class Leaf:
"""
special fields
+ params : white list of params to optimize
+ updates : white list of updates to optimize
"""
is_train = tt.bscalar()
def __call__(self, *args, **kwargs):
raise NotImplementedError("implement in a derived class")
def get_params(self):
if hasattr(self, "params"):
return search_shared(self.params)
return search_shared(getattr(self, s) for s in dir(self))
def get_updates(self):
return search_updates(self)
def optimize(self, cost: tt.Variable, optimizer: Optimizer):
updates = optimizer.updates(self.get_params(), cost)
updates.update(self.get_updates())
return updates
def state_list(self):
return list(p.get_value() for p in self.get_params())
def load_state_list(self, state):
for p, s in zip(self.get_params(), state):
p.set_value(s)
def __init__(self, data, config, fast_predict=False):
self.embedding_shapes = data.embedding_shapes
self.lstm_type = config.lstm_cell
self.lstm_hidden_size = int(config.lstm_hidden_size)
self.num_lstm_layers = int(config.num_lstm_layers)
self.max_grad_norm = float(config.max_grad_norm)
self.vocab_size = data.word_dict.size()
self.label_space_size = data.label_dict.size()
self.unk_id = data.unk_id
# Initialize layers and parameters
self.embedding_layer = EmbeddingLayer(data.embedding_shapes,
data.embeddings)
self.params = [p for p in self.embedding_layer.params]
self.rnn_layers = [None] * self.num_lstm_layers
for l in range(self.num_lstm_layers):
input_dim = self.embedding_layer.output_size if l == 0 else self.lstm_hidden_size
input_dropout = config.input_dropout_prob if (
config.per_layer_dropout or l == 0) else 0.0
recurrent_dropout = config.recurrent_dropout_prob
self.rnn_layers[l] = get_rnn_layer(self.lstm_type)(
input_dim,
self.lstm_hidden_size,
input_dropout_prob=input_dropout,
recurrent_dropout_prob=recurrent_dropout,
fast_predict=fast_predict,
prefix='lstm_{}'.format(l))
self.params.extend(self.rnn_layers[l].params)
self.softmax_layer = SoftmaxLayer(self.lstm_hidden_size,
self.label_space_size)
self.params.extend(self.softmax_layer.params)
# Build model
# Shape of x: [seq_len, batch_size, num_features]
self.x0 = tensor.ltensor3('x')
self.y0 = tensor.lmatrix('y')
self.mask0 = tensor.matrix('mask', dtype=floatX)
self.is_train = tensor.bscalar('is_train')
self.x = self.x0.dimshuffle(1, 0, 2)
self.y = self.y0.dimshuffle(1, 0)
self.mask = self.mask0.dimshuffle(1, 0)
self.inputs = [None] * (self.num_lstm_layers + 1)
self.inputs[0] = self.embedding_layer.connect(self.x)
self.rev_mask = self.mask[::-1]
for l, rnn in enumerate(self.rnn_layers):
outputs = rnn.connect(self.inputs[l],
self.mask if l % 2 == 0 else self.rev_mask,
self.is_train)
self.inputs[l + 1] = outputs[::-1]
self.scores, self.pred = self.softmax_layer.connect(self.inputs[-1])
self.pred0 = self.pred.reshape([self.x.shape[0],
self.x.shape[1]]).dimshuffle(1, 0)
def test_replacements(binomial_model_inference):
d = tt.bscalar()
d.tag.test_value = 1
approx = binomial_model_inference.approx
p = approx.model.p
p_t = p ** 3
p_s = approx.sample_node(p_t)
if theano.config.compute_test_value != 'off':
assert p_s.tag.test_value.shape == p_t.tag.test_value.shape
sampled = [p_s.eval() for _ in range(100)]
assert any(map(
operator.ne,
sampled[1:], sampled[:-1])
) # stochastic
p_d = approx.sample_node(p_t, deterministic=True)
sampled = [p_d.eval() for _ in range(100)]
assert all(map(
operator.eq,
sampled[1:], sampled[:-1])
) # deterministic
p_r = approx.sample_node(p_t, deterministic=d)
sampled = [p_r.eval({d: 1}) for _ in range(100)]
assert all(map(
operator.eq,
sampled[1:], sampled[:-1])
) # deterministic
sampled = [p_r.eval({d: 0}) for _ in range(100)]
assert any(map(
operator.ne,
sampled[1:], sampled[:-1])
) # stochastic
def random_fn(self):
"""
Implements posterior distribution from initial latent space
Parameters
----------
size : number of samples from distribution
no_rand : whether use deterministic distribution
Returns
-------
posterior space (numpy)
"""
In = theano.In
size = tt.iscalar('size')
no_rand = tt.bscalar('no_rand')
posterior = self.random(size, no_rand)
fn = theano.function([
In(size, 'size', 1, allow_downcast=True),
In(no_rand, 'no_rand', 0, allow_downcast=True)
], posterior)
def inner(size=None, no_rand=False):
if size is None:
return fn(1, int(no_rand))[0]
else:
return fn(size, int(no_rand))
return inner
def test_param_allow_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([
Param(a, allow_downcast=True),
Param(b, allow_downcast=False),
Param(c, allow_downcast=None)
], (a + b + c))
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert numpy.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, f, [3], numpy.array([6], dtype='int16'),
1)
# Value too big for a, silently ignored
assert numpy.all(f([2**20], numpy.ones(1, dtype='int8'), 1) == 2)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, f, [3], [312], 1)
# Value too big for c, raises TypeError
self.assertRaises(TypeError, f, [3], [6], 806)
def test_allow_input_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([a, b, c], (a + b + c), allow_input_downcast=True)
# Value too big for a, b, or c, silently ignored
assert f([2**20], [1], 0) == 1
assert f([3], [312], 0) == 59
assert f([3], [1], 806) == 42
g = pfunc([a, b, c], (a + b + c), allow_input_downcast=False)
# All values are in range. Since they're not ndarrays (but lists
# or scalars), they will be converted, and their value checked.
assert numpy.all(g([3], [6], 0) == 9)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, g, [3], numpy.array([6], dtype='int16'),
0)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, g, [3], [312], 0)
h = pfunc([a, b, c], (a + b + c)) # Default: allow_input_downcast=None
# Everything here should behave like with False
assert numpy.all(h([3], [6], 0) == 9)
self.assertRaises(TypeError, h, [3], numpy.array([6], dtype='int16'),
0)
self.assertRaises(TypeError, h, [3], [312], 0)
def test_param_allow_downcast_int(self):
a = tensor.wvector("a") # int16
b = tensor.bvector("b") # int8
c = tensor.bscalar("c") # int8
f = pfunc(
[
In(a, allow_downcast=True),
In(b, allow_downcast=False),
In(c, allow_downcast=None),
],
(a + b + c),
)
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert np.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
with pytest.raises(TypeError):
f([3], np.array([6], dtype="int16"), 1)
# Value too big for a, silently ignored
assert np.all(f([2**20], np.ones(1, dtype="int8"), 1) == 2)
# Value too big for b, raises TypeError
with pytest.raises(TypeError):
f([3], [312], 1)
# Value too big for c, raises TypeError
with pytest.raises(TypeError):
f([3], [6], 806)
def test_param_allow_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([Param(a, allow_downcast=True),
Param(b, allow_downcast=False),
Param(c, allow_downcast=None)],
(a + b + c))
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert numpy.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, f,
[3], numpy.array([6], dtype='int16'), 1)
# Value too big for a, silently ignored
assert numpy.all(f([2 ** 20], numpy.ones(1, dtype='int8'), 1) == 2)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, f, [3], [312], 1)
# Value too big for c, raises TypeError
self.assertRaises(TypeError, f, [3], [6], 806)
def test_allow_input_downcast_int(self):
a = tensor.wvector('a') # int16
b = tensor.bvector('b') # int8
c = tensor.bscalar('c') # int8
f = pfunc([a, b, c], (a + b + c), allow_input_downcast=True)
# Value too big for a, b, or c, silently ignored
assert f([2 ** 20], [1], 0) == 1
assert f([3], [312], 0) == 59
assert f([3], [1], 806) == 42
g = pfunc([a, b, c], (a + b + c), allow_input_downcast=False)
# All values are in range. Since they're not ndarrays (but lists
# or scalars), they will be converted, and their value checked.
assert numpy.all(g([3], [6], 0) == 9)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, g,
[3], numpy.array([6], dtype='int16'), 0)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, g, [3], [312], 0)
h = pfunc([a, b, c], (a + b + c)) # Default: allow_input_downcast=None
# Everything here should behave like with False
assert numpy.all(h([3], [6], 0) == 9)
self.assertRaises(TypeError, h,
[3], numpy.array([6], dtype='int16'), 0)
self.assertRaises(TypeError, h, [3], [312], 0)
def random_fn(self):
"""
Implements posterior distribution from initial latent space
Parameters
----------
size : number of samples from distribution
no_rand : whether use deterministic distribution
Returns
-------
posterior space (numpy)
"""
In = theano.In
size = tt.iscalar('size')
no_rand = tt.bscalar('no_rand')
posterior = self.random(size, no_rand)
fn = theano.function([In(size, 'size', 1, allow_downcast=True),
In(no_rand, 'no_rand', 0, allow_downcast=True)],
posterior)
def inner(size=None, no_rand=False):
if size is None:
return fn(1, int(no_rand))[0]
else:
return fn(size, int(no_rand))
return inner
def __init__(self, n_features):
self.n_features = n_features
self.x = T.fvector("x")
self.y = T.bscalar("y")
self.W = theano.shared(rng.randn(n_features).astype(
theano.config.floatX),
name="W")
self.b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX),
name="b")
def get_cost(self):
self.y = T.bscalar('y')
self.L1 = abs(self.hidden_layer.W).sum() \
+ abs(self.output_layer.W).sum()
self.L2_sqr = (self.hidden_layer.W ** 2).sum() \
+ (self.output_layer.W ** 2).sum()
self.params = self.hidden_layer.params + self.output_layer.params
self.cost = self.negative_log_likelihood(self.y) \
+ self.L2_reg * self.L2_sqr
#+ self.L1_reg * self.L1
return self.cost
def get_cost(self):
self.y = T.bscalar('y')
self.L1 = abs(self.hidden_layer.W).sum() \
+ abs(self.output_layer.W).sum()
self.L2_sqr = (self.hidden_layer.W ** 2).sum() \
+ (self.output_layer.W ** 2).sum()
self.params = self.hidden_layer.params + self.output_layer.params
self.cost = self.negative_log_likelihood(self.y) \
+ self.L2_reg * self.L2_sqr
#+ self.L1_reg * self.L1
return self.cost
def __init__(self,
input=None,
output=None,
n_features=500,
n_states=10,
learning_rate=0.01):
self.n_features = n_features
self.n_states = n_states
# x is a vector
self.x = input
if not self.x:
self.x = T.fvector('x')
# y is a label(0 1 2 3 ..)
self.y = output
if not self.y:
self.y = T.bscalar('y')
# test value
#self.x.tag.test_value = rng.random(n_features).astype(
# theano.config.floatX)
#self.y.tag.test_value = 3
#self.y = T.cast(y, 'int32')
self.b = theano.shared(
numpy.zeros((n_states), dtype=theano.config.floatX),
name='b',
borrow=True,
)
self.W = theano.shared(
value=numpy.zeros((n_features, n_states),
dtype=theano.config.floatX),
name='W',
borrow=True,
)
self.p_y_given_x = T.nnet.softmax(T.dot(self.W, self.x) + self.b)
# get the max index
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
self.get_y_pred = theano.function(inputs=[self.x],
allow_input_downcast=True,
outputs=self.y_pred)
self.learning_rate = learning_rate
self.params = [self.W, self.b]
def test_replacements(binomial_model_inference):
d = tt.bscalar()
d.tag.test_value = 1
approx = binomial_model_inference.approx
p = approx.model.p
p_t = p**3
p_s = approx.apply_replacements(p_t)
sampled = [p_s.eval() for _ in range(100)]
assert any(map(operator.ne, sampled[1:], sampled[:-1])) # stochastic
p_d = approx.apply_replacements(p_t, deterministic=True)
sampled = [p_d.eval() for _ in range(100)]
assert all(map(operator.eq, sampled[1:], sampled[:-1])) # deterministic
p_r = approx.apply_replacements(p_t, deterministic=d)
sampled = [p_r.eval({d: 1}) for _ in range(100)]
assert all(map(operator.eq, sampled[1:], sampled[:-1])) # deterministic
sampled = [p_r.eval({d: 0}) for _ in range(100)]
assert any(map(operator.ne, sampled[1:], sampled[:-1])) # stochastic
def build_trainer(phi_shared, N, loglik_primary_f, logprior_f, hypernet_f,
log_det_dtheta_dz_f=None, primary_f=None):
'''It is assumed every time this is called z_noise will be drawn from a
standard Gaussian. phi_shared are weights to hypernet and N is the total
number of points in the data set.'''
