trax | Trax — Deep Learning with Clear Code and Speed | Machine Learning library
kandi X-RAY | trax Summary
kandi X-RAY | trax Summary
Trax is an end-to-end library for deep learning that focuses on clear code and speed. It is actively used and maintained in the Google Brain team. This notebook (run it in colab) shows how to use Trax and where you can find more information.
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Top functions reviewed by kandi - BETA
- Train model
- Convert x to ndarray
- Print the number of trainable weights
- Logs to stdout
- Configurable terraformer
- Cast this tensor to a given dtype
- Return a reversible serializer
- Creates MathQA inputs
- Convert a tensor
- Build a SubTensor from a list of tokens
- Increments incremental forward algorithm
- Transformer LM model
- Transformer model
- Creates a multiplicative attention layer
- Generalized convolution
- Construct a layer dropout transformer
- LSTM SeqNN layer
- Creates aqua inputs
- Configurable transformer encoder
- Transformer transformer
- Configurable Transformer transformer
- R Hourglass model
- Creates an annotated drop input
- Configurable transformer
- Performs one step of one iteration
- Shorten LSTM
trax Key Features
trax Examples and Code Snippets
Lines of Code : 58License : Permissive (Apache-2.0)
Copyfrom trax.supervised import training
# Training task.
train_task = training.TrainTask(
labeled_data=train_batches_stream,
loss_layer=tl.WeightedCategoryCrossEntropy(),
optimizer=trax.optimizers.Adam(0.01),
n_steps_per_checkpoint=500, 
License : No LicenseCopyfrom trax.supervised import training
# Training task.
train_task = training.TrainTask(
labeled_data=train_batches_stream,
loss_layer=tl.WeightedCategoryCrossEntropy(),
optimizer=trax.optimizers.Adam(0.01),
n_steps_per_checkpoint=500, class Embedding(base.Layer):
"""Trainable layer that maps discrete tokens/IDs to vectors."""
def __init__(self,
vocab_size,
d_feature,
kernel_initializer=init.RandomNormalInitializer(1.0)):
"""Ret [new_list] = [['a','b','c','d']]
print(new_list)
['a','b','c','d']
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
driver = webdriver.Chrome(executable_padef translate(model, sentence, vocab_dir, vocab_file):
empty_state = model.state # save empty state
tokenized_sentence = next(trax.data.tokenize(iter([sentence]), vocab_dir=vocab_dir,
list_names = ['PNP','CHECKERS'..]
mapping = ['PICKNPAY', 'CHECKERS' ..]
for i,item in enumerate(list_names):
trax.loc[trax['Activity_2'].str.contains(item), 'Activity_2'] = mapping[i]
#replaces inplace
df["Price"] = df["Price"].replace(["\$", ','],"", regex=True).astype(int)
print (df)
Condition_Type State Price Year Make Model
0 In Stock SA 24654 2017 Mazda 3
1 Used Car VIC 23162 2016 HCommunity Discussions
Trending Discussions on trax
QUESTION
So I've architected the following model which I will use to classify the MNIST Fashion data.
...ANSWER
Answered 2021-May-10 at 16:39MNIST has 10 classes so your output should be of size [batch_size, 10]. Change the last linear layer to self.dense = nn.Linear(128,10). Then, since your label is of size [batch_size,1], you should use torch.nn.CrossEntropyLoss as the criterion.
Additionally, you need not include the last softmax layer during training, as the aforementioned loss function performs a softmax operation during computation. You can instead use softmax or argmax for inference only.
QUESTION
Currently trying to retrieve data from this url.
I am trying to retrieve the following: Unit Name, Site Street 1, 2, Site Ciy, Province/State, Code, Facility Category, Completed. I have successfully done so but the code seems to appear in one line. For example:
...ANSWER
Answered 2021-May-03 at 01:40You should wait and locate not only the head elements, but their children as well.
QUESTION
I am working on a project involving Google's TRAX package. I encountered the following error message while running a snippet of code on Jupyter notebook.
...ANSWER
Answered 2021-Mar-20 at 02:29As per trax documentation The call must be trax.data rather than trax.Supervised trax.data.inputs.add_loss_weights(generator, id_to_mask=None)
QUESTION
I need a minimum of 3/4 different tts voice but unfortunatenly I have only one voice.
This because I have only one Italian neural voice (Diego) and the others are all standard voice and the quality is much worse.
The final objective is create a voice over for 3/4 persons minimum and I can't use the some exact voice.
For this reason, I like to create some variant started by the only one neural voice that I have, that gives the impression of a voice of other people all of this without seem unnatural.
Actually I have Adobe Audition, Audacity , Ircam Trax, ffmpeg and apart this I can use SSML with API (in this case microsoft Azure).
I don't known what are the effects and in what measure use it without damage the voices.
In short I ask what is the best way to do using the software that I have or other if I will get better results.
Thanks !
...ANSWER
Answered 2021-Mar-01 at 04:09what language are you using? If you are using English, I am sure you can find more than 3-4 neural voices. There are en-US, en-GB, en-CA, en-AU neural voices and all sound natural.
You can also tune the pitch using SSML to make the voice sound different.
If you would like to create different voices, try customvoice.ai with your speech data (or your voice talents).
or, what are the particular 'variances' you are looking for?
QUESTION
Anyone managed to log the loss with TensorBoard? I am using the trax ml library.
I am getting this error TypeError: 'SummaryWriter' object is not callable.
I am using the SummaryWriter from jaxboard and then adding it to callbacks within training.Loop.
ANSWER
Answered 2021-Feb-27 at 15:59Worked when I removed the line with callbacks, summary_writer and instead added this on google colab:
QUESTION
UPDATE : example and add some information.
I have to sort out the number below in numerical order in XSLT 1.0. The "code" follow the pattern below :
for example,
...ANSWER
Answered 2021-Jan-22 at 15:54To do this in pure XSLT 1.0, you will need to do it the pedestrian way:
QUESTION
I am creating Bulk upload shipments in woocommerce. I created a order status name trax-booked and in order admin we can bulk select the order to submit shipments in courier platform.
This is my code to get the orders data:
...ANSWER
Answered 2021-Jan-03 at 09:16There are some mistakes in your code… To skip orders that have _dvs_courier_check as meta data, try the following:
QUESTION
The AttentionQKV layer implemented by Trax is as the following: AttentionQKV
...ANSWER
Answered 2020-Sep-30 at 08:19This code snippet is an implementation of the equation on the top of page 5 of the Attention is all you need paper that introduced the Transformer models in 2017. The computation is illustrated in Figure 2 of the paper:
The hidden states get projection into h attention heads which do the scaled dot-product attention in parallel. The projection can be interpreted as extraction of information that is relevant for the head. Each head then does the probabilistic retrieval based on different (learned) criteria.
QUESTION
I am new to Machine Translation and Trax. My goal is to understand the introductory example on transformers in Trax, which can be found at https://trax-ml.readthedocs.io/en/latest/notebooks/trax_intro.html:
...ANSWER
Answered 2020-Aug-20 at 11:05I found it out myself... one needs to reset the model's state. So the following code works for me:
QUESTION
Getting below Exception::
...ANSWER
Answered 2020-Jul-04 at 12:29What version of Jackrabbit?
Looking at the source I see that the exception is actually caught and logged, and the system should proceed.
Are you really getting an exception in the code?
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install trax
You can use trax like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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