Inkundla yasungulwa ithimba le-Google Brain futhi isetshenziswa ezinsizeni ze-Google ukuze kuqashelwe inkulumo, ukuhlonza ubuso ezithombeni, ukucacisa ukufana kwezithombe, ukuhlunga ugaxekile ku-Gmail,
I-TensorFlow inikeza umtapo wolwazi wokubala amanani enziwe ngomumo osetshenziswa ngamagrafu okugeleza kwedatha. Amanodi akumagrafu anjalo asebenzisa imisebenzi yezibalo noma amaphuzu okufaka/okukhiphayo, kuyilapho amaphethelo egrafu amelela ama-multidimensional data array (ama-tensor) ageleza phakathi kwamanodi.
Ama-Node angabelwa kumadivayisi e-computing futhi enziwe ngokuhambisanayo, kanyekanye acubungule wonke ama-thesors afanele wona ngesikhathi esisodwa, okwenza kube nokwenzeka ukuhlela ukusebenza ngesikhathi esisodwa kwama-node kunethiwekhi ye-neural ngokufanisa nokusebenza kanyekanye kwama-neurons ebuchosheni.
Okugxilwe kakhulu ekulungiseleleni inguqulo entsha kwakuwukwenza lula kanye nokusetshenziswa kalula.
- Kuphakanyiswe i-API entsha yezinga eliphezulu yokwakha nokuqeqeshwa
UKeras , ehlinzeka ngezinketho zokusebenzelana eziningana zamamodeli wokwakha (Ukulandelana, Okusebenzayo, I-Subclassing) enekhono lokuukuqaliswa ngokushesha (ngaphandle kokuhlanganiswa kwangaphambili) kanye nendlela elula yokulungisa iphutha; - I-API eyengeziwe
tf.sabalalisa.Isu okwenhlanganoukufunda okusabalalisiwe amamodeli anezinguquko ezincane kumakhodi akhona. Ngaphezu kwamathuba okusabalalisa izibalo yonkanaama-GPU amaningi , usekelo lokuhlola luyatholakala ekuhlukaniseni inqubo yokufunda ibe amaphrosesa ambalwa azimele kanye nekhono lokusebenzisa ifuI-TPU (Iyunithi yokucubungula i-Tensor); - Esikhundleni semodeli ememezelayo yokwakha igrafu ngokusebenza ngokusebenzisa i-tf.Session, kuyenzeka ukuthi ubhale imisebenzi evamile ku-Python, okuthi, kusetshenziswa ikholi eya ku-tf.function, iguqulelwe ibe amagrafu bese isenziwa ukude, i-serialized, noma ilungiselelwe. ukusebenza okuthuthukisiwe;
- Kwengezwe umhumushi
I-AutoGraph , eguqula ukusakazwa kwemiyalo ye-Python ibe izinkulumo ze-TensorFlow, okuvumela ikhodi ye-Python ukuthi isetshenziswe ngaphakathi kwe-tf.function-decorated, tf.data, tf.distribute, kanye nemisebenzi ye-tf.keras; - I-SavedModel ihlanganisa ifomethi yokushintshanisa imodeli futhi yengeza usekelo lokulondoloza nokubuyisela imodeli yesimo. Amamodeli ahlanganiselwe i-TensorFlow manje angasetshenziswa ku
I-TensorFlow Lite (kumadivayisi eselula),I-TensorFlow JS (kusiphequluli noma ku-Node.js),Isevisi ye-TensorFlow ΠΈIhabhu le-TensorFlow ; - I-tf.train.Optimizers kanye ne-tf.keras.Optimizers APIs ahlanganisiwe; esikhundleni se-compute_gradients, kuphakanyiswe ikilasi elisha lokubala ama-gradient.
I-Gradient Tape ; - Ukusebenza okukhuphuke kakhulu uma usebenzisa i-GPU.
Ijubane lokuqeqeshwa okuyimodeli kumasistimu ane-NVIDIA Volta kanye ne-Turing GPUs likhuphuke lafika izikhathi ezintathu; -
Kwenziwe Ukuhlanzwa okukhulu kwe-API, izingcingo eziningi eziqanjwe kabusha noma zisusiwe, ukusekelwa kokuguquguquka komhlaba wonke ezindleleni zomsizi kumisiwe. Esikhundleni se-tf.app, tf.flags, tf.logging, i-absl-py API entsha iyaphakanyiswa. Ukuze uqhubeke usebenzisa i-API endala, imojuli ye-comat.v1 isilungisiwe.
Source: opennet.ru