def _gpu_matrix_dot(matrix_a, matrix_b, matrix_c=None):
"""
Performs matrix multiplication.
Attempts to use the GPU if it's available. If the matrix multiplication
is too big to fit on the GPU, this falls back to the CPU after throwing
a warning.
Parameters
----------
matrix_a : WRITEME
matrix_b : WRITEME
matrix_c : WRITEME
"""
if not hasattr(ZCA._gpu_matrix_dot, 'theano_func'):
ma, mb = T.matrices('A', 'B')
mc = T.dot(ma, mb)
ZCA._gpu_matrix_dot.theano_func = \
theano.function([ma, mb], mc, allow_input_downcast=True)
theano_func = ZCA._gpu_matrix_dot.theano_func
try:
if matrix_c is None:
return theano_func(matrix_a, matrix_b)
else:
matrix_c[...] = theano_func(matrix_a, matrix_b)
return matrix_c
except MemoryError:
warnings.warn('Matrix multiplication too big to fit on GPU. '
'Re-doing with CPU. Consider using '
'THEANO_FLAGS="device=cpu" for your next '
'preprocessor run')
return np.dot(matrix_a, matrix_b, matrix_c)
def __init__(self,
gen_params, # dictionary of generative model parameters
GEN_MODEL, # class that inherits from GenerativeModel
rec_params, # dictionary of approximate posterior ("recognition model") parameters
REC_MODEL, # class that inherits from RecognitionModel
xDim=2, # dimensionality of latent state
yDim=2 # dimensionality of observations
):
# instantiate rng's
self.srng = RandomStreams(seed=234)
self.nrng = np.random.RandomState(124)
#---------------------------------------------------------
## actual model parameters
self.X, self.Y = T.matrices('X','Y') # symbolic variables for the data
self.xDim = xDim
self.yDim = yDim
# instantiate our prior & recognition models
self.mrec = REC_MODEL(rec_params, self.Y, self.xDim, self.yDim, self.srng, self.nrng)
self.mprior = GEN_MODEL(gen_params, self.xDim, self.yDim, srng=self.srng, nrng = self.nrng)
self.isTrainingRecognitionModel = True;
self.isTrainingGenerativeModel = True;
def sample_parallel():
print "並列"
x, y = T.matrices("a", "b")
diff = x - y
abs_diff = abs(diff)
diff_sq = diff**2
# 2つの行列を入力, 3つの行列のベクトルを出力
f = theano.function([x, y], [diff, abs_diff, diff_sq])
print f([[0,1],[2,3]], [[10,11],[12,13]])
print
def test_grad(self, cls_ofg):
x, y, z = T.matrices('xyz')
e = x + y * z
op = cls_ofg([x, y, z], [e])
f = op(x, y, z)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
assert np.all(11.0 == fn(xv, yv, zv))
def test_grad(self):
x, y, z = T.matrices('xyz')
e = x + y * z
op = OpFromGraph([x, y, z], [e], mode='FAST_RUN', grad_depth=2)
f = op(x, y, z)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX)*3
zv = numpy.ones((2, 2), dtype=config.floatX)*5
assert numpy.all(11.0 == fn(xv, yv, zv))
def test_connection_pattern(self, cls_ofg):
# Basic case
x, y, z = T.matrices('xyz')
out1 = x * y
out2 = y * z
op1 = cls_ofg([x, y, z], [out1, out2])
results = op1.connection_pattern(None)
expect_result = [[True, False],
[True, True],
[False, True]]
assert results == expect_result
# Graph with ops that don't have a 'full' connection pattern
# and with ops that have multiple outputs
m, n, p, q = T.matrices('mnpq')
o1, o2 = op1(m, n, p)
out1, out2 = op1(o1, q, o2)
op2 = cls_ofg([m, n, p, q], [out1, out2])
results = op2.connection_pattern(None)
expect_result = [[True, False],
[True, True],
[False, True],
[True, True]]
assert results == expect_result
# Inner graph where some computation doesn't rely on explicit inputs
srng = RandomStreams(seed=234)
rv_u = srng.uniform((2, 2))
x, y = T.matrices('xy')
out1 = x + rv_u
out2 = y + 3
out3 = 3 + rv_u
op3 = cls_ofg([x, y], [out1, out2, out3])
results = op3.connection_pattern(None)
expect_result = [[True, False, False],
[False, True, False],
[True, False, True]]
assert results == expect_result
def _build(self):
if self._debug:
theano.config.compute_test_value = 'warn'
X,W = T.matrices('X','W')
if self._debug:
X.tag.test_value = np.random.rand(3,1)
W.tag.test_value = np.random.rand(5,3)
Z = T.dot(W,X)
A = self._activation(Z)
self._fpropagate = function([X, W],A)
self._layers = []
self._generate_initial_weights()
def test_grad_grad(self):
x, y, z = T.matrices('xyz')
e = x + y * z
op = OpFromGraph([x, y, z], [e])
f = op(x, y, z)
f = f - T.grad(T.sum(f), y)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
assert numpy.allclose(6.0, fn(xv, yv, zv))
def test_straightforward(self):
x, y, z = T.matrices('xyz')
e = x + y * z
op = OpFromGraph([x, y, z], [e], mode='FAST_RUN')
f = op(x, y, z) - op(y, z, x) # (1+3*5=array of 16) - (3+1*5=array of 8)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX)*3
zv = numpy.ones((2, 2), dtype=config.floatX)*5
#print function, function.__module__
#print fn.maker.fgraph.toposort()
fn(xv, yv, zv)
assert numpy.all(8.0 == fn(xv, yv, zv))
assert numpy.all(8.0 == fn(xv, yv, zv))
def __init__(self, shape):
self.in_size, self.out_size = shape
self.W = init_weights(shape)
self.b = init_bias(self.out_size)
self.gW = init_gradws(shape)
self.gb = init_bias(self.out_size)
D, X = T.matrices("D", "X")
def _active(X):
return T.nnet.sigmoid(T.dot(X, self.W) + self.b)
self.active = theano.function(inputs = [X], outputs = _active(X))
def _derive(D, X):
return D * ((1 - X) * X)
self.derive = theano.function(
inputs = [D, X],
outputs = _derive(D, X)
)
def _propagate(D):
return T.dot(D, self.W.T)
self.propagate = theano.function(inputs = [D], outputs = _propagate(D))
x, dy = T.rows("x","dy")
updates_grad = [(self.gW, self.gW + T.dot(x.T, dy)),
(self.gb, self.gb + dy)]
self.grad = theano.function(
inputs = [x, dy],
updates = updates_grad
)
updates_clear = [
(self.gW, self.gW * 0),
(self.gb, self.gb * 0)]
self.clear_grad = theano.function(
inputs = [],
updates = updates_clear
)
lr = T.scalar()
t = T.scalar()
updates_w = [
(self.W, self.W - self.gW * lr / t),
(self.b, self.b - self.gb * lr / t)]
self.update = theano.function(
inputs = [lr, t],
updates = updates_w
)
def test_setitem(self):
x, y, z, w = T.matrices('x', 'y', 'z', 'w')
i1 = TheanoInterval(x, y)
i2 = TheanoInterval(z, w)
i1[:, 1:3] = i2
f = function([x, y, z, w], [i1.lower, i1.upper])
ex_x = A([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ex_z = A([[20, 30], [50, 60], [80, 90]])
ex_y = 100 * ex_x
ex_w = 100 * ex_z
l, u = f(ex_x, ex_y, ex_z, ex_w)
rl = A([[1., 20., 30.], [4., 50., 60.], [7., 80., 90.]])
