def End2end_Early_stopping(self, numpy_rng, dataset, n_validate, data_name,
batch_size, end2end_lr, algo, norm, patience,
validation):
train_X, test_X, actual = dataset
valid_x = train_X.get_value()[:n_validate]
train_x = train_X.get_value()[n_validate:]
#train_x = train_x[:100]
"for compute tm and vm before optimization process"
t = theano.shared(numpy.asarray(train_x, dtype=theano.config.floatX),
borrow=True)
v = theano.shared(numpy.asarray(valid_x, dtype=theano.config.floatX),
borrow=True)
"Use downhill for training network"
opt = downhill.build(algo=algo,
params=self.params,
loss=self.end2end_cost,
inputs=[self.x])
train = downhill.Dataset(train_x, batch_size=batch_size, rng=numpy_rng)
valid = downhill.Dataset(valid_x,
batch_size=len(valid_x),
rng=numpy_rng)
"for monitoring before optimization process"
stop_ep = 0
for tm1, vm1 in opt.iterate(
train,
valid,
patience=patience,
validate_every=validation,
min_improvement=1e-3,
#learning_rate = end2end_lr,
momentum=0.0,
nesterov=False):
stop_ep = stop_ep + 1
#
## "******* Classification Results after End to End training ******"
# if ((stop_ep%1 == 0) and (stop_ep > 0)):
# lof,cen,dis,kde,svm05,svm01,ae = self.Compute_AUC_Hidden(train_X, test_X, actual, norm, data_name)
# a = [stop_ep, lof, cen, dis, kde, svm05, svm01, ae]
# monitor = np.append(monitor, a)
if (stop_ep >= 1000):
break
#Plotting AUC and save to csv file
# monitor = np.reshape(monitor, (-1,8))
# Plotting_Monitor(monitor, 0.4, 1.0, data_name, path)
# np.savetxt(path + data_name + "_monitor_auc.csv", monitor, delimiter=",", fmt='%f' )
return [stop_ep, vm1['loss'], tm1['loss']]
def End2end_Early_stopping(self, numpy_rng, dataset, n_validate, data_name,
batch_size, end2end_lr, algo, norm, patience,
validation):
train_X, test_X, actual = dataset
valid_x = train_X.get_value()[:n_validate]
train_x = train_X.get_value()[n_validate:]
"for compute tm and vm before optimization process"
"Training network by downhill"
#'adadelta' 'adagrad (default 0.01)' 'adam''esgd' 'nag''rmsprop' 'rprop' 'sgd'
opt = downhill.build(algo=algo,
params=self.params,
loss=self.end2end_cost,
inputs=[self.x])
train = downhill.Dataset(train_x, batch_size=batch_size, rng=numpy_rng)
valid = downhill.Dataset(valid_x,
batch_size=len(valid_x),
rng=numpy_rng)
"***** Monitor before optimization *****"
stop_ep = 0
RE = np.empty([0, 3])
for tm, vm in opt.iterate(
train, # 5, 5, 1e-2, 0.9
valid,
patience=patience, # 10
validate_every=validation, # 5
min_improvement=1e-3, # 1e-3
#learning_rate = end2end_lr, # 1e-4
momentum=0.0,
nesterov=False):
stop_ep = stop_ep + 1
re = np.column_stack([stop_ep, vm['loss'], tm['loss']])
RE = np.append(RE, re)
if (stop_ep >= 1000):
break
RE = np.reshape(RE, (-1, 3))
Plotting_End2End_RE(RE, stop_ep, 0.0, 0.4, data_name, path)
np.savetxt(path + data_name + "_training_error1.csv",
RE,
delimiter=",",
fmt='%f')
np.set_printoptions(precision=6, suppress=True)
print("\n ", RE[stop_ep - 1])
return RE[stop_ep - 1]
def test_batch_size(self):
ds = downhill.Dataset([np.random.randn(40, 2)], batch_size=10, rng=4)
assert len(ds._batches) == 4
assert ds._batches[0][0].shape == (10, 2)
assert ds._batches[1][0].shape == (10, 2)
assert ds._batches[2][0].shape == (10, 2)
assert ds._batches[3][0].shape == (10, 2)
ds = downhill.Dataset([np.random.randn(40, 2)], batch_size=11, rng=4)
assert len(ds._batches) == 4
assert ds._batches[0][0].shape == (11, 2)
assert ds._batches[1][0].shape == (11, 2)
assert ds._batches[2][0].shape == (7, 2)
assert ds._batches[3][0].shape == (11, 2)
def test_batch_size(self):
ds = downhill.Dataset([np.random.randn(40, 2)], batch_size=10, rng=4)
assert len(ds._slices) == 4
assert_size(ds, 0, 10)
