def testMainOpSpeed(self):
# mode = theano.Mode(linker=theano.gof.vm.VM_Linker(
# allow_gc=False,
# use_cloop=True))
for i in range(self.nbTests):
# Generate theano functions to run the op in python and in C
output = self.op(self.s_images_list[i], self.s_hidacts_list[i],
self.frows(i), self.fcols(i))
pyFunction = theano.function([],
output,
mode=theano.Mode(linker='py'))
cFunction = theano.function([],
output,
mode=theano.Mode(linker='c'))
# Run the OP in python
t0 = time.time()
[pyFunction() for i in range(self.n_calls)]
t1 = time.time()
print "py", t1 - t0,
# Run the OP in C and time it
t0 = time.time()
[cFunction() for i in range(self.n_calls)]
t1 = time.time()
print "c", t1 - t0
def test_model_function_mode():
pars = ParameterSet()
weights = pars.declare((2, 3))
pars.alloc()
inpt = T.matrix()
output = T.dot(inpt, weights)
pars.data[...] = np.random.standard_normal(pars.data.shape)
model = Model()
model.exprs = {'inpt': inpt, 'output': output}
model.parameters = pars
mode = theano.Mode()
f = model.function(['inpt'], 'output', mode=mode)
actual_mode = f.theano_func.maker.mode
assert actual_mode is mode, 'wrong mode: %s' % actual_mode
model.mode = theano.Mode()
f = model.function(['inpt'], 'output')
actual_mode = f.theano_func.maker.mode
# Maybe a weird way to compare modes, but it seems to get the job done.
equal = actual_mode.__dict__ == mode.__dict__
assert equal, 'wrong mode: (%s != %s)' % (actual_mode, mode)
def __init__(self, rule_left, operand, rule_right, args,rows,cols):
self.show_log = True
self.rule_left = rule_left
self.rule_right = rule_right
self.operand =operand
self.rule = rule_left + operand + rule_right
self.args_str = ','.join(args)
# define all symbolic arguments
for p in args:
globals()[p] = T.dmatrix(p)
arg_set = eval('[' + ','.join(args) + ']')
if( rule_left.find('sum(' )>=0 or rule_right.find('sum(')>=0 ):
self.f_switch = theano.function(arg_set, self.__rule(arg_set), mode=theano.Mode(linker='vm'))
else:
operands = {'>' : 'T.gt(' + rule_left +','+ rule_right+')',
'<' : 'T.lt(' + rule_left +','+ rule_right+')',
'<=': 'T.le(' + rule_left +','+ rule_right+')',
'>=': 'T.ge(' + rule_left +','+ rule_right+')',
'=' : 'T.eq(' + rule_left +','+ rule_right+')',
}
t_compare = eval( operands[operand] )
self.ones = numpy.ones((rows,cols))
self.zeros = numpy.zeros((rows,cols))
z_switch = T.switch(t_compare, self.ones, self.zeros)
self.f_switch = theano.function(arg_set, z_switch, mode=theano.Mode(linker='vm'))
def compute_Lx_batches(v, g, h, xw_mat, xv_mat, xa, xb, xc, bs, cbs):
xw = xw_mat.flatten()
xv = xv_mat.flatten()
tv = v.reshape((bs // cbs, cbs, v.shape[1]))
tg = g.reshape((bs // cbs, cbs, g.shape[1]))
th = h.reshape((bs // cbs, cbs, h.shape[1]))
final_w1 = T.unbroadcast(T.shape_padleft(T.zeros_like(xw_mat)),0)
final_v1 = T.unbroadcast(T.shape_padleft(T.zeros_like(xv_mat)),0)
final_a1 = T.unbroadcast(T.shape_padleft(T.zeros_like(xa)),0)
final_b1 = T.unbroadcast(T.shape_padleft(T.zeros_like(xb)),0)