ru = rl * 100
assert_array_equal(l, rl)
assert_array_equal(u, ru)
def test_size_changes(self):
x, y, z = T.matrices('xyz')
e = T.dot(x, y)
op = OpFromGraph([x, y], [e], mode='FAST_RUN')
f = op(x, op(y, z))
fn = function([x, y, z], f)
xv = numpy.ones((2, 3), dtype=config.floatX)
yv = numpy.ones((3, 4), dtype=config.floatX)*3
zv = numpy.ones((4, 5), dtype=config.floatX)*5
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert numpy.all(180.0 == res)
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert numpy.all(180.0 == res)
def test_size_changes(self, cls_ofg):
x, y, z = tt.matrices("xyz")
e = tt.dot(x, y)
op = cls_ofg([x, y], [e])
f = op(x, op(y, z))
fn = function([x, y, z], f)
xv = np.ones((2, 3), dtype=config.floatX)
yv = np.ones((3, 4), dtype=config.floatX) * 3
zv = np.ones((4, 5), dtype=config.floatX) * 5
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert np.all(180.0 == res)
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert np.all(180.0 == res)
def test_straightforward(self):
x, y, z = T.matrices('xyz')
e = x + y * z
op = OpFromGraph([x, y, z], [e], mode='FAST_RUN')
f = op(x, y, z) - op(y, z,
x) # (1+3*5=array of 16) - (3+1*5=array of 8)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
#print function, function.__module__
#print fn.maker.fgraph.toposort()
fn(xv, yv, zv)
assert numpy.all(8.0 == fn(xv, yv, zv))
assert numpy.all(8.0 == fn(xv, yv, zv))
def test_size_changes(self, cls_ofg):
x, y, z = T.matrices('xyz')
e = T.dot(x, y)
op = cls_ofg([x, y], [e])
f = op(x, op(y, z))
fn = function([x, y, z], f)
xv = np.ones((2, 3), dtype=config.floatX)
yv = np.ones((3, 4), dtype=config.floatX) * 3
zv = np.ones((4, 5), dtype=config.floatX) * 5
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert np.all(180.0 == res)
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert np.all(180.0 == res)
def test_size_changes(self):
x, y, z = T.matrices('xyz')
e = T.dot(x, y)
op = OpFromGraph([x, y], [e])
f = op(x, op(y, z))
fn = function([x, y, z], f)
xv = numpy.ones((2, 3), dtype=config.floatX)
yv = numpy.ones((3, 4), dtype=config.floatX) * 3
zv = numpy.ones((4, 5), dtype=config.floatX) * 5
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert numpy.all(180.0 == res)
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert numpy.all(180.0 == res)
def test_connection_pattern(self):
# Basic case
x, y, z = T.matrices('xyz')
out1 = x * y
out2 = y * z
op1 = OpFromGraph([x, y, z], [out1, out2])
results = op1.connection_pattern(None)
expect_result = [[True, False], [True, True], [False, True]]
assert results == expect_result
# Graph with ops that don't have a 'full' connection pattern
# and with ops that have multiple outputs
m, n, p, q = T.matrices('mnpq')
o1, o2 = op1(m, n, p)
out1, out2 = op1(o1, q, o2)
op2 = OpFromGraph([m, n, p, q], [out1, out2])
results = op2.connection_pattern(None)
expect_result = [[True, False], [True, True], [False, True],
[True, True]]
assert results == expect_result
# Inner graph where some computation doesn't rely on explicit inputs
srng = RandomStreams(seed=234)
rv_u = srng.uniform((2, 2))
x, y = T.matrices('xy')
out1 = x + rv_u
out2 = y + 3
out3 = 3 + rv_u
op3 = OpFromGraph([x, y], [out1, out2, out3])
results = op3.connection_pattern(None)
expect_result = [[True, False, False], [False, True, False],
[True, False, True]]
assert results == expect_result
def test_straightforward(self, cls_ofg):
x, y, z = T.matrices('xyz')
e = x + y * z
op = cls_ofg([x, y, z], [e])
# (1+3*5=array of 16) - (3+1*5=array of 8)
f = op(x, y, z) - op(y, z, x)
fn = function([x, y, z], f)
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
# print function, function.__module__
# print fn.maker.fgraph.toposort()
fn(xv, yv, zv)
assert np.all(8.0 == fn(xv, yv, zv))
assert np.all(8.0 == fn(xv, yv, zv))
def test_shared(self):
x, y, z = T.matrices('xyz')
s = shared(numpy.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = OpFromGraph([x, y, z], [e])
# (1+3*5=array of 16) - (3+1*5=array of 8)
f = op(x, y, z) - op(y, z, x)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
# print function, function.__module__
# print fn.maker.fgraph.toposort()
assert numpy.allclose(8.0, fn(xv, yv, zv))
assert numpy.allclose(8.0, fn(xv, yv, zv))
def test_straightforward(self, cls_ofg):
x, y, z = T.matrices('xyz')
e = x + y * z
op = cls_ofg([x, y, z], [e])
# (1+3*5=array of 16) - (3+1*5=array of 8)
f = op(x, y, z) - op(y, z, x)
fn = function([x, y, z], f)
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
# print function, function.__module__
# print fn.maker.fgraph.toposort()
fn(xv, yv, zv)
assert np.all(8.0 == fn(xv, yv, zv))
assert np.all(8.0 == fn(xv, yv, zv))
def test_getitem(self):
x, y = T.matrices('x', 'y')
i = TheanoInterval(x, y)
i0, i1, i2 = i[0, 0], i[1, 1], i[2, 2]
l0, l1, l2 = i0.lower, i1.lower, i2.lower
u0, u1, u2 = i0.upper, i1.upper, i2.upper
f = function([x, y], [l0, l1, l2, u0, u1, u2])
ex_x = A([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ex_y = ex_x * 10
[rl0, rl1, rl2, ru0, ru1, ru2] = f(ex_x, ex_y)
assert_equal(rl0, 1)
assert_equal(rl1, 5)
assert_equal(rl2, 9)
assert_equal(ru0, 10)
assert_equal(ru1, 50)
assert_equal(ru2, 90)
def test_shared(self, cls_ofg):
x, y, z = T.matrices('xyz')
s = shared(np.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = cls_ofg([x, y, z], [e])
# (1+3*5=array of 16) - (3+1*5=array of 8)
f = op(x, y, z) - op(y, z, x)
fn = function([x, y, z], f)
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
# print function, function.__module__
# print fn.maker.fgraph.toposort()
assert np.allclose(8.0, fn(xv, yv, zv))
assert np.allclose(8.0, fn(xv, yv, zv))
def __init__(self, shape, X):
prefix = "Softmax_"
self.in_size, self.out_size = shape
self.W = init_weights(shape, prefix + "W")
self.b = init_bias(self.out_size, prefix + "b")
self.gW = init_gradws(shape, prefix + "gW")
self.gb = init_bias(self.out_size, prefix + "gb")
D = T.matrices("D")
self.X = X
def _active(X):
return T.nnet.softmax(T.dot(X, self.W) + self.b)
self.active = theano.function(inputs = [self.X], outputs = _active(self.X))
def _propagate(D):
return T.dot(D, self.W.T)
self.propagate = theano.function(inputs = [D], outputs = _propagate(D))
x, dy = T.rows("x","dy")
updates_grad = [(self.gW, self.gW + T.dot(x.T, dy)),
(self.gb, self.gb + dy)]
self.grad = theano.function(
inputs = [x, dy],
updates = updates_grad
)
updates_clear = [
(self.gW, self.gW * 0),
(self.gb, self.gb * 0)]
self.clear_grad = theano.function(
inputs = [],
updates = updates_clear
)
lr = T.scalar()
t = T.scalar()
updates_w = [
(self.W, self.W - self.gW * lr / t),
(self.b, self.b - self.gb * lr / t)]
self.update = theano.function(
inputs = [lr, t],
updates = updates_w
)
self.params = [self.W, self.b]
def test_max(self):
al = A([[1, 2], [3, 4]])
au = A([[2, 2], [4, 7]])
bl = A([[0, 3], [3, -4]])
bu = A([[2, 4], [3, -3]])
alt, aut, blt, but = T.matrices('alt', 'aut', 'blt', 'but')
ai = TheanoInterval(alt, aut)
bi = TheanoInterval(blt, but)
ci = ai.max(bi)
d = {alt: al, aut: au, blt: bl, but: bu}
res = ci.eval(d)
rl = res[0]
ru = res[1]
ansl = A([[1, 3], [3, 4]])
ansu = A([[2, 4], [4, 7]])
array_almost_equal(rl, ansl)
array_almost_equal(ru, ansu)
def test_shared_grad(self, cls_ofg):
x, y, z = tt.matrices("xyz")
s = shared(np.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = cls_ofg([x, y, z], [e])
f = op(x, y, z)
f = f - tt.grad(tt.sum(f), y)
fn = function([x, y, z], f)
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
assert np.allclose(11.0 + s.get_value(), fn(xv, yv, zv))
# grad again the shared variable
f = op(x, y, z)
f = f - tt.grad(tt.sum(f), s)
fn = function([x, y, z], f)
assert np.allclose(15.0 + s.get_value(), fn(xv, yv, zv))
def test_shared_grad(self):
x, y, z = T.matrices('xyz')
s = shared(numpy.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = OpFromGraph([x, y, z], [e])
f = op(x, y, z)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
assert numpy.allclose(11.0 + s.get_value(), fn(xv, yv, zv))
# grad again the shared variable
f = op(x, y, z)
f = f - T.grad(T.sum(f), s)
fn = function([x, y, z], f)
assert numpy.allclose(15.0 + s.get_value(), fn(xv, yv, zv))
def main():
"""A simple test case."""