assert_size(ds, 1, 10)
assert_size(ds, 2, 10)
assert_size(ds, 3, 10)
ds = downhill.Dataset([np.random.randn(40, 2)], batch_size=11, rng=4)
assert len(ds._slices) == 4
assert_size(ds, 0, 11)
assert_size(ds, 1, 11)
assert_size(ds, 2, 7)
assert_size(ds, 3, 11)
def pretrain_Early_stopping(self, numpy_rng, train_set, n_validate,
data_name, batch_size, pre_lr, corruptions):
RE = np.empty([10000, self.n_layers])
stop_epoch = np.empty([self.n_layers])
for i in range(self.n_layers):
cost, updates = self.dA_layers[i].get_cost_updates(
corruptions[i], pre_lr)
if (i == 0):
train_x1 = train_set.get_value()
else:
train_x1 = self.get_hidden_i(train_set, i - 1)
valid_x = train_x1[:n_validate]
train_x = train_x1[n_validate:]
# adadelta, 'adagrad (default 0.01)' 'adam''esgd' 'nag''rmsprop' 'rprop' 'sgd'
opt = downhill.build(algo='sgd',
params=self.dA_layers[i].params,
loss=cost)
train = downhill.Dataset(train_x,
batch_size=batch_size,
rng=numpy_rng)
valid = downhill.Dataset(valid_x,
batch_size=len(valid_x),
rng=numpy_rng)
epoch = 0
re = np.empty([10000])
for tm1, vm1 in opt.iterate(
train,
valid,
patience=100, #100
validate_every=5, #5
min_improvement=1e-3, #4
learning_rate=pre_lr, #1e-2
momentum=0.0,
nesterov=False):
re[epoch] = tm1['loss']
epoch = epoch + 1
if (epoch == 200):
break
RE[:, i] = re
stop_epoch[i] = epoch
print(' + Stopping epoch:', stop_epoch)
Plotting_Pre_RE1(RE, stop_epoch, self.n_layers, 0.0, 0.1, batch_size,
data_name, path)
def fit(self, train, entities, relations, param):
self.n, self.m, self.l, self.k = entities, relations, entities, param.k
self.setup(param)
train, inputs = self.minibatch(train, param)
opt = downhill.build(param.sgd,
loss=self.loss_opt,
inputs=inputs,
monitor_gradients=True)
train = downhill.Dataset(train, name='train')
it = 0
for _ in opt.iterate(train,
None,
max_updates=param.epoch,
validate_every=10,
patience=5,
max_gradient_norm=1,
learning_rate=param.lr):
it += 1
if it >= param.epoch:
break
def fit(self, train_triples, valid_triples, hparams, n=0,m=0,l=0, scorer = None):
#Set input_dimensions:
if n == 0: #No given dimensions, can be useful for transparent predicton of entities/rels not seen in train
self.set_dims(train_triples, hparams)
else:
self.n, self.m, self.l, self.k = n, m, l, hparams.embedding_size
#Define the downhill loss corresponding to the input dimensions
self.setup_params_for_train(train_triples, valid_triples, hparams)
#get the loss inputs:
train_vals, train_symbs, valid_vals = self.get_loss_args_and_symb_vars(train_triples, valid_triples, hparams)
opt = downhill.build(hparams.learning_rate_policy, loss=self.loss_to_opt, inputs=train_symbs, monitor_gradients=True)
train_vals = downhill.Dataset(train_vals, name = 'train')
#Main SGD loop
it = 0
best_valid_mrr = -1
best_valid_ap = -1
for tm, vm in opt.iterate(train_vals, None,
max_updates=hparams.max_iter,
validate_every=9999999, #I take care of the valiation, with validation metrics instead of loss
patience=9999999, #Number of tolerated imporvements of validation loss that are inferior to min_improvement
max_gradient_norm=1, # Prevent gradient explosion!
learning_rate=hparams.learning_rate):
if it % hparams.valid_scores_every == 0 and scorer is not None:
if valid_triples is not None:
logger.info("Validation metrics:")
res = scorer.compute_scores(self, self.name, hparams, valid_triples)
cv_res = CV_Results()
cv_res.add_res(res, self.name, hparams.embedding_size, hparams.lmbda, self.nb_params)
if scorer.compute_ranking_scores:
metrics = cv_res.print_MRR_and_hits()
#Early stopping on filtered MRR
if best_valid_mrr >= metrics[self.name][2]:
logger.info("Validation filtered MRR decreased, stopping here.")
