Tape | Gradle plugin to modify android app icon | Plugin library

According to Wikipedia, the ASCII is a 7-bit encoding. Since each address (then and now) stores 8 bits, the extraneous 8th bit can bit used as a parity bit.

The committee voted to use a seven-bit code to minimize costs associated with data transmission. Since perforated tape at the time could record eight bits in one position, it also allowed for a parity bit for error checking if desired.[3]:217, 236 §5 Eight-bit machines (with octets as the native data type) that did not use parity checking typically set the eighth bit to 0.

Nothing seems to mandate that the 8th bit in a byte storing an ASCII character has to be 0. Therefore, when decoding ASCII characters, do we have to account for the possibility that the 8th bit may be set to 1? Python doesn't seem to take this into account — should it? Or are we guaranteed that the parity bit is always 0 (by some official standard)?

If the parity bit is 0 (default), then Python can decode a character ('@'):

I need to compute tf.Variable gradients in a class method, but use those gradients to update the variables at a later time, in a different method. I can do this when not using the @tf.function decorator, but I get the TypeError: An op outside of the function building code is being passed a "Graph" tensor error when using @tf.function. I've searched for understanding on this error and how to resolve it, but have come up short.

Just FYI if you're curious, I want to do this because I have variables that are in numerous different equations. Rather than trying to create a single equation that relates all the variables, it is easier (less computationally costly) to keep them separate, compute the gradients at a moment in time for each of those equations, and then incrementally apply the updates. I recognize that these two approaches are not mathematically identical.

Here is my code (a minimal example), followed by the results and error message. Note that when gradients are computed and used to update variables in a single method, .iterate(), there is no error.

I'm trying to build an LSTM encoder. I'm testing it on the MNIST dataset to check any errors before using it on my actual dataset. My code:

I'm working on this image classification problem with keras. I'm trying to use subclassing API's to do almost everything. I've created my custom conv blocks which looks as follows:

I've been trying to translate some PyTorch code to TensorFlow 2, but the TF2 code is around 10 times slower. I've tried looking at where this might come from, and as far as I can tell it comes from the tape.gradient call (performance was the same with keras' .fit function). I've tried to use different data loaders, ways of declaring the model, installations, etc... and the results have been consistent.

Any explanation / solution as to why this is happening would be much appreciated.

Here is a minimalist version of the TF2 code:

Background

I'm currently trying to implement a DDPG framework to control a simple car agent. At first, the car agent would only need to learn how to reach the end of a straight path as quickly as possible by adjusting its acceleration. This task was simple enough, so I decided to introduce an additional steering action as well. I updated my observation and action spaces accordingly.

The lines below are the for loop that runs each episode:

I use tensorflow's Dataset such that y is a dictionary of 6 tensors which I all use in a single loss function which looks likes this:

I am building a DNN with a custom loss function and I am training this DNN using Gradient Tape in TensorFlow.kerasenter code here. The code runs without any errors, however, as far as I can check the weights of the DNN, the weights were not being updated at all. I followed exactly what recommends from the TensorFlow website and search for the answers but still don't understand what is the reason. Here is my code:

Please find the below TF Keras Model in which I am using tanh activation function in the Hidden Layers.

While the value of Logits are proper, the values that are calculated by implementing the tanh function manually is resulting in Nan.

It may be because of the Runtime Warnings shown below:

/home/abc/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:76: RuntimeWarning: overflow encountered in exp

/home/abc/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:76: RuntimeWarning: invalid value encountered in true_divide

Complete reproducible code is mentioned below: