']: # less epochs for track 2, because of more data
if config['early_stopping']:
extensions = [
FinishAfter(after_n_epochs=config['finish_after']/2),
#FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'],
every_n_batches=config['save_freq'])
]
else:
extensions = [
FinishAfter(after_n_epochs=config['finish_after']/2),
#FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'],
every_n_batches=config['save_freq'])
]
else:
if config['early_stopping']:
extensions = [
FinishAfter(after_n_epochs=config['finish_after']),
#FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'],
every_n_batches=config['save_freq'])
]
else:
extensions = [
FinishAfter(after_n_epochs=config['finish_after']),
#FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'],
every_n_batches=config['save_freq'])
]
# Set up beam search and sampling computation graphs if necessary
if config['hook_samples'] >= 1:
logger.info("Building sampling model")
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
generated = decoder.generate(sampling_input, sampling_representation)
search_model = Model(generated)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
# Add sampling
if config['hook_samples'] >= 1:
logger.info("Building sampler")
extensions.append(
Sampler(model=search_model, data_stream=tr_stream,
hook_samples=config['hook_samples'],
#every_n_batches=1,
every_n_batches=config['sampling_freq'],
src_vocab_size=8))
#src_vocab_size=config['src_vocab_size']))
# Add early stopping based on bleu
if config['val_set'] is not None:
logger.info("Building accuracy validator")
extensions.append(
AccuracyValidator(sampling_input, samples=samples, config=config,
model=search_model, data_stream=dev_stream,
after_training=True,
#after_epoch=True))
every_n_epochs=5))
else:
logger.info("No validation set given for this language")
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
# Set up training algorithm
logger.info("Initializing training algorithm")
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=CompositeRule([StepClipping(config['step_clipping']),
eval(config['step_rule'])()])
)
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(
model=training_model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions
)
# Train!
main_loop.run()
def __init__(self, ref_data, output_dim):
input_dim = ref_data.shape[1]
ref_data_sh = theano.shared(numpy.array(ref_data, dtype=numpy.float32),
name='ref_data')
# Construct the model
j = tensor.lvector('j')
r = ref_data_sh[j, :]
x = tensor.fmatrix('x')
y = tensor.ivector('y')
# input_dim must be nr
mlp0 = MLP(activations=activation_functions_0,
dims=[input_dim] + hidden_dims_0,
name='e0')
mlp0vs = MLP(activations=[None],
dims=[hidden_dims_0[-1], input_dim],
name='de0')
mlp1 = MLP(activations=activation_functions_1,
dims=[hidden_dims_0[-1]] + hidden_dims_1 + [n_inter],
name='inter_gen')
mlp2 = MLP(activations=activation_functions_2 + [None],
dims=[n_inter] + hidden_dims_2 + [output_dim],
name='end_mlp')
encod = mlp0.apply(r)
rprime = mlp0vs.apply(encod)
inter_weights = mlp1.apply(encod)
ibias = Bias(n_inter)
ibias.biases_init = Constant(0)
ibias.initialize()
inter = inter_act_fun.apply(ibias.apply(tensor.dot(x, inter_weights)))
final = mlp2.apply(inter)
cost = Softmax().categorical_cross_entropy(y, final)
confidence = Softmax().apply(final)
pred = final.argmax(axis=1)
error_rate = tensor.neq(y, pred).mean()
# Initialize parameters
for brick in [mlp0, mlp0vs, mlp1, mlp2]:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.001)
brick.initialize()
# apply regularization
cg = ComputationGraph([cost, error_rate])
if r_dropout != 0:
# - dropout on input vector r : r_dropout
cg = apply_dropout(cg, [r], r_dropout)
if s_dropout != 0:
# - dropout on intermediate layers of first mlp : s_dropout
s_dropout_vars = list(
set(
VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([inter_weights]))) -
set([inter_weights]))
cg = apply_dropout(cg, s_dropout_vars, s_dropout)
if i_dropout != 0:
# - dropout on input to second mlp : i_dropout
cg = apply_dropout(cg, [inter], i_dropout)
if a_dropout != 0:
# - dropout on hidden layers of second mlp : a_dropout
a_dropout_vars = list(
set(
VariableFilter(bricks=[Tanh], name='output')
(ComputationGraph([final]))) - set([inter_weights]) -
set(s_dropout_vars))
cg = apply_dropout(cg, a_dropout_vars, a_dropout)
if w_noise_std != 0:
# - apply noise on weight variables
weight_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, weight_vars, w_noise_std)
[cost_reg, error_rate_reg] = cg.outputs
# add reconstruction penalty for AE part
penalty_val = tensor.sqrt(((r - rprime)**2).sum(axis=1)).mean()
