# Theano Study Notes (1) -- Algebra

Source: Internet
Author: User
Tags theano

`import theano.tensor as Tfrom theano import functionx = T.dscalar('x')y = T.dscalar('y')z = x + yf = function([x, y], z)`

The input defines two symbol variables to replace the value. The output is a zero-dimensional numpy. ndarray array.

Change the input type. If the dimension of the matrix is different, the numpy broadcast rule is followed.

`import theano.tensor as Tfrom theano import functionx = T.dmatrix('x')y = T.dmatrix('y')z = x + yf = function([x, y], z)`

Define a formula such as a ** 2 + B ** 2 + 2 * a * B

Each variable must be declared separately.

`import theanoa = theano.tensor.vector()b = theano.tensor.vector()out = a ** 2 + b ** 2 + 2 * a * bf = theano.function([a,b],out)print f([0, 1],[1,2])>>> [ 1. 9.]`

Support for multiple outputs

`import theano.tensor as Tfrom theano import functiona, b = T.dmatrices('a', 'b')diff = a - babs_diff = abs(diff)diff_squared = diff**2f = function([a, b], [diff, abs_diff,diff_squared])print f([[1, 1], [1, 1]], [[0, 1], [2,3]])>>> [array([[ 1.,  0.],      [-1., -2.]]), array([[ 1.,  0.],      [ 1.,  2.]]), array([[ 1.,  0.],      [ 1.,  4.]])]`

Set default parameters

Like standard Python, the default parameter must be non-default, and the default variable name can also be defined.

`import theano.tensor as Tfrom theano import functionfrom theano import Paramx, y = T.dscalars('x', 'y')z = x + yf = function([x, Param(y, default=1,name='by_name')],z)print f(33)print f(33, 2)print f(33,by_name=3)>>> 34.035.036.0`

Shared variable

To provide better performance on the GPU, the sharing variable is introduced. The example uses the accumulators.

`import theano.tensor as Tfrom theano import functionfrom theano import sharedstate = shared(0)inc = T.iscalar('inc')accumulator = function([inc], state,updates=[(state, state+inc)])print state.get_value()accumulator(1)print state.get_value()accumulator(300)print state.get_value()state.set_value(-1)print accumulator(3)print state.get_value()>>> 01301-12`

The state value is refreshed only after the function is called. In addition, multiple functions can be defined to share the same shared variable, such as the subtraction operator.

`decrementor = function([inc], state,updates=[(state, state-inc)])print decrementor(2)print state.get_value()>>> 20`

If a function shares the shared variable but does not want to change its value, you can use the given parameter to replace the variable. The old state does not change.

`fn_of_state = state * 2 + incfoo = T.scalar(dtype=state.dtype)skip_shared = function([inc, foo],fn_of_state,                           givens=[(state,foo)])print skip_shared(1, 3)print state.get_value()>>> 70`

Generate random number

Like srand () in C, they are pseudo-random numbers.

`From theano import functionfrom theano. tensor. shared_randomstreamsimport randomstreamssrng = randomstreams (seed = 234) # seed rv_u = SRNG. uniform (2, 2) # uniformly distributed rv_n = SRNG. normal (2, 2) # normal distribution F = function ([], rv_u) # every call, G = function ([], rv_n, no_default_updates = true) will be updated each time) # If this set of random numbers is used all the time, nearly_zeros = function ([], rv_u + rv_u-2 * rv_u) print nearly_zeros () # The function obtains only one random number at a time, even if the expression contains three random numbers`

Seed stream: These two random variables can be set globally for the same seed or separately.

`# Set separately and use. RNG. set_value () function rng_val = rv_u.rng.get_value (borrow = true) # Get the RNG for rv_urng_val.seed (89234) # seeds partition (rng_val, borrow = true) # global settings, use. seed () function SRNG. seed (902340)`

Shared stream between functions

`State_after_v0 = rv_u.rng.get_value (). get_state () # Save the statenearly_zeros () # This affects rv_u's generatorv1 = f () # The first call before the call. Then the state changes to RNG = rv_u.rng.get_value (borrow = true) RNG. set_state (state_after_v0) # restore rv_u.rng.set_value (RNG, borrow = true) for its State v2 = f () # V2! = Random number of state corresponding to V1 output update V3 = f () # V3 = V1 updated again and restored to the original state`

Copy status between two theano Images

`Import theanoimport numpyimport theano. tensor as tfrom theano. sandbox. rng_mrg importmrg_randomstreamsfrom theano. tensor. shared_randomstreamsimport randomstreams class graph (): def _ init _ (self, seed = 123): Self. RNG = randomstreams (SEED) self. y = self. RNG. uniform (size = (1,) G1 = graph (seed = 123) F1 = theano. function ([], g1.y) G2 = graph (seed = 987) F2 = theano. function ([], g2.y) print 'by default, the TW O functionsare out of sync. 'print 'f1 () returns ', F1 () print 'f2 () returns', F2 () # output different random values def copy_random_state (G1, G2 ): if isinstance (g1.rng, mrg_randomstreams): # type judgment: the first parameter is an object, and the second is a list of type names or type names. The return value is boolean. G2.rng. rstate = g1.rng. rstate for (su1, su2) in zip (g1.rng. state_updates, g2.rng. state_updates): # package su2 [0]. set_value (su1 [0]. get_value () # assign the value 'we now copy the state of thetheano random number generators. 'Copy _ random_state (G1, G2) print 'f1 () returns ', F1 () print 'f2 () returns', F2 () # output the same random value >>> by default, the two functions are outof sync. f1 () Returns [0.72803009] F2 () Returns [0.55056769] We now copy the state of the theanorandom number generators. f1 () Returns [1, 0.59044123] F2 () Returns [2, 0.59044123]`

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Theano Study Notes (1) -- Algebra

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