X = T.matrix('x')
y = T.matrix('y') # Assuming multivariate output
z_noise = T.vector('z')
prelim = T.bscalar('prelim')
lr = T.scalar('lr')
elbo = hypernet_elbo(X, y, loglik_primary_f, logprior_f, hypernet_f,
z_noise, N, log_det_dtheta_dz_f=log_det_dtheta_dz_f,
prelim=prelim)
loss = -elbo
grads = T.grad(loss, phi_shared)
updates = lasagne.updates.adam(grads, phi_shared, learning_rate=lr)
trainer = theano.function([X, y, z_noise, lr, prelim], loss, updates=updates)
# Build get_err in case you want to check Jacobian logic
elbo_no_J = hypernet_elbo(X, y, loglik_primary_f, logprior_f, hypernet_f,
z_noise, N, prelim=prelim)
err = T.abs_(elbo - elbo_no_J)
get_err = theano.function([X, y, z_noise, prelim], err)
theta = hypernet_f(z_noise, prelim=prelim)
theta_f = theano.function([z_noise, prelim], theta)
test_loglik = loglik_primary_f(X, y, theta)
test_f = theano.function([X, y, z_noise, prelim], test_loglik)
primary_out = None
if primary_f is not None:
yp = primary_f(X, theta)
primary_out = theano.function([X, z_noise, prelim], yp)
grad_f = theano.function([X, y, z_noise, prelim], grads)
return trainer, get_err, test_f, primary_out, grad_f, theta_f
def test_mlp(learning_rate=0.001, L1_reg=0.0, L2_reg=1, n_epochs=10000,
dataset='Carolyn1_filt_turnclass.csv', batch_size=3, n_hidden=20):
"""
Demonstrate stochastic gradient descent optimization for a multilayer
perceptron
This is demonstrated on MNIST.
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient
:type L1_reg: float
:param L1_reg: L1-norm's weight when added to the cost (see
regularization)
:type L2_reg: float
:param L2_reg: L2-norm's weight when added to the cost (see
regularization)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: the path of the MNIST dataset file from
http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz
"""
datasets = load_data(dataset,'Carolyn1_filt_turnlabels.csv')
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
z = T.bscalar()
rng = numpy.random.RandomState(1234)
# construct the MLP class
classifier = MLP(
rng=rng,
input=x,
n_in=24,
n_hidden=n_hidden,
n_out=2,
ts=z
)
# start-snippet-4
# the cost we minimize during training is the negative log likelihood of
# the model plus the regularization terms (L1 and L2); cost is expressed
# here symbolically
cost = (
classifier.negative_log_likelihood(y)
+ L1_reg * classifier.L1
+ L2_reg * classifier.L2_sqr
)
# end-snippet-4
# compiling a Theano function that computes the mistakes that are made
# by the model on a minibatch
test_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size],
}
)
validate_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size],
}
)
# start-snippet-5
# compute the gradient of cost with respect to theta (sotred in params)
# the resulting gradients will be stored in a list gparams
gparams = [T.grad(cost, param) for param in classifier.params]
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs
# given two lists of the same length, A = [a1, a2, a3, a4] and
# B = [b1, b2, b3, b4], zip generates a list C of same size, where each
# element is a pair formed from the two lists :
# C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
updates = [
(param, param - learning_rate * gparam)
for param, gparam in zip(classifier.params, gparams)
]
# compiling a Theano function `train_model` that returns the cost, but
# in the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size],
}
)
# end-snippet-5
###############
# TRAIN MODEL #
###############
print('... training')
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience // 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
classifier.ts = True
minibatch_avg_cost = train_model(minibatch_index)
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
classifier.ts = False
validation_losses = [validate_model(i) for i
in range(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print(
'epoch %i, minibatch %i/%i, validation error %f %%' %
(
epoch,
minibatch_index + 1,
n_train_batches,
this_validation_loss * 100.
)
)
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if (
this_validation_loss < best_validation_loss *
improvement_threshold
):
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
classifier.ts = False
test_losses = [test_model(i) for i
in range(n_test_batches)]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
print(('Optimization complete. Best validation score of %f %% '
'obtained at iteration %i, with test performance %f %%') %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print(('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.)), file=sys.stderr)
def __init__(
self,
dataset,
learning_rate=0.001,
decrease_constant=0,
hidden_sizes=[500],
random_seed=1234,
batch_size=1,
hidden_activation=T.nnet.sigmoid,
use_cond_mask=False,
direct_input_connect="None",
direct_output_connect=False,
update_rule="None",
dropout_rate=0,
weights_initialization="Uniform",
mask_distribution=0,
):
input_size = dataset["input_size"]
self.shuffled_once = False
class SeedGenerator(object):
# This subclass purpose is to maximize randomness and still keep reproducibility
def __init__(self, random_seed):
self.rng = np.random.mtrand.RandomState(random_seed)
def get(self):
return self.rng.randint(42424242)
self.seed_generator = SeedGenerator(random_seed)
self.trng = T.shared_randomstreams.RandomStreams(self.seed_generator.get())
weights_initialization = getattr(
WeightsInitializer(self.seed_generator.get()), weights_initialization
) # Get the weights initializer by string name
# Building the model's graph
input = T.matrix(name="input")
target = T.matrix(name="target")
is_train = T.bscalar(name="is_train")
# Initialize the mask
self.mask_generator = MaskGenerator(input_size, hidden_sizes, mask_distribution, self.seed_generator.get())
# Initialize layers
input_layer = ConditionningMaskedLayer(
layerIdx=0,
input=input,
n_in=input_size,
n_out=hidden_sizes[0],
activation=hidden_activation,
weights_initialization=weights_initialization,
mask_generator=self.mask_generator,
use_cond_mask=use_cond_mask,
)
self.layers = [dropoutLayerDecorator(input_layer, self.trng, is_train, dropout_rate)]
# Now the hidden layers
for i in range(1, len(hidden_sizes)):
previous_layer = self.layers[i - 1]
hidden_layer = DirectInputConnectConditionningMaskedLayer(
layerIdx=i,
input=previous_layer.output,
n_in=hidden_sizes[i - 1],
n_out=hidden_sizes[i],
activation=hidden_activation,
weights_initialization=weights_initialization,
mask_generator=self.mask_generator,
use_cond_mask=use_cond_mask,
direct_input=input if direct_input_connect == "Full" and previous_layer.output != input else None,
)
self.layers += [dropoutLayerDecorator(hidden_layer, self.trng, is_train, dropout_rate)]
# And the output layer
outputLayerIdx = len(self.layers)
previous_layer = self.layers[outputLayerIdx - 1]
self.layers += [
DirectOutputInputConnectConditionningMaskedOutputLayer(
layerIdx=outputLayerIdx,
input=previous_layer.output,
n_in=hidden_sizes[outputLayerIdx - 1],
n_out=input_size,
activation=T.nnet.sigmoid,
weights_initialization=weights_initialization,
mask_generator=self.mask_generator,
use_cond_mask=use_cond_mask,
direct_input=input
if (direct_input_connect == "Full" or direct_input_connect == "Output")
and previous_layer.output != input
else None,
direct_outputs=[
(layer.layer_idx, layer.n_in, layer.input) for layerIdx, layer in enumerate(self.layers[1:-1])
]
if direct_output_connect
else [],
)
]
# The loss function
output = self.layers[-1].output
pre_output = self.layers[-1].lin_output
log_prob = -T.sum(T.nnet.softplus(-target * pre_output + (1 - target) * pre_output), axis=1)
loss = (-log_prob).mean()
# How to update the parameters
self.parameters = [param for layer in self.layers for param in layer.params]
parameters_gradient = T.grad(loss, self.parameters)
# Initialize update_rule
if update_rule == "None":
self.update_rule = DecreasingLearningRate(learning_rate, decrease_constant)
elif update_rule == "adadelta":
self.update_rule = AdaDelta(decay=decrease_constant, epsilon=learning_rate)
elif update_rule == "adagrad":
self.update_rule = AdaGrad(learning_rate=learning_rate)
elif update_rule == "rmsprop":
self.update_rule = RMSProp(learning_rate=learning_rate, decay=decrease_constant)
elif update_rule == "adam":
self.update_rule = Adam(learning_rate=learning_rate)
elif update_rule == "adam_paper":
self.update_rule = Adam_paper(learning_rate=learning_rate)
updates = self.update_rule.get_updates(zip(self.parameters, parameters_gradient))
# How to to shuffle weights
masks_updates = [layer_mask_update for layer in self.layers for layer_mask_update in layer.shuffle_update]
self.update_masks = theano.function(name="update_masks", inputs=[], updates=masks_updates)
#
# Functions to train and use the model
index = T.lscalar()
self.learn = theano.function(
name="learn",
inputs=[index, is_train],
outputs=loss,
updates=updates,
givens={
input: dataset["train"]["data"][index * batch_size : (index + 1) * batch_size],
target: dataset["train"]["data"][index * batch_size : (index + 1) * batch_size],
},
on_unused_input="ignore",
) # ignore for when dropout is absent
self.use = theano.function(
name="use", inputs=[input, is_train], outputs=output, on_unused_input="ignore"
) # ignore for when dropout is absent
# Test functions
self.valid_log_prob = theano.function(
name="valid_log_prob",
inputs=[is_train],
outputs=log_prob,
givens={input: dataset["valid"]["data"], target: dataset["valid"]["data"]},
on_unused_input="ignore",
) # ignore for when dropout is absent
self.train_log_prob = theano.function(
name="train_log_prob",
inputs=[is_train],
outputs=log_prob,
givens={input: dataset["train"]["data"], target: dataset["train"]["data"]},
on_unused_input="ignore",
) # ignore for when dropout is absent
self.train_log_prob_batch = theano.function(
name="train_log_prob_batch",
inputs=[index, is_train],
outputs=log_prob,
givens={
input: dataset["train"]["data"][index * 1000 : (index + 1) * 1000],
target: dataset["train"]["data"][index * 1000 : (index + 1) * 1000],
},
on_unused_input="ignore",
) # ignore for when dropout is absent
self.test_log_prob = theano.function(
name="test_log_prob",
inputs=[is_train],
outputs=log_prob,
givens={input: dataset["test"]["data"], target: dataset["test"]["data"]},
on_unused_input="ignore",
) # ignore for when dropout is absent
# Functions for verify gradient
self.useloss = theano.function(
name="useloss", inputs=[input, target, is_train], outputs=loss, on_unused_input="ignore"
) # ignore for when dropout is absent
self.learngrad = theano.function(
name="learn",
inputs=[index, is_train],
outputs=parameters_gradient,
givens={
input: dataset["train"]["data"][index * batch_size : (index + 1) * batch_size],
target: dataset["train"]["data"][index * batch_size : (index + 1) * batch_size],
},
on_unused_input="ignore",
) # ignore for when dropout is absent
def evaluate_lenet5(learning_rate=0.1,
n_epochs=200,
dataset='mnist.pkl.gz',
nkerns=[20, 50],
batch_size=500):
""" Demonstrates lenet on MNIST dataset
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: path to the dataset used for training /testing (MNIST here)
:type nkerns: list of ints
:param nkerns: number of kernels on each layer
"""
rng = numpy.random.RandomState(23455)
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
# start-snippet-1
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
layers = []
srng = RandomStreams(25252)
train_flag = T.bscalar('train_flag')
# Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (28, 28) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 1, 28, 28))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
# maxpooling reduces this further to (24/2, 24/2) = (12, 12)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
layer0 = ConvLayer(rng,
data=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5))
layers.append(layer0)
layer1 = PoolLayer(data=layer0.output)
layers.append(layer1)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
# maxpooling reduces this further to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
layer2 = ConvLayer(rng,
data=layer1.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5))
layers.append(layer2)
layer3 = PoolLayer(data=layer2.output)
layers.append(layer3)
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
# or (500, 50 * 4 * 4) = (500, 800) with the default values.
layerd_input = layer3.output.flatten(2)
layerd = DropoutLayer(data=layerd_input,
n_in=nkerns[1] * 4 * 4,
srng=srng,
p=.5,
train_flag=train_flag)
# construct a fully-connected sigmoidal layer
layer4 = FCLayer(rng,
data=layerd.output,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=relu)
layers.append(layer4)
# classify the values of the fully-connected sigmoidal layer
layer5 = LogisticRegression(input=layer4.output, n_in=500, n_out=10)
layers.append(layer5)
# the cost we minimize during training is the NLL of the model
cost = layer5.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[index],
layer5.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size],
train_flag: numpy.cast['int8'](0)
})
validate_model = theano.function(
[index],
layer5.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size],
train_flag: numpy.cast['int8'](0)
})
# create a list of all model parameters to be fit by gradient descent
params = layer5.params + layerd.params + layer4.params + layer3.params + \
layer2.params + layer1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i], grads[i]) pairs.
delta_before = []
for param_i in params:
delta_before_i = theano.shared(value=numpy.zeros(
param_i.get_value().shape, dtype=theano.config.floatX),
borrow=True)
delta_before.append(delta_before_i)
learning_rate = 0.01
momentum = 0.9
weight_decay = 0.0005
updates = []
for param_i, grad_i, delta_before_i in zip(params, grads, delta_before):
delta_i = momentum * delta_before_i - weight_decay * learning_rate * param_i - learning_rate * grad_i
updates.append((delta_before_i, delta_i))
updates.append((param_i, param_i + delta_i))
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size],
train_flag: numpy.cast['int8'](1)
})
# end-snippet-1
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
if gpu_usage is True:
nvmlInit()
handle = nvmlDeviceGetHandleByIndex(0)
info = nvmlDeviceGetMemoryInfo(handle)
print "Total memory:", info.total
print "Free memory:", info.free
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print 'training @ iter = ', iter
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [
validate_model(i) for i in xrange(n_valid_batches)
]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
# improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [
test_model(i) for i in xrange(n_test_batches)
]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
print('Optimization complete.')
