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提交 b1402c8b authored 作者: abergeron's avatar abergeron
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Merge pull request #1802 from nouiz/f32

Fix buildbot tests error in float32
上级 9a44f9bf e2a6481b
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正在显示 包含 160 行增加 和 158 行删除
doc/tutorial/loop.txt
浏览文件 @ b1402c8b
......@@ -36,13 +36,13 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
W = T.matrix("W")
b_sym = T.vector("b_sym")
results, updates = theano.scan(lambda v:T.tanh(T.dot(v,W)+b_sym), sequences=X)
compute_elementwise = theano.function(inputs = [X, W, b_sym], outputs=[results])
results, updates = theano.scan(lambda v: T.tanh(T.dot(v, W) + b_sym), sequences=X)
compute_elementwise = theano.function(inputs=[X, W, b_sym], outputs=[results])
# test values
x = np.eye(2)
w = np.ones((2,2))
b = np.ones((2))
x = np.eye(2, dtype=theano.config.floatX)
w = np.ones((2, 2), dtype=theano.config.floatX)
b = np.ones((2), dtype=theano.config.floatX)
b[1] = 2
print compute_elementwise(x, w, b)[0]
......@@ -51,7 +51,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
print np.tanh(x.dot(w) + b)
**Scan Example: Computing the sequence x(t) = tanh(x(t-1).dot(W) + y(t).dot(U) + p(T-t).dot(V))**
**Scan Example: Computing the sequence x(t) = tanh(x(t - 1).dot(W) + y(t).dot(U) + p(T - t).dot(V))**
.. code-block:: python
import theano
......@@ -67,29 +67,29 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
V = T.matrix("V")
P = T.matrix("P")
results, updates = theano.scan(lambda
y,p,x_tm1:T.tanh(T.dot(x_tm1,W)+T.dot(y,U)+T.dot(p,V)),
sequences=[Y,P[::-1]], outputs_info=[X])
compute_seq = theano.function(inputs = [X, W, Y, U, P, V], outputs=[results])
results, updates = theano.scan(lambda y, p, x_tm1: T.tanh(T.dot(x_tm1, W) + T.dot(y, U) + T.dot(p, V)),
sequences=[Y, P[::-1]], outputs_info=[X])
compute_seq = theano.function(inputs=[X, W, Y, U, P, V], outputs=[results])
# test values
x = np.zeros((2))
x = np.zeros((2), dtype=theano.config.floatX)
x[1] = 1
w = np.ones((2,2))
y = np.ones((5,2))
y[0,:] = -3
u = np.ones((2,2))
p = np.ones((5,2))
p[0,:] = 3
v = np.ones((2,2))
w = np.ones((2, 2), dtype=theano.config.floatX)
y = np.ones((5, 2), dtype=theano.config.floatX)
y[0, :] = -3
u = np.ones((2, 2), dtype=theano.config.floatX)
p = np.ones((5, 2), dtype=theano.config.floatX)
p[0, :] = 3
v = np.ones((2, 2), dtype=theano.config.floatX)
print compute_seq(x,w,y,u,p,v)[0]
print compute_seq(x, w, y, u, p, v)[0]
# comparison with numpy
x_res = np.zeros((5,2))
x_res = np.zeros((5, 2), dtype=theano.config.floatX)
x_res[0] = np.tanh(x.dot(w) + y[0].dot(u) + p[4].dot(v))
for i in range(1,5):
x_res[i] = np.tanh(x_res[i-1].dot(w) + y[i].dot(u) + p[4-i].dot(v))
for i in range(1, 5):
x_res[i] = np.tanh(x_res[i - 1].dot(w) + y[i].dot(u) + p[4-i].dot(v))
print x_res
**Scan Example: Computing norms of lines of X**
......@@ -100,15 +100,15 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
# define tensor variable
X = T.matrix("X")
results, updates = theano.scan(lambda x_i:T.sqrt((x_i**2).sum()), sequences=[X])
compute_norm_lines = theano.function(inputs = [X], outputs=[results])
results, updates = theano.scan(lambda x_i: T.sqrt((x_i ** 2).sum()), sequences=[X])
compute_norm_lines = theano.function(inputs=[X], outputs=[results])
# test value
x = np.diag(np.arange(1,6),1)
x = np.diag(np.arange(1, 6, dtype=theano.config.floatX), 1)
print compute_norm_lines(x)[0]