cost_reg = cost_reg + reconstruction_penalty * penalty_val
self.cost = cost
self.cost_reg = cost_reg
self.error_rate = error_rate
self.error_rate_reg = error_rate_reg
self.pred = pred
self.confidence = confidence
train_stream = get_stream(hdf5_file, 'train', batch_size)
dev_stream = get_stream(hdf5_file, 'dev', batch_size)
# MODEL
x = tensor.matrix('features', dtype='uint8')
y = tensor.matrix('targets', dtype='uint8')
y_hat, cost, cells = nn_fprop(x, y, vocab_size, hidden_size, num_layers, model)
# COST
cg = ComputationGraph(cost)
if dropout > 0:
# Apply dropout only to the non-recurrent inputs (Zaremba et al. 2015)
inputs = VariableFilter(theano_name_regex=r'.*apply_input.*')(cg.variables)
cg = apply_dropout(cg, inputs, dropout)
cost = cg.outputs[0]
# Learning algorithm
step_rules = [RMSProp(learning_rate=learning_rate, decay_rate=decay_rate),
StepClipping(step_clipping)]
algorithm = GradientDescent(cost=cost, parameters=cg.parameters,
step_rule=CompositeRule(step_rules))
# Extensions
gradient_norm = aggregation.mean(algorithm.total_gradient_norm)
step_norm = aggregation.mean(algorithm.total_step_norm)
monitored_vars = [cost, gradient_norm, step_norm]
dev_monitor = DataStreamMonitoring(variables=[cost], after_epoch=True,
before_first_epoch=True, data_stream=dev_stream, prefix="dev")
def set_up(self, config=None, make_prunable=False):
"""Loads and initializes all the theano variables for the
training model and the decoding model.
Args:
config (dict): NMT configuration
"""
if config:
self.config = config
else:
config = self.config
# Create Theano variables
logging.debug('Creating theano variables')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence_mask = tensor.matrix('target_mask')
# Construct model (fs439: Add NoLookup options)
if config['dec_layers'] != 1:
logging.fatal("Only dec_layers=1 supported.")
logging.debug('Building RNN encoder-decoder')
if config['src_sparse_feat_map']:
if config['enc_layers'] != 1:
logging.fatal("Only enc_layers=1 supported for sparse "
"source features.")
source_sentence = tensor.tensor3('source')
self.sampling_input = tensor.tensor3('input')
encoder = NoLookupEncoder(config['enc_embed'], config['enc_nhids'])
else:
source_sentence = tensor.lmatrix('source')
self.sampling_input = tensor.lmatrix('input')
if config['enc_layers'] > 1 and not config['enc_share_weights']:
encoder = DeepBidirectionalEncoder(
config['src_vocab_size'], config['enc_embed'],
config['enc_layers'], config['enc_skip_connections'],
config['enc_nhids'])
else:
encoder = BidirectionalEncoder(config['src_vocab_size'],
config['enc_embed'],
config['enc_layers'],
config['enc_skip_connections'],
config['enc_nhids'])
if config['trg_sparse_feat_map']:
target_sentence = tensor.tensor3('target')
decoder = NoLookupDecoder(
config['trg_vocab_size'], config['dec_embed'],
config['dec_nhids'], config['att_nhids'],
config['maxout_nhids'], config['enc_nhids'] * 2,
config['attention'], config['dec_attention_sources'],
config['dec_readout_sources'], config['memory'],
config['memory_size'], config['seq_len'], config['dec_init'])
else:
target_sentence = tensor.lmatrix('target')
decoder = Decoder(config['trg_vocab_size'],
config['dec_embed'],
config['dec_nhids'],
config['att_nhids'],
config['maxout_nhids'],
config['enc_nhids'] * 2,
config['attention'],
config['dec_attention_sources'],
config['dec_readout_sources'],
config['memory'],
config['memory_size'],
config['seq_len'],
config['dec_init'],
make_prunable=make_prunable)
if config['annotations'] != 'direct':
annotators = []
add_direct = False
for name in config['annotations'].split(','):
if name == 'direct':
add_direct = True
elif name == 'hierarchical':
annotators.append(HierarchicalAnnotator(encoder))
else:
logging.fatal("Annotation strategy %s unknown" % name)
encoder = EncoderWithAnnotators(encoder, annotators, add_direct)
annotations, annotations_mask = encoder.apply(source_sentence,
source_sentence_mask)
self.cost = decoder.cost(annotations, annotations_mask,
target_sentence, target_sentence_mask)
logging.info('Creating computational graph')
self.cg = ComputationGraph(self.cost)
# Initialize model
logging.info('Initializing model')
encoder.weights_init = decoder.weights_init = IsotropicGaussian(
config['weight_scale'])
encoder.biases_init = decoder.biases_init = Constant(0)
encoder.push_initialization_config()
decoder.push_initialization_config()
try:
encoder.bidir.prototype.weights_init = Orthogonal()
except AttributeError:
pass # Its fine, no bidirectional encoder
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
# apply dropout for regularization
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