rgb1, rgb2 = T.matrices('rgb1', 'rgb2')
jab1 = srgb_to_ucs(rgb1, 100, 20, 20, **Surrounds.AVERAGE)
jab2 = srgb_to_ucs(rgb2, 100, 20, 20, **Surrounds.AVERAGE)
loss = delta_e(jab1, jab2)**2
grad_ = T.grad(loss, rgb2)
grad = theano.function([rgb1, rgb2], grad_)
# Inversion of CAM02-UCS via gradient descent
target = floatX([[0.25, 0.25, 1]])
x = np.zeros_like(target) + 0.5
print(x)
for i in range(1500):
g = grad(target, x)
x -= 1e-6 * g
if i % 100 == 99:
print(x)
def test_shared_grad(self):
x, y, z = T.matrices('xyz')
s = shared(numpy.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = OpFromGraph([x, y, z], [e], mode='FAST_RUN')
f = op(x, y, z)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
assert numpy.allclose(11.0 + s.get_value(), fn(xv, yv, zv))
# grad again the shared variable
f = op(x, y, z)
f = f - T.grad(T.sum(f), s)
fn = function([x, y, z], f)
assert numpy.allclose(15.0 + s.get_value(),
fn(xv, yv, zv))
def test_shared_grad(self, cls_ofg):
x, y, z = T.matrices('xyz')
s = shared(np.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = cls_ofg([x, y, z], [e])
f = op(x, y, z)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
assert np.allclose(11.0 + s.get_value(), fn(xv, yv, zv))
# grad again the shared variable
f = op(x, y, z)
f = f - T.grad(T.sum(f), s)
fn = function([x, y, z], f)
assert np.allclose(15.0 + s.get_value(),
fn(xv, yv, zv))
def test_ops1(self):
# __add__, __sub__, __mul__,
x, y, z, w = T.matrices('x', 'y', 'z', 'w')
i1 = TheanoInterval(x, y)
i2 = TheanoInterval(z, w)
l1 = A([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
u1 = l1 * 10 + 1
l2 = l1 * 10 + 2
u2 = l1 * 10 + 3
r_add1 = i1 + i2
r_add2 = i1 + x
r_sub1 = i1 - i2
r_sub2 = i1 - x
r_mul1 = i1 * i2
r_mul2 = i1 * x
fres = [r_add1.lower, r_add1.upper, r_add2.lower, r_add2.upper,
r_sub1.lower, r_sub1.upper, r_sub2.lower, r_sub2.upper,
r_mul1.lower, r_mul1.upper, r_mul2.lower, r_mul2.upper]
f = function([x, y, z, w], fres)
add1l, add1u, add2l, add2u, \
sub1l, sub1u, sub2l, sub2u, \
mul1l, mul1u, mul2l, mul2u, = f(l1, u1, l2, u2)
ops_results = [add1l, add1u, add2l, add2u,
sub1l, sub1u, sub2l, sub2u,
mul1l, mul1u, mul2l, mul2u]
r_add1l = l1 + l2
r_add2l = l1 + l1
r_add1u = u1 + u2
r_add2u = u1 + l1
r_sub1l = l1 - u2
r_sub2l = l1 - l1
r_sub1u = u1 - l2
r_sub2u = u1 - l1
r_mul1l = l1 * l2
r_mul2l = l1 * l1
r_mul1u = u1 * u2
r_mul2u = u1 * l1
results = [r_add1l, r_add1u, r_add2l, r_add2u,
r_sub1l, r_sub1u, r_sub2l, r_sub2u,
r_mul1l, r_mul1u, r_mul2l, r_mul2u]
for i in range(len(ops_results)):
array_almost_equal(ops_results, results)
def test_top_with_numpy_generator():
# Random dataset
dataset = np.random.normal(0, 1, (1000, 10))
# target = 0 if sum>.5 else 1
target = 0. + (np.dot(dataset, np.ones((10, 1))) > .5)
def dataset_generator():
return NumpyDatasetGenerator(dataset=(dataset, target), batch_size=100)
W = theano.shared(np.random.normal(0, 1, (10, 1)).astype(top.up.floatX))
X, T = tensor.matrices('X', 'Y')
Y = tensor.nnet.sigmoid(tensor.dot(X, W))
cost = tensor.nnet.binary_crossentropy(Y, T).mean()
opt = top.Optimizer(W,
cost,
input=[X, T],
method='sgd',
learning_rate=.005)
opt.iterate_epochs(1000, dataset_generator)
print W.get_value()
def test_eval(self):
txl, txu, tyl, tyu = T.matrices('xl', 'xu', 'yl', 'yu')
xl = A([[1, 2], [3, 4]])
xu = A([[2, 4], [6, 9]])
yl = A([[-1, -5], [0, 3]])
yu = A([[4, 2], [0, 3]])
ix = TheanoInterval(txl, txu)
iy = TheanoInterval(tyl, tyu)
iz = ix + iy
d = {txl: xl, txu: xu, tyl: yl, tyu: yu}
zl, zu = iz.eval(d)
array_almost_equal(zl, xl + yl)
array_almost_equal(zu, xu + yu)
i2 = TheanoInterval(theano.shared(1), theano.shared(3))
i2l, i2u = i2.eval()
assert_equal(i2l, 1)
assert_equal(i2u, 3)
i2l, i2u = i2.eval({})
assert_equal(i2l, 1)
assert_equal(i2u, 3)
def SGD(eta, minibatch_size, n_minibatch, n_epochs):
# Testing and Validation data are the outputs of the last inputs.
print 'Calling SGD() ..'
index = T.iscalar('index')
x, y = T.matrices('x', 'y')
tree = TreeLSTM(x, n_in)
updates = [(param, param - eta * gparam) for param, gparam in zip(tree.params, T.grad(tree.loss(y), tree.params))]
train_fn = theano.function([index], tree.loss(y), updates=updates,
givens={x: train_x[:, minibatch_size * n_in * index: (minibatch_size + 1) * n_in * index],
y: train_y[:, minibatch_size * index: (minibatch_size + 1) * index]}
)
# Compilation over
#################
## TRAIN MODEL ##
#################
for epoch in n_epochs:
for idx in range(n_minibatch):
train_fn(idx)
def compile(self,X,n_negative_samples=None):
if n_negative_samples is None:
n_negative_samples = 1000
pos_samples = X.loc[:, self.column_ranges.keys()].values.astype(floatX)
pos_data, neg_data = T.matrices('SigData', 'BckData')
pos_w, neg_w, parameters = T.vectors('SigW', 'BckW', 'parameters')
neg_samples, neg_weight = self.generate_negative_samples(n_negative_samples=n_negative_samples,
strategy=self.sampling_strategy)
givens = {pos_data: pos_samples, neg_data: neg_samples, neg_w: neg_weight}
pdf = self.prepare_pdf()
pdfs, summands = pdf(pos_data, neg_data, neg_weights=neg_w, weights=parameters)
result = - T.mean(pos_w * T.log(pdfs))
self.Tfunction = theano.function([parameters,pos_w], result, givens=givens)
self.Tderivative = theano.function([parameters,pos_w], T.grad(result, parameters), givens=givens)
self.X=X
def __init__(self, layers, mini_batch_size):
"""Takes a list of `layers`, describing the network architecture, and
a value for the `mini_batch_size` to be used during training
by stochastic gradient descent.
"""
self.layers = layers
self.mini_batch_size = mini_batch_size
self.params = [
param for layer in self.layers for param in layer.params
]
self.x = T.matrices("x")
self.y = T.ivector("y")
init_layer = self.layers[0]
init_layer.set_inpt(self.x, self.x, self.mini_batch_size)
for j in range(1, len(self.layers)):
prev_layer, layer = self.layers[j - 1], self.layers[j]
layer.set_inpt(prev_layer.output, prev_layer.output_dropout,
self.mini_batch_size)
self.output = self.layers[-1].output
self.output_dropout = self.layers[-1].output_dropout
def test_4_conditionals():
a, b = T.scalars('a', 'b')
x, y = T.matrices('x', 'y')
f_switch = theano.function([a, b, x, y], T.switch(T.lt(a, b), T.mean(x), T.mean(y)))
f_lazy_ifelse = theano.function([a, b, x, y], ifelse(T.lt(a, b), T.mean(x), T.mean(y)))
x_val = np.ones((100, 100), dtype=theano.config.floatX)*1
y_val = np.ones((100, 100), dtype=theano.config.floatX)*2
# vectorized switch is going to evaluate both options
np.testing.assert_almost_equal(
f_switch(1, 2, x_val, y_val),
1
)
# lazy evaluation is going to evaluate only single option
np.testing.assert_almost_equal(
f_lazy_ifelse(2, 1, x_val, y_val),
2
)
def test_reshape(self):
tl, tu = T.matrices('l', 'u')
xl = A([[1, 2, 3], [4, 5, 6]])
xu = xl + 3
i = TheanoInterval(tl, tu)
i1 = i.reshape((1, 6))
i2 = i.reshape((2, 3))
i3 = i.reshape((3, 2))
i4 = i.reshape((6, 1))
[l1, u1] = i1.eval({tl: xl, tu: xu})
[l2, u2] = i2.eval({tl: xl, tu: xu})
[l3, u3] = i3.eval({tl: xl, tu: xu})
[l4, u4] = i4.eval({tl: xl, tu: xu})
assert_array_equal(l1, xl.reshape((1, 6)))
assert_array_equal(l2, xl.reshape((2, 3)))
assert_array_equal(l3, xl.reshape((3, 2)))
assert_array_equal(l4, xl.reshape((6, 1)))
assert_array_equal(u1, xu.reshape((1, 6)))
assert_array_equal(u2, xu.reshape((2, 3)))
assert_array_equal(u3, xu.reshape((3, 2)))
assert_array_equal(u4, xu.reshape((6, 1)))
def test_broadcasting_pattern(self):
from lasagne.layers import ElemwiseSumLayer, InputLayer
import lasagne
import theano.tensor as T
import numpy as np
import theano
a, b = T.matrices('a', 'b')
a_ = np.ones((2, 1), dtype=theano.config.floatX)
b_ = np.ones((2, 5), dtype=theano.config.floatX)
l_a = InputLayer((2, 1))
l_b = InputLayer((2, 5))
l_o = ElemwiseSumLayer([l_a, l_b])
shp = l_o.output_shape # set broadcastable table
output = lasagne.layers.get_output(l_o, {
l_a: a,
l_b: b
}).eval({
a: a_,
b: b_
})
np.testing.assert_array_almost_equal(output, np.ones((2, 5)) + 1.0)
assert shp == output.shape
# test that None dimensions are not modified
l_a = InputLayer((2, None))
l_b = InputLayer((2, None))
l_o = ElemwiseSumLayer([l_a, l_b])
shp = l_o.output_shape # set broadcastable table
a = T.addbroadcast(a, 1)
output = lasagne.layers.get_output(l_o, {
l_a: a,
l_b: b
}).eval({
a: a_,
b: b_
})
np.testing.assert_array_almost_equal(output, np.ones((2, 5)) + 1.0)
assert shp == (2, None)
self.z = theano.dot(self.inpts, self.W) + self.B
self.pool_size = pool_size
self.filter_shape = filter_shape
conv_out = conv.conv2d(self.inpts,
image_shape=self.image_shape,
filters=self.W,
filter_shape=self.filter_shape)
pool_out = downsample.pool_2d(input=conv_out,
ds=self.pool_size,
ignore_border=True)
self.out = T.nnet.sigmoid(pool_out)
x = T.matrices("x")
y = T.matrices("y")
ly1 = convlyer(x, filter_shape=(5, 5), image_shape=(28, 28), pool_size=(2, 2))
ly2 = lyers(ly1.out, 12, 100, activation="relu")
ly3 = lyers(ly2.out, 100, 10, activation="sigm")
lamda = 0.1
cost = T.mean(1 / 2 * T.square(ly3.outputs - y))
params = [param for ly in [ly1, ly2, ly3] for param in ly.params]
grads = T.grad(cost, params)
update = [(param, param - lamda * grad) for param, grad in zip(params, grads)]
train = theano.function([x, y], cost, updates=update)
predict = theano.function([x], ly3.outputs)
for t_data in training_data[0:10]:
def test_gpu_rowwise_switch():
assert theano.config.device.startswith("gpu"), "Need to test on GPU!"
