Lstm tensorflow tutorial. In particular we will see how. while_loop (): Repeat a Function – TensorFlow Tutorial. If you need help setting up your Python environment, see this post: Mar 13, 2024 · Convolutional Variational Autoencoder. ここでは、2 つの主要部分をサブセクションとともに説明しています Jan 11, 2018 · We will build a two-layer LSTM network with hidden layer sizes of 128 and 64, respectively. 9+) backend. LSTMCell(). Catch up on the latest ML and AI developer updates from Google I/O Watch sessions. Aug 30, 2020 · Gated Recurrent Unit (GRU) is a new generation of Neural Networks and is pretty similar to Long Short Term Memory (LSTM). A model of this type could be helpful within the household in planning expenditures. 要約. In this tutorial we are going to focus on: Preprocessing Mar 30, 2020 · LSTM models are perhaps one of the best models exploited to predict e. unpack may not be able to determine the size of a given axis (use the nums argument if this is the case). Reload to refresh your session. You will train a model using a collection of piano MIDI files from the MAESTRO dataset. All of the code used in this post is available in this colab notebook, which will run end to end (including installing TensorFlow 2. Apr 3, 2024 · Generate music with an RNN. Checkpoints (Object-based saving) One train_step operations. This article assumes some knowledge of text generation, attention and transformer. Import TensorFlow and other dependencies for the examples in this guide. I think we need a index to word matrix and then we should be able to use the output word vector to look up the word in the index to word matrix. Unlike standard feed-forward neural networks, LSTM has feedback connections. A small example: How to use TensorFlow LSTM tutorial for character-level language modeling? 450. You can access GPU hardware Sep 24, 2021 · Approach 6: CNN + LSTM. Sequential API. It uses the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. The following is the sample output when the model in this tutorial trained for 30 epochs, and started with the prompt "Q": Implement a Recurrent Neural Net (RNN) in Tensorflow! RNNs are a class of neural networks that is powerful for modeling sequence data such as time series or Nov 16, 2023 · TensorFlow 2 quickstart for beginners. The opposite is the static tool kit, which includes Theano, Keras, TensorFlow, etc. Sparse models are easier to compress, and we can skip the zeroes during inference for latency improvements. Refer to the migrate section of the guide for more info on migrating your TF1 code to TF2. This change is made to the n_batch parameter in the run () function; for example: 1. Build a neural network machine learning model that classifies images. org で表示. Mar 23, 2024 · Classification on imbalanced data. Our end goal remains to apply the complete model to Natural Language Processing (NLP). Convierte los ejemplos de numeros enteros a numeros de punto flotante: The aim of this tutorial is to show the use of TensorFlow with KERAS for classification and prediction in Time Series Analysis. In this tutorial, you will discover how you can […] You signed in with another tab or window. inputs: it will be batch_size * seq_length * emb_dim, such as 64 * 40 * 200. Schematically, the following Sequential model: # Define Sequential model with 3 layers. Use tf-addons BasicDecoder for decoding. Good options include tf. examples. model = keras. fit(train_dataset, epochs=EPOCHS, callbacks=callbacks) Jul 22, 2020 · This tutorial contains detail content. Jul 24, 2023 · When to use a Sequential model. KerasNLP is a natural language processing library that supports workflows built from modular components that have state-of-the-art preset weights and architectures. It can process not only single data points (such as images) but also entire Oct 20, 2020 · Even though this model is already a nice improvement from the previous tutorial we still can increase the accuracy. If you are new to these dimensions, color_channels refers to (R,G,B). To begin, we're going to start with the exact same code as we used with the basic multilayer-perceptron model: import tensorflow as tf. Mar 23, 2024 · This guide provides a list of best practices for writing code using TensorFlow 2 (TF2), it is written for users who have recently switched over from TensorFlow 1 (TF1). 与往常一样，本文的所有代码都 Jun 19, 2016 · Tensorflow requires input as a tensor (a Tensorflow variable) of the dimensions [batch_size, sequence_length, input_dimension] (a 3d variable). This is very straightforward and available here. Google Colab で実行. In this example, you will train a simple convolutional neural network on the Fashion MNIST dataset May 6, 2024 · TensorFlow is a comprehensive deep learning framework developed by Google, offering a wide range of functionalities beyond neural networks. Jun 14, 2021 · I'm writing a German->English translator using an encoder/decoder pattern, where the encoder connects to the decoder by passing the state output of its last LSTM layer as the input state of the decoder's LSTM. It doesn't require operating system support, any standard C or C++ libraries, or dynamic memory allocation. 1. 1, though our Adadelta optimizer will adapt this over time, and a keep probability of 0. In this tutorial, we consider as examples a deep multi-layer RNN which is unidirectional and uses LSTM as a recurrent unit. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower Mar 23, 2024 · Download notebook. x (t) — token at timestamp t . Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Build Your Own LSTM Model Using TensorFlow: Steps to Create a Customized LSTM – TensorFlow Tutorial. You signed out in another tab or window. Train this neural network. This notebook classifies movie reviews as positive or negative using the text of the review. The data set can be found here. 之前的代码我放在了 Oct 24, 2017 · I am trying to do multi-step time series forecasting using multivariate LSTM in Keras. In our case, batch_size is something we’ll determine later but sequence_length is fixed at 20 and input_dimension is 1 (i. For more details about MultiWorkerMirroredStrategy, consider the following tutorials: Multi-worker training with Keras Model. 