logging.info('Applying dropout')
dropout_inputs = [
x for x in self.cg.intermediary_variables
if x.name == 'maxout_apply_output'
]
self.cg = apply_dropout(self.cg, dropout_inputs, config['dropout'])
# Apply weight noise for regularization
if config['weight_noise_ff'] > 0.0:
logging.info('Applying weight noise to ff layers')
if encoder.lookup:
enc_params = Selector(encoder.lookup).get_parameters().values()
enc_params += Selector(encoder.fwd_fork).get_parameters().values()
enc_params += Selector(encoder.back_fork).get_parameters().values()
dec_params = Selector(
decoder.sequence_generator.readout).get_parameters().values()
dec_params += Selector(
decoder.sequence_generator.fork).get_parameters().values()
self.cg = apply_noise(self.cg, enc_params + dec_params,
config['weight_noise_ff'])
# Print shapes
shapes = [param.get_value().shape for param in self.cg.parameters]
logging.debug("Parameter shapes: ")
for shape, count in Counter(shapes).most_common():
logging.debug(' {:15}: {}'.format(shape, count))
logging.debug("Total number of CG parameters: {}".format(len(shapes)))
# Print parameter names
enc_dec_param_dict = merge(
Selector(encoder).get_parameters(),
Selector(decoder).get_parameters())
logging.debug("Parameter names: ")
for name, value in enc_dec_param_dict.items():
logging.debug(' {:15}: {}'.format(value.get_value().shape,
name))
logging.info("Total number of parameters: {}".format(
len(enc_dec_param_dict)))
# Set up training model
logging.info("Building model")
self.training_model = Model(self.cost)
logging.info("Building sampling model")
src_shape = (self.sampling_input.shape[-2],
self.sampling_input.shape[-1]) # batch_size x sen_length
sampling_representation, _ = encoder.apply(self.sampling_input,
tensor.ones(src_shape))
generated = decoder.generate(src_shape, sampling_representation)
self.search_model = Model(generated)
generated_outputs = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
self.samples = generated_outputs[1]
self.encoder = encoder
self.decoder = decoder
def __init__(self, config, vocab_size):
question = tensor.imatrix('question')
question_mask = tensor.imatrix('question_mask')
context = tensor.imatrix('context')
context_mask = tensor.imatrix('context_mask')
answer = tensor.imatrix('answer')
answer_mask = tensor.imatrix('answer_mask')
bricks = []
question = question.dimshuffle(1, 0)
question_mask = question_mask.dimshuffle(1, 0)
context = context.dimshuffle(1, 0)
context_mask = context_mask.dimshuffle(1, 0)
answer = answer.dimshuffle(1, 0)
answer_mask = answer_mask.dimshuffle(1, 0)
# Embed questions and context
embed = LookupTable(vocab_size,
config.embed_size,
name='question_embed')
embed.weights_init = IsotropicGaussian(0.01)
# Calculate question encoding (concatenate layer1)
qembed = embed.apply(question)
qlstms, qhidden_list = make_bidir_lstm_stack(
qembed, config.embed_size,
question_mask.astype(theano.config.floatX),
config.question_lstm_size, config.question_skip_connections, 'q')
bricks = bricks + qlstms
if config.question_skip_connections:
qenc_dim = 2 * sum(config.question_lstm_size)
qenc = tensor.concatenate([h[-1, :, :] for h in qhidden_list],
axis=1)
else:
qenc_dim = 2 * config.question_lstm_size[-1]
qenc = tensor.concatenate([h[-1, :, :] for h in qhidden_list[-2:]],
axis=1)
qenc.name = 'qenc'
# Calculate context encoding (concatenate layer1)
cembed = embed.apply(context)
cqembed = tensor.concatenate([
cembed,
tensor.extra_ops.repeat(qenc[None, :, :], cembed.shape[0], axis=0)
],
axis=2)
clstms, chidden_list = make_bidir_lstm_stack(
cqembed, config.embed_size + qenc_dim,
context_mask.astype(theano.config.floatX), config.ctx_lstm_size,
config.ctx_skip_connections, 'ctx')
bricks = bricks + clstms
if config.ctx_skip_connections:
cenc_dim = 2 * sum(config.ctx_lstm_size) #2 : fw & bw
cenc = tensor.concatenate(chidden_list, axis=2)
else:
cenc_dim = 2 * config.question_lstm_size[-1]
cenc = tensor.concatenate(chidden_list[-2:], axis=2)
cenc.name = 'cenc'
# Attention mechanism Bilinear
attention_clinear_1 = Linear(input_dim=cenc_dim,
output_dim=qenc_dim,
name='attc_1')
bricks += [attention_clinear_1]
att_start = qenc[None, :, :] * attention_clinear_1.apply(
cenc.reshape(
(cenc.shape[0] * cenc.shape[1], cenc.shape[2]))).reshape(
(cenc.shape[0], cenc.shape[1], cenc.shape[2]))
att_start = att_start.sum(axis=2)
att_start = tensor.nnet.softmax(att_start.T).T
attention_clinear_2 = Linear(input_dim=cenc_dim,
output_dim=qenc_dim,
name='attc_2')
bricks += [attention_clinear_2]
att_end = qenc[None, :, :] * attention_clinear_2.apply(
cenc.reshape(
(cenc.shape[0] * cenc.shape[1], cenc.shape[2]))).reshape(