final_c1 = T.unbroadcast(T.shape_padleft(T.zeros_like(xc)),0)
def comp_step(lv, lg, lh,
acc_w1, acc_v1, acc_a1, acc_b1, acc_c1):
terms1 = compute_Lx_term1(lv, lg, lh, xw, xv, xa, xb, xc)
accs1 = [acc_w1, acc_v1, acc_a1, acc_b1, acc_c1]
rval = []
for (term1, acc) in zip(terms1,accs1):
rval += [acc + term1]
return rval
rvals,_ = theano.sandbox.scan.scan(
comp_step,
sequences=[tv,tg,th],
states=[
final_w1, final_v1, final_a1, final_b1, final_c1],
n_steps=bs // cbs,
profile=0,
mode=theano.Mode(linker='cvm_nogc'),
flags=['no_optimization'] )
accs1 = [x[0]/numpy.float32(bs//cbs) for x in rvals]
accs2 = compute_Lx_term2(v,g,h,xw,xv,xa,xb,xc)
return [x - y for x, y in zip(accs1, accs2)]
def get_Trainer(self,
costname='GenModelLogDensity',
method='adam',
padleft=False):
"""
"""
lr = T.scalar('lr')
the_cost = self.CostsDict[costname](
padleft=padleft
)[0] # the costname always appears at the index 0 of the cost_... method returned value
print('Params', self.get_Params(costname))
updates = {
'adam':
lasagne.updates.adam(-the_cost,
self.get_Params(costname),
learning_rate=lr)
}
train_fn = theano.function(inputs=self.get_CostInputs(costname) +
[theano.In(lr)],
outputs=the_cost,
updates=updates[method],
mode=theano.Mode(linker='vm'),
on_unused_input='warn') #
return train_fn
def generic_compute_Lx_batches(samples, weights, biases, bs, cbs):
tsamples = [x.reshape((bs//cbs, cbs, x.shape[1])) for x in samples]
final_ws = [T.unbroadcast(T.shape_padleft(T.zeros_like(x)),0)
for x in weights]
final_bs = [T.unbroadcast(T.shape_padleft(T.zeros_like(x)),0)
for x in biases]
n_samples = len(samples)
n_weights = len(weights)
n_biases = len(biases)
def comp_step(*args):
lsamples = args[:n_samples]
terms1 = generic_compute_Lx_term1(lsamples, weights, biases)
rval = []
for (term1, acc) in zip(terms1, args[n_samples:]):
rval += [acc + term1]
return rval
rvals,_ = theano.sandbox.scan.scan(
comp_step,
sequences=tsamples,
states=final_ws + final_bs,
n_steps=bs // cbs,
profile=0,
mode=theano.Mode(linker='cvm_nogc'),
flags=['no_optimization'] )
accs1 = [x[0]/numpy.float32(bs//cbs) for x in rvals]
accs2 = generic_compute_Lx_term2(samples,weights,biases)
return [x - y for x, y in zip(accs1, accs2)]
def test_borrow_output(self):
a = T.dmatrix()
f = function([a], Out(a, borrow=False))
o = np.ones((3, 3))
assert o is not f(
o) # function no longer permits aliasing outputs to inputs
f = function([a], Out(a * 4, borrow=False))
o = np.ones((3, 3))
four = f(o)
assert np.all(four == 4)
f(o + .1) # should not clobber the memory used to store four
assert np.all(four == 4)
f = function([a],
Out(a * 4, borrow=True),
mode=theano.Mode('c|py_nogc', 'fast_run'))
o = np.ones((3, 3))
four = f(o)
assert np.all(four == 4)
f(o + .1) # should clobber the memory used to store four
if theano.config.cxx:
assert not np.all(four == 4)
else:
# The Elemwise.perform method don't reuse memory
# as some numpy version don't support that correctly.
assert np.all(four == 4)
def test_shared_input_output():
