A TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit), and oriented toward using or running models rather than training them. In May 2016, Google announced its Tensor processing unit (TPU), an application-specific integrated circuit (ASIC, a hardware chip) built specifically for machine learning and tailored for TensorFlow. In May 2019, Google announced TensorFlow Graphics for deep learning in computer graphics. It became officially available in Sep 2019. In Jan 2019, Google announced TensorFlow 2.0. In March 2018, Google announced TensorFlow.js version 1.0 for machine learning in JavaScript. Kubeflow allows operation and deployment of TensorFlow on Kubernetes. In December 2017, developers from Google, Cisco, RedHat, CoreOS, and CaiCloud introduced Kubeflow at a conference. During the Google I/O Conference in June 2016, Jeff Dean stated that 1,500 repositories on GitHub mentioned TensorFlow, of which only 5 were from Google. The name TensorFlow derives from the operations that such neural networks perform on multidimensional data arrays, which are referred to as tensors. TensorFlow computations are expressed as stateful dataflow graphs. Its flexible architecture allows for the easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.
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TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). Version 1.0.0 was released on February 11, 2017. TensorFlow is Google Brain's second-generation system. In 2009, the team, led by Geoffrey Hinton, had implemented generalized backpropagation and other improvements which allowed generation of neural networks with substantially higher accuracy, for instance a 25% reduction in errors in speech recognition. Google assigned multiple computer scientists, including Jeff Dean, to simplify and refactor the codebase of DistBelief into a faster, more robust application-grade library, which became TensorFlow.
Its use grew rapidly across diverse Alphabet companies in both research and commercial applications. Starting in 2011, Google Brain built DistBelief as a proprietary machine learning system based on deep learning neural networks. _compute_td_err ( state, action, reward, next_state )) + self. use_prioritized_replay : priority = math. _q, state, action ) return td_err def _compute_priority ( self, state, action, reward, next_state ): priority = None if self.
_target_q, next_state )) td_err -= self. _q, next_state ))) else : td_err += self. preprocessing_args )) except ValueError : raise ValueError ( 'Unknown preprocessing method: " ) def _compute_td_err ( self, state, action, reward, next_state ): td_err = reward if next_state is not None : if self. _parameters = QLearningParameters ( config_filename ) # Create preprocessor. o_space: observation space, _space.Tuple is not supported. Args: config_filename: configure file specifying training details. Use either predefined neural network structure (see models.py) or customized network (see customized_models.py). Including: - DQN - Prioritized Experience Replay - Dueling Network - Double Q Learning """ def _init_ ( self, config_filename, o_space, a_space ): """Constructor for Q learning algorithm. Class QLearning ( AgentBaseClass ): """ Q-learning agent.