data = [
# 4 x 2
(np.array([[0.22323515, 0.36703175], [0.82260513, 0.3461504],
[0.82362652, 0.81626087], [0.95270008, 0.2226797]]),
np.array([[0.36341551, 0.20102882], [0.24144639, 0.45237923],
[0.39951822, 0.7348066],
[0.16649647, 0.60306537]]), np.array([1, 0, 1, 1]),
np.array([[0.22323515, 0.36703175], [0.24144639, 0.45237923],
[0.82362652, 0.81626087], [0.95270008, 0.2226797]])),
# 2 x 3 x 4
(np.array([[[0.48769062, 0.82649632, 0.2047115, 0.41437615],
[0.25290664, 0.87164914, 0.80968588, 0.49295084],
[0.71438099, 0.97913502, 0.37598001, 0.76958707]],
[[0.37605973, 0.538358, 0.74304674, 0.84346291],
[0.95310617, 0.61540292, 0.49881143, 0.1028554],
[0.83481996, 0.90969569, 0.40410424, 0.34419989]]]),
np.array([[[0.7289117, 0.97323253, 0.19070121, 0.64164653],
[0.26816493, 0.76093069, 0.95284825, 0.77350426],
[0.55415519, 0.39431256, 0.86588665, 0.50031027]],
[[0.1980869, 0.7753601, 0.26810868, 0.3628802],
[0.2488143, 0.21278388, 0.09724567, 0.58457886],
[0.12295105, 0.75321368, 0.37258797,
0.27756972]]]), np.array([1, 0]),
np.array([[[0.48769062, 0.82649632, 0.2047115, 0.41437615],
[0.25290664, 0.87164914, 0.80968588, 0.49295084],
[0.71438099, 0.97913502, 0.37598001, 0.76958707]],
[[0.1980869, 0.7753601, 0.26810868, 0.3628802],
[0.2488143, 0.21278388, 0.09724567, 0.58457886],
[0.12295105, 0.75321368, 0.37258797, 0.27756972]]]))
]
A2, B2 = T.matrices("AB")
A3, B3 = T.tensor3("A"), T.tensor3("B")
mask = T.ivector("mask")
switch2 = T.switch(mask.dimshuffle(0, "x"), A2, B2)
switch3 = T.switch(mask.dimshuffle(0, "x", "x"), A3, B3)
f2 = theano.function([A2, B2, mask], switch2)
f3 = theano.function([A3, B3, mask], switch3)
print "Graph of 2dim switch:"
theano.printing.debugprint(f2.maker.fgraph.outputs[0])
print "Graph of 3dim switch:"
theano.printing.debugprint(f3.maker.fgraph.outputs[0])
for instance in data:
# Retrieve appropriate function
func = f2 if instance[0].ndim == 2 else f3
# Cast to float-friendly types
instance = [
x.astype(np.float32) if x.dtype.kind == 'f' else x.astype(np.int32)
for x in instance
]
yield tuple([_test_gpu_rowwise_switch_inner, func] + instance)
import theano
import theano.tensor as T
import numpy as np
from breze.arch.construct.layer.distributions import DiagGauss
n, m = 10, 5
A, B = T.matrices('A', 'B')
X = np.empty((3, 4), dtype=np.float32)
print 'X shape =', X.shape
# # this works
# S = theano.shared(np.random.randn(n, m))
# sample = T.tile(S[np.newaxis, :, :], (A.shape[0], 1, 1), ndim=3)
# this does not work
mean_val, var_val = (np.random.randn(n, m) for _ in xrange(2))
var_val = var_val**2 + 1e-5
mean_raw, var_raw = (theano.shared(v) for v in (mean_val, var_val))
mean, var = (T.tile(v[np.newaxis, :, :], (A.shape[0], 1, 1), ndim=3) for v in (mean_raw, var_raw))
gaus = DiagGauss(mean, var)
sample = gaus.sample()
foo_sample_A = theano.function([A], sample)
print 'foo_sample_A.shape =', foo_sample_A(X).shape # (3, 10, 5)
sample += B[0, 0] * 0 # to avoid unused input errorr
def __init__(self,
state = 'x',
measurement = 'z',
motion_transition = None,
measurement_transition = None):
self.N = len(state.split(' '))
self.M = len(measurement.split(' '))
self.X, self.Z = T.fvectors('X','Z')
self.P, self.Q, self.R = T.fmatrices('P','Q','R')
self.F, self.H = T.matrices('F','H')
self.dt = T.scalar('dt')
self.X_ = T.dot(self.F, self.X)
self.fX_ = G.jacobian(T.flatten(self.X_), self.X)
self.P_ = T.dot(T.dot(self.fX_, self.P), T.transpose(self.fX_)) + self.dt * self.Q
self.h = T.dot(self.H, self.X_)
self.y = self.Z - self.h
self.hX_ = G.jacobian(self.h, self.X_)
self.matrix_inv = T.nlinalg.MatrixInverse()
self.S = T.dot(T.dot(self.hX_, self.P_), T.transpose(self.hX_)) + self.R
self.K = T.dot(T.dot(self.P_, T.transpose(self.hX_)), self.matrix_inv(self.S))
self.X__ = self.X_ + T.dot(self.K, self.y)
self.P__ = T.dot(T.identity_like(self.P) - T.dot(self.K, self.hX_), self.P_)
self.prediction = theano.function(inputs = [self.X,
self.P,
self.Q,
self.F,
self.dt],
outputs = [self.X_,
self.P_],
allow_input_downcast = True)
self.update = theano.function(inputs = [self.X,
self.Z,
self.P,
self.Q,
self.R,
self.F,
self.H,
self.dt],
outputs = [self.X__,
self.P__],
allow_input_downcast = True)
if motion_transition == None:
self.motion_transition = np.eye(self.N)
else:
self.motion_transition = np.array(motion_transition)
if measurement_transition == None:
self.measurement_transition = np.eye(self.M)
else:
self.measurement_transition = np.array(motion_transition)
import theano
import theano.tensor as T
import numpy as np
# from OkapiV2.Layers.Activations import SoftmaxLayer
from OkapiV2 import Losses
x, y = T.matrices('xy')
# regular softmax and crossentropy
sm = T.nnet.softmax(x)
cm1 = T.nnet.categorical_crossentropy(sm, y)
g1 = T.grad(cm1.mean(), x)
# numerically stable log-softmax with crossentropy
'''xdev = x-x.max(1, keepdims=True)
lsm = xdev - T.log(T.sum(T.exp(xdev), axis=1, keepdims=True))'''
# lsm = SoftmaxLayer().get_output(x, None) + 1e-7
'''sm2 = T.exp(lsm)
cm2 = -T.sum(y*lsm, axis=1)'''
cm2 = Losses.AltSoftmaxLoss().get_train_loss(x, y, [None])
# cm2 = T.nnet.categorical_crossentropy(sm2, y)
g2 = T.grad(cm2.mean(), x)
# create some inputs into a softmax that are large and labels
a = np.exp(10*np.random.rand(5, 10).astype(theano.config.floatX))
# create some one-hot coded labels
b = np.eye(5, 10).astype(theano.config.floatX)
# show equivalence of softmax and exponentiated numerically stable log-softmax
'''f1 = theano.function([x], [sm, sm2])
sm1, sm2 = f1(a)
import time
import numpy
import theano
from theano import tensor as tt
from theano.ifelse import ifelse
a, b = tt.scalars('a', 'b')
x, y = tt.matrices('x', 'y')
z_switch = tt.switch(tt.lt(a, b), tt.mean(x), tt.mean(y))
z_lazy = ifelse(tt.lt(a, b), tt.mean(x), tt.mean(y))
f_switch = theano.function([a, b, x, y], z_switch)
f_lazyifelse = theano.function([a, b, x, y], z_lazy)
val1 = 0.