print(
'Best validation score of %f %% obtained at iteration %i, '
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def __init__(self,
rng,
n_in,
n_out,
n_h,
dropout=0,
sigma_g=sigmoid,
sigma_c=hyperbolic_tangent,
sigma_h=hyperbolic_tangent,
sigma_y=softmax,
dropout_rate=0,
obj='c'):
'''
:param rng: Numpy RandomState
:param n_in: Input dimension (int)
:param n_out: Output dimension (int)
:param n_h: Hidden dimension (int)
:param sigma_g, sigma_c, sigma_h, sigma_y: activation functions
:param dropout_rate: dropout rate (float)
:param obj: objective type, 'c' for classification with cross entropy loss, 'r' for regression with MSE loss. (['c','r'])
'''
Wf_ = rng.uniform(-np.sqrt(6. / (n_in + n_h)),
np.sqrt(6. / (n_in + n_h)), (n_h, n_in))
Uf_ = rng.uniform(-np.sqrt(6. / (n_h + n_h)),
np.sqrt(6. / (n_h + n_h)), (n_h, n_h))
bf_ = np.zeros(n_h)
Wi_ = rng.uniform(-np.sqrt(6. / (n_in + n_h)),
np.sqrt(6. / (n_in + n_h)), (n_h, n_in))
Ui_ = rng.uniform(-np.sqrt(6. / (n_h + n_h)),
np.sqrt(6. / (n_h + n_h)), (n_h, n_h))
bi_ = np.zeros(n_h)
Wo_ = rng.uniform(-np.sqrt(6. / (n_in + n_h)),
np.sqrt(6. / (n_in + n_h)), (n_h, n_in))
Uo_ = rng.uniform(-np.sqrt(6. / (n_h + n_h)),
np.sqrt(6. / (n_h + n_h)), (n_h, n_h))
bo_ = np.zeros(n_h)
Wc_ = rng.uniform(-np.sqrt(6. / (n_in + n_h)),
np.sqrt(6. / (n_in + n_h)), (n_h, n_in))
Uc_ = rng.uniform(-np.sqrt(6. / (n_h + n_h)),
np.sqrt(6. / (n_h + n_h)), (n_h, n_h))
bc_ = np.zeros(n_h)
Wy_ = rng.uniform(-np.sqrt(6. / (n_out + n_h)),
np.sqrt(6. / (n_out + n_h)), (n_out, n_h))
by_ = np.zeros(n_out)
h0_ = rng.uniform(-np.sqrt(3. / (2. * n_h)), np.sqrt(3. / (2. * n_h)),
n_h)
c0_ = rng.uniform(-np.sqrt(3. / (2. * n_h)), np.sqrt(3. / (2. * n_h)),
n_h)
# Theano: Created shared variables
Wf = theano.shared(name='Wf', value=Wf_.astype(theano.config.floatX))
Uf = theano.shared(name='Uf', value=Uf_.astype(theano.config.floatX))
bf = theano.shared(name='bf', value=bf_.astype(theano.config.floatX))
Wi = theano.shared(name='Wi', value=Wi_.astype(theano.config.floatX))
Ui = theano.shared(name='Ui', value=Ui_.astype(theano.config.floatX))
bi = theano.shared(name='bi', value=bi_.astype(theano.config.floatX))
Wo = theano.shared(name='Wo', value=Wo_.astype(theano.config.floatX))
Uo = theano.shared(name='Uo', value=Uo_.astype(theano.config.floatX))
bo = theano.shared(name='bo', value=bo_.astype(theano.config.floatX))
Wc = theano.shared(name='Wc', value=Wc_.astype(theano.config.floatX))
Uc = theano.shared(name='Uc', value=Uc_.astype(theano.config.floatX))
bc = theano.shared(name='bc', value=bc_.astype(theano.config.floatX))
Wy = theano.shared(name='Wy', value=Wy_.astype(theano.config.floatX))
by = theano.shared(name='by', value=by_.astype(theano.config.floatX))
h0 = theano.shared(name='h0', value=h0_.astype(theano.config.floatX))
c0 = theano.shared(name='c0', value=c0_.astype(theano.config.floatX))
self.p = [
Wf, Uf, bf, Wi, Ui, bi, Wo, Uo, bo, Wc, Uc, bc, Wy, by, c0, h0
]
seq_len = T.iscalar('seq_len')
self.seq_len = seq_len
self.x = T.vector()
x_scan = T.reshape(self.x, [seq_len, n_in], ndim=2)
if dropout_rate > 0:
np.random.seed(int(time.time()))
# for training
def masked_forward_prop_step(x_t, h_t_prev, c_t_prev):
f_t = sigma_g(Wf.dot(x_t) + Uf.dot(h_t_prev) + bf)
i_t = sigma_g(Wi.dot(x_t) + Ui.dot(h_t_prev) + bi)
o_t = sigma_g(Wo.dot(x_t) + Uo.dot(h_t_prev) + bo)
c_t = i_t * sigma_c(Wc.dot(x_t) + Uc.dot(h_t_prev) + bc)
c_t += c_t_prev * f_t
h_t = o_t * sigma_h(c_t)
y_t = Wy.dot(h_t) + by
mask = np.random.binomial(np.ones(n_h, dtype=int),
1.0 - dropout_rate)
masked_h_t = h_t * T.cast(mask, theano.config.floatX)
return [y_t, masked_h_t, c_t]
# for testing
def forward_prop_step(x_t, h_t_prev, c_t_prev):
f_t = sigma_g(Wf.dot(x_t) + Uf.dot(h_t_prev) + bf)
i_t = sigma_g(Wi.dot(x_t) + Ui.dot(h_t_prev) + bi)
o_t = sigma_g(Wo.dot(x_t) + Uo.dot(h_t_prev) + bo)
c_t = i_t * sigma_c(Wc.dot(x_t) + Uc.dot(h_t_prev) + bc)
c_t += c_t_prev * f_t
h_t = o_t * sigma_h(c_t)
h_t = (1.0 - dropout_rate) * h_t
y_t = Wy.dot(h_t) + by
return [y_t, h_t, c_t]
[o_train, _, _], _ = theano.scan(masked_forward_prop_step,
sequences=[x_scan],
outputs_info=[None, h0, c0],
n_steps=seq_len)
[o_test, _, _], _ = theano.scan(forward_prop_step,
sequences=[x_scan],
outputs_info=[None, h0, c0],
n_steps=seq_len)
else:
def forward_prop_step(x_t, h_t_prev, c_t_prev):
f_t = sigma_g(Wf.dot(x_t) + Uf.dot(h_t_prev) + bf)
i_t = sigma_g(Wi.dot(x_t) + Ui.dot(h_t_prev) + bi)
o_t = sigma_g(Wo.dot(x_t) + Uo.dot(h_t_prev) + bo)
c_t = i_t * sigma_c(Wc.dot(x_t) + Uc.dot(h_t_prev) + bc)
c_t += c_t_prev * f_t
h_t = o_t * sigma_h(c_t)
y_t = Wy.dot(h_t) + by
return [y_t, h_t, c_t]
[o_train, _, _], _ = theano.scan(forward_prop_step,
sequences=[x_scan],
outputs_info=[None, h0, c0],
n_steps=seq_len)
o_test = o_train
if obj == 'c': # classification task
self.y = T.bscalar('y')
self.o_train = sigma_y(o_train[-1])
self.o_test = sigma_y(o_test[-1])
#obj function to compute grad, use dropout
self.cost = T.nnet.categorical_crossentropy(
self.o_train,
T.eye(n_out)[self.y])
#compute accuracy use average of dropout rate
self.accuracy = T.switch(T.eq(T.argmax(self.o_test), self.y), 1.,
0.)
self.prediction = np.argmax(self.o_test)
elif obj == 'r': # regression task
self.y = T.dscalar('y')
self.o_train = o_train[-1]
self.o_test = o_test[-1]
#obj function to compute grad, use dropout
self.cost = (self.o_train[0] - self.y)**2
#compute accuracy use average of dropout rate
self.accuracy = (self.o_test[0] - self.y)**2
self.prediction = self.o_test[0]
self.optimiser = sgd_optimizer(self, 'LSTM')
parse_mode=opts.parse_mode)
log("train_stats %s %s" % (len(train_x), train_stats))
dev_x, dev_y, dev_stats = util.load_data(opts.dev_set,
vocab,
update_vocab=False,
max_egs=int(opts.num_from_dev),
parse_mode=opts.parse_mode)
log("dev_stats %s %s" % (len(dev_x), dev_stats))
# input/output example vars
s1_idxs = T.ivector('s1') # sequence for sentence one
s2_idxs = T.ivector('s2') # sequence for sentence two
actual_y = T.ivector('y') # single for sentence pair label; 0, 1 or 2
# dropout keep prob for post concat, pre MLP
apply_dropout = T.bscalar(
'apply_dropout') # dropout.{APPLY_DROPOUT|NO_DROPOUT}
keep_prob = theano.shared(opts.keep_prob) # recall 1.0 => noop
keep_prob = T.cast(keep_prob,
'float32') # shared weirdity, how to set in init (?)
# keep track of different "layers" that handle their own gradients.
# includes rnns, final concat & softmax and, potentially, special handling for
# tied embeddings
layers = []
# decide set of sequence idxs we'll be processing. there will always the two
# for the forward passes over s1 and s2 and, optionally, two more for the
# reverse pass over s1 & s2 in the bidirectional case.
idxs = [s1_idxs, s2_idxs]
names = ["s1f", "s2f"]
if opts.bidirectional:
non_sequences=[n_words, word_dim],
sequences=[index1, index2],
outputs_info=[result_mat],
)
return (output[-1])
def sents_ind_2vec(self, sents):
# Create Input moddule contain positional encoding scheme
# the input sents presents the index of words
# this will convert each fact into a vector as output
shape_input = sents.shape
bach_size, n_sents, n_words = shape_input
positional_encode_matrix = self.positional_encoding_scheme(n_words)
p_e_m_shuffle = positional_encode_matrix.dimshuffle("x", "x", 0, 1)
sents_emb = self.words_ind_2vec(sents) * p_e_m_shuffle
return (sents_emb.sum(axis=2))
# Debug ONLY
if __name__ == "__main__":
rng = np.random.RandomState(220495)
arrSents = T.itensor3()
nn = T.bscalar()
EMBD = EncodingLayer(32, 10, rng=rng)
Word2Vec = theano.function(inputs=[arrSents],
outputs=EMBD.sents_ind_2vec(arrSents))
sents = [[[3, 14, 0], [0, 0, 0]], [[3, 14, 0], [1, 2, 6]]]
Vec = Word2Vec(sents)
print("Val: ", Vec)
print("Dim: ", Vec.shape)
def test_mlp(learning_rate=0.01, L1_reg=0.000, L2_reg=0.0002, n_epochs=10000,
dataset='data_nn.csv', batch_size=15, n_hidden1=200, n_hidden2=100):
"""
Demonstrate stochastic gradient descent optimization for a multilayer
perceptron
This is demonstrated on MNIST.
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient
:type L1_reg: float
:param L1_reg: L1-norm's weight when added to the cost (see
regularization)
:type L2_reg: float
:param L2_reg: L2-norm's weight when added to the cost (see
regularization)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: the path of the MNIST dataset file from
http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz
"""
datasets, grad_test = load_data(dataset,'theano_labels.csv')
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
z = T.bscalar()
rng = numpy.random.RandomState(1234)
# construct the MLP class
classifier = MLP(
rng=rng,
input=x,
n_in=96,
n_hidden1=n_hidden1,
n_hidden2=n_hidden2,
n_out=4,
ts=z
)
# start-snippet-4
# the cost we minimize during training is the negative log likelihood of
# the model plus the regularization terms (L1 and L2); cost is expressed
# here symbolically
cost = (
classifier.crossentropy(y)
+ L1_reg * classifier.L1
+ L2_reg * classifier.L2_sqr
)
# end-snippet-4
# compiling a Theano function that computes the mistakes that are made
# by the model on a minibatch
test_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size],
}
)
validate_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size],
}
)
# start-snippet-5
# compute the gradient of cost with respect to theta (sotred in params)
# the resulting gradients will be stored in a list gparams
gparams = [T.grad(cost, param) for param in classifier.params]
#ginput = T.grad(cost, classifier.input)
ginput = theano.gradient.jacobian(cost, classifier.input)
#J, updates = theano.scan(lambda i, y,x : T.grad(y[i], x), sequences=T.arange(y.shape[0]), non_sequences=[y,x])
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs
# given two lists of the same length, A = [a1, a2, a3, a4] and
# B = [b1, b2, b3, b4], zip generates a list C of same size, where each
# element is a pair formed from the two lists :
# C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
updates = [
(param, param - learning_rate * gparam)
for param, gparam in zip(classifier.params, gparams)
]
# compiling a Theano function `train_model` that returns the cost, but
# in the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size],
}
)
#get_gradient = theano.function(
#outputs=ginput,
#givens={
# x: train_set_x[0],
# y: train_set_y[0],
#}
#)
# end-snippet-5
###############
# TRAIN MODEL #
###############
print('... training')
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience // 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
classifier.ts = True
minibatch_avg_cost = train_model(minibatch_index)
# iteration number
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
classifier.ts = False
validation_losses = [validate_model(i) for i
in range(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print(
'epoch %i, minibatch %i/%i, validation error %f %%' %
(
epoch,
minibatch_index + 1,
n_train_batches,
this_validation_loss * 100.
