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提交 b0c8223d authored 作者: Iban Harlouchet's avatar Iban Harlouchet 提交者： Arnaud Bergeron
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testcode for doc/tutorial/loop.txt

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doc/tutorial/loop.txt
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...@@ -26,7 +26,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`. ...@@ -26,7 +26,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
**Scan Example: Computing tanh(x(t).dot(W) + b) elementwise** **Scan Example: Computing tanh(x(t).dot(W) + b) elementwise**
.. code-block:: python .. testcode::
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -46,15 +46,21 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`. ...@@ -46,15 +46,21 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
b = np.ones((2), dtype=theano.config.floatX) b = np.ones((2), dtype=theano.config.floatX)
b[1] = 2 b[1] = 2
print compute_elementwise(x, w, b)[0] print(compute_elementwise(x, w, b)[0])
# comparison with numpy # comparison with numpy
print np.tanh(x.dot(w) + b) print(np.tanh(x.dot(w) + b))
.. testoutput::
[[ 0.96402758 0.99505475]
[ 0.96402758 0.99505475]]
[[ 0.96402758 0.99505475]
[ 0.96402758 0.99505475]]
**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 .. testcode::
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -84,18 +90,31 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`. ...@@ -84,18 +90,31 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
p[0, :] = 3 p[0, :] = 3
v = np.ones((2, 2), dtype=theano.config.floatX) 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 # comparison with numpy
x_res = np.zeros((5, 2), dtype=theano.config.floatX) 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)) x_res[0] = np.tanh(x.dot(w) + y[0].dot(u) + p[4].dot(v))
for i in range(1, 5): 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)) x_res[i] = np.tanh(x_res[i - 1].dot(w) + y[i].dot(u) + p[4-i].dot(v))
print x_res print(x_res)
.. testoutput::
[[-0.99505475 -0.99505475]
[ 0.96471973 0.96471973]
[ 0.99998585 0.99998585]
[ 0.99998771 0.99998771]
[ 1. 1. ]]
[[-0.99505475 -0.99505475]
[ 0.96471973 0.96471973]
[ 0.99998585 0.99998585]
[ 0.99998771 0.99998771]
[ 1. 1. ]]
**Scan Example: Computing norms of lines of X** **Scan Example: Computing norms of lines of X**
.. code-block:: python .. testcode::
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -108,14 +127,19 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`. ...@@ -108,14 +127,19 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
# test value # test value
x = np.diag(np.arange(1, 6, dtype=theano.config.floatX), 1) x = np.diag(np.arange(1, 6, dtype=theano.config.floatX), 1)
print compute_norm_lines(x)[0] print(compute_norm_lines(x)[0])
# comparison with numpy # comparison with numpy
print np.sqrt((x ** 2).sum(1)) print(np.sqrt((x ** 2).sum(1)))
.. testoutput::
[ 1. 2. 3. 4. 5. 0.]
[ 1. 2. 3. 4. 5. 0.]
**Scan Example: Computing norms of columns of X** **Scan Example: Computing norms of columns of X**
.. code-block:: python .. testcode::
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -128,14 +152,19 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`. ...@@ -128,14 +152,19 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
# test value # test value
x = np.diag(np.arange(1, 6, dtype=theano.config.floatX), 1) x = np.diag(np.arange(1, 6, dtype=theano.config.floatX), 1)
print compute_norm_cols(x)[0] print(compute_norm_cols(x)[0])
# comparison with numpy # comparison with numpy
print np.sqrt((x ** 2).sum(0)) print(np.sqrt((x ** 2).sum(0)))
.. testoutput::
[ 0. 1. 2. 3. 4. 5.]
[ 0. 1. 2. 3. 4. 5.]
**Scan Example: Computing trace of X** **Scan Example: Computing trace of X**
.. code-block:: python .. testcode::
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -153,14 +182,20 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`. ...@@ -153,14 +182,20 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
# test value # test value
x = np.eye(5, dtype=theano.config.floatX) x = np.eye(5, dtype=theano.config.floatX)
x[0] = np.arange(5, dtype=theano.config.floatX) x[0] = np.arange(5, dtype=theano.config.floatX)
print compute_trace(x)[0] print(compute_trace(x)[0])
# comparison with numpy # comparison with numpy
print np.diagonal(x).sum() print(np.diagonal(x).sum())
.. testoutput::
4.0
4.0
**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 .. testcode::
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -187,7 +222,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`. ...@@ -187,7 +222,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
n = 10 n = 10
b = np.ones((2), dtype=theano.config.floatX) 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 # comparison with numpy
x_res = np.zeros((10, 2)) x_res = np.zeros((10, 2))
...@@ -197,11 +232,35 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`. ...@@ -197,11 +232,35 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
for i in range(2, 10): for i in range(2, 10):
x_res[i] = (x_res[i - 2].dot(u) + x_res[i - 1].dot(v) + x_res[i] = (x_res[i - 2].dot(u) + x_res[i - 1].dot(v) +
np.tanh(x_res[i - 1].dot(w) + b)) np.tanh(x_res[i - 1].dot(w) + b))
print x_res print(x_res)
.. testoutput::
[array([[ 1.40514825, 1.40514825],
[ 2.88898899, 2.38898899],
[ 4.34018291, 4.34018291],
[ 6.53463142, 6.78463142],
[ 9.82972243, 9.82972243],
[ 14.22203814, 14.09703814],
[ 20.07439936, 20.07439936],
[ 28.12291843, 28.18541843],
[ 39.1913681 , 39.1913681 ],
[ 54.28407732, 54.25282732]])]
[[ 1.40514825 1.40514825]
[ 2.88898899 2.38898899]
[ 4.34018291 4.34018291]
[ 6.53463142 6.78463142]
[ 9.82972243 9.82972243]
[ 14.22203814 14.09703814]
[ 20.07439936 20.07439936]
[ 28.12291843 28.18541843]
[ 39.1913681 39.1913681 ]
[ 54.28407732 54.25282732]]
**Scan Example: Computing the Jacobian of y = tanh(v.dot(A)) wrt x** **Scan Example: Computing the Jacobian of y = tanh(v.dot(A)) wrt x**
.. code-block:: python .. testcode::
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -221,13 +280,22 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`. ...@@ -221,13 +280,22 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
print compute_jac_t(w, x)[0] print compute_jac_t(w, x)[0]
# compare with numpy # compare with numpy
print ((1 - np.tanh(x.dot(w)) ** 2) * w).T print(((1 - np.tanh(x.dot(w)) ** 2) * w).T)
.. testoutput::
[[ 0.41997434 0. 0.41997434 0. 0. ]
[ 0. 1. 1. 0. 0. ]
[ 0. 0. 1. 0. 0. ]]
[[ 0.41997434 0. 0.41997434 0. 0. ]
[ 0. 1. 1. 0. 0. ]
[ 0. 0. 1. 0. 0. ]]
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. 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.
**Scan Example: Accumulate number of loop during a scan** **Scan Example: Accumulate number of loop during a scan**
.. code-block:: python .. testcode::
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -246,7 +314,7 @@ Note that we need to iterate over the indices of ``y`` and not over the elements ...@@ -246,7 +314,7 @@ Note that we need to iterate over the indices of ``y`` and not over the elements
**Scan Example: Computing tanh(v.dot(W) + b) * d where d is binomial** **Scan Example: Computing tanh(v.dot(W) + b) * d where d is binomial**
.. code-block:: python .. testcode::
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -268,13 +336,26 @@ Note that we need to iterate over the indices of ``y`` and not over the elements ...@@ -268,13 +336,26 @@ Note that we need to iterate over the indices of ``y`` and not over the elements
w = np.ones((2, 2), dtype=theano.config.floatX) w = np.ones((2, 2), dtype=theano.config.floatX)
b = np.ones((2), dtype=theano.config.floatX) b = np.ones((2), dtype=theano.config.floatX)
print compute_with_bnoise(x, w, b) print(compute_with_bnoise(x, w, b))
.. testoutput::
[array([[ 0.96402758, 0. ],
[ 0. , 0.96402758],
[ 0. , 0. ],
[ 0.76159416, 0.76159416],
[ 0.76159416, 0. ],
[ 0. , 0.76159416],
[ 0. , 0.76159416],
[ 0. , 0.76159416],
[ 0. , 0. ],
[ 0.76159416, 0.76159416]])]
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. 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 .. testcode::
import theano import theano
import theano.tensor as T import theano.tensor as T
...@@ -298,13 +379,16 @@ Note that if you want to use a random variable ``d`` that will not be updated th ...@@ -298,13 +379,16 @@ 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, power = theano.function(inputs=[A, k], outputs=final_result,
updates=updates) updates=updates)
print power(range(10), 2) print(power(range(10), 2))
#[ 0. 1. 4. 9. 16. 25. 36. 49. 64. 81.]
.. testoutput::
[ 0. 1. 4. 9. 16. 25. 36. 49. 64. 81.]
**Scan Example: Calculating a Polynomial** **Scan Example: Calculating a Polynomial**
.. code-block:: python .. testcode::
import numpy import numpy
import theano import theano
...@@ -329,7 +413,10 @@ Note that if you want to use a random variable ``d`` that will not be updated th ...@@ -329,7 +413,10 @@ Note that if you want to use a random variable ``d`` that will not be updated th
test_coeff = numpy.asarray([1, 0, 2], dtype=numpy.float32) test_coeff = numpy.asarray([1, 0, 2], dtype=numpy.float32)
print calculate_polynomial(test_coeff, 3) print calculate_polynomial(test_coeff, 3)
# 19.0
.. testoutput::
19.0
......
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