Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support
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Updated
Aug 26, 2020 - Python
Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support
Faster R-CNN R-101-FPN model was implemented with TensorFlow2.0 eager execution.
PyTorch, TensorFlow, JAX and NumPy — all of them natively using the same code
TensorFlow implementation of Deformable Convolutional Layer
TensorFlow Eager implementation of NEAT and Adaptive HyperNEAT
A Tensorflow Keras implementation (Graph and eager execution) of Mnasnet: MnasNet: Platform-Aware Neural Architecture Search for Mobile.
Cyclic learning rate TensorFlow implementation.
TensorFlow 2.0 implementation of MAML.
TensorFlow implementation of DropBlock
Just another YOLO V2 implementation. Train your own dataset in a jupyter notebook!
Tutorials of TensorFlow eager execution
In this project I implement Neural Machine Translation using Attention mechanism. The code is written using the TensorFlow library in Python. I have used TensorFlow functionalities like tf.data.Dataset to manage the input pipeline, Eager Execution and Model sub classing to create the model architecture.
Implementing Style Transfer in Tensorflow 2.0, using the VGG19 network architecture, which composes the content image in the style of the reference picture, both input by the user.
Eagerly Experimentable!!!
An example of semantic segmentation using tensorflow in eager execution.
Eager Execution enables you to run operations immediately
Foolbox Native brings native performance to Foolbox
Tensor utilities, reinforcement learning, and more!
A proposal for distribution-based loss function in neural network
MobileNetV2 written in tensorflow, training with eager mode and estimator API
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