trains | Magical Experiment Manager & Version Control | Machine Learning library
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kandi X-RAY | trains Summary
TRAINS - Auto-Magical Experiment Manager & Version Control for AI - NOW WITH AUTO-MAGICAL DEVOPS!
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of trains
trains Key Features
trains Examples and Code Snippets
Lines of Code : 199License : Permissive (MIT)
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"trainDock": "D",
"trainHour": "26 
License : No LicenseCopyThis is the documentation for the use of Python's ``argparse`` to implement a CLI. This approach is no longer
recommended, and people are encouraged to use the new `LightningCLI <../cli/lightning_cli.html>`_ class instead.
from argparse import .. raw:: html
import os.path as ops
import lightning as L
from quick_start.components import PyTorchLightningScript, ImageServeGradio
class TrainDeploy(L.LightningFlow):
def __init__(self):
super().__init__()
self.trai """
.. _model-dgmg:
Generative Models of Graphs
===========================================
**Author**: `Mufei Li `_,
`Lingfan Yu `_, Zheng Zhang
.. warning::
The tutorial aims at gaining insights into the paper, with code as a mea def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_wei """
Stochastic Training of GNN for Link Prediction
==============================================
This tutorial will show how to train a multi-layer GraphSAGE for link
prediction on ``ogbn-arxiv`` provided by `Open Graph Benchmark
(OGB) `__. The dat arch = models.alexnet(); pic_x = 227
dummy_input = torch.zeros((1,3, pic_x, pic_x))
torch.onnx.export(arch, dummy_input, "alexnet.onnx", verbose=True, export_params=True, )
graph(%input.1 : Float(1, 3, 2Distances are measured by training univariate XGBoost models
of y for all the features, and then predicting the output of these
models using univariate XGBoost models of other features. If one
feature can effectively predict the output oimport matplotlib.pylot as plt
output = df_clean.pivot_table("QUANTITYORDERED","PRODUCTLINE","COUNTRY","sum")
f = plt.figure()
output.plot.bar(stacked=True, ax=f.gca())
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))
RNN_layer_1 = LSTM(units=64, return_sequences=False)(x)
RNN_layer_1 = LSTM(units=64, return_sequences=True)(x)
Community Discussions
Trending Discussions on trains
QUESTION
I have recently sourced and curated a lot of reddit data from Google Bigquery.
The dataset looks like this:
Before passing this data to word2vec to create a vocabulary and be trained, it is required that I properly tokenize the 'body_cleaned' column.
I have attempted the tokenization with both manually created functions and NLTK's word_tokenize, but for now I'll keep it focused on using word_tokenize.
Because my dataset is rather large, close to 12 million rows, it is impossible for me to open and perform functions on the dataset in one go. Pandas tries to load everything to RAM and as you can understand it crashes, even on a system with 24GB of ram.
I am facing the following issue:
- When I tokenize the dataset (using NTLK word_tokenize), if I perform the function on the dataset as a whole, it correctly tokenizes and word2vec accepts that input and learns/outputs words correctly in its vocabulary.
- When I tokenize the dataset by first batching the dataframe and iterating through it, the resulting token column is not what word2vec prefers; although word2vec trains its model on the data gathered for over 4 hours, the resulting vocabulary it has learnt consists of single characters in several encodings, as well as emojis - not words.
To troubleshoot this, I created a tiny subset of my data and tried to perform the tokenization on that data in two different ways:
- Knowing that my computer can handle performing the action on the dataset, I simply did:
ANSWER
Answered 2021-May-27 at 18:28First & foremost, beyond a certain size of data, & especially when working with raw text or tokenized text, you probably don't want to be using Pandas dataframes for every interim result.
They add extra overhead & complication that isn't fully 'Pythonic'. This is particularly the case for:
- Python
listobjects where each word is a separate string: once you've tokenized raw strings into this format, as for example to feed such texts to Gensim'sWord2Vecmodel, trying to put those into Pandas just leads to confusing list-representation issues (as with your columns where the same text might be shown as either['yessir', 'shit', 'is', 'real']– which is a true Python list literal – or[yessir, shit, is, real]– which is some other mess likely to break if any tokens have challenging characters). - the raw word-vectors (or later, text-vectors): these are more compact & natural/efficient to work with in raw Numpy arrays than Dataframes
So, by all means, if Pandas helps for loading or other non-text fields, use it there. But then use more fundamntal Python or Numpy datatypes for tokenized text & vectors - perhaps using some field (like a unique ID) in your Dataframe to correlate the two.
Especially for large text corpuses, it's more typical to get away from CSV and instead use large text files, with one text per newline-separated line, and any each line being pre-tokenized so that spaces can be fully trusted as token-separated.
That is: even if your initial text data has more complicated punctuation-sensative tokenization, or other preprocessing that combines/changes/splits other tokens, try to do that just once (especially if it involves costly regexes), writing the results to a single simple text file which then fits the simple rules: read one text per line, split each line only by spaces.