break
else:
best_valid_mrr = metrics[self.name][2]
else:
logger.info("Validation AP: " + str(res.ap))
#Early stopping on Average Precision
if best_valid_ap >= res.ap:
logger.info("Validation AP decreased, stopping here.")
break
else:
best_valid_ap = res.ap
it += 1
if it >= hparams.max_iter: #Avoid downhill resetting the parameters when max_iter is reached
break
def test_iteration_size(self):
def batches_unchanged(previous):
return all(
np.allclose(a, b) for a, b in zip(ds._batches, previous))
ds = downhill.Dataset([np.random.randn(40, 2)],
batch_size=5,
iteration_size=3)
previous = list(ds._batches)
c = sum(1 for _ in ds)
assert c == 3, 'got {}'.format(c)
assert ds._index == 3, 'got {}'.format(ds._index)
assert batches_unchanged(previous)
previous = list(ds._batches)
c = sum(1 for _ in ds)
assert c == 3
assert ds._index == 6, 'got {}'.format(ds._index)
assert batches_unchanged(previous)
previous = list(ds._batches)
c = sum(1 for _ in ds)
assert c == 3
assert ds._index == 1, 'got {}'.format(ds._index)
assert not batches_unchanged(previous)
def create_dataset(self, data, **kwargs):
'''Create a dataset for this experiment.
Parameters
----------
data : sequence of ndarray or callable
The values that you provide for data will be encapsulated inside a
:class:`Dataset <downhill.Dataset>` instance; see that class for
documentation on the types of things it needs. In particular, you
can currently pass in either a list/array/etc. of data, or a
callable that generates data dynamically.
Returns
-------
data : :class:`Dataset <downhill.Dataset>`
A dataset capable of providing mini-batches of data to a training
algorithm.
'''
default_axis = 0
if not callable(data) and not callable(data[0]) and len(
data[0].shape) == 3:
default_axis = 1
name = kwargs.get('name', 'dataset')
b, i, s = 'batch_size', 'iteration_size', '{}_batches'.format(name)
return downhill.Dataset(data,
name=name,
batch_size=kwargs.get(b, 32),
iteration_size=kwargs.get(i, kwargs.get(s)),
axis=kwargs.get('axis', default_axis))
def dnn_sep(M, W1, W2, hh=.0001, ep=5000, d=0, sp=.0001, spb=3, al='rprop'):
# GPU cached data
_M = theano.shared(M.T.astype(float64))
dum = Th.vector('dum')
# Get layer sizes
K = []
for i in range(len(W1)):
K.append([W1[i].shape[0], W2[i].shape[0]])
K.append([M.T.shape[1], M.T.shape[1]])
# We have weights to discover, init = 2/(Nin+Nout)
H = theano.shared(
sqrt(2. / (K[0][0] + K[0][1] + M.shape[1])) *
random.rand(M.T.shape[0], K[0][0] + K[0][1]).astype(float64))
fI = InputLayer(shape=(M.T.shape[0], K[0][0] + K[0][1]), input_var=H)
# Split in two pathways, one for each source's autoencoder
H1 = (len(W1) + 1) * [None]
H2 = (len(W1) + 1) * [None]
H1[0] = SliceLayer(fI, indices=slice(0, K[0][0]), axis=1)
H2[0] = SliceLayer(fI, indices=slice(K[0][0], K[0][0] + K[0][1]), axis=1)
# Put the subsequent layers
for i in range(len(W1)):
H1[i + 1] = DenseLayer(H1[i],
num_units=K[i + 1][0],
W=W1[i].astype(float64),
nonlinearity=lambda x: psoftplus(x, spb),