val2 = 1.
big_mat1 = numpy.ones((10000, 1000))
big_mat2 = numpy.ones((10000, 1000))
n_times = 10
tic = time.clock()
for i in xrange(n_times):
f_switch(val1, val2, big_mat1, big_mat2)
print 'time spent evaluating both values %f sec' % (time.clock() - tic)
tic = time.clock()
for i in xrange(n_times):
# IfElse v.s. Switch
# (1) Both ops build a condition over symbolic variables.
# (2) IfElse takes a boolean condition and two variables as inputs.
# (3) Swith takes a tensor as condition an two variables as inputs. switch
# is an elementwise operation and is thus more general than ifelse.
# (4) Whereas switch evaluates both output variables, ifelse is lazy and
# only evaluates one variable with respect to the condition.
# Example
from theano import tensor as T
from theano.ifelse import ifelse
import theano, time, numpy
a, b = T.scalars('a', 'b')
x, y = T.matrices('x', 'y')
z_switch = T.switch(T.lt(a, b), T.mean(x), T.mean(y))
z_lazy = ifelse(T.lt(a, b), T.mean(x), T.mean(y))
f_switch = theano.function([a, b, x, y],
z_switch,
mode=theano.Mode(linker='vm'))
f_lazyifelse = theano.function([a, b, x, y],
z_lazy,
mode=theano.Mode(linker='vm'))
val1 = 0.0
val2 = 1.0
big_mat1 = numpy.ones((10000, 20000), dtype=numpy.float32)
big_mat2 = numpy.ones((10000, 20000), dtype=numpy.float32)
x=T.dscalar('x')
y=T.dscalar('y')
z=x+y
f=function([x,y],z)
print f(2,3)
print f(132.5,322.4)
print z.eval({x:1,y:2})
#Add two mitrix
x=T.dmatrix('x')
y=T.dmatrix('y')
z=x+y
f=function([x,y],z)
print f([[1,2],[1,2]],[[10,20],[10,20]])
#multiply outputs
a,b=T.matrices('a','b')
diff=a-b
abs_diff = abs(diff)
diff_squared = diff ** 2
f=theano.function([a,b],[diff,abs_diff,diff_squared])
d,e,f=f([[1,2,3],[10,20,30]],[[100,200,300],[1,2,3]])
print "diff is : \n"
print d
print "abs_diff is : \n"
print e
print "diff_squared is : \n"
print f
import theano.tensor as T
from theano import function
a, b = T.matrices('a', 'b')
diff = a - b
abs_diff = abs(diff)
diff_squared = diff ** 2
f = function([a, b], [diff, abs_diff, diff_squared])
print f([[1, 1], [1, 1]], [[0, 1], [2, 3]])
from theano import function
import theano
from theano.ifelse import ifelse
import time
if __name__ == '__main__':
from minitest import *
inject(numpy.allclose, 'must_close')
# http://deeplearning.net/software/theano/tutorial/conditions.html
with test("if"):
a,b = T.scalars('a', 'b')
x,y = T.matrices('x', 'y')
z_lazy = ifelse(T.lt(a, b), T.mean(x), T.mean(y))
f_lazyifelse = theano.function([a, b, x, y], z_lazy,
mode=theano.Mode(linker='vm'))
val1 = 0.
val2 = 1.
big_mat1 = numpy.ones((2, 2))
big_mat2 = numpy.ones((2, 2))
f_lazyifelse(val1, val2, big_mat1, big_mat2).must_close(
[1.0])
with test("if with value"):
a = T.scalar()
def make_train_fun(
self,
agent,
sequence_length=25, # how many steps to make before updating weights
observation_shape=(1, 64,
64), # same as env.observation_space.shape
reward_scale=1e-3, #rewards are multiplied by this. May be useful if they are large.
gamma=0.99, #discount from TD
):
"""Compiles a function to train for one step"""
#make replay environment
observations = T.tensor(theano.config.floatX,
broadcastable=(False, ) *
(2 + len(observation_shape)),
name="observations[b,t,color,width,height]")
actions = T.imatrix("actions[b,t]")
rewards, is_alive = T.matrices("rewards[b,t]", "is_alive[b,t]")
prev_memory = [l.input_var for l in agent.agent_states.values()]
replay = SessionBatchEnvironment(observations, [observation_shape],
actions=actions,
rewards=rewards,
is_alive=is_alive)
#replay sessions
_, _, _, _, (logits_seq, V_seq) = agent.get_sessions(
replay,
session_length=sequence_length,
experience_replay=True,
initial_hidden=prev_memory,
unroll_scan=
False, #speeds up compilation 10x, slows down training by 20% (still 4x faster than TF :P )
)
rng_updates = agent.get_automatic_updates(
) #updates of random states (will be passed to a function)
# compute pi(a|s) and log(pi(a|s)) manually [use logsoftmax]
# we can't guarantee that theano optimizes logsoftmax automatically since it's still in dev
logits_flat = logits_seq.reshape([-1, logits_seq.shape[-1]])
policy_seq = T.nnet.softmax(logits_flat).reshape(logits_seq.shape)
logpolicy_seq = T.nnet.logsoftmax(logits_flat).reshape(
logits_seq.shape)
# get policy gradient
elwise_actor_loss, elwise_critic_loss = a2c.get_elementwise_objective(
policy=logpolicy_seq,
treat_policy_as_logpolicy=True,
state_values=V_seq[:, :, 0],
actions=replay.actions[0],
rewards=replay.rewards * reward_scale,
is_alive=replay.is_alive,
gamma_or_gammas=gamma,
n_steps=None,
return_separate=True)
# add losses with magic numbers
# (you can change them more or less harmlessly, this usually just makes learning faster/slower)
# also regularize to prioritize exploration
reg_logits = T.mean(logits_seq**2)
reg_entropy = T.mean(T.sum(policy_seq * logpolicy_seq, axis=-1))
loss = 0.1 * elwise_actor_loss.mean() + 0.25 * elwise_critic_loss.mean(
) + 1e-3 * reg_entropy + 1e-2 * reg_logits
# Compute weight updates, clip by norm
grads = T.grad(loss, self.weights)
grads = lasagne.updates.total_norm_constraint(grads, 10)
updates = lasagne.updates.adam(grads, self.weights, 1e-4)
# compile train function
inputs = [observations, actions, rewards, is_alive] + prev_memory
return theano.function(inputs,
updates=rng_updates + updates,
allow_input_downcast=True)
def __init__(self, rng, layer, shape, X, is_train = 1, batch_size = 1, p = 0.5):
prefix = "GRU_"
self.in_size, self.out_size = shape
self.W_xr = init_weights((self.in_size, self.out_size), prefix + "W_xr" + "_" + layer)
self.W_hr = init_weights((self.out_size, self.out_size), prefix + "W_hr" + "_" + layer)
self.b_r = init_bias(self.out_size, prefix + "b_r" + "_" + layer)
self.W_xz = init_weights((self.in_size, self.out_size), prefix + "W_xz" + "_" + layer)
self.W_hz = init_weights((self.out_size, self.out_size), prefix + "W_hz" + "_" + layer)
self.b_z = init_bias(self.out_size, prefix + "b_z" + "_" + layer)
self.W_xh = init_weights((self.in_size, self.out_size), prefix + "W_xh" + "_" + layer)
self.W_hh = init_weights((self.out_size, self.out_size), prefix + "W_hh" + "_" + layer)
self.b_h = init_bias(self.out_size, prefix + "b_h" + "_" + layer)
# for gradients
self.gW_xr = init_gradws((self.in_size, self.out_size), prefix + "gW_xr" + "_" + layer)
self.gW_hr = init_gradws((self.out_size, self.out_size), prefix + "gW_h" + "_" + layer)
self.gb_r = init_bias(self.out_size, prefix + "gb_r" + "_" + layer)
self.gW_xz = init_gradws((self.in_size, self.out_size), prefix + "gW_xz" + "_" + layer)
self.gW_hz = init_gradws((self.out_size, self.out_size), prefix + "gW_hz" + "_" + layer)
self.gb_z = init_bias(self.out_size, prefix + "gb_z" + "_" + layer)
self.gW_xh = init_gradws((self.in_size, self.out_size), prefix + "gW_xh" + "_" + layer)
self.gW_hh = init_gradws((self.out_size, self.out_size), prefix + "gW_hh" + "_" + layer)
self.gb_h = init_bias(self.out_size, prefix + "gb_h" + "_" + layer)
def _active(x, pre_h):
r = T.nnet.sigmoid(T.dot(x, self.W_xr) + T.dot(pre_h, self.W_hr) + self.b_r)
z = T.nnet.sigmoid(T.dot(x, self.W_xz) + T.dot(pre_h, self.W_hz) + self.b_z)
gh = T.tanh(T.dot(x, self.W_xh) + T.dot(r * pre_h, self.W_hh) + self.b_h)
h = z * pre_h + (1 - z) * gh
return r, z, gh, h
self.X = X
H = T.matrix("H")
[r, z, gh, h], updates = theano.scan(_active, sequences=[self.X], outputs_info=[None, None, None, H])
self.active = theano.function(
inputs = [self.X, H],
outputs = [r, z, gh, h]
)
h = T.reshape(h, (self.X.shape[0], self.out_size))
# dropout
if p > 0:
srng = T.shared_randomstreams.RandomStreams(rng.randint(999999))
mask = srng.binomial(n = 1, p = 1-p, size = h.shape, dtype = theano.config.floatX)