Lots of algorithms, like Gensim's Word2Vec or FastText, can either stream such files directly or via very low-overhead iterable-wrappers - so the text is never completely in memory, only read as needed, repeatedly, for multiple training iterations.
For more details on this efficient way to work with large bodies of text, see this artice: https://rare-technologies.com/data-streaming-in-python-generators-iterators-iterables/
QUESTION
I want to get through Fashion_Mnist data, I would like to see the output gradient which might be mean squared sum between first and second layer
My code first below
...ANSWER
Answered 2021-May-30 at 12:28The error is caused by the number of samples in the dataset and the batch size.
In more detail, the training MNIST dataset includes 60,000 samples, your current batch_size is 128 and you will need 60000/128=468.75 loops to finish training on one epoch. So the problem comes from here, for 468 loops, your data will have 128 samples but the last loop just contains 60000 - 468*128 = 96 samples.
To solve this problem, I think you need to find the suitable batch_size and the number of neural in your model as well.
I think it should work for computing loss
QUESTION
I am working on the pytorch to learn.
And There is a question how to check the output gradient by each layer in my code.
My code is below
...ANSWER
Answered 2021-May-29 at 11:31Well, this is a good question if you need to know the inner computation within your model. Let me explain to you!
So firstly when you print the model variable you'll get this output:
QUESTION
I'm using this tutorial to learn how to train a model on the MNIST dataset here: https://www.tensorflow.org/tutorials/quickstart/beginner
Currently, the model only trains on the accuracy, but I want to figure out the F1-score of the model (starting with precision and recall first).
...ANSWER
Answered 2021-May-28 at 02:46suppose you predicted using code:
QUESTION
The code below works just fine it trains the machine to multiply any given value by 10.
What I would like to figure out is how to train it with larger numbers without receiving a NaN when it tries to print. For instance I would like to put 100 = 200 when training the bot but anything over 10 for the training input and it throws a NaN.
...ANSWER
Answered 2021-May-23 at 19:57If you know the range of the values that you model is supposed to be able to handle, you can just normalize the values and train the model on the normalized values. If you, for example, know that you maximum input will be 1000, then you can just divide all inputs to your model by 1000 to have only inputs in range [0, 1]. Then you use the model to predict the output value and scale the values up again.
QUESTION
What I am trying to do is build a graph in NetworkX, in which every node is a train station. To build the nodes I did:
...ANSWER
Answered 2021-May-20 at 20:03You can simply use the add_path method in a loop:
QUESTION
I've been experimenting with SALT but am encountering consistent Load problems that seem to only affect raw function-trains. I hoped for any advice on ensuring all functions Load correctly.
To illustrate, in a clear workspace I'll create some example functions for converting hex and octal. Some are dfns, others are raw trains:
...ANSWER
Answered 2021-May-18 at 21:53As per the current SALT User Guide:
🛈 Nameclasses 3.3 (primitive or derived function) and 4.3 (primitive or derived operator) cannot be manipulated using SALT – attempting to do so can result in a loss of data.
As mentioned in Paul Mansour's comment, Dyalog Ltd. recommends transitioning from SALT to Link, especially when using Dyalog APL version 18.1, due to be released in the upcoming months. However, note that even Link does not currently handle tacit functions:
Functions, operators and namespaces without text source (
⎕NCof 3.3 or 4.3, namely derived functions/operators, trains and named primitives), are not supported.
As opposed to SALT, which is not scheduled to receive any major feature additions, this is likely to change in the near future.
While it is awkward to wrap tacit functions in tradfns by hand, the “Lazy” library makes this a breeze.
QUESTION
I am trying to parallelize this equation:
...ANSWER
Answered 2021-May-17 at 09:37The expensive operation here seems to be the code following the computation of the cosine similarity. You may want to use heap data structure to get the top ten.
Here is an attempt to improve the performance (while ensuring low space complexity) by parallelizing cosine similarity computation. Reference: https://docs.python.org/3/library/multiprocessing.html
QUESTION
I'm trying to research the best hyperparameters for my boosted decision tree training. Here's the code for just two instances:
...ANSWER
Answered 2021-May-14 at 18:37The problem in your code is that the expression nestimators[i] for i in range(2) is not a list (as you may think). That is a generator, and it doesn't produce any values until you explicitly do that. For example, this code:
QUESTION
I am using an encoder from using the TextVectorization object from preprocessing class. I then adapt my train data like so:
ANSWER
Answered 2021-May-13 at 17:53This is because you haven't specified the argument that indicates what the output shape of encoder will be, i.e output_sequence_length.
output_sequence_length: If set, the output will have its time dimension padded or truncated to exactly output_sequence_length values, resulting in a tensor of shape [batch_size, output_sequence_length] regardless of how many tokens resulted from the splitting step. Defaults to None.
If you set it to a number, you will see that the output shape of the layer will be defined:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install trains
You can use trains 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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