During inference, precision in floats is not needed and can be reduced to using 8 bits instead of 32 bits this allows to bin continuous values to discrete ranges, and therefore is known as Quantization. This enables us to increase the bandwith sent (or increase the memory footprint in caseContinue Reading
After training we can optimize a frozen graph or even a dynamic graph by removes training-specific and debug-specific nodes, fusing common operations, and removes code that isn’t used/reached. Code Example from tensorflow.python.tools import optimize_for_inference_lib inputGraph = tf.GraphDef() #read in a frozen model with tf.gfile.Open(‘frozentensorflowModel.pb’, “rb”) as f: data2read = f.read() inputGraph.ParseFromString(data2read) outputGraph = optimize_for_inference_lib.optimize_for_inference(inputGraph, [“inputTensor”], Continue Reading
In prediction/inference mode, variable types are unnecessary, so by freezing the graph we convert all variables in a graph and checkpoint into constants. Also there are structure nodes and operations related to training that we don’t need and can be removed by the freezing process as well. The freezing processContinue Reading
Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobservable (i.e. hidden) states. In simpler Markov models (like a Markov chain), the state is directly visible to the observer, and therefore the state transition probabilities areContinue Reading
Hidden states are the unknowns we try to detect or predict. The Hidden states have a relationship amongst themselves called the transition probabilities. Observations are the evidence variables that we have a priori. Observations and states have a relationship between them called the emission probabilities.Continue Reading
A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. (“memorylessness”)Continue Reading
To perform experience replay we store the agent’s experiences et=(st,at,rt,st+1) Then we use a random sample of these prior actions instead of the most recent action to proceed. * This removes correlations in the observation sequence and smooths changes in the data distribution. * Iterative update adjusts Q towards targetContinue Reading
Learns a policy which tells an agent what action to take under what circumstances. Q-learning learns a policy that is optimal in the sense that it maximizes the expected value of the total reward over any and all successive steps, starting from the current state.Continue Reading
* Is a discrete time stochastic control process for decision making in situations where outcomes are partly random and partly under the control of a decision maker. * At each discrete time step, the process is in some state s, and the decision maker may choose any action a thatContinue Reading
A variational autoencoder provides a probability distribution for describing an observation/attribute in latent/hidden space.Continue Reading