# Test bug reported on the mailing list by Alberto Orlandi
# https://groups.google.com/d/topic/theano-users/6dLaEqc2R6g/discussion
# The shared variable is both an input and an output of the function.
if not theano.config.cxx:
pytest.skip("Need cxx for this test")
inc = theano.tensor.iscalar("inc")
state = theano.shared(0)
state.name = "state"
linker = theano.gof.CLinker()
mode = theano.Mode(linker=linker)
f = theano.function([inc], state, updates=[(state, state + inc)], mode=mode)
g = theano.function([inc], state, updates=[(state, state + inc)])
# Initial value
f0 = f(0)
g0 = g(0)
assert f0 == g0 == 0, (f0, g0)
# Increment state via f, returns the previous value.
f2 = f(2)
assert f2 == f0, (f2, f0)
f0 = f(0)
g0 = g(0)
assert f0 == g0 == 2, (f0, g0)
# Increment state via g, returns the previous value
g3 = g(3)
assert g3 == g0, (g3, g0)
f0 = f(0)
g0 = g(0)
assert f0 == g0 == 5, (f0, g0)
vstate = theano.shared(np.zeros(3, dtype="int32"))
vstate.name = "vstate"
fv = theano.function([inc], vstate, updates=[(vstate, vstate + inc)], mode=mode)
gv = theano.function([inc], vstate, updates=[(vstate, vstate + inc)])
# Initial value
fv0 = fv(0)
gv0 = gv(0)
assert np.all(fv0 == 0), fv0
assert np.all(gv0 == 0), gv0
# Increment state via f, returns the previous value.
fv2 = fv(2)
assert np.all(fv2 == fv0), (fv2, fv0)
fv0 = fv(0)
gv0 = gv(0)
assert np.all(fv0 == 2), fv0
assert np.all(gv0 == 2), gv0
# Increment state via g, returns the previous value
gv3 = gv(3)
assert np.all(gv3 == gv0), (gv3, gv0)
fv0 = fv(0)
gv0 = gv(0)
assert np.all(fv0 == 5), fv0
assert np.all(gv0 == 5), gv0
def get_insilico_knockout_tensor_op(lisa_prediction, precompute, coef, original_median=None):
""" use theano tensor operation to speed up
return a theano.function
lisa_prediction: numpy array
precompute: numpy array
coef: pandas DataFrame
"""
x = T.imatrix('E') # each motif tensor
precomp = theano.shared(precompute.astype(theano.config.floatX), name='precompute')
r = theano.shared(lisa_prediction.astype(theano.config.floatX), name='Lisa RP')
c = theano.shared(coef.iloc[:, 0].values.astype(theano.config.floatX), name='coefficients')
m = theano.shared(original_median.astype(theano.config.floatX), name='original_rp_median')
# sample x (gene1_bin1, gene1_bin2...gene2_bin1,gene2_bin2...)
y = T.extra_ops.repeat(x, precompute.shape[0], axis=0)
tensor_del = y * precomp # sample x (gene,bin)
tensor_del = T.reshape(tensor_del, (c.shape[0],r.shape[0],200)) # sample x gene x bin
tensor_del = T.transpose(T.sum(tensor_del, axis=2), (1,0)) + T.constant(1) # one motif
##tensor_del_med = T.mean(tensor_del, axis=0) # one motif
##log_tensor_del = T.log2(tensor_del) - T.log2(tensor_del_med)
log_tensor_del = T.log2(tensor_del) - m # original median already take log2
tensor_delta = r - T.dot(log_tensor_del, c)
mode = theano.Mode(linker='cvm', optimizer='fast_run')
theano.config.exception_verbosity = 'high'
# theano.config.openmp = True
theano_delta_rp = theano.function([x], tensor_delta, mode=mode)
return theano_delta_rp
def __init__(self, network, config=None, batch_size=20):
"""
Create a SGD trainer.
