σ ΰΖχXc@`s‘ddlmZmZmZddlZddlZddlmZddlZ ddl Z ddl m Z ddl jZddljjZddlmZddlmZy0ddlmZmZdd lmZeZWnek rφeZnXej d ƒZ!d „Z"d „Z#ddd „Z%ddd„Z&da'da(da)d„Z*d„Z+e+ej,ƒZ-e+ej.ƒZ/e+d„ƒZ0defd„ƒYZ1dS(i(tabsolute_importtprint_functiontdivisionN(tStringIO(tconfig(tModei(tget_mode(t GpuArrayTypet _name_for_ctx(tGpuArraystheano.compile.nanguardmodecC`s‘t|tjjjƒrtSt|tjjj ƒr8tS|rWt |j dtƒrWtSt|t ƒrjtS|dkrztS|jdkrtStS(s~ Checks a variable against non-numeric types such as types, slices, empty arrays, and None, that need not be checked for NaN and Inf values. Parameters ---------- arr : the data of that correspond to any Theano Variable var : The corresponding Theano variable Returns ------- is_non_numeric : bool `True` the value is non-numeric. tis_rngiN(t isinstancettheanotgofttypet _cdata_typetFalsetnptrandomtmtrandt RandomStatetgetattrttagtslicetNonetsizetTrue(tarrtvar((s;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pyt_is_numeric_values cC`sxt|tttjfƒrmg}xP|D]>}t|ttfƒrY|jt|ƒƒq(|j|ƒq(Wn|gS|S(sχ Turns a nested graph of lists/tuples/other objects into a list of objects. Parameters ---------- l : list/tuple/other objects Might be nested. Returns ------- object A flattened list of objects. (R tlistttuplet collectionst ValuesViewtextendtflattentappend(tltrvaltelem((s;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pyR#9s cC`sαt||ƒstStjr—t|tjƒr—|rettjdƒret|j tjj j ƒretSt t ttƒtjt|j|jƒƒƒSn4trΛt|tƒrΛtjt|j|jƒƒƒStjtj|ƒƒS(sε Test whether a numpy.ndarray contains any `np.nan` values. Parameters ---------- arr : np.ndarray or output of any Theano op node : None or an Apply instance. If arr is the output of a Theano op, the node associated to it. var : The Theano symbolic variable. Returns ------- contains_nan : bool `True` if the array contains any `np.nan` values, `False` otherwise. Notes ----- Tests for the presence of `np.nan`'s using `np.isnan(np.min(ndarray))`. This approach is faster and more memory efficient than the obvious alternative, calling `np.any(np.isnan(ndarray))`, which requires the construction of a boolean array with the same shape as the input array. trng_mrg(RRtcudatcuda_availableR t CudaNdarraythasattrR tsandboxtopR(tGPU_mrg_uniformtcompile_gpu_funcRRtisnantf_gpumintreshapeRtpygpu_availableR t f_gpua_mintmin(RtnodeR((s;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pyt contains_nanTs"cC`s;t||ƒstStjrΈt|tjƒrΈ|rettjdƒret|j tjj j ƒretSt tt tƒtjt|j|jƒƒƒp΄tjt|j|jƒƒƒSnUtr t|tƒr tjt|j|jƒƒƒp tjt|j|jƒƒƒStjtj|ƒƒp:tjtj|ƒƒS(s Test whether a numpy.ndarray contains any `np.inf` values. Parameters ---------- arr : np.ndarray or output of any Theano op node : None or an Apply instance. If the output of a Theano op, the node associated to it. var : The Theano symbolic variable. Returns ------- contains_inf : bool `True` if the array contains any `np.inf` values, `False` otherwise. Notes ----- Tests for the presence of `np.inf`'s by determining whether the values returned by `np.nanmin(arr)` and `np.nanmax(arr)` are finite. This approach is more memory efficient than the obvious alternative, calling `np.any(np.isinf(ndarray))`, which requires the construction of a boolean array with the same shape as the input array. R((RRR)R*R R+R,R R-R.R(R/R0RRtisinfR2R3Rtf_gpumaxR4R R5t f_gpua_maxtnanmaxtnanmin(RR7R((s;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pyt contains_inf~s!"!cC`sHtjs dStjdƒ}t}|s.