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danielknobe/blobbyvolley2 68

Official continuation of the famous Blobby Volley 1.x arcade game.

ngc92/quadgym 7

OpenAI gym Environments for Quadrotor Control

ngc92/DeepQLearn 1

deep q learning with experience replay

ngc92/Generative-Adversarial-Networks 1

Implementation of several Generative Adversarial Networks in tensorflow.

ngc92/gym 1

A toolkit for developing and comparing reinforcement learning algorithms.

Mesocopic/branchedflowsim 0

A software package for the simulation of branched flows on random media.

ngc92/baselines 0

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

ngc92/Box2D 0

Box2D is a 2D physics engine for games

ngc92/branchedflowsim 0

A software package for the simulation of branched flows on random media.

PR opened tzutalin/labelImg

adds a utility function to hide a QT5/QT4 discrepancy

The old code needed to trim the returned text at multiple places. This method changed names between QT4 and QT5, so I've consolidated this into a utility function. The implementation of this function is chosen at import time.

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adds a utility function to hide a QT5/QT4 discrepancy

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🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images

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issue commenttensorflow/tensorflow

Partial shapes for keras.InputLayer with sparse=True

It appears that this works now. It is also shown in the documentation here


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issue closedtensorflow/tensorflow

Partial shapes for keras.InputLayer with sparse=True

System information

  • TensorFlow version (you are using): 2.1
  • Are you willing to contribute it (Yes/No): Yes

Describe the feature and the current behavior/state. It is currently not possible to use input SparseTensor into e.g. keras.Sequential with partially defined shapes. This is because SparseTensors need to be declared specifically, thus necessitating the use of keras.InputLayer as the fiirst layer in the model.

If the shape of keras.InputLayer for sparse inputs is not fully defined (including the batch size), the produced SparseTensor will have dense_shape where only the rank is defined. This happens because unless the dense_shapeis fully defined, a placeholder will be used for feeding in the dense_shape, removing the partial shape information.

Will this change the current api? How? No

Who will benefit with this feature? This will make models of the form

   InputLayer(shape=(num_features,), sparse=True), 

work, as they require partial shape for the input of the Dense layer.

closed time in 2 months