:type network:
:type config: deepy.conf.TrainerConfig
:return:
"""
super(DelayedBatchSGDTrainer, self).__init__(network, config)
self.learning_rate = self.config.learning_rate
self.batch_size = batch_size
logging.info('compiling %s learning function', self.__class__.__name__)
network_updates = list(network.updates) + list(
network._learning_updates)
learning_updates = list(self.learning_updates())
update_list = network_updates + learning_updates
logging.info("network updates: %s" %
" ".join(map(str, [x[0] for x in network_updates])))
logging.info("learning updates: %s" %
" ".join(map(str, [x[0] for x in learning_updates])))
self.learning_func = theano.function(
network.inputs,
self.training_variables,
updates=update_list,
allow_input_downcast=True,
mode=theano.Mode(linker=THEANO_LINKER))
def test_reallocation():
x = tensor.scalar("x")
y = tensor.scalar("y")
z = tensor.tanh(3 * x + y) + tensor.cosh(x + 5 * y)
# The functinality is currently implement for non lazy and non c VM only.
for linker in [
vm.VM_Linker(allow_gc=False, lazy=False, use_cloop=False),
vm.VM_Linker(allow_gc=True, lazy=False, use_cloop=False),
]:
m = theano.compile.get_mode(theano.Mode(linker=linker))
m = m.excluding("fusion", "inplace")
f = theano.function([x, y], z, name="test_reduce_memory", mode=m)
output = f(1, 2)
assert output
storage_map = f.fn.storage_map
def check_storage(storage_map):
from theano.tensor.var import TensorConstant
for i in storage_map:
if not isinstance(i, TensorConstant):
keys_copy = list(storage_map.keys())[:]
keys_copy.remove(i)
for o in keys_copy:
if storage_map[i][
0] and storage_map[i][0] is storage_map[o][0]:
return [True, storage_map[o][0]]
return [False, None]
assert check_storage(storage_map)[0]
assert len(set(id(v) for v in storage_map.values())) < len(storage_map)
def test_clinker_literal_cache():
mode = theano.Mode(linker="c")
A = theano.tensor.matrix()
input1 = theano.tensor.vector()
normal_svd = np.array(
[
[5.936276e01, -4.664007e-07, -2.56265e-06],
[-4.664007e-07, 9.468691e-01, -3.18862e-02],
[-2.562651e-06, -3.188625e-02, 1.05226e00],
],
dtype=theano.config.floatX,
)
orientationi = np.array(
[59.36276866, 1.06116353, 0.93797339], dtype=theano.config.floatX
)
for out1 in [A - input1[0] * np.identity(3), input1[0] * np.identity(3)]:
benchmark = theano.function(
inputs=[A, input1], outputs=[out1], on_unused_input="ignore", mode=mode
)
out1 = benchmark(normal_svd, orientationi)
def test_reallocation():
x = tensor.scalar('x')
y = tensor.scalar('y')
z = tensor.tanh(3 * x + y) + tensor.cosh(x + 5 * y)
for l in ['vm_nogc', 'vm', 'vm_nogc', 'vm']:
m = theano.compile.get_mode(theano.Mode(linker=l))
m = m.excluding('fusion', 'inplace')
f = theano.function([x, y], z, name="test_reduce_memory",
mode=m)
output = f(1, 2)
assert output
storage_map = f.fn.storage_map
def check_storage(storage_map):
from theano.tensor.var import TensorConstant
for i in storage_map.keys():
if not isinstance(i, TensorConstant):
keys_copy = storage_map.keys()[:]
keys_copy.remove(i)
for o in keys_copy:
if (storage_map[i][0] and
storage_map[i][0] is storage_map[o][0]):
return [True, storage_map[o][0]]
return [False, None]
assert check_storage(storage_map)[0]
assert len(set([id(v) for v in
storage_map.values()])) < len(storage_map)
def _gradient_function(self):
if not self._gradient_func:
params = self.network.parameters
inputs = self.network.input_variables + self.network.target_variables
self._gradient_func = theano.function(inputs, T.grad(self.cost, params),
allow_input_downcast=True, mode=theano.Mode(linker=THEANO_LINKER))
return self._gradient_func
def likelihood_gradient(observations = {}, learning_rate = 0.1):
all_vars = ancestors(list(observations.keys()))
for o in observations:
assert o in all_vars
if not is_raw_rv(o):
raise TypeError(o)
RVs = [v for v in all_vars if is_raw_rv(v)]
free_RVs = [v for v in RVs if v not in observations]
# Instantiate actual values for the different random variables:
params = dict()
for v in free_RVs:
f = theano.function([], v, mode=theano.Mode(linker='py', optimizer=None))
params[v] = theano.shared(f())
# Compute the full log likelihood:
full_observations = dict(observations)
full_observations.update(params)
log_likelihood = full_log_likelihood(full_observations)
# Construct the update equations for learning:
updates = dict()
for frvs in params.values():
updates[frvs] = frvs + learning_rate * tensor.grad(log_likelihood, frvs)
return params, updates, log_likelihood
def test_col_scale():
x = theano.sparse.csc_dmatrix()
s = theano.tensor.dvector()
def d(x, s):
return sp.sp_sum(sp.col_scale(x, s), sparse_grad=True)
rng = numpy.random.RandomState(8723)
R = 5
C = 8
x_val_dense = numpy.zeros((R, C), dtype='d')
for idx in [(0, 0), (4, 1), (2, 1), (3, 3), (4, 4), (3, 7), (2, 7)]:
x_val_dense.__setitem__(idx, rng.randn())
x_val = scipy.sparse.csc_matrix(x_val_dense)
s_val = rng.randn(C)
f = theano.function([x, s], sp.col_scale(x, s))
print 'A', f(x_val, s_val).toarray()
print 'B', (x_val_dense * s_val)
assert numpy.all(f(x_val, s_val).toarray() == (x_val_dense * s_val))
if 0:
tensor.verify_grad(None,
d, [x_val, s_val],
mode=theano.Mode(linker='py',
optimizer='fast_compile'))
else:
print >> sys.stderr, "WARNING: skipping gradient test because verify_grad doesn't support sparse arguments"
def test_clinker_literal_cache():
# This caused bugs in the past related to the cache.
if not theano.config.cxx:
raise SkipTest("G++ not available, so we need to skip this test.")
mode = theano.Mode(linker='c')
A = theano.tensor.matrix()
input1 = theano.tensor.vector()
normal_svd = numpy.array([[5.936276e+01, -4.664007e-07, -2.56265e-06],
[-4.664007e-07, 9.468691e-01, -3.18862e-02],
[-2.562651e-06, -3.188625e-02, 1.05226e+00]],
dtype=theano.config.floatX)
orientationi = numpy.array([59.36276866, 1.06116353, 0.93797339],
dtype=theano.config.floatX)
for out1 in [
A - input1[0] * numpy.identity(3), input1[0] * numpy.identity(3)
]:
benchmark = theano.function(inputs=[A, input1],
outputs=[out1],
on_unused_input='ignore',
mode=mode)
out1 = benchmark(normal_svd, orientationi)
def compute_Gv(*args):
idx0 = const([0])
ep = [
TT.alloc(const(0), 1, *shp) for shp in model.params_shape
]
def Gv_step(*gv_args):
idx = TT.cast(gv_args[0], 'int32')
nw_inps = [x[idx * options['cbs']: \
(idx + 1) * options['cbs']] for x in
loc_inputs]
replace = dict(zip(model.inputs, nw_inps))
nw_cost, nw_preactiv_out = safe_clone(
[model.train_cost, model.preactiv_out], replace)
nw_gvs = TT.Lop(
nw_preactiv_out, model.params,
TT.Rop(TT.grad(nw_cost, nw_preactiv_out), model.params,
args))
Gvs = [
ogv + ngv for (ogv, ngv) in zip(gv_args[1:], nw_gvs)
]