# comparison with numpy
print np.sqrt((x**2).sum(1))
print np.sqrt((x ** 2).sum(1))
**Scan Example: Computing norms of columns of X**
......@@ -119,15 +119,15 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
# define tensor variable
X = T.matrix("X")
results, updates = theano.scan(lambda x_i:T.sqrt((x_i**2).sum()), sequences=[X.T])
compute_norm_cols = theano.function(inputs = [X], outputs=[results])
results, updates = theano.scan(lambda x_i: T.sqrt((x_i ** 2).sum()), sequences=[X.T])
compute_norm_cols = theano.function(inputs=[X], outputs=[results])
# test value
x = np.diag(np.arange(1,6),1)
x = np.diag(np.arange(1, 6, dtype=theano.config.floatX), 1)
print compute_norm_cols(x)[0]
# comparison with numpy
print np.sqrt((x**2).sum(0))
print np.sqrt((x ** 2).sum(0))
**Scan Example: Computing trace of X**
......@@ -139,21 +139,21 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
# define tensor variable
X = T.matrix("X")
results, updates = theano.scan(lambda i, j, t_f:T.cast(X[i,j]+t_f, floatX), \
sequences=[T.arange(X.shape[0]), T.arange(X.shape[1])], \
results, updates = theano.scan(lambda i, j, t_f: T.cast(X[i, j] + t_f, floatX),
sequences=[T.arange(X.shape[0]), T.arange(X.shape[1])],
outputs_info=np.asarray(0., dtype=floatX))
result = results[-1]
compute_trace = theano.function(inputs = [X], outputs=[result])
compute_trace = theano.function(inputs=[X], outputs=[result])
# test value
x = np.eye(5)
x[0] = np.arange(5)
compute_trace(x)[0]
x = np.eye(5, dtype=theano.config.floatX)
x[0] = np.arange(5, dtype=theano.config.floatX)
print compute_trace(x)[0]
# comparison with numpy
print np.diagonal(x).sum()
**Scan Example: Computing the sequence x(t) = x(t-2).dot(U) + x(t-1).dot(V) + tanh(x(t-1).dot(W) + b)**
**Scan Example: Computing the sequence x(t) = x(t - 2).dot(U) + x(t - 1).dot(V) + tanh(x(t - 1).dot(W) + b)**
.. code-block:: python
import theano
......@@ -168,31 +168,30 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
V = T.matrix("V")
n_sym = T.iscalar("n_sym")
results, updates = theano.scan(lambda x_tm2,x_tm1:T.dot(x_tm2,U) + T.dot(x_tm1,V) \
+ T.tanh(T.dot(x_tm1,W) + b_sym), \
n_steps=n_sym, outputs_info=[dict(initial = X, taps = [-2,-1])])
compute_seq2 = theano.function(inputs = [X, U, V, W, b_sym, n_sym], outputs=[results])
results, updates = theano.scan(lambda x_tm2, x_tm1: T.dot(x_tm2, U) + T.dot(x_tm1, V) + T.tanh(T.dot(x_tm1, W) + b_sym),
n_steps=n_sym, outputs_info=[dict(initial=X, taps=[-2, -1])])
compute_seq2 = theano.function(inputs=[X, U, V, W, b_sym, n_sym], outputs=[results])
# test values
x = np.zeros((2,2)) # the initial value must be able to return x[-2]
x[1,1] = 1
w = 0.5*np.ones((2,2))
u = 0.5*(np.ones((2,2))-np.eye(2))
v = 0.5*np.ones((2,2))
x = np.zeros((2, 2), dtype=theano.config.floatX) # the initial value must be able to return x[-2]
x[1, 1] = 1
w = 0.5 * np.ones((2, 2), dtype=theano.config.floatX)
u = 0.5 * (np.ones((2, 2), dtype=theano.config.floatX) - np.eye(2, dtype=theano.config.floatX))
v = 0.5 * np.ones((2, 2), dtype=theano.config.floatX)
n = 10
b = np.ones((2))
b = np.ones((2), dtype=theano.config.floatX)
print compute_seq2(x,u,v,w,b,n)
print compute_seq2(x, u, v, w, b, n)
# comparison with numpy
x_res = numpy.zeros((10,2))
x_res[0] = x[0].dot(u) + x[1].dot(v) + numpy.tanh(x[1].dot(w) + b)
x_res[1] = x[1].dot(u) + x_res[0].dot(v) + numpy.tanh(x_res[0].dot(w) + b)
x_res[2] = x_res[0].dot(u) + x_res[1].dot(v) \
+ numpy.tanh(x_res[1].dot(w) + b)
for i in range(2,10):
x_res[i] = (x_res[i-2].dot(u) + x_res[i-1].dot(v) \
+ numpy.tanh(x_res[i-1].dot(w) + b))
x_res = np.zeros((10, 2))
x_res[0] = x[0].dot(u) + x[1].dot(v) + np.tanh(x[1].dot(w) + b)
x_res[1] = x[1].dot(u) + x_res[0].dot(v) + np.tanh(x_res[0].dot(w) + b)
x_res[2] = x_res[0].dot(u) + x_res[1].dot(v) + np.tanh(x_res[1].dot(w) + b)
for i in range(2, 10):
x_res[i] = (x_res[i - 2].dot(u) + x_res[i - 1].dot(v) +
np.tanh(x_res[i - 1].dot(w) + b))
print x_res
**Scan Example: Computing the Jacobian of y = tanh(v.dot(A)) wrt x**
......@@ -204,18 +203,18 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
# define tensor variables
v = T.vector()
A = T.matrix()
y = T.tanh(T.dot(v,A))
results, updates = theano.scan(lambda i:T.grad(y[i], v), sequences = [T.arange(y.shape[0])])
compute_jac_t = theano.function([A,v], [results], allow_input_downcast = True) # shape (d_out, d_in)
y = T.tanh(T.dot(v, A))
results, updates = theano.scan(lambda i: T.grad(y[i], v), sequences=[T.arange(y.shape[0])])
compute_jac_t = theano.function([A, v], [results], allow_input_downcast=True) # shape (d_out, d_in)
# test values
x = np.eye(5)[0]
w = np.eye(5,3)
w[2] = np.ones((3))
print compute_jac_t(w,x)[0]
x = np.eye(5, dtype=theano.config.floatX)[0]
w = np.eye(5, 3, dtype=theano.config.floatX)
w[2] = np.ones((3), dtype=theano.config.floatX)
print compute_jac_t(w, x)[0]
# compare with numpy
print ((1 - np.tanh(x.dot(w))**2)*w).T
print ((1 - np.tanh(x.dot(w)) ** 2) * w).T
Note that we need to iterate over the indices of ``y`` and not over the elements of ``y``. The reason is that scan create a placeholder variable for its internal function and this placeholder variable does not have the same dependencies than the variables that will replace it.
......@@ -230,14 +229,14 @@ Note that we need to iterate over the indices of ``y`` and not over the elements
k = theano.shared(0)
n_sym = T.iscalar("n_sym")
results, updates = theano.scan(lambda:{k:(k+1)}, n_steps=n_sym)
accumulator = theano.function([n_sym], [], updates=updates, allow_input_downcast = True)
results, updates = theano.scan(lambda:{k:(k + 1)}, n_steps=n_sym)
accumulator = theano.function([n_sym], [], updates=updates, allow_input_downcast=True)
k.get_value()
accumulator(5)
k.get_value()
**Scan Example: Computing tanh(v.dot(W) + b)*d where b is binomial**
**Scan Example: Computing tanh(v.dot(W) + b) * d where b is binomial**
.. code-block:: python
import theano
......@@ -253,18 +252,18 @@ Note that we need to iterate over the indices of ``y`` and not over the elements
trng = T.shared_randomstreams.RandomStreams(1234)
d=trng.binomial(size=W[1].shape)
results, updates = theano.scan(lambda v:T.tanh(T.dot(v,W)+b_sym)*d, sequences=X)
compute_with_bnoise = theano.function(inputs = [X, W, b_sym], outputs=[results], \
updates=updates, allow_input_downcast = True)
x = np.eye(10,2)
w = np.ones((2,2))
b = np.ones((2))
results, updates = theano.scan(lambda v: T.tanh(T.dot(v, W) + b_sym) * d, sequences=X)
compute_with_bnoise = theano.function(inputs=[X, W, b_sym], outputs=[results],
updates=updates, allow_input_downcast=True)
x = np.eye(10, 2, dtype=theano.config.floatX)
w = np.ones((2, 2), dtype=theano.config.floatX)
b = np.ones((2), dtype=theano.config.floatX)
print compute_with_bnoise(x, w, b)
Note that if you want to use a random variable ``d`` that will not be updated through scan loops, you should pass this variable as a ``non_sequences`` arguments.
**Scan Example: Computing pow(A,k)**
**Scan Example: Computing pow(A, k)**
.. code-block:: python
......@@ -290,7 +289,7 @@ Note that if you want to use a random variable ``d`` that will not be updated th
power = theano.function(inputs=[A, k], outputs=final_result,
updates=updates)
print power(range(10),2)
print power(range(10), 2)
#[ 0. 1. 4. 9. 16. 25. 36. 49. 64. 81.]
......
theano/tensor/tests/test_opt.py
浏览文件 @ b1402c8b
......@@ -1146,7 +1146,8 @@ class test_fusion(unittest.TestCase):
#we need the optimisation enabled and the canonicalize.
#the canonicalize is needed to merge multiplication/addition by constant.
mode._optimizer = mode._optimizer.including(
'local_elemwise_fusion', 'canonicalize')
'local_elemwise_fusion', 'composite_elemwise_fusion',
'canonicalize')
self.do(mode, shared, shp)
@attr('slow')
......@@ -1156,7 +1157,8 @@ class test_fusion(unittest.TestCase):
#we need the optimisation enabled and the canonicalize.
#the canonicalize is needed to merge multiplication/addition by constant.
mode._optimizer = mode._optimizer.including(
'local_elemwise_fusion', 'canonicalize')
'local_elemwise_fusion', 'composite_elemwise_fusion',
'canonicalize')
self.do(mode, shared, shp)
def test_gpu_fusion(self):
......@@ -1164,10 +1166,12 @@ class test_fusion(unittest.TestCase):
#we need the optimisation enabled, debug do this.
if theano.config.mode == "FAST_COMPILE":
mode = theano.compile.mode.get_mode("FAST_RUN").including(
'local_elemwise_fusion', 'canonicalize', 'gpu')
'local_elemwise_fusion', 'composite_elemwise_fusion',
'canonicalize', 'gpu')
else:
mode = theano.compile.mode.get_default_mode().including(
'local_elemwise_fusion', 'canonicalize', 'gpu')
'local_elemwise_fusion', 'composite_elemwise_fusion',
'canonicalize', 'gpu')
import theano.sandbox.cuda as cuda
if not cuda.cuda_available:
raise SkipTest("cuda not available")
......@@ -1179,10 +1183,12 @@ class test_fusion(unittest.TestCase):
#we need the optimisation enabled, debug do this.
if theano.config.mode == "FAST_COMPILE":
mode = theano.compile.mode.get_mode("FAST_RUN").including(
'local_elemwise_fusion', 'canonicalize', 'gpu')
'local_elemwise_fusion', 'composite_elemwise_fusion',
'canonicalize', 'gpu')
else:
mode = theano.compile.mode.get_default_mode().including(
'local_elemwise_fusion', 'canonicalize', 'gpu')
'local_elemwise_fusion', 'composite_elemwise_fusion',
'canonicalize', 'gpu')
import theano.sandbox.cuda as cuda
if not cuda.cuda_available:
raise SkipTest("cuda not available")
......@@ -1278,7 +1284,8 @@ class test_fusion(unittest.TestCase):
#we need the optimisation enabled and the canonicalize.
#the canonicalize is needed to merge multiplication/addition by constant.
mode._optimizer = mode._optimizer.including(
'local_elemwise_fusion', 'canonicalize', 'inplace')
'local_elemwise_fusion', 'composite_elemwise_fusion',
'canonicalize', 'inplace')
x, y, z = dmatrices('xyz')
f = theano.function([x, y, z], tensor.dot(x, y) + x + y + z, mode=mode)
......
theano/tests/test_tutorial.py
浏览文件 @ b1402c8b
......@@ -1137,6 +1137,7 @@ class T_graphstructures(unittest.TestCase):
assert e.owner.inputs[1].owner.inputs[0] is y
assert e.owner.inputs[1].owner.inputs[1] is z
class T_scan(unittest.TestCase):
## All tests here belong to
## http://deeplearning.net/software/theano/tutorial/loop.html
......@@ -1144,21 +1145,20 @@ class T_scan(unittest.TestCase):
## Any change you do here also add it to the tutorial !
def test_elemwise(self):
# defining the tensor variables
X = T.matrix("X")
W = T.matrix("W")
b_sym = T.vector("b_sym")
results, updates = theano.scan(lambda v:T.tanh(T.dot(v,W)+b_sym), \
results, updates = theano.scan(lambda v: T.tanh(T.dot(v, W) + b_sym),
sequences=X)
compute_elementwise = theano.function(inputs = [X, W, b_sym], \
compute_elementwise = theano.function(inputs=[X, W, b_sym],
outputs=[results])
# test values
x = numpy.eye(2)
w = numpy.ones((2,2))
b = numpy.ones((2))
x = numpy.eye(2, dtype=theano.config.floatX)
w = numpy.ones((2, 2), dtype=theano.config.floatX)
b = numpy.ones((2), dtype=theano.config.floatX)
b[1] = 2
print "Scan results:", compute_elementwise(x, w, b)[0]
......@@ -1167,7 +1167,6 @@ class T_scan(unittest.TestCase):
print "Numpy results:", numpy.tanh(x.dot(w) + b)
def test_sequence(self):
# define tensor variables
X = T.vector("X")
W = T.matrix("W")
......@@ -1177,49 +1176,49 @@ class T_scan(unittest.TestCase):
V = T.matrix("V")
P = T.matrix("P")
results, updates = theano.scan(lambda \
y,p,x_tm1:T.tanh(T.dot(x_tm1,W) + \
T.dot(y,U)+T.dot(p,V)), \
sequences=[Y,P[::-1]], outputs_info=[X])
results, updates = theano.scan(
lambda y, p, x_tm1: T.tanh(T.dot(x_tm1, W) +
T.dot(y, U) + T.dot(p, V)),
sequences=[Y, P[::-1]], outputs_info=[X])
compute_seq = theano.function(inputs = [X, W, Y, U, P, V], \
compute_seq = theano.function(inputs=[X, W, Y, U, P, V],
outputs=[results])
# test values
x = numpy.zeros((2))
x = numpy.zeros((2), dtype=theano.config.floatX)
x[1] = 1
w = numpy.ones((2,2))
y = numpy.ones((5,2))
y[0,:] = -3
u = numpy.ones((2,2))
p = numpy.ones((5,2))
p[0,:] = 3
v = numpy.ones((2,2))
w = numpy.ones((2, 2), dtype=theano.config.floatX)
y = numpy.ones((5, 2), dtype=theano.config.floatX)
y[0, :] = -3
u = numpy.ones((2, 2), dtype=theano.config.floatX)
p = numpy.ones((5, 2), dtype=theano.config.floatX)
p[0, :] = 3
v = numpy.ones((2, 2), dtype=theano.config.floatX)
print "Scan results", compute_seq(x,w,y,u,p,v)[0]
print "Scan results", compute_seq(x, w, y, u, p, v)[0]
# comparison with numpy
x_res = numpy.zeros((5,2))
x_res = numpy.zeros((5, 2), dtype=theano.config.floatX)
x_res[0] = numpy.tanh(x.dot(w) + y[0].dot(u) + p[4].dot(v))
