scan函數(shù)

theano.scan(fn, sequences=None, outputs_info=None,non_sequences=None, n_steps=None, truncate_gradient=-1,go_backwards=False, mode=None, name=None, profile=False)

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 outputs_info is the list of Theano variables or dictionaries describing the initial state of the outputs computed recurrently.

fn是每一步所用的函數(shù)，sequences是輸入，outputs_info是scan輸出在起始的狀態(tài)。sequences and outputs_info are all parameters of fn in ordered sequence.

scan(fn, sequences = [ dict(input= Sequence1, taps = [-3,2,-1]) , Sequence2 , dict(input =Sequence3, taps = 3) ]  , outputs_info = [ dict(initial =Output1, taps = [-3,-5]) , dict(initial = Output2, taps = None) , Output3 ]  , non_sequences = [ Argument1, Argument2])

fn should expect the following arguments in this given order:

1. Sequence1[t-3]
2. Sequence1[t+2]
3. Sequence1[t-1]
4. Sequence2[t]
5. Sequence3[t+3]
6. Output1[t-3]
7. Output1[t-5]
8. Output3[t-1]
9. Argument1
10. Argument2

import theano
import theano.tensor as T
mode = theano.Mode(linker='cvm')
import numpy as np

def fun(a,b):
return a+b
input=T.vector("input")
output,update=theano.scan(fun,sequences=input,outputs_info=[T.as_tensor_variable(np.asarray(1,input.dtype))])

out=theano.function(inputs=[input],outputs=output)

in1=numpy.array([1,2,3])
print out(in1)

 def fun(a,b):
return a+b
input=T.matrix("input")
output,update=theano.scan(fun,sequences=input,outputs_info=[T.as_tensor_variable(np.asarray([0,0,0],input.dtype))])

out=theano.function(inputs=[input,],outputs=output)

in1=numpy.array([[1,2,3],[4,5,6]])
print(in1)
print out(in1)

shared variables相當于全局變量，The value can be accessed and modified by the.get_value() and .set_value() methods.  在function里用updata來修改可以并行。

scan的輸出是一個symbol，用來在后面的theano function里作為output和update的規(guī)則。當sequences=None時，n_steps應有一個值來限制對后面theano function里的input的循環(huán)次數(shù)。當sequences不為空時，theano function直接對sequences循環(huán)：

components, updates = theano.scan(fn=lambda coefficient, power, free_variable: coefficient * (free_variable ** power),  outputs_info=None,  sequences=[coefficients, theano.tensor.arange(max_coefficients_supported)],  non_sequences=x)

這個例子中，

theano.tensor.arange(max_coefficients_supported)類似于enumerate的index，coefficientes相當與enumerate里到序列值。這里根據(jù)順序，x為free_variable.

Debug：

http://deeplearning.net/software/theano/tutorial/debug_faq.html

theano.config.compute_test_value = 'warn'

• off: Default behavior. This debugging mechanism is inactive.
• raise: Compute test values on the fly. Any variable for which a test value is required, but not provided by the user, is treated as an error. An exception is raised accordingly.
• warn: Idem, but a warning is issued instead of an Exception.
• ignore: Silently ignore the computation of intermediate test values, if a variable is missing a test value.

import theanodef inspect_inputs(i, node, fn):  print i, node, "input(s) value(s):", [input[0] for input in fn.inputs],def inspect_outputs(i, node, fn):  print "output(s) value(s):", [output[0] for output in fn.outputs]x = theano.tensor.dscalar('x')f = theano.function([x], [5 * x],   mode=theano.compile.MonitorMode( pre_func=inspect_inputs, post_func=inspect_outputs))f(3)

mode = 'DEBUG_MODE' 很慢，無效？

使用print

x = theano.tensor.dvector('x')x_printed = theano.printing.Print('this is a very important value')(x)f = theano.function([x], x * 5)f_with_print = theano.function([x], x_printed * 5)#this runs the graph without any printingassert numpy.all( f([1, 2, 3]) == [5, 10, 15])#this runs the graph with the message, and value printedassert numpy.all( f_with_print([1, 2, 3]) == [5, 10, 15])

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