return [gv_args[0] + const(1)] + Gvs
states = [idx0] + ep
n_steps = options['mbs'] // options['cbs']
rvals, updates = scan(Gv_step,
states=states,
n_steps=n_steps,
mode=theano.Mode(linker='cvm'),
name='Gv_step',
profile=options['profile'])
final_Gvs = [x[0] / const(n_steps) for x in rvals[1:]]
return final_Gvs, updates
def scalar_armijo_search(phi,
phi0,
derphi0,
c1=constant(1e-4),
n_iters=10,
profile=0):
"""
.. todo::
WRITEME
"""
alpha0 = one
phi_a0 = phi(alpha0)
alpha1 = -(derphi0) * alpha0 ** 2 / 2.0 /\
(phi_a0 - phi0 - derphi0 * alpha0)
phi_a1 = phi(alpha1)
csol1 = phi_a0 <= phi0 + c1 * derphi0
csol2 = phi_a1 <= phi0 + c1 * alpha1 * derphi0
def armijo(alpha0, alpha1, phi_a0, phi_a1):
factor = alpha0**2 * alpha1**2 * (alpha1 - alpha0)
a = alpha0 ** 2 * (phi_a1 - phi0 - derphi0 * alpha1) - \
alpha1 ** 2 * (phi_a0 - phi0 - derphi0 * alpha0)
a = a / factor
b = -alpha0 ** 3 * (phi_a1 - phi0 - derphi0 * alpha1) + \
alpha1 ** 3 * (phi_a0 - phi0 - derphi0 * alpha0)
b = b / factor
alpha2 = (-b + TT.sqrt(abs(b**2 - 3 * a * derphi0))) / (3.0 * a)
phi_a2 = phi(alpha2)
end_condition = phi_a2 <= phi0 + c1 * alpha2 * derphi0
end_condition = TT.bitwise_or(TT.isnan(alpha2), end_condition)
end_condition = TT.bitwise_or(TT.isinf(alpha2), end_condition)
alpha2 = TT.switch(
TT.bitwise_or(alpha1 - alpha2 > alpha1 / constant(2.),
one - alpha2 / alpha1 < 0.96), alpha1 / constant(2.),
alpha2)
return [alpha1, alpha2, phi_a1, phi_a2], \
theano.scan_module.until(end_condition)
states = []
states += [TT.unbroadcast(TT.shape_padleft(alpha0), 0)]
states += [TT.unbroadcast(TT.shape_padleft(alpha1), 0)]
states += [TT.unbroadcast(TT.shape_padleft(phi_a0), 0)]
states += [TT.unbroadcast(TT.shape_padleft(phi_a1), 0)]
# print 'armijo'
rvals, _ = scan(armijo,
states=states,
n_steps=n_iters,
name='armijo',
mode=theano.Mode(linker='cvm'),
profile=profile)
sol_scan = rvals[1][0]
a_opt = ifelse(csol1, one, ifelse(csol2, alpha1, sol_scan))
score = ifelse(csol1, phi_a0, ifelse(csol2, phi_a1, rvals[2][0]))
return a_opt, score
def testMainOpSpeed(self):
# mode = theano.Mode(linker=theano.gof.vm.VM_Linker(
# allow_gc=False,
# use_cloop=True))
for i in range(self.nbTests):
# Generate theano functions to run the op in python and in C
output = self.op(self.s_filters_list[i], self.s_hidacts_list[i],
self.irows(i), self.icols(i))
output_omp = self.op_omp(self.s_filters_list[i],
self.s_hidacts_list[i], self.irows(i),
self.icols(i))
pyFunction = theano.function([],
output,
mode=theano.Mode(linker='py'))
cFunction = theano.function([],
output,
mode=theano.Mode(linker='c'))
cFunction2 = theano.function([],
output_omp,
mode=theano.Mode(linker='c'))
# Run the OP in python
t0 = time.time()
[pyFunction() for i in range(self.n_calls)]
t1 = time.time()
py_t = t1 - t0
print "py", py_t
# Run the OP in C and time it
t0 = time.time()
[cFunction() for i in range(self.n_calls)]
t1 = time.time()
c_t = t1 - t0
print "c", c_t, "speed up python", py_t / c_t
# Run the Op in C with openmp
if theano.config.openmp:
t0 = time.time()
[cFunction2() for i in range(self.n_calls)]
t1 = time.time()
c_t2 = t1 - t0
print "omp c", c_t2, "speed up python", py_t / c_t2, "speed up c", c_t / c_t2
def test_grad_abs():
a = theano.tensor.fscalar("a")
b = theano.tensor.nnet.relu(a)
c = theano.grad(b, a)
f = theano.function([a], c, mode=theano.Mode(optimizer=None))