self.activation = T.switch(T.eq(is_train, 1), h * mask, h * (1 - p)) # is_train = 1
else:
self.activation = T.switch(T.eq(is_train, 1), h, h) # is_train
# TODO ->scan
def _derive(prop, r, post_r, z, gh, pre_h, post_dh, post_dgh, post_dr, post_dz):
dh = prop + T.dot(post_dr, self.W_hr.T) + T.dot(post_dz, self.W_hz.T) + T.dot(post_dgh * post_r, self.W_hh.T) + post_dh * z
dgh = dh * (1 - z) * (1 - gh ** 2)
dr = T.dot(dgh * pre_h, self.W_hh.T) * ((1 - r) * r)
dz = (dh * (pre_h - gh)) * ((1 - z) * z)
return dh, dgh, dr, dz
prop, r, z, gh, pre_h, post_dh, post_dgh, post_dr, post_dz, post_r = \
T.matrices("prop", "r", "z", "gh", "pre_h", "post_dh", "post_dgh", "post_dr", "post_dz", "post_r")
self.derive = theano.function(
inputs = [prop, r, post_r, z, gh, pre_h, post_dh, post_dgh, post_dr, post_dz],
outputs = _derive(prop, r, post_r, z, gh, pre_h, post_dh, post_dgh, post_dr, post_dz)
)
x, dz, dr, dgh = T.rows("x", "dz", "dr", "dgh")
updates_grad = [(self.gW_xr, self.gW_xr + T.dot(x.T, dr)),
(self.gW_xz, self.gW_xz + T.dot(x.T, dz)),
(self.gW_xh, self.gW_xh + T.dot(x.T, dgh)),
(self.gW_hr, self.gW_hr + T.dot(pre_h.T, dr)),
(self.gW_hz, self.gW_hz + T.dot(pre_h.T, dz)),
(self.gW_hh, self.gW_hh + T.dot((r * pre_h).T, dgh)),
(self.gb_r, self.gb_r + dr),
(self.gb_z, self.gb_z + dz),
(self.gb_h, self.gb_h + dgh)]
self.grad = theano.function(
inputs = [x, r, pre_h, dz, dr, dgh],
updates = updates_grad
)
updates_clear = [
(self.gW_xr, self.gW_xr * 0),
(self.gW_xz, self.gW_xz * 0),
(self.gW_xh, self.gW_xh * 0),
(self.gW_hr, self.gW_hr * 0),
(self.gW_hz, self.gW_hz * 0),
(self.gW_hh, self.gW_hh * 0),
(self.gb_r, self.gb_r * 0),
(self.gb_z, self.gb_z * 0),
(self.gb_h, self.gb_h * 0)]
self.clear_grad = theano.function(
inputs = [],
updates = updates_clear
)
lr = T.scalar()
t = T.scalar()
tm1 = T.scalar()
updates_w = [
(self.W_xr, self.W_xr - self.gW_xr * lr / t),
(self.W_xz, self.W_xz - self.gW_xz * lr / t),
(self.W_xh, self.W_xh - self.gW_xh * lr / t),
(self.W_hr, self.W_hr - self.gW_hr * lr / tm1),
(self.W_hz, self.W_hz - self.gW_hz * lr / tm1),
(self.W_hh, self.W_hh - self.gW_hh * lr / tm1),
(self.b_r, self.b_r - self.gb_r * lr / t),
(self.b_z, self.b_z - self.gb_z * lr / t),
(self.b_h, self.b_h - self.gb_h * lr / t)]
self.update = theano.function(
inputs = [lr, t, tm1],
updates = updates_w
)
DZ, DR, DGH = T.matrices("DZ", "DR", "DGH")
def _propagate(DR, DZ, DGH):
return (T.dot(DR, self.W_xr.T) + T.dot(DZ, self.W_xz.T) + T.dot(DGH, self.W_xh.T))
self.propagate = theano.function(inputs = [DR, DZ, DGH], outputs = _propagate(DR, DZ, DGH))
self.params = [self.W_xr, self.W_hr, self.b_r,
self.W_xz, self.W_hz, self.b_z,
self.W_xh, self.W_hh, self.b_h]
# Dataset generation parameters
ns = 300 # number of source points
nt = 20 # number of target points
nb_tr = 3 # number of trials for averaging
num_src = 5 # number of source domains
# variables to stock the results
lambdaWa = []
Wdista = []
MMDa = []
true_errora = []
# variables used in teano for mmd calculation
Xth, Yth = T.matrices('X', 'Y')
sigmath = T.scalar('sigma')
fn = theano.function([Xth, Yth, sigmath], mmd.rbf_mmd2(Xth, Yth,
sigma=sigmath))
a, b = np.ones((ns, )) / ns, np.ones(
(nt, )) / nt # empirical distributions for source and target domains
reg = 1e-1 # entropic regularization for \lambda computation
Mb = ot.utils.dist0(ns) # cost matrix on bins
Mb /= Mb.max() # normalization
if plot_moons: # to plot the data (avoid when len(theta_range)>5)
fig, axes = plt.subplots(len(theta_range), num_src, figsize=(21, 16))
for j, it in enumerate(theta_range):
lambdaW = []
from theano import tensor as T
from theano.ifelse import ifelse
import theano, time, numpy
a,b = T.matrices('a', 'b')
x,y = T.matrices('x', 'y')
#I would think of it as just another operator that acts on three symbolic variables, if the first is true, return the second, else return the third.
#But for many operators (like - and +) theano has overloaded them for symbolic variables, so probably you don't feel the difference.
#For example, if a and b are numbers, then c=a+b creates a variable c with the value of a+b. If a and b are symbolic variables, then c=a+b creates another symbolic variable c, that will apply (element-wise) addition to a and b when the corresponded function gets called/evaluated.
#Here's an introduction on theano operators and graphs. http://deeplearning.net/software/theano/extending/graphstructures.html
val1 = numpy.zeros((100, 100))
val2 = numpy.ones((100, 100))
big_mat1 = numpy.ones((100, 100))
big_mat2 = numpy.zeros((100, 100))
z_switch = T.switch(T.lt(a-b,0),big_mat1, big_mat2)
#set = (a,b)
#z_lazy = ifelse(T.lt(*set), big_mat1, big_mat2)
f_switch = theano.function([a,b], z_switch,
mode=theano.Mode(linker='vm'))
#f_lazyifelse = theano.function([a,b], z_lazy,
# mode=theano.Mode(linker='vm'))
n_times = 10
tic = time.clock()
def make_gradient(self,
steps=30,
interpolator='PchipInterpolator',
bg=BgColors.NEUTRAL,
diff_weight=1e4,
callback=None):
global opfunc
start = time.perf_counter()
def _loss(y, ideal_jab, ideal_diff):
jab = ucs.symbolic.srgb_to_ucs(y, 80, 16,
ucs.srgb_to_xyz(bg)[1] * 80,
**Surrounds.AVERAGE)
diff = jab[1:, :] - jab[:-1, :]
ucs_loss = T.sum(T.sqr(jab - ideal_jab))
diff_loss = T.mean(T.sqr(diff - ideal_diff))
return ucs_loss + diff_loss * diff_weight
if opfunc is None:
rgb, _ideal_jab, _ideal_diff = T.matrices('rgb', 'ideal_jab',
'ideal_diff')
loss_sym = _loss(rgb, _ideal_jab, _ideal_diff)
grad_sym = T.grad(loss_sym, rgb)
# Ensure this function is compiled
ucs.srgb_to_ucs([1, 1, 1])
print('Building opfunc()...', file=sys.stderr)
opfunc = theano.function([rgb, _ideal_jab, _ideal_diff],
[loss_sym, grad_sym],
allow_input_downcast=True,
on_unused_input='ignore')
print('Done building functions in {:.3g} seconds.'.format(
time.perf_counter() - start),
file=sys.stderr)
# If the method was called only to precompile Theano functions, return early
if self.x is None:
return
if self.colorspace == 'rgb':
conds = Conditions(Y_w=100, Y_b=ucs.srgb_to_xyz(self.bg)[1] * 100)
jmh = ucs.jab_to_jmh(ucs.srgb_to_ucs(self.y, conds))
elif self.colorspace == 'jmh':
jmh = self.y.copy()
jmh[:, 2] = ucs.H_to_h(self.y[:, 2])
else:
raise ValueError('colorspace must be RGB or JMH')
jmh[:, 2] = np.rad2deg(np.unwrap(np.deg2rad(jmh[:, 2])))
if self.periodic:
jmh[-1] = jmh[0]
interp = interpolate.CubicSpline(self.x,
jmh,
axis=0,
bc_type='periodic')
else:
if not hasattr(interpolate, interpolator):
raise ValueError(
'interpolator must exist in scipy.interpolate')
interp = getattr(interpolate, interpolator)(self.x, jmh, axis=0)
ideal_jmh = np.zeros((steps, 3))
x = np.linspace(self.x[0], self.x[-1], steps)
for i, n in enumerate(x):
ideal_jmh[i] = interp(n)
ideal_jab = ucs.jmh_to_jab(ideal_jmh)
ideal_diff = ideal_jab[1:, :] - ideal_jab[:-1, :]
y = floatX(np.random.uniform(-1e-8, 1e-8, size=ideal_jab.shape)) + 0.5
opt = AdamOptimizer(y,
opfunc=lambda y: opfunc(y, ideal_jab, ideal_diff),
proj=lambda y: np.clip(y, 0, 1))
for i in opt:
if i % 100 == 0:
loss_ = float(opfunc(y, ideal_jab, ideal_diff)[0])
if callback is not None:
callback('Iteration {:d}, loss = {:.3f}'.format(i, loss_))
# i = 0
# y_shape = y.shape
# def lbfgs_callback(y_opt):
# nonlocal i
# i += 1
# if i % 100 == 0:
# loss_ = float(opfunc(y_opt.reshape(y_shape), ideal_jab, ideal_diff)[0])
# if callback is not None:
# callback('Iteration {:d}, loss = {:.3f}'.format(i, loss_))
# y = lbfgs(y, lambda y: opfunc(y, ideal_jab, ideal_diff), callback=lbfgs_callback)
done = time.perf_counter()
s = (
'Loss was {:.3f} after {:d} iterations; make_gradient() took {:.3f} seconds.'
