tape | reverse proxy capable Web server for local JavaScript

by metajack Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets(5)Community Discussions(10)Vulnerabilities Install Support

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A simple, reverse proxy capable Web server for local JavaScript application development.

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tape Key Features

No Key Features are available at this moment for tape.

tape Examples and Code Snippets

Using enzyme with Tape and AVA

npm

Lines of Code : 1

License : No License

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npm i --save-dev enzyme enzyme-adapter-react-16

Using enzyme with Tape and AVA

npm

Lines of Code : 1

License : No License

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npm i --save-dev enzyme enzyme-adapter-react-16

Decorator for gradients .

python

Lines of Code : 50

License : Non-SPDX (Apache License 2.0)

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def grad_pass_through(f):
  """Creates a grad-pass-through op with the forward behavior provided in f.

  Use this function to wrap any op, maintaining its behavior in the forward
  pass, but replacing the original op in the backward graph with an id

Call the function .

python

Lines of Code : 17

License : Non-SPDX (Apache License 2.0)

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def __call__(self, device, token, args):
    """Calls `self._func` in eager mode, recording the tape if needed."""
    use_tape_cache = (
        self._support_graph_mode_gradient or tape_lib.could_possibly_record())

    if use_tape_cache:
      wit

Initialize the tape .

python

Lines of Code : 6

License : Non-SPDX (Apache License 2.0)

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def __init__(self, persistent=False):
    self._c_tape = _tape.Tape(persistent)
    ctx = context_stack.get_default()
    self._tape_context = _tape.TapeContext(
        ctx, self._c_tape, gradient_registry.get_global_registry())
    self._ctx_manage

Community Discussions

Trending Discussions on tape

Model.evaluate returns 0 loss when using custom model

When decoding ASCII, should the parity bit be deliberately omitted?

In Tensorflow how can I (1) compute gradients and (2) update variables in *separate* @tf.function methods?

No gradients provided for any variable - LSTM autoencoder

NotImplementedError: When subclassing the `Model` class, you should implement a `call` method

TF2 code 10 times slower than equivalent PyTorch code for a Conv1D network

TensorFlow 2.0 : ValueError - No Gradients Provided (After Modifying DDPG Actor)

Single loss with Multiple output model in TF.Keras

Weights were not updated using Gradient Tape and apply_gradients()

Manual Calculation of tanh in Tensorflow Keras Model is resulting in Nan

QUESTION

Model.evaluate returns 0 loss when using custom model

Asked 2021-Jun-15 at 15:52

I am trying to use my own train step in with Keras by creating a class that inherits from Model. It seems that the training works correctly but the evaluate function always returns 0 on the loss even if I send to it the train data, which have a big loss value during the training. I can't share my code but was able to reproduce using the example form the Keras api in https://keras.io/guides/customizing_what_happens_in_fit/ I changed the Dense layer to have 2 units instead of one, and made its activation to sigmoid.

The code:

...

ANSWER

Answered 2021-Jun-12 at 17:27

As you manually use the loss and metrics function in the train_step (not in the .compile) for the training set, you should also do the same for the validation set or by defining the test_step in the custom model in order to get the loss score and metrics score. Add the following function to your custom model.

Source https://stackoverflow.com/questions/67951244

QUESTION

When decoding ASCII, should the parity bit be deliberately omitted?

Asked 2021-Jun-12 at 12:19

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)?

Example

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

...

ANSWER

Answered 2021-Jun-12 at 11:39

The fact that the parity bit CAN be set is just an observation, not a generally followed protocol. That being said, I know of no programming languages that actually care about parity when decoding ASCII. If the highest bit is set, the number is simply treated as >=128, which is out of range of the known ASCII characters.

Source https://stackoverflow.com/questions/67948490

QUESTION

In Tensorflow how can I (1) compute gradients and (2) update variables in *separate* @tf.function methods?

Asked 2021-Jun-11 at 18:28

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.

...

ANSWER

Answered 2021-Jun-11 at 18:28

Please check the quick fix below corresponding to your question.

Source https://stackoverflow.com/questions/67940720

QUESTION

No gradients provided for any variable - LSTM autoencoder

Asked 2021-Jun-09 at 19:28

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:

...

ANSWER

Answered 2021-Jun-09 at 19:28

You need to pass x_train and y_train into the fit statement.

Source https://stackoverflow.com/questions/67909447

QUESTION

NotImplementedError: When subclassing the `Model` class, you should implement a `call` method

Asked 2021-Jun-07 at 16:42

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:

...

ANSWER

Answered 2021-Jun-07 at 16:40

In your custom model with subclassed API, implement the call method as follows:

Source https://stackoverflow.com/questions/67870304

QUESTION

TF2 code 10 times slower than equivalent PyTorch code for a Conv1D network

Asked 2021-Jun-06 at 11:34

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:

...

ANSWER

Answered 2021-Jun-06 at 11:34

You're using tf.GradientTape correctly, but both your models and data are different in the snippets you provided.

Here is the TF code that uses the same data and model architecture as your Pytorch model.

Source https://stackoverflow.com/questions/67848459

QUESTION

TensorFlow 2.0 : ValueError - No Gradients Provided (After Modifying DDPG Actor)

Asked 2021-Jun-05 at 19:06

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:

...

ANSWER

Answered 2021-Jun-05 at 19:06

The issue has been resolved thanks to some simple but helpful advice I received on Reddit. I was disrupting the tracking of my variables by making changes using my custom for-loop. I should have used a TensorFlow function instead. The following changes fixed the problem for me:

Source https://stackoverflow.com/questions/67845026

QUESTION

Single loss with Multiple output model in TF.Keras

Asked 2021-Jun-02 at 14:49

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:

...

ANSWER

Answered 2021-Jun-02 at 10:45

Here is one approach for your case. We will still use a custom training loop but also take the leverage of the convenient .fit method by customizing this method. Please check the document for more details of this: Customizing what happens in fit()

Here is one simple demonstration, extending your reproducible code.

Source https://stackoverflow.com/questions/67796503

QUESTION

Weights were not updated using Gradient Tape and apply_gradients()

Asked 2021-Jun-02 at 11:05

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:

...

ANSWER

Answered 2021-Jun-02 at 11:05

The weight does change. You can check as follows; after building the model save your weights file (these are initial weight).

Source https://stackoverflow.com/questions/67803574

QUESTION

Manual Calculation of tanh in Tensorflow Keras Model is resulting in Nan

Asked 2021-May-31 at 09:48

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: