Result for 0FF3726A9C71CF7688A446B1269D756C07E37559

Query result

Key Value
FileName./usr/share/doc/keras-doc/examples/lstm_text_generation.py
FileSize3341
MD524F1FD1BF7A366F377B95FECA9E1A25A
SHA-10FF3726A9C71CF7688A446B1269D756C07E37559
SHA-256544E2543D8AC1EA168880EEA271F7BD5AC00E7C962E7322BDA6347372B09ABA0
SSDEEP96:THD1jG4ALe1bJqIdQDrbjDzhbnlrRofVoaNewb8wrU:THBjG4ALUbJ7d0rbjDzh1aNjb8wrU
TLSHT16961477138E73E06824792BE9DD6EF12633A02635F4CA83070DC5629DFA745952A9CFC
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize942032
MD59EE8D4352481D1DAC8F8C26E165B1DA1
PackageDescriptionCPU/GPU math expression compiler for Python (docs) Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian, but coming up). . This package contains the documentation for Keras.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamekeras-doc
PackageSectiondoc
PackageVersion2.1.1-1
SHA-1D1759E97E0267A861FA9A6EB5568E23251D070B1
SHA-256EF91FD03A896D2F8D5E0C260E64F27DEA9395684BD6FDCAD82E0CB480C84E03E