(cenc.shape[0], cenc.shape[1], cenc.shape[2]))
att_end = att_end.sum(axis=2)
att_end = tensor.nnet.softmax(att_end.T).T
att_start = tensor.dot(
tensor.le(
tensor.tile(
theano.tensor.arange(context.shape[0])[None, :],
(context.shape[0], 1)),
tensor.tile(
theano.tensor.arange(context.shape[0])[:, None],
(1, context.shape[0]))), att_start)
att_end = tensor.dot(
tensor.ge(
tensor.tile(
theano.tensor.arange(context.shape[0])[None, :],
(context.shape[0], 1)),
tensor.tile(
theano.tensor.arange(context.shape[0])[:, None],
(1, context.shape[0]))), att_end)
# add attention from left and right
att_weights = att_start * att_end
att_target = tensor.eq(
tensor.tile(answer[None, :, :], (context.shape[0], 1, 1)),
tensor.tile(context[:, None, :],
(1, answer.shape[0], 1))).sum(axis=1).clip(0, 1)
self.predictions = tensor.gt(att_weights, 0.25) * context
att_target = att_target / (att_target.sum(axis=0) + 0.00001)
att_weights = att_weights / (att_weights.sum(axis=0) + 0.00001)
#cost = (tensor.nnet.binary_crossentropy(att_weights, att_target) * context_mask).sum() / context_mask.sum()
cost = (((att_weights - att_target)**2) *
context_mask).sum() / context_mask.sum()
# Apply dropout
cg = ComputationGraph([cost])
if config.w_noise > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, config.w_noise)
if config.dropout > 0:
cg = apply_dropout(cg, qhidden_list + chidden_list, config.dropout)
[cost_reg] = cg.outputs
# Other stuff
cost.name = 'cost'
cost_reg.name = 'cost_reg'
att_start.name = 'att_start'
att_end.name = 'att_end'
att_weights.name = 'att_weights'
att_target.name = 'att_target'
self.predictions.name = 'pred'
self.sgd_cost = cost_reg
self.monitor_vars = [[cost_reg]]
self.monitor_vars_valid = [[cost_reg]]
self.analyse_vars = [
cost, self.predictions, att_start, att_end, att_weights, att_target
]
# Initialize bricks
embed.initialize()
for brick in bricks:
brick.weights_init = config.weights_init
brick.biases_init = config.biases_init
brick.initialize()
def main(feature_maps=None, mlp_hiddens=None, conv_sizes=None, pool_sizes=None, batch_size=None, num_batches=None):
if feature_maps is None:
feature_maps = [32, 48, 64, 80, 128, 128]
if mlp_hiddens is None:
mlp_hiddens = [1000]
if conv_sizes is None:
conv_sizes = [7, 5, 5, 5, 5, 4]
if pool_sizes is None:
pool_sizes = [3, 2, 2, 2, 2, 1]
if batch_size is None:
batch_size = 64
conv_steps = [2, 1, 1, 1, 1, 1] # same as stride
image_size = (256, 256)
output_size = 2
learningRate = 0.001
drop_prob = 0.5
weight_noise = 0.75
num_epochs = 250
num_batches = None
host_plot = "http://*****:*****@ %s" % (graph_name, datetime.datetime.now(), socket.gethostname()),
channels=[["train_error_rate", "valid_error_rate"], ["train_total_gradient_norm"]],
after_epoch=True,
server_url=host_plot,
)
)
PLOT_AVAILABLE = True
except ImportError:
PLOT_AVAILABLE = False
extensions.append(Checkpoint(save_to, after_epoch=True, after_training=True, save_separately=["log"]))
logger.info("Building the model")
model = Model(cost)
########### Loading images #####################
main_loop = MainLoop(algorithm, stream_data_train, model=model, extensions=extensions)
main_loop.run()
def main(config,
tr_stream,
dev_stream,
source_vocab,
target_vocab,
use_bokeh=False):
# Create Theano variables
logger.info('Creating theano variables')
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence = tensor.lmatrix('target')
target_sentence_mask = tensor.matrix('target_mask')
initial_context = tensor.matrix('initial_context')
# Construct model
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(config['src_vocab_size'],
config['enc_embed'], config['enc_nhids'])
# let user specify the target transition class name in config,
# eval it and pass to decoder
target_transition_name = config.get(
'target_transition', 'GRUInitialStateWithInitialStateSumContext')
target_transition = eval(target_transition_name)
logger.info('Using target transition: {}'.format(target_transition_name))
decoder = InitialContextDecoder(config['trg_vocab_size'],
config['dec_embed'], config['dec_nhids'],
config['enc_nhids'] * 2,
config['context_dim'], target_transition)
cost = decoder.cost(encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence,
target_sentence_mask, initial_context)
cost.name = 'decoder_cost'
# Initialize model
logger.info('Initializing model')
encoder.weights_init = decoder.weights_init = IsotropicGaussian(
config['weight_scale'])
encoder.biases_init = decoder.biases_init = Constant(0)
encoder.push_initialization_config()
decoder.push_initialization_config()
encoder.bidir.prototype.weights_init = Orthogonal()
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# GRAPH TRANSFORMATIONS FOR BETTER TRAINING