for i in range(1,5):
x_res[i] = numpy.tanh(x_res[i-1].dot(w) \
+ y[i].dot(u) + p[4-i].dot(v))
for i in range(1, 5):
x_res[i] = numpy.tanh(x_res[i-1].dot(w) +
y[i].dot(u) + p[4-i].dot(v))
print "Numpy results:", x_res
def test_norm(self):
# define tensor variable
X = T.matrix("X")
results, updates = theano.scan(lambda x_i:T.sqrt((x_i**2).sum()), \
results, updates = theano.scan(lambda x_i: T.sqrt((x_i**2).sum()),
sequences=[X])
compute_norm_lines = theano.function(inputs = [X], outputs=[results])
compute_norm_lines = theano.function(inputs=[X], outputs=[results])
results, updates = theano.scan(lambda x_i:T.sqrt((x_i**2).sum()), \
results, updates = theano.scan(lambda x_i: T.sqrt((x_i**2).sum()),
sequences=[X.T])
compute_norm_cols = theano.function(inputs = [X], outputs=[results])
compute_norm_cols = theano.function(inputs=[X], outputs=[results])
# test value
x = numpy.diag(numpy.arange(1,6),1)
x = numpy.diag(numpy.arange(1, 6, dtype=theano.config.floatX), 1)
print "Scan results:", compute_norm_lines(x)[0], \
compute_norm_cols(x)[0]
......@@ -1228,29 +1227,27 @@ class T_scan(unittest.TestCase):
numpy.sqrt((x**2).sum(0))
def test_trace(self):
# define tensor variable
X = T.matrix("X")
results, updates = theano.scan(lambda i, j, t_f:T.cast(X[i,j] + \
t_f, theano.config.floatX), \
sequences=[T.arange(X.shape[0]), \
T.arange(X.shape[1])], \
outputs_info=numpy.asarray(0., \
dtype=theano.config.floatX))
results, updates = theano.scan(lambda i, j, t_f: T.cast(X[i,j] +
t_f, theano.config.floatX),
sequences=[T.arange(X.shape[0]),
T.arange(X.shape[1])],
outputs_info=numpy.asarray(
0., dtype=theano.config.floatX))
result = results[-1]
compute_trace = theano.function(inputs = [X], outputs=[result])
compute_trace = theano.function(inputs=[X], outputs=[result])
# test value
x = numpy.eye(5)
x[0] = numpy.arange(5)
x = numpy.eye(5, dtype=theano.config.floatX)
x[0] = numpy.arange(5, dtype=theano.config.floatX)
print "Scan results:", compute_trace(x)[0]
# comparison with numpy
print "Numpy results:", numpy.diagonal(x).sum()
def test_taps(self):
# define tensor variables
X = T.matrix("X")
W = T.matrix("W")
......@@ -1259,55 +1256,55 @@ class T_scan(unittest.TestCase):
V = T.matrix("V")
n_sym = T.iscalar("n_sym")
results, updates = theano.scan(lambda x_tm2,x_tm1:T.dot(x_tm2,U) \
+ T.dot(x_tm1,V) + T.tanh(T.dot(x_tm1,W) + b_sym), \
n_steps=n_sym, \
outputs_info=[dict(initial = X, taps = [-2,-1])])
results, updates = theano.scan(
lambda x_tm2,x_tm1: T.dot(x_tm2,U) + T.dot(x_tm1,V) + T.tanh(T.dot(x_tm1,W) + b_sym),
n_steps=n_sym,
outputs_info=[dict(initial=X, taps=[-2, -1])])
compute_seq2 = theano.function(inputs = [X, U, V, W, b_sym, \
n_sym], outputs=[results])
compute_seq2 = theano.function(inputs=[X, U, V, W, b_sym, n_sym],
outputs=[results])
# test values
x = numpy.zeros((2,2))
x = numpy.zeros((2, 2), dtype=theano.config.floatX)
# the initial value must be able to return x[-2]
x[1,1] = 1
w = 0.5*numpy.ones((2,2))
u = 0.5*(numpy.ones((2,2))-numpy.eye(2))
v = 0.5*numpy.ones((2,2))