|rtdkry(tj|gtj |ƒddƒaWqt k r{t }qXn|rέ| rέt dkrέy(tj|gtj |ƒddƒa Wqέt k rΩt }qέXn|rD| rDtdkrDy1tj|gtj tj|ƒƒddƒaWqDt k r@t }qDXndS(sD compile utility function used by contains_nan and contains_inf Nt nan_guardtmodetFAST_RUN(R)R*tfvectorRR2RR tfunctiontTR6t RuntimeErrorRR:tmaxt f_gpuabsmaxtabs_(t nan_is_errort inf_is_errort big_is_errort guard_inputtcuda_compile_failed((s;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pyR0―s2         c`s"‡‡fd†‰tƒˆ_ˆS(Nc`sΌ|j}t|jƒ}||f}ˆjj|dƒ}|dkr²tt|ƒtfd|ƒƒ}t dƒj dƒ}t j |gˆ|ƒd|dtƒ}|ˆj|μst NanGuardModecB`s&eZdZdddddd„ZRS(sŸ A Theano compilation Mode that makes the compiled function automatically detect NaNs and Infs and detect an error if they occur. Parameters ---------- nan_is_error : bool If True, raise an error anytime a NaN is encountered. inf_is_error : bool If True, raise an error anytime an Inf is encountered. Note that some pylearn2 modules currently use np.inf as a default value (e.g. mlp.max_pool) and these will cause an error if inf_is_error is True. big_is_error : bool If True, raise an error when a value greater than 1e10 is encountered. Note ---- We ignore the linker parameter tdefaultc `s||_ˆdkr$tjj‰nˆdkr?tjj‰nˆdkrZtjj‰nˆsrˆsrˆsrt‚tj rŽt ˆˆˆƒnd‡‡‡fd†‰‡fd†}‡fd†}t j j jd|d|ƒ}tt|ƒj|d|jƒdS(Nc`skt}tƒ}ˆrCt|||ƒrCtdd|ƒt}qCnˆrwt|||ƒrwtdd|ƒt}qwnˆrZt}t||ƒs›t}ntjrηt |tj ƒrηt tttƒt |j |jƒƒdk}nQtrt |tƒrt|j |jƒƒdk}ntj|ƒjƒdk}|rZtdd|ƒt}qZn|rg|r›tdd|ƒttjj|ddƒd|ƒntdd|ƒ|rΑ|jd }nttjjj|ƒd|ƒ|jƒ}tjjd kr t |ƒ‚qgtjjd krBt|ƒd d l!}|j"ƒqgtjjd krgt#j$|ƒqgnd S(sW Checks `value` for NaNs / Infs. If detected, raises an exception and / or prints information about `nd`, `f`, and `is_input` to help the user determine the cause of the invalid values. Parameters ---------- value : numpy.ndarray The value to be checked. nd : theano.gof.Apply The Apply node being executed. var : theano.gof.Variable Not used if nd is there. Otherwise, used to print the stack trace for inputs of the graph. s NaN detectedtfiles Inf detectedg _ BsBig value detectedsENanGuardMode found an error in the output of a node in this variable:RUs5NanGuardMode found an error in an input of the graph.itraisetpdbNtwarn(%RRR8tprintRR>RR)R*R R+R0RGR3RR4R t f_gpua_absmaxRtabsRFR tprintingt debugprinttoutputsR tutilstget_variable_trace_stringtgetvalueRRatactiontAssertionErrorRet set_tracetloggerterror(tvaluetndRRttsioterrtmsgRe(RKRJRI(s;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pyt do_check_onsV    !!  %     c`sSxL|jD]A}||dr t|jdtƒr ˆ||d|ƒq q WdS(Nitnan_guard_mode_check(RlRRR(R7tthunkt storage_mapt compute_mapR(Rz(s;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pyt nan_checkTsc`s/t|jdtƒr+ˆ|dd|ƒndS(NR{R(RRRR(RRu(Rz(s;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pytnan_check_inputZstcallbacktcallback_inputt optimizer(tprovided_optimizerRRRaRIRJRKRqR)t cuda_enabledR0R R tvmt VM_Linkertsupert__init__( tselfRIRJRKRƒtlinkerRR€t wrap_linker((RKRzRJRIs;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pyR‰s"     @ N(t__name__t __module__t__doc__RR‰(((s;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pyRaοs (2t __future__RRRR tloggingt six.movesRtnumpyRR ttheano.configparserRt theano.tensorttensorRDttheano.sandbox.cudaR-R)ttheano.compileRR@Rttheano.gpuarray.typeRRtpygpu.gpuarrayR RR4t ImportErrorRt getLoggerRsRR#RR8R>R2R:RGR0R^R6R5RFR;RhRa(((s;/tmp/pip-build-X4mzal/theano/theano/compile/nanguardmode.pyts<         *, +