).format(
float(opfunc(y, ideal_jab, ideal_diff)[0]),
i,
done - start,
)
return x, y, s
def SGD(eta, n_minibatch, n_epochs, valid_steps, valid_interval, valid_headwords):
# Testing and Validation data are the outputs of the last inputs.
print 'Calling SGD() ..'
t0 = time.time()
index = T.iscalar('index')
x, y = T.matrices('x', 'y')
print 'The length of valid_headwords is', len(valid_headwords)
print 'n_epochs * n_minibatch * n_in is', n_epochs * n_minibatch * n_in / valid_interval
# minibatch size needs to be added later in assert statement.
assert len(valid_headwords) >= (n_epochs * n_minibatch)/ valid_interval, 'The length of valid_headwords need to be' \
'greater than n_epochs * n_minibatch/ valid_interval'
# Need to put (* minibatch_size) when support for minibatches is introduced.
model = SentenceLSTMLayers(n_in_tree, n_nodes)
model_output = model.output(x)
valid_output = model.output(x, n_steps=valid_steps, valid=True)
loss = model.loss(y, model_output)
pred = model.pred(model_output)
validation_pred = model.pred(valid_output)
params = model.params
updates = [(param, param - eta * gparam) for param, gparam in zip(params, T.grad(loss, params))]
# No need to return the updates by scan function within RNNStackedLayers. Only needed when shared variables are
# updated within step function. This is not the case here. See this source
train_fn = theano.function([index], [loss, pred], updates=updates,
givens={x: train_x[:, n_in * index: n_in * (index + 1)],
y: train_y[:, index: index + 1]})
valid_fn = theano.function([x], validation_pred)
# Compilation over
#################
## TRAIN MODEL ##
#################
words_seen = 0
match_idx = 0
valid_idx = 0
training_loss = []
for i in range(n_epochs):
print 'The current epoch number is', i
t1 = time.time()
for idx in range(n_minibatch):
train_loss, train_pred = train_fn(idx)
training_loss.append(train_loss)
if (i*n_minibatch+idx) % valid_interval == 0:
valid_op = []
valid_headword = valid_headwords[valid_idx]
valid_input = word_vecs[valid_headword]
print '---------------------------------------------------------------'
assert valid_input.shape[0] == vec_dims, 'ASSERTION 1 FALSE'
print 'The validation headword for validation index =', valid_idx, 'is', \
valid_headword
valid_pred = valid_fn(valid_input)
valid_op.append(valid_pred)
print 'The validation prediction is', ' '.join([mappings_words[idx] for idx in valid_op])
print 'Time taken by this epoch is', time.time()-t1
print 'Time taken by __main__ and training is', time.time()-t0
print 'Total words seen is', words_seen
print 'Total words matched is', match_idx
print 'Ratio is', match_idx/words_seen
from __future__ import absolute_import, print_function, division
import time
import numpy
import theano
from theano import tensor as tt
from six.moves import xrange
from theano.ifelse import ifelse
a, b = tt.scalars('a', 'b')
x, y = tt.matrices('x', 'y')
z_switch = tt.switch(tt.lt(a, b), tt.mean(x), tt.mean(y))
z_lazy = ifelse(tt.lt(a, b), tt.mean(x), tt.mean(y))
f_switch = theano.function([a, b, x, y], z_switch)
f_lazyifelse = theano.function([a, b, x, y], z_lazy)
val1 = 0.
val2 = 1.
big_mat1 = numpy.ones((10000, 1000))
big_mat2 = numpy.ones((10000, 1000))
n_times = 10
tic = time.clock()
for i in xrange(n_times):
f_switch(val1, val2, big_mat1, big_mat2)
print('time spent evaluating both values %f sec' % (time.clock() - tic))
def test_clone(self):
# Data for unit testing
X_unit = ['abcdef', 'abcdef', 'qwerty']
X_unit = [[ord(c) for c in w] for w in X_unit]
X_unit = np.array(X_unit, dtype='int8')
n_alerts_unit, l_alerts_unit = X_unit.shape
mask_unit = np.ones(X_unit.shape, dtype='int8')
# Dimensions
n_alerts = None
l_alerts = None
n_alphabet = 2**7 # All ASCII chars
num_units = 10
# Symbolic variables
input_var, input_var2 = T.imatrices('inputs', 'inputs2')
mask_var, mask_var2 = T.matrices('masks', 'masks2')
target_var = T.dvector('targets')
# build net for testing
l_in = InputLayer(shape=(n_alerts, l_alerts),
input_var=input_var,
name='INPUT-LAYER')
l_emb = EmbeddingLayer(l_in,
n_alphabet,
n_alphabet,
W=np.eye(n_alphabet),
name='EMBEDDING-LAYER')
l_emb.params[l_emb.W].remove('trainable') # Fix weight
l_mask = InputLayer(shape=(n_alerts, l_alerts),
input_var=mask_var,
name='MASK-INPUT-LAYER')
l_lstm = LSTMLayer(l_emb,
num_units=num_units,
name='LSTM-LAYER',
mask_input=l_mask)
l_slice = SliceLayer(l_lstm, indices=-1, axis=1,
name="SLICE-LAYER") # Only last timestep
net = l_slice
# clone
l_in2 = InputLayer(shape=(n_alerts, l_alerts),
input_var=input_var2,
name='INPUT-LAYER2')
l_mask2 = InputLayer(shape=(n_alerts, l_alerts),
input_var=mask_var2,
name='MASK-INPUT-LAYER2')
net2 = lstm_rnn_tied_weights.clone(net, l_in2, l_mask2)
self.assertNotEqual(repr(net), repr(net2))
pred_unit = layers.get_output(net,
inputs={
l_in: input_var,
l_mask: mask_var
}).eval({
input_var: X_unit,
mask_var: mask_unit
})
pred_unit2 = layers.get_output(net2,
inputs={
l_in2: input_var2,
l_mask2: mask_var2
}).eval({
input_var2: X_unit,
mask_var2: mask_unit
})
self.assert_array_equal(pred_unit, pred_unit2)
def test_gpu_rowwise_switch():
assert theano.config.device.startswith("gpu"), "Need to test on GPU!"