b=None)
H2[i + 1] = DenseLayer(H2[i],
num_units=K[i + 1][1],
W=W2[i].astype(float64),
nonlinearity=lambda x: psoftplus(x, spb),
b=None)
# Add the two approximations
R = ElemwiseSumLayer([H1[-1], H2[-1]])
# Cost function
Ro = get_output(R) + eps
cost = Th.mean(_M * (Th.log(_M + eps) - Th.log(Ro)) - _M +
Ro) + 0 * Th.mean(dum)
for i in range(len(H1) - 1):
cost += sp * Th.mean(abs(get_output(H1[i]))) + sp * Th.mean(
abs(get_output(H2[i])))
# Train it using Lasagne
opt = downhill.build(al, loss=cost, inputs=[dum], params=[H])
train = downhill.Dataset(array([d]).astype(float64), batch_size=0)
er = downhill_train(opt, train, hh, ep, None)
# Get outputs
_r = nget(R, dum, array([0]).astype(float64)).T + eps
_r1 = nget(H1[-1], dum, array([0]).astype(float64)).T
_r2 = nget(H2[-1], dum, array([0]).astype(float64)).T
return _r, _r1, _r2, er
def dnn_model(M,
K=[20, 20],
hh=.0001,
ep=5000,
d=0,
wsp=0.0001,
hsp=0,
spb=3,
bt=0,
al='rprop'):
# Sort out the activation
from inspect import isfunction
if isfunction(spb):
act = spb
else:
act = lambda x: psoftplus(x, spb)
# Copy key variables to GPU
_M = Th.matrix('_M')
# Input and forward transform
I = InputLayer(shape=(None, M.shape[0]), input_var=_M)
# Setup the layers
L = K + [M.T.shape[1]]
H = len(L) * [None]
Hd = len(L) * [None]
# First layer
H[0] = DenseLayer(I, num_units=K[0], nonlinearity=act, b=None)
# All the rest
for k in range(1, len(L)):
# Optional dropout
Hd[k - 1] = DropoutLayer(H[k - 1], d)
# Next layer
H[k] = DenseLayer(Hd[k - 1], num_units=L[k], nonlinearity=act, b=None)
# Cost function
Ro = get_output(H[-1]) + eps
cost = Th.mean(_M * (Th.log(_M + eps) - Th.log(Ro)) - _M + Ro)
for k in range(len(L) - 1):
cost += wsp * Th.mean(abs(H[k].W)) + hsp * Th.mean(get_output(H[k]))
# Train it using Lasagne
opt = downhill.build(al,
loss=cost,
inputs=[_M],
params=get_all_params(H[-1]))
train = downhill.Dataset(M.T.astype(float64), batch_size=bt)
er = downhill_train(opt, train, hh, ep, None)
# Get approximation
h = [nget(H[k], _M, M.T.astype(float64)).T for k in range(len(L))]
w = [H[k].W.get_value() for k in range(len(L))]
return h, w, er
def downhill_models(M,
P,
FE,
z,
K=20,
hh=.001,
ep=5000,
dp=0,
wsp=.001,
plt=False):
from paris.signal import bss_eval
rng = theano.tensor.shared_randomstreams.RandomStreams(0)
# Shared variables to use
x = Th.matrix('x')
y = theano.shared(M.astype(theano.config.floatX))
d = theano.shared(float32(dp))
# Network weights
W0 = theano.shared(
sqrt(2. / (K + M.shape[0])) *
random.randn(K, M.shape[0]).astype(theano.config.floatX))
W1 = theano.shared(
sqrt(2. / (K + M.shape[0])) *
random.randn(M.shape[0], K).astype(theano.config.floatX))
# First layer is the transform to a non-negative subspace
h = psoftplus(W0.dot(x), 3.)
# Dropout
if dp > 0:
h *= (1. / (1. - d) * (rng.uniform(size=h.shape) > d).astype(
theano.config.floatX)).astype(theano.config.floatX)
# Second layer reconstructs the input
r = psoftplus(W1.dot(h), 3.)