# TODO: validate performance with/without regularization
if config.get('l2_regularization', False) is True:
l2_reg_alpha = config['l2_regularization_alpha']
logger.info(
'Applying l2 regularization with alpha={}'.format(l2_reg_alpha))
model_weights = VariableFilter(roles=[WEIGHT])(cg.variables)
for W in model_weights:
cost = cost + (l2_reg_alpha * (W**2).sum())
# why do we need to name the cost variable? Where did the original name come from?
cost.name = 'decoder_cost_cost'
cg = ComputationGraph(cost)
# apply dropout for regularization
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
# this is the probability of dropping out, so you probably want to make it <=0.5
logger.info('Applying dropout')
dropout_inputs = [
x for x in cg.intermediary_variables
if x.name == 'maxout_apply_output'
]
cg = apply_dropout(cg, dropout_inputs, config['dropout'])
# Print shapes
shapes = [param.get_value().shape for param in cg.parameters]
logger.info("Parameter shapes: ")
for shape, count in Counter(shapes).most_common():
logger.info(' {:15}: {}'.format(shape, count))
logger.info("Total number of parameters: {}".format(len(shapes)))
# Print parameter names
enc_dec_param_dict = merge(
Selector(encoder).get_parameters(),
Selector(decoder).get_parameters())
logger.info("Parameter names: ")
for name, value in enc_dec_param_dict.items():
logger.info(' {:15}: {}'.format(value.get_value().shape, name))
logger.info("Total number of parameters: {}".format(
len(enc_dec_param_dict)))
# Set up training model
logger.info("Building model")
training_model = Model(cost)
# create the training directory, and copy this config there if directory doesn't exist
if not os.path.isdir(config['saveto']):
os.makedirs(config['saveto'])
shutil.copy(config['config_file'], config['saveto'])
# Set extensions
# TODO: add checking for existing model and loading
logger.info("Initializing extensions")
extensions = [
FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'], every_n_batches=config['save_freq'])
]
# Create the theano variables that we need for the sampling graph
sampling_input = tensor.lmatrix('input')
sampling_context = tensor.matrix('context_input')
# Set up beam search and sampling computation graphs if necessary
if config['hook_samples'] >= 1 or config.get('bleu_script',
None) is not None:
logger.info("Building sampling model")
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
generated = decoder.generate(sampling_input, sampling_representation,
sampling_context)
search_model = Model(generated)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
# Add sampling
if config['hook_samples'] >= 1:
logger.info("Building sampler")
extensions.append(
Sampler(
model=search_model,
data_stream=tr_stream,
hook_samples=config['hook_samples'],
every_n_batches=config['sampling_freq'],
src_vocab=source_vocab,
trg_vocab=target_vocab,
src_vocab_size=config['src_vocab_size'],
))
# Add early stopping based on bleu
if config.get('bleu_script', None) is not None:
logger.info("Building bleu validator")
extensions.append(
BleuValidator(sampling_input,
sampling_context,
samples=samples,
config=config,
model=search_model,
data_stream=dev_stream,
src_vocab=source_vocab,
trg_vocab=target_vocab,
normalize=config['normalized_bleu'],
every_n_batches=config['bleu_val_freq']))
# Add early stopping based on Meteor
if config.get('meteor_directory', None) is not None:
logger.info("Building meteor validator")
extensions.append(
MeteorValidator(sampling_input,
sampling_context,
samples=samples,
config=config,
model=search_model,
data_stream=dev_stream,
src_vocab=source_vocab,
trg_vocab=target_vocab,
normalize=config['normalized_bleu'],
every_n_batches=config['bleu_val_freq']))
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot(config['model_save_directory'],
channels=[[
'decoder_cost', 'validation_set_bleu_score',
'validation_set_meteor_score'
]],
every_n_batches=10))
# Set up training algorithm
logger.info("Initializing training algorithm")
# if there is dropout or random noise, we need to use the output of the modified graph
if config['dropout'] < 1.0 or config['weight_noise_ff'] > 0.0:
algorithm = GradientDescent(cost=cg.outputs[0],
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(config['step_clipping']),
eval(config['step_rule'])()
]))
else:
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(config['step_clipping']),
eval(config['step_rule'])()
]))
# enrich the logged information
extensions.append(Timing(every_n_batches=100))
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(model=training_model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions)