data = [
# 4 x 2
(np.array([[ 0.22323515, 0.36703175],
[ 0.82260513, 0.3461504 ],
[ 0.82362652, 0.81626087],
[ 0.95270008, 0.2226797 ]]),
np.array([[ 0.36341551, 0.20102882],
[ 0.24144639, 0.45237923],
[ 0.39951822, 0.7348066 ],
[ 0.16649647, 0.60306537]]),
np.array([1, 0, 1, 1]),
np.array([[ 0.22323515, 0.36703175],
[ 0.24144639, 0.45237923],
[ 0.82362652, 0.81626087],
[ 0.95270008, 0.2226797 ]])),
# 2 x 3 x 4
(np.array([[[ 0.48769062, 0.82649632, 0.2047115 , 0.41437615],
[ 0.25290664, 0.87164914, 0.80968588, 0.49295084],
[ 0.71438099, 0.97913502, 0.37598001, 0.76958707]],
[[ 0.37605973, 0.538358 , 0.74304674, 0.84346291],
[ 0.95310617, 0.61540292, 0.49881143, 0.1028554 ],
[ 0.83481996, 0.90969569, 0.40410424, 0.34419989]]]),
np.array([[[ 0.7289117 , 0.97323253, 0.19070121, 0.64164653],
[ 0.26816493, 0.76093069, 0.95284825, 0.77350426],
[ 0.55415519, 0.39431256, 0.86588665, 0.50031027]],
[[ 0.1980869 , 0.7753601 , 0.26810868, 0.3628802 ],
[ 0.2488143 , 0.21278388, 0.09724567, 0.58457886],
[ 0.12295105, 0.75321368, 0.37258797, 0.27756972]]]),
np.array([1, 0]),
np.array([[[ 0.48769062, 0.82649632, 0.2047115 , 0.41437615],
[ 0.25290664, 0.87164914, 0.80968588, 0.49295084],
[ 0.71438099, 0.97913502, 0.37598001, 0.76958707]],
[[ 0.1980869 , 0.7753601 , 0.26810868, 0.3628802 ],
[ 0.2488143 , 0.21278388, 0.09724567, 0.58457886],
[ 0.12295105, 0.75321368, 0.37258797, 0.27756972]]]))
]
A2, B2 = T.matrices("AB")
A3, B3 = T.tensor3("A"), T.tensor3("B")
mask = T.ivector("mask")
switch2 = T.switch(mask.dimshuffle(0, "x"), A2, B2)
switch3 = T.switch(mask.dimshuffle(0, "x", "x"), A3, B3)
f2 = theano.function([A2, B2, mask], switch2)
f3 = theano.function([A3, B3, mask], switch3)
print "Graph of 2dim switch:"
theano.printing.debugprint(f2.maker.fgraph.outputs[0])
print "Graph of 3dim switch:"
theano.printing.debugprint(f3.maker.fgraph.outputs[0])
for instance in data:
# Retrieve appropriate function
func = f2 if instance[0].ndim == 2 else f3
# Cast to float-friendly types
instance = [x.astype(np.float32) if x.dtype.kind == 'f'
else x.astype(np.int32) for x in instance]
yield tuple([_test_gpu_rowwise_switch_inner, func] + instance)
def __init__(self, input, n_in, n_out):
hidden_size = 36
batch_size = 32
self._w_h = init_weights((n_in, hidden_size))
self._b_h = init_b_weights((1, hidden_size))
# self._b_h = init_b_weights((hidden_size,))
self._w_h2 = init_weights((hidden_size, hidden_size))
self._b_h2 = init_b_weights((1, hidden_size))
# self._b_h2 = init_b_weights((hidden_size,))
# self._w_o = init_tanh(hidden_size, n_out)
self._w_o = init_weights((hidden_size, n_out))
self._b_o = init_b_weights((1, n_out))
# self._b_o = init_b_weights((n_out,))
self.updateTargetModel()
self._w_h_old = init_weights((n_in, hidden_size))
self._w_h2_old = init_weights((hidden_size, hidden_size))
self._w_o_old = init_tanh(hidden_size, n_out)
# print ("Initial W " + str(self._w_o.get_value()) )
self._learning_rate = 0.00025
self._discount_factor = 0.99
self._weight_update_steps = 5000
self._updates = 0
# data types for model
State = T.dmatrix("State")
State.tag.test_value = np.random.rand(batch_size, 2)
ResultState = T.dmatrix("ResultState")
ResultState.tag.test_value = np.random.rand(batch_size, 2)
Reward = T.col("Reward")
Reward.tag.test_value = np.random.rand(batch_size, 1)
Action = T.icol("Action")
Action.tag.test_value = np.zeros((batch_size, 1),
dtype=np.dtype('int32'))
# Q_val = T.fmatrix()
# model = T.nnet.sigmoid(T.dot(State, self._w) + self._b.reshape((1, -1)))
# self._model = theano.function(inputs=[State], outputs=model, allow_input_downcast=True)
_py_xA = self.model(State, self._w_h, self._b_h, self._w_h2,
self._b_h2, self._w_o, self._b_o, 0.0, 0.0)
_py_xB = self.model(State, self._w_h_old, self._b_h_old,
self._w_h2_old, self._b_h2_old, self._w_o_old,
self._b_o_old, 0.0, 0.0)
self._y_predA = T.argmax(_py_xA, axis=1)
self._y_predB = T.argmax(_py_xB, axis=1)
self._q_funcA = T.mean(
(self.model(State, self._w_h, self._b_h, self._w_h2, self._b_h2,
self._w_o, self._b_o, 0.0,
0.0))[T.arange(batch_size),
Action.reshape((-1, ))].reshape((-1, 1)))
self._q_funcB = T.mean(
(self.model(State, self._w_h_old, self._b_h_old, self._w_h2_old,
self._b_h2_old, self._w_o_old, self._b_o_old, 0.0,
0.0))[T.arange(batch_size),
Action.reshape((-1, ))].reshape((-1, 1)))
# q_val = py_x
# noisey_q_val = self.model(ResultState, self._w_h, self._b_h, self._w_h2, self._b_h2, self._w_o, self._b_o, 0.2, 0.5)
# L1 norm ; one regularization option is to enforce L1 norm to
# be small
self._L1_A = (abs(self._w_h).sum() + abs(self._w_h2).sum() +
abs(self._w_o).sum())
self._L1_B = (abs(self._w_h_old).sum() + abs(self._w_h2_old).sum() +
abs(self._w_o_old).sum())
self._L1_reg = 0.0
self._L2_reg = 0.001
# L2 norm ; one regularization option is to enforce
# L2 norm to be small
self._L2_A = ((self._w_h**2).sum() + (self._w_h2**2).sum() +
(self._w_o**2).sum())
self._L2_B = ((self._w_h_old**2).sum() + (self._w_h2_old**2).sum() +
(self._w_o_old**2).sum())
# cost = T.mean(T.nnet.categorical_crossentropy(py_x, Y))
# delta = ((Reward.reshape((-1, 1)) + (self._discount_factor * T.max(self.model(ResultState), axis=1, keepdims=True)) ) - self.model(State))
deltaA = ((Reward + (self._discount_factor * T.max(self.model(
ResultState, self._w_h_old, self._b_h_old, self._w_h2_old,
self._b_h2_old, self._w_o_old, self._b_o_old, 0.2, 0.5),
axis=1,
keepdims=True))) -
(self.model(State, self._w_h, self._b_h, self._w_h2,
self._b_h2, self._w_o, self._b_o, 0.2,
0.5))[T.arange(Action.shape[0]),
Action.reshape((-1, ))].reshape((-1, 1)))
deltaB = (
(Reward +
(self._discount_factor *
T.max(self.model(ResultState, self._w_h, self._b_h, self._w_h2,
self._b_h2, self._w_o, self._b_o, 0.2, 0.5),
axis=1,
keepdims=True))) -
(self.model(State, self._w_h_old, self._b_h_old, self._w_h2_old,
self._b_h2_old, self._w_o_old, self._b_o_old, 0.2,
0.5))[T.arange(Action.shape[0]),
Action.reshape((-1, ))].reshape((-1, 1)))
# bellman_cost = T.mean( 0.5 * ((delta) ** 2 ))
bellman_costA = T.mean(0.5 * ((deltaA)**2)) + (
self._L2_reg * self._L2_A) + (self._L1_reg * self._L1_A)
bellman_costB = T.mean(0.5 * ((deltaB)**2)) + (
self._L2_reg * self._L2_B) + (self._L1_reg * self._L1_B)
paramsA = [
self._w_h, self._b_h, self._w_h2, self._b_h2, self._w_o, self._b_o
]
paramsB = [
self._w_h_old, self._b_h_old, self._w_h2_old, self._b_h2_old,
self._w_o_old, self._b_o_old
]
# updates = sgd(bellman_cost, params, lr=self._learning_rate)
updatesA = rlTDSGD(self._q_funcA,
T.mean(deltaA),
paramsA,
lr=self._learning_rate)
updatesB = rlTDSGD(self._q_funcB,
T.mean(deltaB),
paramsB,
lr=self._learning_rate)
# updates = RMSprop(bellman_cost, params, lr=self._learning_rate)
# updates = RMSpropRL(q_func, T.mean(delta), params, lr=self._learning_rate)
# updates = lasagne.updates.rmsprop(bellman_cost, params, self._learning_rate, 0.95, 0.01)
# updatesA = lasagne.updates.rmsprop(self._q_funcA, paramsA, self._learning_rate * -T.mean(deltaA), 0.95, 0.01)
# updatesB = lasagne.updates.rmsprop(self._q_funcB, paramsB, self._learning_rate * -T.mean(deltaB), 0.95, 0.01)
self._trainA = theano.function(
inputs=[State, Action, Reward, ResultState],
outputs=bellman_costA,
updates=updatesA,
allow_input_downcast=True)
self._trainB = theano.function(
inputs=[State, Action, Reward, ResultState],
outputs=bellman_costB,
updates=updatesB,
allow_input_downcast=True)
self._bellman_errorA = theano.function(
inputs=[State, Action, Reward, ResultState],
outputs=deltaA,
allow_input_downcast=True)
self._bellman_errorB = theano.function(
inputs=[State, Action, Reward, ResultState],
outputs=deltaB,
allow_input_downcast=True)
self._q_valuesA = theano.function(inputs=[State],
outputs=_py_xA,
allow_input_downcast=True)
self._q_valuesB = theano.function(inputs=[State],
outputs=_py_xB,
allow_input_downcast=True)
self._py_xA = theano.function(inputs=[State],
outputs=_py_xA,
allow_input_downcast=True)
self._py_xB = theano.function(inputs=[State],
outputs=_py_xB,
allow_input_downcast=True)
x, y = T.matrices('x', 'y')
z_lazy = ifelse(T.gt(T.max(x, axis=1)[0],
T.max(y, axis=1)[0]), T.argmax(x, axis=1),
T.argmax(y, axis=1))
self._f_lazyifelse = theano.function([x, y],
z_lazy,
mode=theano.Mode(linker='vm'))