x[1, 1] = 1
w = 0.5 * numpy.ones((2, 2), dtype=theano.config.floatX)
u = 0.5 * (numpy.ones((2, 2), dtype=theano.config.floatX) -
numpy.eye(2, dtype=theano.config.floatX))
v = 0.5 * numpy.ones((2, 2), dtype=theano.config.floatX)
n = 10
b = numpy.ones((2))
b = numpy.ones((2), dtype=theano.config.floatX)
print "Scan results:", compute_seq2(x,u,v,w,b,n)
print "Scan results:", compute_seq2(x, u, v, w, b, n)
# comparison with numpy
x_res = numpy.zeros((10,2))
x_res = numpy.zeros((10, 2), dtype=theano.config.floatX)
x_res[0] = x[0].dot(u) + x[1].dot(v) + numpy.tanh(x[1].dot(w) + b)
x_res[1] = x[1].dot(u) + x_res[0].dot(v) \
+ numpy.tanh(x_res[0].dot(w) + b)
x_res[2] = x_res[0].dot(u) + x_res[1].dot(v) \
+ numpy.tanh(x_res[1].dot(w) + b)
for i in range(2,10):
x_res[i] = (x_res[i-2].dot(u) + x_res[i-1].dot(v) \
+ numpy.tanh(x_res[i-1].dot(w) + b))
for i in range(2, 10):
x_res[i] = (x_res[i-2].dot(u) + x_res[i-1].dot(v) +
numpy.tanh(x_res[i-1].dot(w) + b))
print "Numpy results:", x_res
def test_jacobian(self):
# define tensor variables
v = T.vector()
A = T.matrix()
y = T.tanh(T.dot(v,A))
results, updates = theano.scan(lambda i:T.grad(y[i], v), \
sequences = [T.arange(y.shape[0])])
compute_jac_t = theano.function([A,v], [results], \
allow_input_downcast = True) # shape (d_out, d_in)
y = T.tanh(T.dot(v, A))
results, updates = theano.scan(lambda i: T.grad(y[i], v),
sequences=[T.arange(y.shape[0])])
compute_jac_t = theano.function([A, v], [results],
allow_input_downcast=True) # shape (d_out, d_in)
# test values
x = numpy.eye(5)[0]
w = numpy.eye(5,3)
w = numpy.eye(5, 3)
w[2] = numpy.ones((3))
print "Scan results:", compute_jac_t(w,x)[0]
print "Scan results:", compute_jac_t(w, x)[0]
# compare with numpy
print "Numpy results:", ((1 - numpy.tanh(x.dot(w))**2)*w).T
......@@ -1317,9 +1314,9 @@ class T_scan(unittest.TestCase):
k = theano.shared(0)
n_sym = T.iscalar("n_sym")
results, updates = theano.scan(lambda:{k:(k+1)}, n_steps=n_sym)
accumulator = theano.function([n_sym], [], updates=updates, \
allow_input_downcast = True)
results, updates = theano.scan(lambda: {k: (k + 1)}, n_steps=n_sym)
accumulator = theano.function([n_sym], [], updates=updates,
allow_input_downcast=True)
print "Before 5 steps:", k.get_value()
accumulator(5)
......@@ -1333,17 +1330,16 @@ class T_scan(unittest.TestCase):
# define shared random stream
trng = T.shared_randomstreams.RandomStreams(1234)
d=trng.binomial(size=W[1].shape)
d = trng.binomial(size=W[1].shape)
results, updates = theano.scan(lambda v:T.tanh(T.dot(v,W) \
+ b_sym)*d, sequences=X)
compute_with_bnoise = theano.function(inputs = [X, W, b_sym], \
outputs=[results], \
updates=updates, \
results, updates = theano.scan(lambda v: T.tanh(T.dot(v, W) + b_sym) * d,
sequences=X)
compute_with_bnoise = theano.function(inputs=[X, W, b_sym],
outputs=[results],
updates=updates,
allow_input_downcast = True)
x = numpy.eye(10,2)
w = numpy.ones((2,2))
b = numpy.ones((2))
print compute_with_bnoise(x, w, b)
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