Metadata-Version: 1.1
Name: cadl
Version: 1.1.0
Summary: Creative Applications of Deep Learning with TensorFlow
Home-page: https://github.com/pkmital/pycadl
Author: Parag Mital
Author-email: parag@pkmital.com
License: UNKNOWN
Download-URL: https://github.com/pkmital/pycadl/archive/v1.1.0.tar.gz
Description-Content-Type: UNKNOWN
Description: # Introduction
        This package is part of the Kadenze Academy program [Creative Applications of Deep Learning w/ TensorFlow](https://www.kadenze.com/programs/creative-applications-of-deep-learning-with-tensorflow).
        
        [COURSE 1: Creative Applications of Deep Learning with TensorFlow I](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow-iv/info) (Free to Audit)  
        Session 1: Introduction to TensorFlow  
        Session 2: Training A Network W/ TensorFlow  
        Session 3: Unsupervised And Supervised Learning  
        Session 4: Visualizing And Hallucinating Representations  
        Session 5: Generative Models  
        
        [COURSE 2: Creative Applications of Deep Learning with TensorFlow II](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow-ii/info) (Program exclusive)  
        Session 1: Cloud Computing, GPUs, Deploying  
        Session 2: Mixture Density Networks  
        Session 3: Modeling Attention with RNNs, DRAW  
        Session 4: Image-to-Image Translation with GANs  
        
        [COURSE 3: Creative Applications of Deep Learning with TensorFlow III](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow-iii-iii/info) (Program exclusive)  
        Session 1: Modeling Music and Art: Google Brain’s Magenta Lab  
        Session 2: Modeling Language: Natural Language Processing  
        Session 3: Autoregressive Image Modeling w/ PixelCNN  
        Session 4: Modeling Audio w/ Wavenet and NSynth  
        
        # Requirements
        
        Python 3.5+
        
        # Installation
        
        `pip install cadl`
        
        Then in python, you can import any module like so:
        
        `from cadl import vaegan`
        
        Or see a list of possible modules in an interactive console by typing:
        
        `from cadl import ` and then pressing tab to see the list of available modules.
        
        # Documentation
        
        [cadl.readthedocs.io](http://cadl.readthedocs.io)
        
        # Contents 
        
        This package contains various models, architectures, and building blocks covered in the Kadenze Academy program including:
        
        * Autoencoders  
        * Character Level Recurrent Neural Network (CharRNN)  
        * Conditional Pixel CNN  
        * CycleGAN  
        * Deep Convolutional Generative Adversarial Networks (DCGAN)  
        * Deep Dream  
        * Deep Recurrent Attentive Writer (DRAW)  
        * Gated Convolution  
        * Generative Adversarial Networks (GAN)  
        * Global Vector Embeddings (GloVe)  
        * Illustration2Vec  
        * Inception  
        * Mixture Density Networks (MDN)  
        * PixelCNN  
        * NSynth  
        * Residual Networks 
        * Sequence2Seqeuence (Seq2Seq) w/ Attention (both bucketed and dynamic rnn variants available)  
        * Style Net  
        * Variational Autoencoders (VAE)  
        * Variational Autoencoding Generative Adversarial Networks (VAEGAN)  
        * Video Style Net  
        * VGG16  
        * WaveNet / Fast WaveNet Generation w/ Queues / WaveNet Autoencoder (NSynth)  
        * Word2Vec  
        
        and more.  It also includes various datasets, preprocessing, batch generators, input pipelines, and plenty more for datasets such as:
        
        * CELEB  
        * CIFAR  
        * Cornell  
        * MNIST  
        * TedLium  
        * LibriSpeech  
        * VCTK  
        
        and plenty of utilities for working with images, GIFs, sound (wave) files, MIDI, video, text, TensorFlow, TensorBoard, and their graphs.
        
        Examples of each module's use can be found in the tests folder.
        
        # Contributing
        
        Contributions, such as other model architectures, bug fixes, dataset handling, etc... are welcome and should be filed on the GitHub.
        
        # Troubleshooting
        
        ## Error: alsa/asoundlib.h: No such file or directory
        
        ```
          src/RtMidi.cpp:1101:28: fatal error: alsa/asoundlib.h: No such file or directory
          compilation terminated.
          error: command 'gcc' failed with exit status 1
        ```
        
        This is a dependency of `magenta` (`python-rtmidi`) which requires `libasound`.
        
        ### Solution: Install ALSA
        
        CentOS:
        
        ```
        sudo yum install alsa-lib-devel alsa-utils
        ```
        
        Ubuntu:
        
        ```
        sudo apt-get install libasound2-dev
        ```
        
        ### More info:
        
        https://python-rtmidi.readthedocs.io/en/latest/installation.html
        https://github.com/tensorflow/magenta/issues/781
        
        ## Error: jack/jack.h: No such file or directory
        
        ```
        src/RtMidi.cpp:2448:23: fatal error: jack/jack.h: No such file or directory
        compilation terminated.
        ```
        
        ### Solution: Install Jack
        
        Ubuntu:
        ```
        sudo apt install libjack-dev
        ```
        
        ### More info:
        
        https://python-rtmidi.readthedocs.io/en/latest/installation.html
        https://github.com/tensorflow/magenta/issues/781
        
        
        
        # 1.1.0
        
        * Requirements now points to 1.5.0 TensorFlow
        
        # 1.0.9
        
        * Residual block in CycleGAN was not using first convolutional layer
        
        # 1.0.8
        
        * Batch loading support from magenta repo for FastGen config
        
        # 1.0.7
        
        * NSynth batch processing code from magenta repo
        * `get_model` function in `nsynth` module now attempts to download and untar the model from the magenta website.
        * `utils.download` functions default to local dir
        * Separate encode functionality in nsynth module.
        
        # 1.0.6
        
        * MDN activation fn
        
        # 1.0.5
        
        * Fix gauss pdf
        
        # 1.0.4
        
        * Allow for batch=1 in DRAW code
        
        # 1.0.3
        
        * Add mdn to init
        
        # 1.0.2
        
        * Remove tanh activation from variational layer
        * Add librispeech train code to fastwavenet module
        * Add Mixture Density Network code from course in mdn module
        
        # 1.0.1
        
        Fixed model loading during charrnn infer method.  No longer checks for ckpt name and will attempt to load regardless.
        
        # 1.0.0
        
        Initial release
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
