lstm | clean example of lstm neural network training | Machine Learning library
kandi X-RAY | lstm Summary
kandi X-RAY | lstm Summary
A basic lstm network can be written from scratch in a few hundred lines of python, yet most of us have a hard time figuring out how lstm's actually work. The original Neural Computation paper is too technical for non experts. Most blogs online on the topic seem to be written by people who have never implemented lstm's for people who will not implement them either. Other blogs are written by experts (like this blog post) and lack simplified illustrative source code that actually does something. The Apollo library built on top of caffe is terrific and features a fast lstm implementation. However, the downside of efficient implementations is that the source code is hard to follow. This repo features a minimal lstm implementation for people that are curious about lstms to the point of wanting to know how lstm's might be implemented. The code here follows notational conventions set forth in this well written tutorial introduction. This article should be read before trying to understand this code (at least the part about lstm's). By running python test.py you will have a minimal example of an lstm network learning to predict an output sequence of numbers in [-1,1] by using a Euclidean loss on the first element of each node's hidden layer. Play with code, add functionality, and try it on different datasets. Pull requests welcome. Please read my blog article if you want details on the backprop part of the code. This sample code has been ported to the D programming language by Mathias Baumann: as well as Julia by @hyperdo Alfiuman in C++ (with CUDA) and Ascari in JavaScript (for nodejs)
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Top functions reviewed by kandi - BETA
- Calculates the top_diff and the parameters
- Computes the loss of the loss of a loss of loss .
- Initialize parameters .
- apply_diff_diff
- Assigns the bottom data to the network .
- Add an x - node .
- Generate random permutation
- Compute sigmoid function .
- Calculate sigmoid derivative .
- Calculate tanh derivative .
lstm Key Features
lstm Examples and Code Snippets
Lines of Code : 34License : Permissive (Apache-2.0)
CopyRelu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha Relu(x) - max(0, x)
Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) - 1/(1 + e^{-x})
(NOTE: Below are optional)
Affine(x) - alpha*x + beta
LeakyRelu(x) - x if x >= 0 else alpha import argparse
import collections
import os
import time
import warnings
import zipfile
os.environ["DGLBACKEND"] = "mxnet"
os.environ["MXNET_GPU_MEM_POOL_TYPE"] = "Round"
import mxnet as mx
import numpy as np
from mxnet import gluon
from tree_lstm import argparse
import collections
import time
import numpy as np
import torch as th
import torch.nn.functional as F
import torch.nn.init as INIT
import torch.optim as optim
from torch.utils.data import DataLoader
from tree_lstm import TreeLSTM
imp #! /usr/bin/python
# -*- coding: utf-8 -*-
"""Example of Synced sequence input and output.
This is a reimpmentation of the TensorFlow official PTB example in :
tensorflow/models/rnn/ptb
The batch_size can be seem as how many concurrent computations Community Discussions
Trending Discussions on lstm
QUESTION
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.
...ANSWER
Answered 2022-Feb-17 at 10:47You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)
A running exampleJust modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
QUESTION
I'm trying to use GridSearchCV to find the best hyperparameters for an LSTM model, including the best parameters for vocab size and the word embeddings dimension. First, I prepared my testing and training data.
ANSWER
Answered 2022-Feb-02 at 08:53I tried with scikeras but I got errors because it doesn't accept not-numerical inputs (in our case the input is in str format). So I came back to the standard keras wrapper.
The focal point here is that the model is not built correctly. The TextVectorization must be put inside the Sequential model like shown in the official documentation.
So the build_model function becomes:
QUESTION
I am approaching a problem that Keras must offer an excellent solution for, but I am having problems developing an approach (because I am such a neophyte concerning anything for deep learning). I have sales data. It contains 11106 distinct customers, each with its time series of purchases, of varying length (anyway from 1 to 15 periods).
I want to develop a single model to predict each customer's purchase amount for the next period. I like the idea of an LSTM, but clearly, I cannot make one for each customer; even if I tried, there would not be enough data for an LSTM in any case---the longest individual time series only has 15 periods.