)
)
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if (
this_validation_loss < best_validation_loss *
improvement_threshold
):
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
classifier.ts = False
test_losses = [test_model(i) for i
in range(n_test_batches)]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
print(('Optimization complete. Best validation score of %f %% '
'obtained at iteration %i, with test performance %f %%') %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print(('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.)), file=sys.stderr)
f = theano.function([classifier.input, y], ginput)
print (pp(f.maker.fgraph.outputs[0]))
theano.printing.pydotprint(f.maker.fgraph.outputs[0])
test_in = train_set = theano.shared([-0.85932366, 0.58308557, 0.57291266, -2.73567018, 0.5720682 ,
0.69788969, 0.01782218, 0.89483408, 3.28290884, -0.38769847,
-0.76087236, -0.50285087, -1.24656495, -0.73529842, -0.99193669,
-1.95702179, 1.57430759, 0.24463588, 3.06210202, 2.45264677,
-0.25134517, -0.04829522, -0.55535032, 0.0503641 , -1.91432708,
0.77470853, 0.7401515 , -2.71915318, 0.4963475 , 1.00522374,
0.27958163, -1.35574041, 0.58434732, -0.67177877, -1.07827181,
-2.11205369, -1.48408336, -0.80823029, -0.95141343, -1.98320406,
1.46513513, -0.09303374, -0.76959049, 0.85930122, -0.86362607,
-0.78979288, -0.0444948 , 0.45982332, -2.31018994, 0.85091726,
0.77935362, -2.70620804, 0.44422539, 1.24119296, 0.09150836,
-2.86868139, -1.16801813, -0.50896104, -0.05604379, -0.57235696,
-1.08455522, -1.17935154, -1.12812324, -1.9744183 , 0.19983282,
-0.11654747, -1.15473115, -0.07447867, -0.28972877, -0.94642741,
0.26084976, 0.46156281, 2.1851348 , -0.77191925, -0.73766559,
1.8115434 , 0.83390925, -0.91492798, 0.06507779, 2.07655773,
2.62112977, 0.04236459, -0.34407471, -0.03113814, 0.67895545,
1.1023399 , 0.77840311, 1.18688628, 1.31362216, 0.86287225,
2.23127128, 1.32033075, 0.07084121, 0.45882767, -0.52361762,
-0.24316931])
test_out = theano.shared(numpy.asarray(4,dtype=theano.config.floatX))
print (f([test_in,test_out]))
def sgd_optimization_mnist(learning_rate=0.2, n_epochs=1000, batch_size=5):
datasets = load_data()
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
index = T.bscalar()
x = T.matrix('x')
y = T.ivector('y')
classifier = LogisticRegression(input=x, n_in=img_size, n_out=9)
cost = classifier.negative_log_likelihood(y)
test_model = theano.function(inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]})
validate_model = theano.function(inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]})
g_W = T.grad(cost=cost, wrt=classifier.W)
g_b = T.grad(cost=cost, wrt=classifier.b)
updates = [(classifier.W, classifier.W - learning_rate * g_W),
(classifier.b, classifier.b - learning_rate * g_b)]
train_model = theano.function(inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]})
###############
# TRAIN MODEL #
###############
print '... training the model'
patience = 5000
patience_increase = 2
improvement_threshold = 0.995
validation_frequency = min(n_train_batches, patience / 2)
best_params = None
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
validation_losses = [validate_model(i)
for i in xrange(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
if this_validation_loss < best_validation_loss:
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
test_losses = [test_model(i)
for i in xrange(n_test_batches)]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of best'
' model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print(('Optimization complete with best validation score of %f %%,'
'with test performance %f %%') %
(best_validation_loss * 100., test_score * 100.))
print 'The code run for %d epochs, with %f epochs/sec' % (
epoch, 1. * epoch / (end_time - start_time))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.1fs' % ((end_time - start_time)))
import theano
import theano.tensor as tensor
#global variables to use to toggle training and rng, etc
from theano.sandbox.rng_mrg import MRG_RandomStreams
layer_train_rng = MRG_RandomStreams()
layer_train_enable = tensor.bscalar()
layer_train_epoch = tensor.iscalar()
layer_train_it = theano.shared(0)
def get_rng():
global layer_train_rng
return layer_train_rng
def set_rng_seed(v):
global layer_train_rng
layer_train_rng.seed(v)
def get_train():
global layer_train_enable
return layer_train_enable
def get_epoch():
global layer_train_epoch
return layer_train_epoch
def __init__(self,
dataset,
learning_rate=0.001,
decrease_constant=0,
hidden_sizes=[500],
random_seed=1234,
batch_size=1,
hidden_activation=T.nnet.sigmoid,
use_cond_mask=False,
direct_input_connect="None",
direct_output_connect=False,
update_rule="None",
dropout_rate=0,
weights_initialization="Uniform",
mask_distribution=0):
input_size = dataset['input_size']
self.shuffled_once = False
class SeedGenerator(object):
# This subclass purpose is to maximize randomness and still keep reproducibility
def __init__(self, random_seed):
self.rng = np.random.mtrand.RandomState(random_seed)
def get(self):
return self.rng.randint(42424242)
self.seed_generator = SeedGenerator(random_seed)
self.trng = T.shared_randomstreams.RandomStreams(
self.seed_generator.get())
weights_initialization = getattr(
WeightsInitializer(self.seed_generator.get()),
weights_initialization
) # Get the weights initializer by string name
# Building the model's graph
input = T.matrix(name="input")
target = T.matrix(name="target")
is_train = T.bscalar(name="is_train")
# Initialize the mask
self.mask_generator = MaskGenerator(input_size, hidden_sizes,
mask_distribution,
self.seed_generator.get())
# Initialize layers
input_layer = ConditionningMaskedLayer(
layerIdx=0,
input=input,
n_in=input_size,
n_out=hidden_sizes[0],
activation=hidden_activation,
weights_initialization=weights_initialization,
mask_generator=self.mask_generator,
use_cond_mask=use_cond_mask)
self.layers = [
dropoutLayerDecorator(input_layer, self.trng, is_train,
dropout_rate)
]
# Now the hidden layers
for i in range(1, len(hidden_sizes)):
previous_layer = self.layers[i - 1]
hidden_layer = DirectInputConnectConditionningMaskedLayer(
layerIdx=i,
input=previous_layer.output,
n_in=hidden_sizes[i - 1],
n_out=hidden_sizes[i],
activation=hidden_activation,
weights_initialization=weights_initialization,
mask_generator=self.mask_generator,
use_cond_mask=use_cond_mask,
direct_input=input if direct_input_connect == "Full"
and previous_layer.output != input else None)
self.layers += [
dropoutLayerDecorator(hidden_layer, self.trng, is_train,
dropout_rate)
]
# And the output layer
outputLayerIdx = len(self.layers)
previous_layer = self.layers[outputLayerIdx - 1]
self.layers += [
DirectOutputInputConnectConditionningMaskedOutputLayer(
layerIdx=outputLayerIdx,
input=previous_layer.output,
n_in=hidden_sizes[outputLayerIdx - 1],
n_out=input_size,
activation=T.nnet.sigmoid,
weights_initialization=weights_initialization,
mask_generator=self.mask_generator,
use_cond_mask=use_cond_mask,
direct_input=input if (direct_input_connect == "Full"
or direct_input_connect == "Output")
and previous_layer.output != input else None,
direct_outputs=[
(layer.layer_idx, layer.n_in, layer.input)
for layerIdx, layer in enumerate(self.layers[1:-1])
] if direct_output_connect else [])
]
# The loss function
output = self.layers[-1].output
pre_output = self.layers[-1].lin_output
log_prob = -T.sum(T.nnet.softplus(-target * pre_output +
(1 - target) * pre_output),
axis=1)
loss = (-log_prob).mean()
# How to update the parameters
self.parameters = [
param for layer in self.layers for param in layer.params
]
parameters_gradient = T.grad(loss, self.parameters)
# Initialize update_rule
if update_rule == "None":
self.update_rule = DecreasingLearningRate(learning_rate,
decrease_constant)
elif update_rule == "adadelta":
self.update_rule = AdaDelta(decay=decrease_constant,
epsilon=learning_rate)
elif update_rule == "adagrad":
self.update_rule = AdaGrad(learning_rate=learning_rate)
elif update_rule == "rmsprop":
self.update_rule = RMSProp(learning_rate=learning_rate,
decay=decrease_constant)
elif update_rule == "adam":
self.update_rule = Adam(learning_rate=learning_rate)
elif update_rule == "adam_paper":
self.update_rule = Adam_paper(learning_rate=learning_rate)
updates = self.update_rule.get_updates(
zip(self.parameters, parameters_gradient))
# How to to shuffle weights
masks_updates = [
layer_mask_update for layer in self.layers
for layer_mask_update in layer.shuffle_update
]
self.update_masks = theano.function(name='update_masks',
inputs=[],
updates=masks_updates)
#
# Functions to train and use the model
index = T.lscalar()
self.learn = theano.function(
name='learn',
inputs=[index, is_train],
outputs=loss,
updates=updates,
givens={
input:
dataset['train']['data'][index * batch_size:(index + 1) *
batch_size],
target:
dataset['train']['data'][index * batch_size:(index + 1) *
batch_size]
},
on_unused_input='ignore') # ignore for when dropout is absent
self.use = theano.function(
name='use',
inputs=[input, is_train],
outputs=output,
on_unused_input='ignore') # ignore for when dropout is absent
# Test functions
self.valid_log_prob = theano.function(
name='valid_log_prob',
inputs=[is_train],
outputs=log_prob,
givens={
input: dataset['valid']['data'],
target: dataset['valid']['data']
},
on_unused_input='ignore') # ignore for when dropout is absent
self.train_log_prob = theano.function(
name='train_log_prob',
inputs=[is_train],
outputs=log_prob,
givens={
input: dataset['train']['data'],
target: dataset['train']['data']
},
on_unused_input='ignore') # ignore for when dropout is absent
self.train_log_prob_batch = theano.function(
name='train_log_prob_batch',
inputs=[index, is_train],
outputs=log_prob,
givens={
input:
dataset['train']['data'][index * 1000:(index + 1) * 1000],
target:
dataset['train']['data'][index * 1000:(index + 1) * 1000]
},
on_unused_input='ignore') # ignore for when dropout is absent
self.test_log_prob = theano.function(
name='test_log_prob',
inputs=[is_train],
outputs=log_prob,
givens={
input: dataset['test']['data'],
target: dataset['test']['data']
},
on_unused_input='ignore') # ignore for when dropout is absent
# Functions for verify gradient
self.useloss = theano.function(
name='useloss',
inputs=[input, target, is_train],
outputs=loss,
on_unused_input='ignore') # ignore for when dropout is absent
self.learngrad = theano.function(
name='learn',
inputs=[index, is_train],
outputs=parameters_gradient,
givens={
input:
dataset['train']['data'][index * batch_size:(index + 1) *
batch_size],
target:
dataset['train']['data'][index * batch_size:(index + 1) *
batch_size]
},
on_unused_input='ignore') # ignore for when dropout is absent
def test_mlp(
learning_rate=0.01,
L1_reg=0.00,
L2_reg=0.0001,
cor_reg=0.00,
cor_scaling=1.0,
rand_seed=1234,
dropout=False,
n_epochs=1000,
dataset="mnist.pkl.gz",
batch_size=20,
n_hidden=500,
save_correlations=False,
):
"""
Demonstrate stochastic gradient descent optimization for a multilayer
perceptron
This is demonstrated on MNIST.
