The ``inputs`` argument to ``theano.function`` is a list, containing the ``Variable`` instances for which values will be specified at the time of the function call. But inputs can be more than just Variables.
The ``inputs`` argument to ``theano.function`` is a list, containing the ``Variable`` instances for which values will be specified at the time of the function call. But inputs can be more than just Variables.
``In`` instances let us attach properties to ``Variables`` to tell function more about how to use them.
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@@ -50,7 +50,7 @@ The ``inputs`` argument to ``theano.function`` is a list, containing the ``Varia
compiled function to modify the Python object being used as the
default value. The default value is ``False``.
``strict``: Bool (default: ``False`` ). ``True`` means that the value
``strict``: Bool (default: ``False`` ). ``True`` means that the value
you pass for this input must have exactly the right type. Otherwise, it
may be cast automatically to the proper type.
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@@ -90,7 +90,7 @@ Since we provided a ``value`` for ``s`` and ``x``, we can call it with just a va
>>> inc(5) # update s with 10+3*5
[]
>>> print inc[s]
>>> print(inc[s])
25.0
The effect of this call is to increment the storage associated to ``s`` in ``inc`` by 15.
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@@ -100,18 +100,18 @@ If we pass two arguments to ``inc``, then we override the value associated to
>>> inc(3, 4) # update s with 25 + 3*4
[]
>>> print inc[s]
>>> print(inc[s])
37.0
>>> print inc[x] # the override value of 4 was only temporary
>>> print(inc[x]) # the override value of 4 was only temporary
3.0
If we pass three arguments to ``inc``, then we override the value associated
If we pass three arguments to ``inc``, then we override the value associated
with ``x`` and ``u`` and ``s``.
Since ``s``'s value is updated on every call, the old value of ``s`` will be ignored and then replaced.
>>> inc(3, 4, 7) # update s with 7 + 3*4
[]
>>> print inc[s]
>>> print(inc[s])
19.0
We can also assign to ``inc[s]`` directly:
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@@ -286,13 +286,13 @@ The ``outputs`` argument to function can be one of
- a Variable or ``Out`` instance, or
- a list of Variables or ``Out`` instances.
An ``Out`` instance is a structure that lets us attach options to individual output ``Variable`` instances,
An ``Out`` instance is a structure that lets us attach options to individual output ``Variable`` instances,
similarly to how ``In`` lets us attach options to individual input ``Variable`` instances.
**Out(variable, borrow=False)** returns an ``Out`` instance:
* ``borrow``
If ``True``, a reference to function's internal storage
is OK. A value returned for this output might be clobbered by running
the function again, but the function might be faster.
@@ -277,7 +277,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
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]
print(compute_jac_t(w, x)[0])
# compare with numpy
print(((1 - np.tanh(x.dot(w)) ** 2) * w).T)
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@@ -351,7 +351,7 @@ Note that we need to iterate over the indices of ``y`` and not over the elements
[ 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)**
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@@ -412,7 +412,7 @@ Note that if you want to use a random variable ``d`` that will not be updated th