# Currently Theano return 0.5, but it isn't sure it won't change
# in the futur.
ret = f(0.)
assert ret == 0.5, ret
def mh_sample(s_rng, outputs, observations={}):
all_vars = ancestors(list(outputs) + list(observations.keys()))
for o in observations:
assert o in all_vars
if not is_raw_rv(o):
raise TypeError(o)
RVs = [v for v in all_vars if is_raw_rv(v)]
free_RVs = [v for v in RVs if v not in observations]
# Draw sample from the proposal
free_RVs_state = []
for v in free_RVs:
f = theano.function([],
v,
mode=theano.Mode(linker='py', optimizer=None))
free_RVs_state.append(theano.shared(f()))
log_likelihood = theano.shared(numpy.array(float('-inf')))
U = s_rng.uniform(low=0.0, high=1.0)
def mcmc(ll, *frvs):
proposals = [
s_rng.local_proposal(v, rvs) for v, rvs in zip(free_RVs, frvs)
]
proposals_rev = [
s_rng.local_proposal(v, rvs)
for v, rvs in zip(free_RVs, proposals)
]
full_observations = dict(observations)
full_observations.update(
dict([(rv, s) for rv, s in zip(free_RVs, proposals)]))
new_log_likelihood = full_log_likelihood(full_observations)
logratio = new_log_likelihood - ll \
+ tensor.add(*[tensor.sum(lpdf(p, r)) for p, r in zip(proposals_rev, frvs)]) \
- tensor.add(*[tensor.sum(lpdf(p, r)) for p, r in zip(proposals, proposals)])
accept = tensor.gt(logratio, tensor.log(U))
return [tensor.switch(accept, new_log_likelihood, ll)] + \
[tensor.switch(accept, p, f) for p, f in zip(proposals, frvs)], \
{}, theano.scan_module.until(accept)
samples, updates = theano.scan(mcmc,
outputs_info=[log_likelihood] +
free_RVs_state,
n_steps=100)
updates[log_likelihood] = samples[0][-1]
updates.update(
dict([(f, s[-1]) for f, s in zip(free_RVs_state, samples[1:])]))
return [free_RVs_state[free_RVs.index(out)]
for out in outputs], log_likelihood, updates
def test_composite_neg_bool(self):
# Check that taking the negation of a Boolean intermediate value
# works correctly with Python code. It used to be an issue because
# `-numpy.bool_(True)` is False and `-numpy.bool_(False)` is True.
x = floats('x')
y = - (x > 0)
z = Composite([x], [y]).make_node(x).outputs[0]
f = theano.function([x], z, mode=theano.Mode(linker='py'))
for inp, out in zip([-1, 0, 1], [0, 0, -1]):
self.assertTrue(f(inp) == out)
def as_int(o):
if hasattr(o, 'data'):
return int(o.data)
elif hasattr(o, 'type'):
f = theano.function([],
o,
mode=theano.Mode(linker='py', optimizer=None))
return f()
else:
return int(o)
def test_deepcopy():
a = cuda.fmatrix()
a_v = cuda.CudaNdarray(numpy.zeros((3, 4), dtype='float32'))
# We force the c code to check that we generate c code
mode = theano.Mode("c", mode_with_gpu.optimizer)
f = theano.function([a], a, mode=mode)
theano.printing.debugprint(f)
out = f(a_v)
assert out is not a_v
assert numpy.allclose(numpy.asarray(a_v), numpy.asarray(out))
# We force the python linker as the default code should work for this op
mode = theano.Mode("py", mode_with_gpu.optimizer)
f = theano.function([a], a, mode=mode)
theano.printing.debugprint(f)
out = f(a_v)
assert out is not a_v
assert numpy.allclose(numpy.asarray(a_v), numpy.asarray(out))
def version():
"""
return the current cuDNN version we compile with.