I have used types of Markov chains, clustering, and regression in the past to model this kind of data. I am asking the question here, though, about what type of model in Keras is suited to this type of prediction. A complication is that all customers can be clustered by their overall patterns. Some belong together based on similarity; others do not; e.g., some customers spend with patterns like $100-$100-$100, others like $100-$100-$1000-$10000, and so on.
Can anyone point me to a type of sequential model supported by Keras that might handle this well? Thank you.
I am trying to achieve this in R. Haven't been able to build a model that gives me more than about .3 accuracy.
...ANSWER
Answered 2022-Jan-31 at 18:55Hi here's my suggestion and I will edit it later to provide you with more information
Since its a sequence problem you should use RNN based models: LSTM, GRU's
QUESTION
I have an input that is a time series of 5 dimensions:
a = [[8,3],[2] , [4,5],[1], [9,1],[2]...] #total 100 timestamps. For each element, dims 0,1 are numerical data and dim 2 is a numerical encoding of a category. This is per sample, 3200 samples
The category has 3 possible values (0,1,2)
I want to build a NN such that the last dimension (the category) will go through an embedding layer with output size 8, and then will be concatenated back to the first two dims (the numerical data).
So, this will be something like:
...ANSWER
Answered 2021-Dec-12 at 11:08There are a couple of issues you are having here. First let me give you a working example and explain along the way how to solve your issues.
Imports and Data GenerationQUESTION
ANSWER
Answered 2021-Sep-16 at 19:53text input must of type str (single example), List[str] (batch or single pretokenized example) or List[List[str]] (batch of pretokenized examples).
Solution to the above error:
Just use text_input = 'text'
instead of
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
QUESTION
I am trying to train a BiLSTM-CRF on detecting new NER entities with Pytorch. To do so, I am using a snippet of code derivated from the Pytorch Advanced tutorial. This snippet implements batch training.
I followed the READ-ME in order to present data as required. Everything works great on CPU, but when I'm trying to get it to GPU, the following error occur :
...ANSWER
Answered 2021-Jun-22 at 15:58Within PadSequence function (which acts as a collate_fn which gathers samples and makes a batch from them) you are explicitly casting to cuda device, namely:
QUESTION
I want to do time series multi-class classification with time-series data. Here the data set I have got needs to be preprocessed heavily and that just to get an idea of how to implement the model I have used the IRIS data set(not suitable for LSTM) since it has the exact same structure of the time series data I have( 4 input features,1 output feature, 120 samples). I have the following code implemented but it causes me the invalid shape error when fitting the model with a batch size of 5 (changed the batch size many times but didn't seem to make any change)
...ANSWER
Answered 2021-Jun-21 at 12:48Your y_true and y_pred are not in the same shape. You may need to define your LSTM in the following way
QUESTION
tensorflow version 2.3.1 numpy version 1.20
below the code
...ANSWER
Answered 2021-Feb-15 at 11:55I solved with numpy downgrade to 1.18.5
QUESTION
I'm trying to train a model using LSTM layers. I'm using a GPU and all needed libraries are loaded.
When I'm building the model this way:
...ANSWER
Answered 2021-Apr-20 at 23:59I found the solution... kinda.
So it works as it should when I downgraded tensorflow to 2.1.0, CUDA to 10.1 and cudnn to 7.6.5 (at the time 4th combination from this list on TensorFlow website)
I don't know why it didn't work at the newest version, or at the valid combination for tensorflow 2.4.0.
It's working well so my issue is solved. Nonetheless it would be nice to know why using LSTM with cudnn on higher versions didn't work for me, as I haven't found this issue anywhere.
QUESTION
I am a beginner with LSTMs so sorry if this is a basic question. I've been trying to make a simple LSTM model that loads data from a csv text file for training
...ANSWER
Answered 2021-Apr-19 at 06:54trainX.shape = (35, 150) which means that you have 35 samples of 150. But you need to pass the data with the batch_size in the first position according to Keras. So you would have to expand the 2D input to 3D:
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Install lstm
You can use lstm 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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