# Approximate input using KL-like distance
cost = Th.mean(y * (Th.log(y + eps) - Th.log(r + eps)) - y +
r) + wsp * Th.mean(abs(W1))
# Make an optimizer and define the training input
opt = downhill.build('rprop', loss=cost, inputs=[x], params=[W0, W1])
train = downhill.Dataset(M.astype(theano.config.floatX), batch_size=0)
# Train it
downhill_train(opt, train, hh, ep, None)
# Get approximation
d = 0
_, _r = theano.function(inputs=[x], outputs=[h, r],
updates=[])(M.astype(theano.config.floatX))
o = FE.ife(_r, P)
sxr = bss_eval(o, 0, array([z]))
return W1.get_value(), sxr
def build_model(algo):
loss_value = []
W1.set_value(W1_val)
b1.set_value(b1_val)
W2.set_value(W2_val)
b2.set_value(b2_val)
opt = downhill.build(algo, loss=loss)
train = downhill.Dataset([train_X[:-1000], train_y_onehot[:-1000]],
batch_size=1,
iteration_size=1)
valid = downhill.Dataset([train_X[-1000:], train_y_onehot[-1000:]])
iterations = 0
for tm, vm in opt.iterate(train, valid, patience=1000):
iterations += 1
loss_value.append(vm['loss'])
if iterations > 1000:
break
return loss_value
def cnn_model(M,
K=20,
T=1,
hh=.0001,
ep=5000,
d=0,
hsp=0.0001,
wsp=0,
spb=3,
bt=0,
al='rprop'):
# Facilitate reasonable convolutions core
theano.config.dnn.conv.algo_fwd = 'fft_tiling'
theano.config.dnn.conv.algo_bwd_filter = 'none'
theano.config.dnn.conv.algo_bwd_data = 'none'
# Reformat input data
M3 = reshape(M.astype(float32), (1, M.shape[0], M.shape[1]))
# Copy key variables to GPU
_M = Th.tensor3('_M')
# Input and forward transform
I = InputLayer(shape=M3.shape, input_var=_M)
# First layer is the transform to a non-negative subspace
H = Conv1DLayer(I,
filter_size=T,
num_filters=K,
pad='same',
nonlinearity=lambda x: psoftplus(x, spb),
b=None)
# Upper layer is the synthesizer
R = Conv1DLayer(H,
filter_size=T,
num_filters=M.shape[0],
pad='same',
nonlinearity=lambda x: psoftplus(x, spb),
b=None)
# Cost function
Ro = get_output(R) + eps
cost = Th.mean( _M*(Th.log( _M+eps) - Th.log( Ro)) - _M + Ro) \
+ hsp*Th.mean( get_output( H))
# Train it using Lasagne
opt = downhill.build(al, loss=cost, inputs=[_M], params=get_all_params(R))
train = downhill.Dataset(M3, batch_size=bt)
er = downhill_train(opt, train, hh, ep, None)
# Get approximation and hidden state
_r = squeeze(nget(R, _M, M3))
_h = squeeze(nget(H, _M, M3))
return _r, R.W.get_value(), er, _h
def create_dataset(data, **kwargs):
name = kwargs.get('name', 'dataset')
s = '{}_batches'.format(name)
return downhill.Dataset(data,
name=name,
batch_size=kwargs.get('batch_size', 32),
iteration_size=kwargs.get(
'iteration_size', kwargs.get(s)),
axis=kwargs.get('axis', 0),
rng=kwargs['rng'])
def test_shared(self):
x = theano.shared(np.random.randn(40, 2))
ds = downhill.Dataset([x], batch_size=10, rng=4)
assert len(ds._slices) == 4
assert_size(ds, 0, 10)
assert_size(ds, 1, 10)
assert_size(ds, 2, 10)
assert_size(ds, 3, 10)
f = list(ds)[0][0]
assert isinstance(f, TT.TensorVariable), type(f)
def test_sparse_csr(self):
import scipy.sparse as ss
x = ss.csr_matrix(np.random.randn(40, 2))
ds = downhill.Dataset([x], batch_size=10, rng=4)
assert len(ds._slices) == 4
assert_size(ds, 0, 10)
assert_size(ds, 1, 10)
assert_size(ds, 2, 10)
assert_size(ds, 3, 10)
f = list(ds)[0][0]
assert isinstance(f, ss.csr.csr_matrix), type(f)
def test_pandas(self):
import pandas as pd
x = pd.DataFrame(np.random.randn(40, 2))
ds = downhill.Dataset([x], batch_size=10, rng=4)
assert len(ds._slices) == 4
assert_size(ds, 0, 10)
assert_size(ds, 1, 10)
