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1. Watch Me 2.0.9 Online
2. Watch Me 2015

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Version 2.0.9. Installing WatchMe with MacUpdate Desktop. If nothing happens, you might not have MacUpdate Desktop installed. Apr 18, 2018 WatchMe is a small macOS application designed to help you find and download episodes from TV series, with minimal effort. The app finds multiple online sources for each episode, and can also convert the media content to MP4, MOV, or MPEG file formats.

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Watch Me 2.0.9 Online

fchollet released this Nov 1, 2017 · 1328 commits to master since this release

Areas of improvement

• RNN improvements:
  • Refactor RNN layers to rely on atomic RNN cells. This makes the creation of custom RNN very simple and user-friendly, via the RNN base class.
  • Add ability to create new RNN cells by stacking a list of cells, allowing for efficient stacked RNNs.
  • Add CuDNNLSTM and CuDNNGRU layers, backend by NVIDIA's cuDNN library for fast GPU training & inference.
  • Add RNN Sequence-to-sequence example script.
  • Add constants argument in RNN's call method, making RNN attention easier to implement.
• Easier multi-GPU data parallelism via keras.utils.multi_gpu_model.
• Bug fixes & performance improvements (in particular, native support for NCHW data layout in TensorFlow).
• Documentation improvements and examples improvements.

API changes

• Add 'fashion mnist' dataset as keras.datasets.fashion_mnist.load_data()
• Add Minimum merge layer as keras.layers.Minimum (class) and keras.layers.minimum(inputs) (function)
• Add InceptionResNetV2 to keras.applications.
• Support bool variables in TensorFlow backend.
• Add dilation to SeparableConv2D.
• Add support for dynamic noise_shape in Dropout
• Add keras.layers.RNN() base class for batch-level RNNs (used to implement custom RNN layers from a cell class).
• Add keras.layers.StackedRNNCells() layer wrapper, used to stack a list of RNN cells into a single cell.
• Add CuDNNLSTM and CuDNNGRU layers.
• Deprecate implementation=0 for RNN layers.
• The Keras progbar now reports time taken for each past epoch, and average time per step.
• Add option to specific resampling method in keras.preprocessing.image.load_img().
• Add keras.utils.multi_gpu_model for easy multi-GPU data parallelism.
• Add constants argument in RNN's call method, used to pass a list of constant tensors to the underlying RNN cell.

Breaking changes

• Implementation change in keras.losses.cosine_proximity results in a different (correct) scaling behavior.
• Implementation change for samplewise normalization in ImageDataGenerator results in a different normalization behavior.

Credits

Thanks to our 59 contributors whose commits are featured in this release!

Watch Me 2015

@alok, @Danielhiversen, @Dref360, @HelgeS, @JakeBecker, @MPiecuch, @MartinXPN, @RitwikGupta, @TimZaman, @adammenges, @aeftimia, @ahojnnes, @akshaychawla, @alanyee, @aldenks, @andhus, @apbard, @aronj, @bangbangbear, @bchu, @bdwyer2, @bzamecnik, @cclauss, @colllin, @datumbox, @deltheil, @dhaval067, @durana, @ericwu09, @facaiy, @farizrahman4u, @fchollet, @flomlo, @fran6co, @grzesir, @hgaiser, @icyblade, @jsaporta, @julienr, @jussihuotari, @kashif, @lucashu1, @mangerlahn, @myutwo150, @nicolewhite, @noahstier, @nzw0301, @olalonde, @ozabluda, @patrikerdes, @podhrmic, @qin, @raelg, @roatienza, @shadiakiki1986, @smgt, @souptc, @taehoonlee, @y0z