# Train!
main_loop.run()
def main(config, tr_stream, dev_stream):
# Create Theano variables
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence = tensor.lmatrix('target')
target_sentence_mask = tensor.matrix('target_mask')
sampling_input = tensor.lmatrix('input')
# Construct model
encoder = BidirectionalEncoder(config['src_vocab_size'], config['enc_embed'],
config['enc_nhids'])
decoder = Decoder(config['trg_vocab_size'], config['dec_embed'],
config['dec_nhids'], config['enc_nhids'] * 2)
cost = decoder.cost(encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence, target_sentence_mask)
# Initialize model
encoder.weights_init = decoder.weights_init = IsotropicGaussian(config['weight_scale'])
encoder.biases_init = decoder.biases_init = Constant(0)
encoder.push_initialization_config()
decoder.push_initialization_config()
encoder.bidir.prototype.weights_init = Orthogonal()
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
cg = ComputationGraph(cost)
# apply dropout for regularization
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
dropout_inputs = [x for x in cg.intermediary_variables
if x.name == 'maxout_apply_output']
cg = apply_dropout(cg, dropout_inputs, config['dropout'])
# Apply weight noise for regularization
if config['weight_noise_ff'] > 0.0:
enc_params = Selector(encoder.lookup).get_params().values()
enc_params += Selector(encoder.fwd_fork).get_params().values()
enc_params += Selector(encoder.back_fork).get_params().values()
dec_params = Selector(decoder.sequence_generator.readout).get_params().values()
dec_params += Selector(decoder.sequence_generator.fork).get_params().values()
dec_params += Selector(decoder.transition.initial_transformer).get_params().values()
cg = apply_noise(cg, enc_params+dec_params, config['weight_noise_ff'])
cost = cg.outputs[0]
# Print shapes
shapes = [param.get_value().shape for param in cg.parameters]
logger.info("Parameter shapes: ")
for shape, count in Counter(shapes).most_common():
logger.info(' {:15}: {}'.format(shape, count))
logger.info("Total number of parameters: {}".format(len(shapes)))
# Print parameter names
enc_dec_param_dict = merge(Selector(encoder).get_params(),
Selector(decoder).get_params())
logger.info("Parameter names: ")
for name, value in enc_dec_param_dict.iteritems():
logger.info(' {:15}: {}'.format(value.get_value().shape, name))
logger.info("Total number of parameters: {}".format(len(enc_dec_param_dict)))
# Set up training algorithm
if args.subtensor_fix:
assert config['step_rule'] == 'AdaDelta'
from subtensor_gradient import GradientDescent_SubtensorFix, AdaDelta_SubtensorFix, subtensor_params
lookups = subtensor_params(cg, [encoder.lookup, decoder.sequence_generator.readout.feedback_brick.lookup])
algorithm = GradientDescent_SubtensorFix(
subtensor_params=lookups,
cost=cost, params=cg.parameters,
step_rule=CompositeRule([StepClipping(config['step_clipping']),
RemoveNotFinite(0.9),
AdaDelta_SubtensorFix(subtensor_params=lookups)])
)
else:
algorithm = GradientDescent(
cost=cost, params=cg.parameters,
step_rule=CompositeRule([StepClipping(config['step_clipping']),
RemoveNotFinite(0.9),
eval(config['step_rule'])()])
)
# Set up beam search and sampling computation graphs
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
generated = decoder.generate(sampling_input, sampling_representation)
search_model = Model(generated)
samples, = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is the next_outputs
# Set up training model
training_model = Model(cost)
# Set extensions
extensions = [
Sampler(
model=search_model, config=config, data_stream=tr_stream,
src_eos_idx=config['src_eos_idx'],
trg_eos_idx=config['trg_eos_idx'],
every_n_batches=config['sampling_freq']),
BleuValidator(
sampling_input, samples=samples, config=config,
model=search_model, data_stream=dev_stream,
src_eos_idx=config['src_eos_idx'],
trg_eos_idx=config['trg_eos_idx'],
every_n_batches=config['bleu_val_freq']),
TrainingDataMonitoring([cost], after_batch=True),
#Plot('En-Fr', channels=[['decoder_cost_cost']],
# after_batch=True),
Printing(after_batch=True),
Dump(config['saveto'], every_n_batches=config['save_freq'])
]
# Reload model if necessary
if config['reload']:
extensions += [LoadFromDumpWMT15(config['saveto'])]
# Initialize main loop
main_loop = MainLoop(
model=training_model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions
)