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient
:type L1_reg: float
:param L1_reg: L1-norm's weight when added to the cost (see
regularization)
:type L2_reg: float
:param L2_reg: L2-norm's weight when added to the cost (see
regularization)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: the path of the MNIST dataset file from
http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz
"""
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
######################
# BUILD ACTUAL MODEL #
######################
print "... building the model"
# allocate symbolic variables for the data
cor_reg_var = theano.shared(cor_reg) # symbolic variable storing cor_reg value
alpha = T.dscalar("alpha") # scaling factor for weight decay
is_train = T.bscalar("is_train") # boolean for switching between training and prediction
index = T.lscalar() # index to a [mini]batch
perm = T.lvector() # permutation of the indices of the training samples
x = T.matrix("x") # the data is presented as rasterized images
y = T.ivector("y") # the labels are presented as 1D vector of
# [int] labels
rng = np.random.RandomState(rand_seed)
# construct the MLP class
classifier = MLP(rng=rng, input=x, n_in=28 * 28, n_hidden=n_hidden, n_out=10, dropout=dropout)
# start-snippet-4
# the cost we minimize during training is the negative log likelihood of
# the model plus the regularization terms (L1 and L2); cost is expressed
# here symbolically
if cor_reg == 0:
cost = classifier.negative_log_likelihood(y) + L1_reg * classifier.L1 + L2_reg * classifier.L2_sqr
else:
cost = (
classifier.negative_log_likelihood(y)
+ L1_reg * classifier.L1
+ L2_reg * classifier.L2_sqr
+ cor_reg_var * classifier.cor_sqr_sum
)
# end-snippet-4
# compiling a Theano function that computes the mistakes that are made
# by the model on a minibatch
if save_correlations:
validate_model = theano.function(
inputs=[index],
outputs=[classifier.errors(y), classifier.activation_correlation],
givens={
x: valid_set_x[index * batch_size : (index + 1) * batch_size],
y: valid_set_y[index * batch_size : (index + 1) * batch_size],
is_train: np.cast["int8"](0),
},
)
else:
validate_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size : (index + 1) * batch_size],
y: valid_set_y[index * batch_size : (index + 1) * batch_size],
is_train: np.cast["int8"](0),
},
)
# start-snippet-5
# compute the gradient of cost with respect to theta (sotred in params)
# the resulting gradients will be stored in a list gparams
gparams = [T.grad(cost, param) for param in classifier.params]
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs
# given two lists of the same length, A = [a1, a2, a3, a4] and
# B = [b1, b2, b3, b4], zip generates a list C of same size, where each
# element is a pair formed from the two lists :
# C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
updates = [(param, param - learning_rate * gparam) for param, gparam in zip(classifier.params, gparams)]
# compiling a Theano function `train_model` that returns the cost, but
# in the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(
inputs=[index, perm],
outputs=cost,
updates=updates,
givens={
x: train_set_x[perm[index * batch_size : (index + 1) * batch_size]],
y: train_set_y[perm[index * batch_size : (index + 1) * batch_size]],
is_train: np.cast["int8"](1),
},
)
# end-snippet-5
# update the symbolic cor_reg variable
update_cor_reg = theano.function(inputs=[alpha], outputs=cor_reg_var, updates=[(cor_reg_var, cor_reg_var * alpha)])
###############
# TRAIN MODEL #
###############
print "... training"
best_validation_loss = np.inf
best_epoch = 0
start_time = timeit.default_timer()
# Open file for writing validation losses, and write the header
valid_loss_filename = "ValidationLoss_Epoch%i_Batch%i_Cor%f_Drop%i_Scale%f.csv" % (
n_epochs,
n_epochs * n_train_batches,
cor_reg,
dropout,
cor_scaling,
)
valid_loss_filepath = os.path.join(os.path.split(__file__)[0], "..", "output", "MLP", valid_loss_filename)
valid_loss_outfile = open(valid_loss_filepath, "w")
valid_loss_outfile.write("Epoch,Iteration,Error\n")
if save_correlations:
flat_corr_filename = "FlatCorrelations_Epoch%i_Batch%i_Cor%f_Drop%i_Scale%f.csv" % (
n_epochs,
n_epochs * n_train_batches,
cor_reg,
dropout,
cor_scaling,
)
flat_corr_filepath = os.path.join(os.path.split(__file__)[0], "..", "output", "MLP", flat_corr_filename)
flat_corr_outfile = open(flat_corr_filepath, "w")
epoch = 0
while epoch < n_epochs:
epoch += 1
index_perm = rng.permutation(train_set_x.get_value(borrow=True).shape[0]) # generate new permutation of indices
# perform 1 epoch of training
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index, index_perm)
print "Hidden layer after training:\n"
print classifier.hiddenLayer.output.get_value()
# compute zero-one loss on validation set
if save_correlations: # compute and save the average pairwise correlations
validation_losses = []
mean_correlations = 0 # contains mean correlation matrix once loop is finished
for i in xrange(n_valid_batches):
valid_loss, valid_corr = validate_model(i)
validation_losses.append(valid_loss)
mean_correlations += 1.0 * valid_corr / n_valid_batches # iteratively constructs mean to save memory
this_validation_loss = np.mean(validation_losses)
flat_mean_correlation = flatten_correlation_matrix(mean_correlations)
flat_corr_outfile.write(str(epoch) + "," + ",".join(map(str, flat_mean_correlation)) + "\n")
else:
validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
this_validation_loss = np.mean(validation_losses)
# Write this epoch's validation error to the file
valid_loss_outfile.write(("%i,%i,%f\n") % (epoch, epoch * n_train_batches, this_validation_loss))
# ********COMMENT THIS OUT WHEN RUNNING MULTIPLE PARAMS OVERNIGHT********
print (
"epoch %i (iteration %i), validation error %f %%, cor_reg %f"
% (epoch, epoch * n_train_batches, this_validation_loss * 100.0, cor_reg_var.get_value())
)
# current_time = timeit.default_timer()
# print('epoch %i (iteration %i), validation error %f %%, cor_reg %f, time elapsed %.2fm' % (epoch, epoch*n_train_batches, this_validation_loss * 100., cor_reg_var.get_value(), (current_time - start_time) / 60.))
print "Hidden layer after validation:\n"
print classifier.hiddenLayer.output.get_value()
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
best_validation_loss = this_validation_loss
best_epoch = epoch
# Update the value of cor_reg for the next epoch
# Only makes a difference when cor_scaling != 1, because multiplication
if cor_scaling != 1:
old_cor_reg = update_cor_reg(cor_scaling)
valid_loss_outfile.close()
if save_correlations:
flat_corr_outfile.close()
end_time = timeit.default_timer()
print (
("Optimization complete. Best validation score of %f %% " "obtained following epoch %i (iteration %i)")
% (best_validation_loss * 100.0, best_epoch, best_epoch * n_train_batches)
)
print "Training process ran for %.2fm" % ((end_time - start_time) / 60.0)
def __init__(self):
super(DropoutModel, self).__init__()
self.is_train = T.bscalar('is_train')
def optimiser(self, num_samples, update, update_kwargs, saved_update=None):
latent_dim = T.bscalar('latent_dim')
batch = T.matrix('batch')
batch_rep = T.repeat(batch, num_samples, axis=0)
h_regular_rep = self.recognition_model.get_samples_latents_regular(
batch_rep)
h_overdisp_rep = self.recognition_model.get_samples_latents_overdisp(
batch_rep, latent_dim)
h_rep = T.set_subtensor(h_regular_rep[:, latent_dim], h_overdisp_rep)
log_p_h = self.generative_model.log_p_h(h_rep)
log_p_x = self.generative_model.log_p_x(h_rep, batch_rep)
entropies_h = self.recognition_model.entropies_latents(
h_rep, batch_rep)
imp_wts = self.recognition_model.importance_weights_latents(
h_overdisp_rep, batch_rep, latent_dim)
elbos_rep = imp_wts * (log_p_h + log_p_x + entropies_h)
elbos_matrix = elbos_rep.reshape((batch.shape[0], num_samples))
elbo = T.sum(T.mean(elbos_matrix, axis=1, keepdims=True))
params = self.generative_model.get_params(
) + self.recognition_model.get_params()[:-1]
grads = T.grad(-elbo, params)
tau = self.recognition_model.get_params()[-1]
all_grads, _ = theano.scan(
lambda s, E: T.grad(-T.sum(E[s]), params),
sequences=[T.arange(elbos_matrix.T.shape[0])],
non_sequences=[elbos_matrix.T],
)
variance = T.sum([T.sum(T.var(g, axis=0)) for g in all_grads])
grad_tau = T.grad(variance, tau)
grads += [grad_tau]
params = self.generative_model.get_params(
) + self.recognition_model.get_params()
update_kwargs['loss_or_grads'] = grads
update_kwargs['params'] = params
updates = update(**update_kwargs)
if saved_update is not None:
for u, v in zip(updates, saved_update.keys()):
u.set_value(v.get_value())
optimiser = theano.function(
inputs=[batch, latent_dim],
outputs=elbo,
updates=updates,
)
return optimiser, updates
def __init__(self, rng, n_in, n_out, n_h, n_layers, f_act=leaky_relu, obj='single', dropout_rate = 0):
'''
:param rng: Numpy RandomState
:param n_in: Input dimension (int)
:param n_out: Output dimension (int)
:param n_h: Hidden dimension (int)
:param n_layers: Number of hidden layers (int)
:param f_act: Hidden-to-hidden activation function
:param f_out: Output activation function
'''
if obj=='single':
f_out = softmax
elif obj=='multi':
f_out = sigmoid
self.x = T.vector()
# construct hidden layers
assert(n_layers>=1)
first_hiddenLayer = HiddenLayer(
rng=rng,
input=self.x,
predict_input=self.x,
n_in=n_in,
n_out=n_h,
activation=f_act,
dropout_rate = dropout_rate,
nametag='0'
)
self.hidden_layers = [first_hiddenLayer]
self.p = first_hiddenLayer.params[:]
for i in range(n_layers-1):
cur_hiddenLayer = ResNetLayer(
rng=rng,
input=self.hidden_layers[-1].output,
predict_input=self.hidden_layers[-1].predict_output,
n_h=n_h,
activation=f_act,
dropout_rate = dropout_rate,
nametag=str(i+1)
)
self.hidden_layers.append(cur_hiddenLayer)
self.p.extend(cur_hiddenLayer.params[:])
# params for output layer
self.outputLayer = HiddenLayer(
rng=rng,
input=self.hidden_layers[-1].output,
predict_input=self.hidden_layers[-1].predict_output,
n_in=n_h,
n_out=n_out,
activation=f_out,
dropout_rate = 0,
nametag='o'
)
self.p.extend(self.outputLayer.params[:])
self.n_layers = n_layers + 1
self.obj = obj
if obj=='single':
self.y = T.bscalar('y')
self.o = self.outputLayer.output
self.cost = T.nnet.categorical_crossentropy(self.o, T.eye(n_out)[self.y])
self.accuracy = T.switch(T.eq(T.argmax(self.o), self.y), 1., 0.)
self.prediction = np.argmax(self.o)
elif obj=='multi':
self.y = T.bvector('y')
self.o = self.outputLayer.output
self.cost = T.nnet.binary_crossentropy(self.o, self.y).mean()
self.prediction = T.argsort(self.o)
self.accuracy = self.y[T.argmax(self.o)]
self.accuracy3 = (1.0/3.0) * (self.y[self.prediction[-3]]+self.y[self.prediction[-2]]+self.y[self.prediction[-1]])
self.accuracy5 = (1.0/5.0) * (self.y[self.prediction[-5]]+self.y[self.prediction[-4]]+self.y[self.prediction[-3]]+self.y[self.prediction[-2]]+self.y[self.prediction[-1]])
self.optimiser = sgd_optimizer(self, 'ResNet')
def SGD(self,
training_data,
no_improvement_in,
mini_batch_size,
eta,
validation_data,
test_data,
lmbda=0.0,
monitor_test=False):
"""Train the network using mini-batch stochastic gradient descent."""
training_x, training_y = training_data
validation_x, validation_y = validation_data
test_x, test_y = test_data
# compute number of minibatches for training, validation and testing
num_training_batches = int(size(training_data) / mini_batch_size)
num_validation_batches = int(size(validation_data) / mini_batch_size)
num_test_batches = int(size(test_data) / mini_batch_size)
# define the (regularized) cost function, symbolic gradients, and updates
l2_norm_squared = sum([(layer.w**2).sum() for layer in self.layers])
cost = self.layers[-1].cost(self)+\
0.5*lmbda*l2_norm_squared/num_training_batches
grads = T.grad(cost, self.params)
updates = [(param, param - eta * grad)
for param, grad in zip(self.params, grads)]
# define functions to train a mini-batch, and to compute the
# accuracy in validation and test mini-batches.
i = T.lscalar() # mini-batch index
n_class = T.bscalar() # number of the class
train_mb = theano.function(
[i],
cost,
updates=updates,
givens={
self.x:
training_x[i * self.mini_batch_size:(i + 1) *
self.mini_batch_size],
self.y:
training_y[i * self.mini_batch_size:(i + 1) *
self.mini_batch_size]
})
validate_mb_accuracy = theano.function(
[i],
self.layers[-1].accuracy(self.y),
givens={
self.x:
validation_x[i * self.mini_batch_size:(i + 1) *
self.mini_batch_size],
self.y:
validation_y[i * self.mini_batch_size:(i + 1) *
self.mini_batch_size]
})
test_mb_accuracy = theano.function(
[i],
self.layers[-1].accuracy(self.y),
givens={
self.x:
test_x[i * self.mini_batch_size:(i + 1) *
self.mini_batch_size],
self.y:
test_y[i * self.mini_batch_size:(i + 1) * self.mini_batch_size]
})
test_mb_accuracies_by_class = theano.function(
[i, n_class],
self.layers[-1].accuracies_by_class(self.y, n_class),
givens={
self.x:
test_x[i * self.mini_batch_size:(i + 1) *
self.mini_batch_size],
self.y:
test_y[i * self.mini_batch_size:(i + 1) * self.mini_batch_size]
},
on_unused_input='ignore')
test_mb_per_by_class = theano.function(
[i, n_class],
self.layers[-1].per_by_class(self.y, n_class),
givens={
self.x:
test_x[i * self.mini_batch_size:(i + 1) *
self.mini_batch_size],
self.y:
test_y[i * self.mini_batch_size:(i + 1) * self.mini_batch_size]
},
on_unused_input='ignore')
self.test_mb_predictions = theano.function(
[i],
self.layers[-1].y_out,
givens={
self.x:
test_x[i * self.mini_batch_size:(i + 1) * self.mini_batch_size]
})
# Do the actual training
best_test_accuracy = 0.0
best_validation_accuracy = 0.0
epoch = 0
it = -1
#Keep trace of the test accuracy
if monitor_test:
test_acc = []
test_acc_by_class = []
while True:
it += 1
epoch += 1
for minibatch_index in range(num_training_batches):
iteration = num_training_batches * epoch + minibatch_index
cost_ij = train_mb(minibatch_index)
if (iteration + 1) % num_training_batches == 0:
validation_accuracy = np.mean([
validate_mb_accuracy(j)
for j in range(num_validation_batches)
])
if validation_accuracy >= best_validation_accuracy:
#print("This is the best total validation accuracy to date.")