assert_size(ds, 2, 10)
assert_size(ds, 3, 10)
f = list(ds)[0][0]
assert isinstance(f, pd.DataFrame), type(f)
def lasagne_models(M,
P,
FE,
z,
K=20,
hh=.0001,
ep=5000,
d=0,
wsp=0.0001,
plt=True):
from paris.signal import bss_eval
# Copy key variables to GPU
_M = Th.matrix('_M')
# Input and forward transform
I = InputLayer(shape=M.T.shape, input_var=_M)
# First layer is the transform to a non-negative subspace
H0 = DenseLayer(I,
num_units=K,
nonlinearity=lambda x: psoftplus(x, 3.),
b=None)
# Optional dropout
H = DropoutLayer(H0, d)
# Compute source modulator
R = DenseLayer(H,
num_units=M.T.shape[1],
nonlinearity=lambda x: psoftplus(x, 3.),
b=None)
# Cost function
cost = (_M*(Th.log(_M+eps) - Th.log( get_output( R)+eps)) - _M + get_output( R)).mean() \
+ wsp*Th.mean( abs( R.W))
# Train it using Lasagne
opt = downhill.build('rprop',
loss=cost,
inputs=[_M],
params=get_all_params(R))
train = downhill.Dataset(M.T.astype(float32), batch_size=0)
er = downhill_train(opt, train, hh, ep, None)[-1]
# Get approximation
_r = nget(R, _M, M.T.astype(float32)).T
_h = nget(H, _M, M.T.astype(float32)).T
o = FE.ife(_r, P)
sxr = bss_eval(o, 0, array([z]))
return R, sxr
def test_callable_length(self):
class Batches:
called = 0
def __call__(self):
self.called += 1
return 'hello'
def __len__(self):
return 10
batches = Batches()
ds = downhill.Dataset(batches, iteration_size=10)
assert list(ds) == ['hello'] * 10
assert batches.called == 10
def test_minimize(self):
x = theano.shared(-3 + np.zeros((2, ), 'f'), name='x')
data = downhill.Dataset(np.zeros((1, 1), 'f'), batch_size=1)
data._slices = [[]]
downhill.minimize(
(100 * (x[1:] - x[:-1]**2)**2 + (1 - x[:-1])**2).sum(),
data,
algo='nag',
learning_rate=0.001,
momentum=0.9,
patience=1,
min_improvement=0.1,
max_gradient_norm=1,
)
assert np.allclose(x.get_value(), [1, 1]), x.get_value()
def rnn_model(M,
K=20,
hh=.0001,
ep=5000,
d=0,
wsp=0.0001,
hsp=0,
spb=3,
bt=0,
al='rmsprop',
t=5):
# Copy key variables to GPU
_M = Th.matrix('_M')
# Input and forward transform
I = InputLayer(shape=(None, M.shape[0]), input_var=_M)
# First layer is the transform to a non-negative subspace
H0 = DenseLayer(I,
num_units=K,
nonlinearity=lambda x: psoftplus(x, spb),
b=None)
# Optional dropout
H = DropoutLayer(H0, d)
# Compute output
R = RecurrentLayer(H,
num_units=M.T.shape[1],
nonlinearity=lambda x: psoftplus(x, spb),
gradient_steps=t,
b=None)
# Cost function
Ro = get_output(R) + eps
cost = Th.mean( _M*(Th.log( _M+eps) - Th.log( Ro)) - _M + Ro) \
+ hsp*Th.mean( get_output( H0))
# Train it using Lasagne
opt = downhill.build(al, loss=cost, inputs=[_M], params=get_all_params(R))
train = downhill.Dataset(M.T.astype(float32), batch_size=bt)
er = downhill_train(opt, train, hh, ep, None)
# Get approximation
_r = nget(R, _M, M.T.astype(float32)).T
_h = nget(H, _M, M.T.astype(float32)).T
return _r, (R.W_in_to_hid.get_value(), R.W_hid_to_hid.get_value()), er, _h
def nn_model(M,
K=20,
hh=.0001,
ep=5000,
d=0,
wsp=0.0001,
hsp=0,
spb=3,
bt=0,
al='rprop'):
# Sort out the activation
from inspect import isfunction
if isfunction(spb):
act = spb
else:
act = lambda x: psoftplus(x, spb)
# Copy key variables to GPU
_M = Th.matrix('_M')
# Input and forward transform
I = InputLayer(shape=(None, M.shape[0]), input_var=_M)
# First layer is the transform to a non-negative subspace
H0 = DenseLayer(I, num_units=K, nonlinearity=act, b=None)
# Optional dropout
H = DropoutLayer(H0, d)
# Compute output
R = DenseLayer(H, num_units=M.T.shape[1], nonlinearity=act, b=None)