This only check the header version, the the library we link with.
"""
if version.v is None:
f = theano.function([], DnnVersion()(), theano.Mode(optimizer=None))
version.v = f()
return version.v
def get_Trainer(self,
costname='ELBO',
method='adam',
padleft=False,
sample_strategy='with_symb_noise'):
"""
Returns a theano.function with updates that trains the statistical model (Adam trainer).
Evaluate this output function as in:
f(y, lr)
where y is the data to be fitted and lr is the desired learning rate.
"""
lr = T.scalar('lr')
adlr = T.scalar('adlr')
rho = T.scalar('rho')
eps = T.scalar('eps')
the_cost = self.CostsDict[costname](
sample_strategy=sample_strategy
)[0] # the costname always appears at the index 0 of the cost_... method
print('Params', self.get_Params(costname))
updates = {
'adam':
lasagne.updates.adam(-the_cost,
self.get_Params(costname),
learning_rate=lr),
'adadelta':
lasagne.updates.adadelta(-the_cost,
self.get_Params(costname),
learning_rate=adlr,
rho=rho,
epsilon=eps)
}
updates_Inputs = {
'adam': [theano.In(lr)],
'adadelta': [theano.In(adlr),
theano.In(rho),
theano.In(eps)]
}
with open('./debugprint', 'wb+') as debugfile:
so = sys.stdout
sys.stdout = debugfile
theano.printing.debugprint(the_cost)
sys.stdout = so
train_fn = theano.function(inputs=self.get_CostInputs(costname) +
updates_Inputs[method],
outputs=the_cost,
updates=updates[method],
mode=theano.Mode(linker='vm'),
on_unused_input='warn')
return train_fn
def encode(self, x):
"""
Encode given input.
"""
if not self._encode_func:
x_var = T.vector()
self._encode_func = theano.function(
[x_var],
self.layers[0].encode_func(x),
allow_input_downcast=True,
mode=theano.Mode(linker=THEANO_LINKER))
return self._encode_func(x)
def test_diag_grad():
def d(x):
sp_x = theano.sparse.csc_from_dense(x)
diag_x = sp.diag(sp_x)
return diag_x.sum()
diag_mat = numpy.zeros((4, 4))
for idx in xrange(4):
diag_mat[idx, idx] += idx * 0.1
utt.verify_grad(d, [diag_mat],
mode=theano.Mode(linker='py', optimizer='fast_compile'))
def __init__(self, learning_rate, epsilon, mode=theano.Mode(linker='cvm')):
self.learning_rate = learning_rate
assert type(self.learning_rate) is FloatType or IntType, "Learning rate must be an integer or float: {0!r}".format(self.learning_rate)
assert 0 < self.learning_rate, "Learning rate must be positive: {0!r}".format(self.learning_rate)
self.epsilon = epsilon
assert type(self.epsilon) is FloatType or IntType, "Epsilon must be an integer or float: {0!r}".format(self.epsilon)
assert 0 < self.epsilon, "Epsilon must be positive: {0!r}".format(self.epsilon)
self.opt_parameters = {'opt': 'AdaGrad', 'learning_rate':self.learning_rate, 'epsilon':self.epsilon}
self.mode = mode
logging.info('Optimizer loaded. Type: {0}, learning rate: {1},'
' epsilon: {3}'.format(self.opt_parameters['opt'], self.opt_parameters['lr'],
self.opt_parameters['e']))