# Train!
main_loop.run()
def __init__(self, config, vocab_size, id_to_vocab, logger):
question = tensor.imatrix('question')
question_mask = tensor.imatrix('question_mask')
context = tensor.imatrix('context')
context_mask = tensor.imatrix('context_mask')
answer = tensor.ivector('answer')
candidates = tensor.imatrix('candidates')
candidates_mask = tensor.imatrix('candidates_mask')
# question_actual = tensor.imatrix('question_actual')
# context_actual = tensor.imatrix('context_actual')
# answer_actual = tensor.imatrix('answer_actual')
bricks = []
question = question.dimshuffle(1, 0)
question_mask = question_mask.dimshuffle(1, 0)
context = context.dimshuffle(1, 0)
context_mask = context_mask.dimshuffle(1, 0)
# Embed questions and cntext
embed = LookupTable(vocab_size, config.embed_size, name='question_embed')
bricks.append(embed)
qembed = embed.apply(question)
cembed = embed.apply(context)
qlstms, qhidden_list = make_bidir_lstm_stack(qembed, config.embed_size, question_mask.astype(theano.config.floatX),
config.question_lstm_size, config.question_skip_connections, 'q')
clstms, chidden_list = make_bidir_lstm_stack(cembed, config.embed_size, context_mask.astype(theano.config.floatX),
config.ctx_lstm_size, config.ctx_skip_connections, 'ctx')
bricks = bricks + qlstms + clstms
# Calculate question encoding (concatenate layer1)
if config.question_skip_connections:
qenc_dim = 2*sum(config.question_lstm_size)
qenc = tensor.concatenate([h[-1,:,:] for h in qhidden_list], axis=1)
else:
qenc_dim = 2*config.question_lstm_size[-1]
#u
qenc = tensor.concatenate([h[-1,:,:] for h in qhidden_list[-2:]], axis=1)
qenc.name = 'qenc'
# Calculate context encoding (concatenate layer1)
if config.ctx_skip_connections:
cenc_dim = 2*sum(config.ctx_lstm_size)
cenc = tensor.concatenate(chidden_list, axis=2)
else:
cenc_dim = 2*config.ctx_lstm_size[-1]
cenc = tensor.concatenate(chidden_list[-2:], axis=2)
cenc.name = 'cenc'
# Attention mechanism MLP
attention_mlp = MLP(dims=config.attention_mlp_hidden + [1],
activations=config.attention_mlp_activations[1:] + [Identity()],
name='attention_mlp')
attention_qlinear = Linear(input_dim=qenc_dim, output_dim=config.attention_mlp_hidden[0], name='attq')
attention_clinear = Linear(input_dim=cenc_dim, output_dim=config.attention_mlp_hidden[0], use_bias=False, name='attc')
bricks += [attention_mlp, attention_qlinear, attention_clinear]
layer1 = Tanh().apply(attention_clinear.apply(cenc.reshape((cenc.shape[0]*cenc.shape[1], cenc.shape[2])))
.reshape((cenc.shape[0],cenc.shape[1],config.attention_mlp_hidden[0]))
+ attention_qlinear.apply(qenc)[None, :, :])
layer1.name = 'layer1'
att_weights = attention_mlp.apply(layer1.reshape((layer1.shape[0]*layer1.shape[1], layer1.shape[2])))
att_weights.name = 'att_weights_0'
att_weights = att_weights.reshape((layer1.shape[0], layer1.shape[1]))
att_weights.name = 'att_weights'
#r
attended = tensor.sum(cenc * tensor.nnet.softmax(att_weights.T).T[:, :, None], axis=0)
attended.name = 'attended'
# Now we can calculate our output
out_mlp = MLP(dims=[cenc_dim + qenc_dim] + config.out_mlp_hidden + [config.n_entities],
activations=config.out_mlp_activations + [Identity()],
name='out_mlp')
bricks += [out_mlp]
# g^AR
probs = out_mlp.apply(tensor.concatenate([attended, qenc], axis=1))
probs.name = 'probs'
is_candidate = tensor.eq(tensor.arange(config.n_entities, dtype='int32')[None, None, :],
tensor.switch(candidates_mask, candidates, -tensor.ones_like(candidates))[:, :, None]).sum(axis=1)
probs = tensor.switch(is_candidate, probs, -1000 * tensor.ones_like(probs))
# Calculate prediction, cost and error rate
pred = probs.argmax(axis=1)
cost = Softmax().categorical_cross_entropy(answer, probs).mean()
error_rate = tensor.neq(answer, pred).mean()
# Apply dropout
cg = ComputationGraph([cost, error_rate])
if config.w_noise > 0:
noise_vars = VariableFilter(roles=[WEIGHT])(cg)
cg = apply_noise(cg, noise_vars, config.w_noise)
if config.dropout > 0:
cg = apply_dropout(cg, qhidden_list + chidden_list, config.dropout)
[cost_reg, error_rate_reg] = cg.outputs
# Other stuff
cost_reg.name = cost.name = 'cost'
error_rate_reg.name = error_rate.name = 'error_rate'
self.sgd_cost = cost_reg
self.monitor_vars = [[cost_reg], [error_rate_reg]]
self.monitor_vars_valid = [[cost], [error_rate]]
# Initialize bricks
for brick in bricks:
brick.weights_init = config.weights_init
brick.biases_init = config.biases_init
brick.initialize()
def train(save_to, num_epochs, feature_maps=None, mlp_hiddens=None,
conv_sizes=None, pool_sizes=None, batch_size=500):
# Initialize the training set
train = CIFAR10(("train",))
train_stream = DataStream.default_stream(
train, iteration_scheme=ShuffledScheme(
train.num_examples, batch_size))
test = CIFAR10(("test",))
test_stream = DataStream.default_stream(
test,
iteration_scheme=ShuffledScheme(
test.num_examples, batch_size))
# ConvMLP Parameters
image_size = (32, 32)
num_channels = 3
num_conv = 3 # Number of Convolutional Layers
if feature_maps is None:
feature_maps = [20, 30, 30]
if not len(feature_maps) == num_conv:
raise ValueError('Must specify more feature maps')
if conv_sizes is None:
conv_sizes = [5] * num_conv
if pool_sizes is None:
pool_sizes = [2] * num_conv
if mlp_hiddens is None:
mlp_hiddens = [500]
output_size = 10
# Use ReLUs everywhere and softmax for the final prediction
conv_activations = [Rectifier() for _ in feature_maps]
mlp_activations = [Rectifier() for _ in mlp_hiddens] + [Softmax()]
convnet = ConvMLP(conv_activations, num_channels, image_size,
filter_sizes=zip(conv_sizes, conv_sizes),
feature_maps=feature_maps,
pooling_sizes=zip(pool_sizes, pool_sizes),
top_mlp_activations=mlp_activations,
top_mlp_dims=mlp_hiddens + [output_size],
border_mode='full',
weights_init=Uniform(width=.2),
biases_init=Constant(0))
# We push initialization config to set different initialization schemes
# for convolutional layers.
convnet.push_initialization_config()
for i in range(num_conv):
convnet.layers[i].weights_init = Uniform(width=.2)
convnet.top_mlp.linear_transformations[0].weights_init = Uniform(width=.08)
convnet.top_mlp.linear_transformations[1].weights_init = Uniform(width=.11)
convnet.initialize()
logging.info("Input dim: {} {} {}".format(
*convnet.children[0].get_dim('input_')))
for i, layer in enumerate(convnet.layers):
logging.info("Layer {} dim: {} {} {}".format(
i, *layer.get_dim('output')))
x = tensor.tensor4('features')
y = tensor.lmatrix('targets')
# Normalize input and apply the convnet
probs = convnet.apply(x)
cost = named_copy(CategoricalCrossEntropy().apply(y.flatten(),
probs), 'cost')
error_rate = named_copy(MisclassificationRate().apply(y.flatten(), probs),
'error_rate')
cg = ComputationGraph([cost, error_rate])
# Apply Dropout to outputs of rectifiers
from blocks.roles import OUTPUT
vs = VariableFilter(roles=[OUTPUT])(cg.variables)
vs1 = [v for v in vs if v.name.startswith('rectifier')]
vs1 = vs1[0: -2] # Only first two layers
cg = apply_dropout(cg, vs1, 0.5)
# Train with simple SGD
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=AdaDelta())
# `Timing` extension reports time for reading data, aggregating a batch
# and monitoring;
# `ProgressBar` displays a nice progress bar during training.
extensions = [Timing(),
FinishAfter(after_n_epochs=num_epochs),
DataStreamMonitoring(
[cost, error_rate],
test_stream,
prefix="test"),
TrainingDataMonitoring(
[cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
Checkpoint(save_to),
ProgressBar(),
Printing()]
model = Model(cost)
main_loop = MainLoop(
algorithm,
train_stream,
model=model,
extensions=extensions)
main_loop.run()
classifier_fn = 'convmlp_cifar10.zip'
with open(classifier_fn, 'w') as f:
dump(convnet, f)