best_validation_accuracy = validation_accuracy
best_iteration = iteration
test_accuracies_by_class = [0] * self.layers[-1].n_out
test_per_by_class = [0] * self.layers[-1].n_out
it = -1
if test_data:
test_accuracy = np.mean([
test_mb_accuracy(j)
for j in range(num_test_batches)
])
if monitor_test:
test_acc.append(test_accuracy)
if test_accuracy > best_test_accuracy:
best_test_accuracy = test_accuracy
best_iteration_acc = iteration
# If you want to track visually your progress, uncomment the following lines:
#print("Epoch {0}: test accuracy {1:.2%}".format(
# epoch, test_accuracy))
if monitor_test:
for i in range(self.layers[-1].n_out):
test_accuracies_by_class[i] = np.mean([test_mb_accuracies_by_class(j,i) for j in range(num_test_batches)]) / \
np.mean([test_mb_per_by_class(j,i) for j in range(num_test_batches)])
test_acc_by_class.append(test_accuracies_by_class)
if it >= no_improvement_in:
break
print("Finished training network after {} epochs.".format(epoch))
print("Best test accuracy of {0:.2%} obtained at iteration {1}".format(
best_test_accuracy, best_iteration_acc))
print("Best validation accuracy of {0:.2%} obtained at iteration {1}".
format(best_validation_accuracy, best_iteration))
if monitor_test:
return test_acc, np.transpose(test_acc_by_class)
def __init__(self,
rng,
n_in,
n_out,
n_h,
f_act=leaky_relu,
f_out=softmax,
orth_init=True,
dropout_rate=0,
obj='c'):
'''
:param rng: Numpy RandomState
:param n_in: Input dimension (int)
:param n_out: Output dimension (int)
:param n_h: Hidden dimension (int)
:param f_act: Hidden-to-hidden activation function
:param f_out: Output activation function
:param orth_init: if true, the initialize transition matrix to be orthogonal (bool)
:param dropout_rate: dropout rate (float)
:param obj: objective type, 'c' for classification with cross entropy loss, 'r' for regression with MSE loss. (['c','r'])
'''
if orth_init:
Whh_ = rvs(rng, n_h)
else:
Whh_ = rng.uniform(-np.sqrt(6. / (n_h + n_h)),
np.sqrt(6. / (n_h + n_h)), (n_h, n_h))
Whi_ = rng.uniform(-np.sqrt(6. / (n_in + n_h)),
np.sqrt(6. / (n_in + n_h)), (n_h, n_in))
bh_ = np.zeros(n_h)
Woh_ = rng.uniform(-np.sqrt(6. / (n_out + n_h)),
np.sqrt(6. / (n_h + n_out)), (n_out, n_h))
bo_ = np.zeros(n_out)
h0_ = rng.uniform(-np.sqrt(3. / (2. * n_h)), np.sqrt(3. / (2. * n_h)),
n_h)
# Theano: Created shared variables
Whh = theano.shared(name='Whh',
value=Whh_.astype(theano.config.floatX))
Whi = theano.shared(name='Whi',
value=Whi_.astype(theano.config.floatX))
bh = theano.shared(name='bh', value=bh_.astype(theano.config.floatX))
Woh = theano.shared(name='Woh',
value=Woh_.astype(theano.config.floatX))
bo = theano.shared(name='bo', value=bo_.astype(theano.config.floatX))
h0 = theano.shared(name='h0', value=h0_.astype(theano.config.floatX))
self.p = [Whh, Whi, Woh, bh, bo, h0]
seq_len = T.iscalar('seq_len')
self.seq_len = seq_len
self.dropout_rate = dropout_rate
self.x = T.vector()
x_scan = T.reshape(self.x, [seq_len, n_in], ndim=2)
if dropout_rate > 0:
np.random.seed(int(time.time()))
# for training
def masked_forward_prop_step(x_t, h_t_prev):
h_t = f_act(Whi.dot(x_t) + Whh.dot(h_t_prev) + bh)
o_t = Woh.dot(h_t) + bo
mask = np.random.binomial(np.ones(n_h, dtype=int),
1 - dropout_rate)
masked_h_t = h_t * T.cast(mask, theano.config.floatX)
return [o_t, masked_h_t]
# for testing
def forward_prop_step(x_t, h_t_prev):
h_t = f_act(Whi.dot(x_t) + Whh.dot(h_t_prev) + bh)
o_t = Woh.dot(h_t) + bo
h_t = (1.0 - dropout_rate) * h_t
return [o_t, h_t]
[o_train, _], _ = theano.scan(masked_forward_prop_step,
sequences=[x_scan],
outputs_info=[None, h0],
n_steps=seq_len)
[o_test, _], _ = theano.scan(forward_prop_step,
sequences=[x_scan],
outputs_info=[None, h0],
n_steps=seq_len)
else:
def forward_prop_step(x_t, h_t_prev):
h_t = f_act(Whi.dot(x_t) + Whh.dot(h_t_prev) + bh)
o_t = Woh.dot(h_t) + bo
return [o_t, h_t]
[o_train, _], _ = theano.scan(forward_prop_step,
sequences=[x_scan],
outputs_info=[None, h0],
n_steps=seq_len)
o_test = o_train
if obj == 'c': # classification task
self.y = T.bscalar('y')
self.o_train = f_out(o_train[-1])
self.o_test = f_out(o_test[-1])
#obj function to compute grad, use dropout
self.cost = T.nnet.categorical_crossentropy(
self.o_train,
T.eye(n_out)[self.y])
#compute accuracy use average of dropout rate
self.accuracy = T.switch(T.eq(T.argmax(self.o_test), self.y), 1.,
0.)
self.prediction = np.argmax(self.o_test)
elif obj == 'r': # regression task
self.y = T.dscalar('y')
self.o_train = o_train[-1]
self.o_test = o_test[-1]
#obj function to compute grad, use dropout
self.cost = (self.o_train[0] - self.y)**2
#compute accuracy use average of dropout rate
self.accuracy = (self.o_test[0] - self.y)**2
self.prediction = self.o_test[0]
_, self.Sigma, _ = T.nlinalg.SVD(full_matrices=1,
compute_uv=1)(self.p[0])
self.max_singular = T.max(self.Sigma)
self.min_singular = T.min(self.Sigma)
self.optimiser = sgd_optimizer(self, 'RNN')
def __init__(self,
rng,
n_in,
n_out,
n_h,
n_r,
margin=1.0,
sig_mean=1.0,
f_act=leaky_relu,
f_out=softmax,
obj='c'):
'''
:param rng: Numpy RandomState
:param n_in: Input dimension (int)
:param n_out: Output dimension (int)
:param n_h: Hidden dimension (int)
:param n_r: Number of reflection vectors (int)
:param f_act: Hidden-to-hidden activation function
:param f_out: Output activation function
:param obj: objective type, 'c' for classification with cross entropy loss, 'r' for regression with MSE loss. (['c','r'])
'''
U_ = np.tril(rng.normal(0, 0.01, (n_h, n_r)))
norms_U_ = np.linalg.norm(U_, axis=0)
U_ = 1. / norms_U_ * U_
V_ = np.tril(rng.normal(0, 0.01, (n_h, n_r)))
norms_V_ = np.linalg.norm(V_, axis=0)
V_ = 1. / norms_V_ * V_
#Sig_ = np.ones( n_h)
P_ = np.zeros(n_h)
Whi_ = rng.uniform(-np.sqrt(6. / (n_in + n_h)),
np.sqrt(6. / (n_in + n_h)), (n_h, n_in))
bh_ = np.zeros(n_h)
Woh_ = rng.uniform(-np.sqrt(6. / (n_out + n_h)),
np.sqrt(6. / (n_h + n_out)), (n_out, n_h))
bo_ = np.zeros(n_out)
h0_ = rng.uniform(-np.sqrt(3. / (2. * n_h)), np.sqrt(3. / (2. * n_h)),
n_h)
# Theano: Created shared variables
Whi = theano.shared(name='Whi',
value=Whi_.astype(theano.config.floatX))
U = theano.shared(name='U', value=U_.astype(theano.config.floatX))
V = theano.shared(name='V', value=V_.astype(theano.config.floatX))
#Sig = theano.shared(name='Sig', value=Sig_.astype(theano.config.floatX))
P = theano.shared(name='P', value=P_.astype(theano.config.floatX))
bh = theano.shared(name='bh', value=bh_.astype(theano.config.floatX))
Woh = theano.shared(name='Woh',
value=Woh_.astype(theano.config.floatX))
bo = theano.shared(name='bo', value=bo_.astype(theano.config.floatX))
h0 = theano.shared(name='h0', value=h0_.astype(theano.config.floatX))
#self.p = [U, V, Sig, Whi, Woh, bh, bo, h0]
self.p = [U, V, P, Whi, Woh, bh, bo, h0]
seq_len = T.iscalar('seq_len')
self.seq_len = seq_len
self.x = T.vector()
#x_scan = T.shape_padright(self.x)
x_scan = T.reshape(self.x, [seq_len, n_in], ndim=2)
if n_h != n_r: # Number of reflection vectors is less than the hidden dimension
def forward_prop_step(x_t, h_t_prev):
Sig = 2 * margin * (sigmoid(P) - 0.5) + sig_mean
h_t = f_act(Whi.dot(x_t) + svd_H_wy(U, V, Sig, h_t_prev) + bh)
o_t = Woh.dot(h_t) + bo
return [o_t, h_t]
else:
def forward_prop_step(x_t, h_t_prev):
Sig = 2 * margin * (sigmoid(P) - 0.5) + sig_mean
Hu1SigHv1 = T.set_subtensor(Sig[-1],
Sig[-1] * U[-1, -1] * V[-1, -1])
h_t = f_act(
Whi.dot(x_t) +
svd_H_wy(U[:, :-1], V[:, :-1], Hu1SigHv1, h_t_prev) + bh)
o_t = Woh.dot(h_t) + bo
return [o_t, h_t]
[o_scan, _], _ = theano.scan(forward_prop_step,
sequences=[x_scan],
outputs_info=[None, h0],
n_steps=seq_len)
if obj == 'c': # classification task
self.y = T.bscalar('y')
self.o = f_out(o_scan[-1])
#obj function to compute grad, use dropout
self.cost = T.nnet.categorical_crossentropy(
self.o,
T.eye(n_out)[self.y])
#compute accuracy use average of dropout rate
self.accuracy = T.switch(T.eq(T.argmax(self.o), self.y), 1., 0.)
self.prediction = np.argmax(self.o)
elif obj == 'r': # regression task
self.y = T.dscalar('y')
self.o = o_scan[-1]
#obj function to compute grad, use dropout
self.cost = (self.o[0] - self.y)**2
#compute accuracy use average of dropout rate
self.accuracy = (self.o[0] - self.y)**2
self.prediction = self.o[0]
self.max_singular = 2 * margin * (sigmoid(T.max(self.p[2])) -
0.5) + sig_mean
self.min_singular = 2 * margin * (sigmoid(T.min(self.p[2])) -
0.5) + sig_mean
self.optimiser = sgd_optimizer(self, 'svdRNN')
def __init__(self, n_features):
self.n_features = n_features
self.x = T.fvector("x")
self.y = T.bscalar("y")
self.W = theano.shared(rng.randn(n_features).astype(theano.config.floatX), name="W")
self.b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name="b")
valid_x = theano.shared(valid_x, borrow=True)
train_y = T.cast(theano.shared(train_y, borrow=True), dtype='int32')
valid_y = T.cast(theano.shared(valid_y, borrow=True), dtype='int32')
# allocate learning rate and momentum shared variables
learning_rate = theano.shared(
np.array(learning_rate_schedule[0], dtype=theano.config.floatX))
momentum = theano.shared(
np.array(momentum_schedule[0], dtype=theano.config.floatX))
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of [int] labels
dropout_active = T.bscalar(
'dropout_active') # a flag to enable and disable dropout
######################
# BUILD ACTUAL MODEL #
######################
print 'Building the model ...'