# Cost function
Ro = get_output(R) + eps
cost = Th.mean( _M*(Th.log( _M+eps) - Th.log( Ro)) - _M + Ro) \
+ wsp*Th.mean( abs( R.W[0])) + hsp*Th.mean( get_output( H0))
# Train it using Lasagne
opt = downhill.build(al, loss=cost, inputs=[_M], params=get_all_params(R))
train = downhill.Dataset(M.T.astype(float64), batch_size=bt)
er = downhill_train(opt, train, hh, ep, None)
# Get approximation
_r = nget(R, _M, M.T.astype(float64)).T
_h = nget(H, _M, M.T.astype(float64)).T
return _r, R.W.get_value(), er, _h
y_hat = T.nnet.softmax(z2)
loss_reg = 1. / batch_size * reg_lambda / 2 * (T.sum(T.sqr(W1)) +
T.sum(T.sqr(W2)))
loss = T.nnet.categorical_crossentropy(y_hat, y).mean() + loss_reg
prediction = T.argmax(y_hat, axis=1)
predict = theano.function([X], prediction)
#Store the training and vlidation loss
train_loss = []
validation_loss = []
opt = downhill.build('sgd', loss=loss)
#Set up training and validation dataset splits, use only one example in a batch
#and use only one batch per step/epoc
#Use everything except last 1000 examples for training
train = downhill.Dataset([train_X[:-1000], train_y_onehot[:-1000]],
batch_size=batch_size,
iteration_size=1)
#Use last 1000 examples for valudation
valid = downhill.Dataset([train_X[-1000:], train_y_onehot[-1000:]])
#SGD
iterations = 0
for tm, vm in opt.iterate(train, valid, patience=10000):
iterations += 1
# Record the training and validation loss
train_loss.append(tm['loss'])
validation_loss.append(vm['loss'])
if iterations > 1000:
break
def build_model(algo):
def lasagne_separate(M,
P,
FE,
W1,
W2,
z1,
z2,
hh=.0001,
ep=5000,
d=0,
wsp=.0001,
plt=True):
# Gt dictionary shapes
K = [W1.shape[0], W2.shape[0]]
# GPU cached data
_M = theano.shared(M.astype(float32))
# Input is the learned dictionary set
lW = hstack((W1.T, W2.T)).astype(float32)
_lW = Th.matrix('_lW')
fI = InputLayer(shape=lW.shape, input_var=_lW)
# Split in two paths
fW1 = SliceLayer(fI, indices=slice(0, K[0]), axis=1)
fW2 = SliceLayer(fI, indices=slice(K[0], K[0] + K[1]), axis=1)
# Dropout?
dfW1 = DropoutLayer(fW1, d)
dfW2 = DropoutLayer(fW2, d)
N_sequence = 10
# # Compute source modulators
# R1 = LSTMLayer(dfW1, N_sequence)
# R2 = LSTMLayer(dfW2, N_sequence)
# Bring to standard orientation
R = ElemwiseSumLayer([R1, R2])
# Cost function
cost = (
_M * (Th.log(_M + eps) - Th.log(get_output(R) + eps)) - _M +
get_output(R)).mean() + wsp * (Th.mean(abs(R1.W)) + Th.mean(abs(R2.W)))
# Train it using Lasagne
opt = downhill.build('rprop',
loss=cost,
inputs=[_lW],
params=get_all_params(R))
train = downhill.Dataset(lW, batch_size=0)
er = downhill_train(opt, train, hh, ep, None)[-1]
# Get outputs
_r = nget(R, _lW, lW) + eps
_r1 = nget(R1, _lW, lW)
_r2 = nget(R2, _lW, lW)
o1 = FE.ife(_r1 * (M / _r), P)
o2 = FE.ife(_r2 * (M / _r), P)
sxr = bss_eval(o1, 0, vstack((z1, z2))) + bss_eval(o2, 1, vstack((z1, z2)))
return o1, o2, (array(sxr[:3]) + array(sxr[3:])) / 2.
def rnn_sep(M,
W1,
W2,
hh=.0001,
ep=5000,
d=0,
sp=.0001,
spb=3,
al='rmsprop',
t=5):
# Get dictionary shapes
K = [W1[0].shape[0], W2[0].shape[0]]
# GPU cached data
_M = theano.shared(M.T.astype(float32))
dum = Th.vector('dum')
# We have weights to discover
H = theano.shared(
sqrt(2. / (K[0] + K[1] + M.shape[1])) *
random.rand(M.T.shape[0], K[0] + K[1]).astype(float32))
fI = InputLayer(shape=(M.T.shape[0], K[0] + K[1]), input_var=H)
# Split in two pathways
fW1 = SliceLayer(fI, indices=slice(0, K[0]), axis=1)
fW2 = SliceLayer(fI, indices=slice(K[0], K[0] + K[1]), axis=1)
# Dropout?
dfW1 = DropoutLayer(fW1, dum[0])
dfW2 = DropoutLayer(fW2, dum[0])
# Compute source modulators using previously learned dictionaries
R1 = RecurrentLayer(dfW1,
num_units=M.T.shape[1],
b=None,
W_in_to_hid=W1[0].astype(float32),
W_hid_to_hid=W1[1].astype(float32),
nonlinearity=lambda x: psoftplus(x, spb),
gradient_steps=5)
R2 = RecurrentLayer(dfW2,
num_units=M.T.shape[1],
b=None,
W_in_to_hid=W2[0].astype(float32),
W_hid_to_hid=W2[1].astype(float32),
nonlinearity=lambda x: psoftplus(x, spb),
gradient_steps=5)
# Add the two approximations
R = ElemwiseSumLayer([R1, R2])
# Cost function
Ro = get_output(R) + eps
cost = (_M*(Th.log(_M+eps) - Th.log( Ro+eps)) - _M + Ro).mean() \
+ sp*Th.mean( abs( H)) + 0*Th.mean( dum)
# Train it using Lasagne
opt = downhill.build(al, loss=cost, inputs=[dum], params=[H])
train = downhill.Dataset(array([d]).astype(float32), batch_size=0)
er = downhill_train(opt, train, hh, ep, None)
# Get outputs
_r = nget(R, dum, array([0]).astype(float32)).T + eps
_r1 = nget(R1, dum, array([0]).astype(float32)).T
_r2 = nget(R2, dum, array([0]).astype(float32)).T
return _r, _r1, _r2, er
def test_name(self):
ds = downhill.Dataset([np.random.randn(40, 2)], name='foo')
assert ds.name == 'foo'
ds = downhill.Dataset([np.random.randn(40, 2)])
assert ds.name.startswith('dataset')
assert ds.name[7:].isdigit()
def cnn_sep(M, W1, W2, hh=.0001, ep=5000, d=0, sp=.0001, spb=3, al='rprop'):
# Facilitate reasonable convolutions core
theano.config.dnn.conv.algo_fwd = 'fft_tiling'
theano.config.dnn.conv.algo_bwd_filter = 'none'
theano.config.dnn.conv.algo_bwd_data = 'none'
# Reformat input data
M3 = reshape(M.astype(float32), (1, M.shape[0], M.shape[1]))
# Copy key variables to GPU
_M = theano.shared(M3.astype(float32))
# Get dictionary shapes
K = [W1.shape[1], W2.shape[1]]
T = W1.shape[2]
# We have weights to discover
H = theano.shared(
sqrt(2. / (K[0] + K[1] + M.shape[1])) *
random.rand(1, K[0] + K[1], M.T.shape[0]).astype(float32))
fI = InputLayer(shape=(1, K[0] + K[1], M.T.shape[0]), input_var=H)
# Split in two pathways
H1 = SliceLayer(fI, indices=slice(0, K[0]), axis=1)
H2 = SliceLayer(fI, indices=slice(K[0], K[0] + K[1]), axis=1)
# Compute source modulators using previously learned convolutional dictionaries
R1 = Conv1DLayer(H1,
filter_size=T,
W=W1,
num_filters=M.shape[0],
pad='same',
nonlinearity=lambda x: psoftplus(x, spb),
b=None)
R2 = Conv1DLayer(H2,
filter_size=T,
W=W2,
num_filters=M.shape[0],
pad='same',
nonlinearity=lambda x: psoftplus(x, spb),
b=None)
# Add the two approximations
R = ElemwiseSumLayer([R1, R2])
# Cost function
dum = Th.vector('dum')
Ro = get_output(R) + eps
cost = Th.mean(_M * (Th.log(_M + eps) - Th.log(Ro)) - _M +
Ro) + 0 * Th.mean(dum) + sp * Th.mean(abs(H))
# Train it using Lasagne
opt = downhill.build(al, loss=cost, inputs=[dum], params=[H])
train = downhill.Dataset(array([0]).astype(float32), batch_size=0)
er = downhill_train(opt, train, hh, ep, None)
# Get outputs
_r = squeeze(nget(R, dum, array([0]).astype(float32))) + eps
_r1 = squeeze(nget(R1, dum, array([0]).astype(float32)))
_r2 = squeeze(nget(R2, dum, array([0]).astype(float32)))
return _r, _r1, _r2, er
def test_callable(self):
def batches():
return 'hello'
ds = downhill.Dataset(batches, iteration_size=10)
assert list(ds) == ['hello'] * 10
def test_rng(self):
ds = downhill.Dataset([np.random.randn(40, 2)], rng=4)
assert ds.rng.randint(10) == 7
ds = downhill.Dataset([np.random.randn(40, 2)],
rng=np.random.RandomState(4))
assert ds.rng.randint(10) == 7