# Reshape matrix of rasterized images of shape (batch_size, 48 * 48)
# to a 4D tensor, compatible with our ConvPoolLayer
layer0_input = x.reshape((batch_size, 1, 48, 48))
# layer10 = ConvPoolLayer(
# rng,
# input=layer0_input,
# image_shape=(batch_size, 1, 98, 98),
# filter_shape=(nkerns[0], 1, 4, 4),
max_egs=int(opts.num_from_train),
parse_mode=opts.parse_mode)
log("train_stats %s %s" % (len(train_x), train_stats))
dev_x, dev_y, dev_stats = util.load_data(opts.dev_set, vocab,
update_vocab=False,
max_egs=int(opts.num_from_dev),
parse_mode=opts.parse_mode)
log("dev_stats %s %s" % (len(dev_x), dev_stats))
# input/output example vars
s1_idxs = T.ivector('s1') # sequence for sentence one
s2_idxs = T.ivector('s2') # sequence for sentence two
actual_y = T.ivector('y') # single for sentence pair label; 0, 1 or 2
# dropout keep prob for post concat, pre MLP
apply_dropout = T.bscalar('apply_dropout') # dropout.{APPLY_DROPOUT|NO_DROPOUT}
keep_prob = theano.shared(opts.keep_prob) # recall 1.0 => noop
keep_prob = T.cast(keep_prob, 'float32') # shared weirdity, how to set in init (?)
# keep track of different "layers" that handle their own gradients.
# includes rnns, final concat & softmax and, potentially, special handling for
# tied embeddings
layers = []
# decide set of sequence idxs we'll be processing. there will always the two
# for the forward passes over s1 and s2 and, optionally, two more for the
# reverse pass over s1 & s2 in the bidirectional case.
idxs = [s1_idxs, s2_idxs]
names = ["s1f", "s2f"]
if opts.bidirectional:
idxs.extend([s1_idxs[::-1], s2_idxs[::-1]])
def __init__(self,
rng,
n_in,
n_per_base,
n_out,
n_layer=1,
basefuncs1=None,
basefuncs2=None,
gradient=None,
with_shortcuts=False):
"""Initialize the parameters for the multilayer function graph
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_layer: int
:param n_layer: number of hidden layers
:type n_per_base: int
:param n_per_base: number of nodes per basis function see FGLayer
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
:type basefuncs1: [int]
:param basefuncs1: see FGLayer
:type basefuncs2: [int]
:param basefuncs2: see FGLayer
:type gradient: string
:param gradient: type of gradient descent algo (None=="sgd+","adagrad","adadelta","nag")
:type with_shortcuts: bool
:param with_shortcuts: whether to use shortcut connections (output is connected to all units)
"""
self.input = T.matrix('input') # the data is presented as vector input
self.labels = T.matrix(
'labels') # the labels are presented as vector of continous values
self.rng = rng
self.n_layers = n_layer
self.hidden_layers = []
self.params = []
self.n_in = n_in
self.n_out = n_out
self.with_shortcuts = with_shortcuts
self.fixL0 = False
for l in xrange(n_layer):
if l == 0:
layer_input = self.input
n_input = n_in
else:
layer_input = self.hidden_layers[l - 1].output
n_input = self.hidden_layers[l - 1].n_out
hiddenLayer = FGLayer(
rng=rng,
inp=layer_input,
n_in=n_input,
n_per_base=n_per_base,
basefuncs1=basefuncs1,
basefuncs2=basefuncs2,
layer_idx=l,
)
self.hidden_layers.append(hiddenLayer)
self.params.extend(hiddenLayer.params)
div_thresh = T.scalar("div_thresh")
# The linear output layer, either it gets as input the output of ALL previous layers
if self.with_shortcuts:
output_layer_inp = T.concatenate(
[l.output for l in reversed(self.hidden_layers)], axis=1)
output_layer_n_in = sum([l.n_out for l in self.hidden_layers])
else: # or just of the last hidden layer
output_layer_inp = self.hidden_layers[-1].output
output_layer_n_in = self.hidden_layers[-1].n_out
self.output_layer = DivisionRegression(rng=rng,
inp=output_layer_inp,
n_in=output_layer_n_in,
n_out=n_out,
div_thresh=div_thresh)
self.params.extend(self.output_layer.params)
self.evalfun = theano.function(
inputs=[self.input, In(div_thresh, value=0.0001)],
outputs=self.output_layer.output)
L1_reg = T.scalar('L1_reg')
L2_reg = T.scalar('L2_reg')
fixL0 = T.bscalar('fixL0')
self.L1 = self.output_layer.L1 + sum(
[l.L1 for l in self.hidden_layers])
self.L2_sqr = self.output_layer.L2_sqr + sum(
[l.L2_sqr for l in self.hidden_layers])
self.penalty = self.output_layer.penalty
self.loss = self.output_layer.loss
self.errors = self.loss
self.cost = (self.loss(self.labels) + L1_reg * self.L1 +
L2_reg * self.L2_sqr + self.penalty)
#Extrapol penalty
self.extrapol_cost = self.output_layer.extrapol_loss
learning_rate = T.scalar('learning_rate')
def process_updates(par, newp):
# print par.name
if par.name == "W":
# if fixL0 is True, then keep small weights at 0
return par, ifelse(
fixL0, T.switch(T.abs_(par) < 0.001, par * 0, newp), newp)
return par, newp
print "Gradient:", gradient
update = None
if gradient == 'sgd+' or gradient == 'sgd' or gradient == None:
gparams = [T.grad(self.cost, param) for param in self.params]
update = OrderedDict([
(param, param - (learning_rate * gparam).clip(-1.0, 1.0))
for param, gparam in zip(self.params, gparams)
])
elif gradient == 'adam':
update = Lupdates.adam(self.cost,
self.params,
learning_rate,
epsilon=1e-04)
elif gradient == 'adadelta':
update = Lupdates.adadelta(self.cost, self.params, learning_rate)
elif gradient == 'rmsprop':
update = Lupdates.rmsprop(self.cost, self.params, learning_rate)
elif gradient == 'nag':
update = Lupdates.nesterov_momentum(self.cost, self.params,
learning_rate)
else:
assert ("unknown gradient " + gradient)
#Extrapol sanity gradient computation:
extrapol_updates = Lupdates.adam(self.extrapol_cost,
self.params,
learning_rate,
epsilon=1e-04)
updates = [process_updates(*up) for up in update.items()]
self.train_model = theano.function(
inputs=[
self.input, self.labels, L1_reg, L2_reg, fixL0, learning_rate,
div_thresh
],
outputs=self.cost,
updates=updates,
)
# avoid too large outputs in extrapolation domain
self.remove_extrapol_error = theano.function(
inputs=[self.input, learning_rate, div_thresh],
outputs=self.extrapol_cost,
updates=extrapol_updates,
)
self.test_model = theano.function(
inputs=[self.input, self.labels,
In(div_thresh, value=0.0001)],
outputs=self.errors(self.labels),
)
self.validate_model = theano.function(
inputs=[self.input, self.labels,
In(div_thresh, value=0.0001)],
outputs=self.errors(self.labels),
)
self.L1_loss = theano.function(
inputs=[],
outputs=self.L1,
)
self.MSE = theano.function(
inputs=[self.input, self.labels,
In(div_thresh, value=0.0001)],
outputs=self.errors(self.labels),
)
def __init__(self, dataset,
learning_rate=0.001,
decrease_constant=0,
hidden_sizes=[500],
random_seed=1234,
batch_size=1,
hidden_activation=T.nnet.sigmoid,
use_cond_mask=False,
direct_input_connect="None",
direct_output_connect=False,
update_rule="None",
dropout_rate=0,
weights_initialization="Uniform",
mask_distribution=0):
input_size = dataset['input_size']
self.shuffled_once = False
self.seed_generator = SeedGenerator(random_seed)
self.trng = T.shared_randomstreams.RandomStreams(self.seed_generator.get())
# Get the weights initializer by string name
weights_initialization = getattr(
WeightsInitializer(self.seed_generator.get()), weights_initialization)
# Building the model's graph
input = T.matrix(name="input")
target = T.matrix(name="target")
is_train = T.bscalar(name="is_train")
# Initialize the mask
self.mask_generator = MaskGenerator(
input_size, hidden_sizes, mask_distribution, self.seed_generator.get())
# Initialize layers
input_layer = ConditionningMaskedLayer(layerIdx=0,
input=input,
n_in=input_size,
n_out=hidden_sizes[0],
activation=hidden_activation,
weights_initialization=weights_initialization,
mask_generator=self.mask_generator,
use_cond_mask=use_cond_mask)
self.layers = [dropoutLayerDecorator(input_layer, self.trng, is_train, dropout_rate)]
# Now the hidden layers
for i in range(1, len(hidden_sizes)):
previous_layer = self.layers[i - 1]
hidden_layer = DirectInputConnectConditionningMaskedLayer(layerIdx=i,
input=previous_layer.output,
n_in=hidden_sizes[i - 1],
n_out=hidden_sizes[i],
activation=hidden_activation,
weights_initialization=weights_initialization,
mask_generator=self.mask_generator,
use_cond_mask=use_cond_mask,
direct_input=input if direct_input_connect == "Full" and previous_layer.output != input else None)
self.layers += [dropoutLayerDecorator(hidden_layer, self.trng, is_train, dropout_rate)]
# And the output layer
outputLayerIdx = len(self.layers)
previous_layer = self.layers[outputLayerIdx - 1]
self.layers += [DirectOutputInputConnectConditionningMaskedOutputLayer(layerIdx=outputLayerIdx,
input=previous_layer.output,
n_in=hidden_sizes[
outputLayerIdx - 1],
n_out=input_size,
activation=T.nnet.sigmoid,
weights_initialization=weights_initialization,
mask_generator=self.mask_generator,
use_cond_mask=use_cond_mask,
direct_input=input if (
direct_input_connect == "Full" or direct_input_connect == "Output") and previous_layer.output != input else None,
direct_outputs=[(layer.layer_idx, layer.n_in, layer.input) for layerIdx, layer in enumerate(self.layers[1:-1])] if direct_output_connect else [])]
# The loss function
output = self.layers[-1].output
pre_output = self.layers[-1].lin_output
log_prob = - \
T.sum(T.nnet.softplus(-target * pre_output + (1 - target) * pre_output), axis=1)
# log_prob = T.sum(target * T.log(output) + (1 - target) * T.log(1 - output), axis=1)
loss = (-log_prob).mean()
# How to update the parameters
self.parameters = [param for layer in self.layers for param in layer.params]
parameters_gradient = T.grad(loss, self.parameters)
# Initialize update_rule
if update_rule == "None":
self.update_rule = DecreasingLearningRate(learning_rate, decrease_constant)
elif update_rule == "adadelta":
self.update_rule = AdaDelta(decay=decrease_constant, epsilon=learning_rate)
elif update_rule == "adagrad":
self.update_rule = AdaGrad(learning_rate=learning_rate)
elif update_rule == "rmsprop":
self.update_rule = RMSProp(learning_rate=learning_rate, decay=decrease_constant)
elif update_rule == "adam":
self.update_rule = Adam(learning_rate=learning_rate)
elif update_rule == "adam_paper":
self.update_rule = Adam_paper(learning_rate=learning_rate)
updates = self.update_rule.get_updates(list(zip(self.parameters, parameters_gradient)))
# How to to shuffle weights
masks_updates = [
layer_mask_update for layer in self.layers for layer_mask_update in layer.shuffle_update]
self.update_masks = theano.function(name='update_masks',
inputs=[],
updates=masks_updates)
#
# Functions to train and use the model
index = T.lscalar()
self.learn = theano.function(name='learn',
inputs=[index, is_train],
outputs=loss,
updates=updates,
givens={input: dataset['train']['data'][
index * batch_size:(index + 1) * batch_size], target: dataset['train']['data'][index * batch_size:(index + 1) * batch_size]},
on_unused_input='ignore') # ignore for when dropout is absent
self.use = theano.function(name='use',
inputs=[input, is_train],
outputs=output,
on_unused_input='ignore') # ignore for when dropout is absent
# Test functions
self.valid_log_prob = theano.function(name='valid_log_prob',
inputs=[is_train],
outputs=log_prob,
givens={
input: dataset['valid']['data'], target: dataset['valid']['data']},
on_unused_input='ignore') # ignore for when dropout is absent
self.train_log_prob = theano.function(name='train_log_prob',
inputs=[is_train],
outputs=log_prob,
givens={
input: dataset['train']['data'], target: dataset['train']['data']},
on_unused_input='ignore') # ignore for when dropout is absent
self.train_log_prob_batch = theano.function(name='train_log_prob_batch',
inputs=[index, is_train],
outputs=log_prob,
givens={input: dataset['train']['data'][
index * 1000:(index + 1) * 1000], target: dataset['train']['data'][index * 1000:(index + 1) * 1000]},
on_unused_input='ignore') # ignore for when dropout is absent
self.test_log_prob = theano.function(name='test_log_prob',
inputs=[is_train],
outputs=log_prob,
givens={
input: dataset['test']['data'], target: dataset['test']['data']},
on_unused_input='ignore') # ignore for when dropout is absent
# Functions for verify gradient
self.useloss = theano.function(name='useloss',
inputs=[input, target, is_train],
outputs=loss,
on_unused_input='ignore') # ignore for when dropout is absent
self.learngrad = theano.function(name='learn',
inputs=[index, is_train],
outputs=parameters_gradient,
givens={input: dataset['train']['data'][
index * batch_size:(index + 1) * batch_size], target: dataset['train']['data'][index * batch_size:(index + 1) * batch_size]},
on_unused_input='ignore') # ignore for when dropout is absent
#
# adding functions to extract embeddings from each layer
self.embedding_funcs = [theano.function(name='embedding-{}'.format(i),
inputs=[input, is_train],
outputs=layer.output,
# givens={input: dataset['train']['data'][
# index * batch_size:(index + 1) * batch_size]},
on_unused_input='ignore')
for i, layer in enumerate(self.layers[:-1])]
#
# NOTE: the predict method (for decoding) is possible only when there is no skip
# connections to the output layer
if direct_input_connect == 'None' and not direct_output_connect:
print('No skip connections! defining decoding function')
pred_threshold = T.vector()
last_layer_embeddings = T.matrix(name="ll-embeddings")
output_probs = T.matrix(name="output-probs")
# T.dot(last_layer_embeddings, self.layers[-1].W) + self.layers[-1].b
pred_probs = output
predictions = T.switch(pred_probs < pred_threshold, 0, 1)
thresholded_output = T.switch(output_probs < pred_threshold, 0, 1)
self.predict_probs = theano.function(name='predict_probs',
inputs=[last_layer_embeddings, is_train],
outputs=pred_probs,
givens={self.layers[-1].input:
last_layer_embeddings},
on_unused_input='ignore')
self.threshold_probs = theano.function(name='threshold_probs',
inputs=[output_probs, pred_threshold],
outputs=thresholded_output,
on_unused_input='ignore')
self.predict_func = theano.function(name='predict',
inputs=[last_layer_embeddings, pred_threshold],
outputs=predictions,
givens={self.layers[-1].input:
last_layer_embeddings},
on_unused_input='ignore')
else:
self.predict_func = None
print('Skip connections detected! decoding will fail!')
"""Wrapper for creating Theano functions for training/inference mode"""
import theano
from theano import tensor as T
# 1 = training mode
# 0 = inference mode
train_mode = T.bscalar('train_mode')
def function(inputs=[], outputs=[], default_mode=0, **kwargs):
inputs = list(inputs)
outputs_list = list(outputs) if type(outputs) in (list, tuple) \
else [outputs]
use_train_mode = train_mode in theano.gof.graph.ancestors(
inputs + outputs_list)
extra_args = [train_mode] if use_train_mode else []
f = theano.function(
list(inputs)+extra_args,
outputs,
on_unused_input='warn',
**kwargs)
def g(*args):
if default_mode is None:
# args[-1] is the train_mode value
if use_train_mode:
# f() includes train_mode, pass arguments directly
return f(*args)
else:
# f() does not include train_mode, drop the last argument
return f(*(args[:-1]))
def __init__(self, layer_sizes, n_samples, alpha, learning_rate, v_prior,
batch_size, X_train, y_train, N_train):
layer_sizes = copy.copy(layer_sizes)
layer_sizes[0] = layer_sizes[0] + 1
print layer_sizes
self.batch_size = batch_size
self.N_train = N_train
self.X_train = X_train
self.y_train = y_train
self.rate = learning_rate
# We create the network
self.network = network.Network(layer_sizes, n_samples, v_prior,
N_train)
# index to a batch
index = T.lscalar()
self.indexes = T.vector('index', dtype='int32')
indexes_train = theano.shared(value=np.array(range(0, N_train),
dtype=np.int32),
borrow=True)
self.x = T.tensor3('x', dtype=theano.config.floatX)
self.y = T.matrix('y', dtype=theano.config.floatX)
self.lr = T.fscalar()
# The logarithm of the values for the likelihood factors
sampl = T.bscalar()
self.fwpass = theano.function(outputs=self.network.output(
self.x, False, samples=sampl, use_indices=False),
inputs=[self.x, sampl],
allow_input_downcast=True)
ll_train = self.network.log_likelihood_values(self.x, self.y,
self.indexes, 0.0, 1.0)
self.estimate_marginal_ll = (-1.0 * N_train / (self.x.shape[ 1 ] * alpha) * \
T.sum(LogSumExp(alpha * (T.sum(ll_train, 2) - self.network.log_f_hat() - self.network.log_f_hat_z()), 0)+ \
T.log(1.0 / n_samples)) - self.network.log_normalizer_q() - 1.0 * N_train / self.x.shape[ 1 ] * self.network.log_normalizer_q_z() + \
self.network.log_Z_prior())
# We create a theano function for updating q
upd = adam(self.estimate_marginal_ll,
self.network.params,
indexes_train[index * batch_size:(index + 1) * batch_size],
self.rate,
rescale_local=np.float32(N_train / batch_size))
self.process_minibatch = theano.function([ index], self.estimate_marginal_ll, \
updates = upd, \
givens = { self.x: T.tile(self.X_train[ index * batch_size: (index + 1) * batch_size] , [ n_samples, 1, 1 ]),
self.y: self.y_train[ index * batch_size: (index + 1) * batch_size ],
self.indexes: indexes_train[ index * batch_size : (index + 1) * batch_size ] })
# We create a theano function for making predictions
self.error_minibatch_train = theano.function(
[index],
T.sum((T.mean(
self.network.output(self.x, self.indexes), 0,
keepdims=True)[0, :, :] - self.y)**2) / layer_sizes[-1],
givens={
self.x:
T.tile(
self.X_train[index * batch_size:(index + 1) * batch_size],
[n_samples, 1, 1]),
self.y:
self.y_train[index * batch_size:(index + 1) * batch_size],
self.indexes:
indexes_train[index * batch_size:(index + 1) * batch_size]
})
self.ll_minibatch_train = theano.function([ index ], T.sum(LogSumExp(T.sum(ll_train, 2), 0) + T.log(1.0 / n_samples)), \
givens = { self.x: T.tile(self.X_train[ index * batch_size: (index + 1) * batch_size ], [ n_samples, 1, 1 ]),
self.y: self.y_train[ index * batch_size: (index + 1) * batch_size ],
self.indexes: indexes_train[ index * batch_size : (index + 1) * batch_size ] })
def __init__(self,
rng,
n_in,
n_out,
n_h,
n_r,
f_act=leaky_relu,
f_out=softmax,
obj='c'):
'''
:param rng: Numpy RandomState
:param n_in: Input dimension (int)
:param n_out: Output dimension (int)
:param n_h: Hidden dimension (int)
:param n_r: Number of reflection vectors (int)
:param f_act: Hidden-to-hidden activation function
:param f_out: Output activation function
:param obj: objective type, 'c' for classification with cross entropy loss, 'r' for regression with MSE loss. (['c','r'])
'''
U_ = np.tril(rng.normal(0, 0.01, (n_h, n_r)))
norms = np.linalg.norm(U_, axis=0)
U_ = 1. / norms * U_
Whi_ = rng.uniform(-np.sqrt(6. / (n_in + n_h)),
np.sqrt(6. / (n_in + n_h)), (n_h, n_in))
bh_ = np.zeros(n_h)
Woh_ = rng.uniform(-np.sqrt(6. / (n_out + n_h)),
np.sqrt(6. / (n_h + n_out)), (n_out, n_h))
bo_ = np.zeros(n_out)
h0_ = rng.uniform(-np.sqrt(3. / (2. * n_h)), np.sqrt(3. / (2. * n_h)),
n_h)
# Theano: Created shared variables
Whi = theano.shared(name='Whi',
value=Whi_.astype(theano.config.floatX))
U = theano.shared(name='U', value=U_.astype(theano.config.floatX))
bh = theano.shared(name='bh', value=bh_.astype(theano.config.floatX))
Woh = theano.shared(name='Woh',
value=Woh_.astype(theano.config.floatX))
bo = theano.shared(name='bo', value=bo_.astype(theano.config.floatX))
h0 = theano.shared(name='h0', value=h0_.astype(theano.config.floatX))
self.p = [U, Whi, Woh, bh, bo, h0]
seq_len = T.iscalar('seq_len')
self.seq_len = seq_len
self.x = T.vector()
#x_scan = T.shape_padright(self.x)
x_scan = T.reshape(self.x, [seq_len, n_in], ndim=2)
if n_h != n_r: # Number of reflection vectors is less than the hidden dimension
def forward_prop_step(x_t, h_t_prev):
h_t = f_act(Whi.dot(x_t) + H_wy(U, h_t_prev) + bh)
o_t = Woh.dot(h_t) + bo
return [o_t, h_t]
else:
def forward_prop_step(x_t, h_t_prev):
h_t_prev = T.set_subtensor(h_t_prev[-1],
h_t_prev[-1] * U[-1, -1])
h_t = f_act(Whi.dot(x_t) + H_wy(U[:, :-1], h_t_prev) + bh)
o_t = Woh.dot(h_t) + bo
return [o_t, h_t]
## For loop version below (when n_r < n_h)
# def forward_prop_step(x_t, h_t_prev):
# Wh = h_t_prev
# for i in range(n_r):
# Wh -= 2. * U[:, n_r - i - 1] * T.dot(U[:, n_r - i - 1], Wh)
# h_t = f_act(Whi.dot(x_t) + Wh + bh)
# o_t = Woh.dot(h_t) + bo
# return [o_t, h_t]
[o_scan, _], _ = theano.scan(forward_prop_step,
sequences=[x_scan],
outputs_info=[None, h0],
n_steps=seq_len)
if obj == 'c': # classification task
self.y = T.bscalar('y')
self.o = f_out(o_scan[-1])
#obj function to compute grad, use dropout
self.cost = T.nnet.categorical_crossentropy(
self.o,
T.eye(n_out)[self.y])
#compute accuracy use average of dropout rate
self.accuracy = T.switch(T.eq(T.argmax(self.o), self.y), 1., 0.)
self.prediction = np.argmax(self.o)
elif obj == 'r': # regression task
self.y = T.dscalar('y')
self.o = o_scan[-1]
#obj function to compute grad, use dropout
self.cost = (self.o[0] - self.y)**2
#compute accuracy use average of dropout rate
self.accuracy = (self.o[0] - self.y)**2
self.prediction = self.o[0]
self.optimiser = sgd_optimizer(self, 'oRNN')
def make_node(self, *inputs):
inputs = [tt.as_tensor_variable(i) for i in inputs]
outputs = [tt.bscalar()]
return gof.Apply(self, inputs, outputs)
def optimiser(self, num_samples, update, update_kwargs, saved_update=None):
overdisp_dim = T.bscalar('overdisp_dim')
overdisp_s = T.bscalar('overdisp_s')
batch = T.matrix('batch')
batch_rep = T.repeat(batch, num_samples, axis=0)
h_regular_rep = self.recognition_model.get_samples_latents_regular(
batch_rep)
h_overdisp = self.recognition_model.get_samples_latents_overdisp(
batch, overdisp_dim)
h_rep = T.set_subtensor(
h_regular_rep[overdisp_s::num_samples, overdisp_dim], h_overdisp)
log_p_h = self.generative_model.log_p_h(h_rep)
log_p_x = self.generative_model.log_p_x(h_rep, batch_rep)
entropies_h = self.recognition_model.entropies_latents(
h_rep, batch_rep)
log_w_rep = log_p_x + entropies_h + log_p_h
log_w_matrix = log_w_rep.reshape((batch.shape[0], num_samples))
v = self.recognition_model.importance_weights_latents(
h_overdisp, batch, overdisp_dim)
log_u_matrix = T.repeat(v, num_samples).reshape(
(batch.shape[0], num_samples)) + log_w_matrix
log_u_rep = log_u_matrix.flatten()
log_u_minus_max = log_u_matrix - T.max(
log_u_matrix, axis=1, keepdims=True)
u_matrix = T.exp(log_u_minus_max)
u_normalized_matrix = u_matrix / T.sum(u_matrix, axis=1, keepdims=True)
u_normalized_rep = T.reshape(u_normalized_matrix, log_w_rep.shape)
params = self.generative_model.get_params(
) + self.recognition_model.get_params()[:-1]
dummy_vec = T.vector(dtype=theano.config.floatX)
grads = theano.clone(T.grad(-T.dot(log_u_rep, dummy_vec), params),
replace={dummy_vec: u_normalized_rep})
tau = self.recognition_model.get_params()[-1]
all_grads, _ = theano.scan(
lambda s, log_u, u_norm: theano.clone(
T.grad(-T.dot(log_u[s], dummy_vec), params),
replace={dummy_vec: u_norm[s]}),
sequences=[T.arange(log_u_matrix.T.shape[0])],
non_sequences=[log_u_matrix.T, u_normalized_matrix.T],
)
variance = T.sum([T.sum(T.var(g, axis=0)) for g in all_grads])
grad_tau = T.grad(variance, tau)
grads += [grad_tau]
params = self.generative_model.get_params(
) + self.recognition_model.get_params()
update_kwargs['loss_or_grads'] = grads
update_kwargs['params'] = params
updates = update(**update_kwargs)
if saved_update is not None:
for u_matrix, v in zip(updates, saved_update.keys()):
u_matrix.set_value(v.get_value())
optimiser = theano.function(
inputs=[batch, overdisp_dim, overdisp_s],
outputs=T.dot(log_u_rep, u_normalized_rep),
updates=updates,
)
return optimiser, updates