5. 0, the built-in LSTM and GRU layers have been updated to leverage CuDNN kernels by default when a GPU is available. We’ll start with a quick review of Keras configurations you should keep in mind when constructing and training your own CNNs. rnn_cell. Feb 3, 2024 · To see an example of structural pruning, run the tutorial Structural pruning with 2 by 4 sparsity. The application that we will cover in this article is the use of an LSTM to generate new text. Unlike a traditional autoencoder, which maps the input Apr 11, 2017 · In this section, we look at halving the batch size from 4 to 2. c (t-1) — previous cell state, and 2 outputs —. This step is the same whether you are distributing the training or not. Keras, on the other hand, is a high-level neural networks API that can run on top of TensorFlow and other deep learning frameworks. Jun 22, 2022 · We will use a sequential neural network created in Tensorflow based on bidirectional LSTM layers to capture the patterns in the univariate sequences that we will input to the model. A VAE is a probabilistic take on the autoencoder, a model which takes high dimensional input data and compresses it into a smaller representation. We set both of them are 0, you can customize them. Mar 19, 2024 · Generative Adversarial Networks (GANs) are one of the most interesting ideas in computer science today. TensorFlow. Running the example shows the same general trend in performance as a batch size of 4, perhaps with a higher RMSE on the final epoch. The Transformer was originally proposed in "Attention is all you need" by Vaswani et al. This tutorial also assumes you have scikit-learn, Pandas, NumPy, and Matplotlib installed. To know more tf. Some important parameters. emb_dim: the dimension of word embeddings, such as 200. A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor. Note: LSTM recurrent neural networks can be slow to train, and it is highly recommended that you train them on GPU hardware. Consider you’re dealing with data that is captured in regular Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain. It is responsible for keeping the information or forgetting it so the sigmoid activation function is このチュートリアルは、TensorFlow を使用した時系列予測を紹介します。. The easiest way to get started processing text in TensorFlow is to use KerasNLP. 事前トレーニング済み Apr 16, 2024 · Intro to Autoencoders. In a regression problem, the aim is to predict the output of a continuous value, like a price or a probability. At the end of the notebook, there is an exercise for you to try, in which you'll train a multi-class classifier to predict the tag for a programming Jan 17, 2021 · This tutorial assumes you have Keras (v2. 代码将松散地遵循这里找到的TensorFlow team教程，但是进行了更新和我自己的大量修改。. Train the model. The training for this tutorial runs for many short epochs. It will walk you through all the components in a Reinforcement Learning (RL) pipeline for training, evaluation and data collection. Setup. To implement bayesian LSTM we start with base LSMT class from tensorflow and override the call function by adding the variational posterior to the weights, after which we compute gates f,i,o,c and h as usual. This short introduction uses Keras to: Load a prebuilt dataset. The rest is pretty Interested readers can find more information about RNNs and LSTM on this blog post. set_printoptions(precision=3, suppress=True) import tensorflow as tf from tensorflow. np. While TensorFlow provides flexibility and scalability for complex machine 因为工作和学习比较忙，所以这些内容也没办法一下子完成。. First, we import the libraries and make sure our TensorFlow is the right version. The source code of BiLSTM model is below: Where. fit on the model and passing in the dataset created at the beginning of the tutorial. The performance of the model is evaluated based on the model’s ability to recreate Jan 6, 2022 · This tutorial will focus on the following steps: Experiment setup and HParams summary; Adapt TensorFlow runs to log hyperparameters and metrics; Start runs and log them all under one parent directory; Visualize the results in TensorBoard's HParams dashboard; Note: The HParams summary APIs and dashboard UI are in a preview stage and will change May 22, 2021 · In this tutorial, you will implement a CNN using Python and Keras. EpochDots which simply prints a . You will use a portion of the Speech Commands dataset ( Warden, 2018 ), which contains short (one-second or less Jul 28, 2023 · KerasNLP. ノートブックをダウンロード. import tensorflow as tf. Concatenate(axis=-1). for each epoch, and a full set of metrics every 100 epochs. LSTM is the child of RNN where it can store long-term information and overcome the drawback of vanishing gradient. In addition, they have been used widely for sequence modeling. Each cell is composed of 3 inputs —. We will cover the following points: I: Calling Keras layers on TensorFlow tensors. このチュートリアルでは、転移学習を使用して、事前トレーニング済みネットワークから猫や犬の画像を分類する方法を紹介します。. 1 and Aug 3, 2016 · Kick-start your project with my new book Deep Learning for Natural Language Processing, including step-by-step tutorials and the Python source code files for all examples. In order to make the performance of our custom lstm network be the same to tf. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). matmul(state_below, U) + b. Nov 16, 2019 · The LSTM layer expects the number of time steps and the number of features to work properly. current input, previous hidden state and current cell input which you eventually use to predict the future stock market prices. LSTMCell(), we should initialize weights and biases in our custom lstm like tf. I'm stuck, though, because I don't know how to interpret the output of the encoder's LSTM. It could also be helpful on the supply side for planning electricity demand for a specific household. 0+) or Theano (v0. Strategy —a TensorFlow API that provides an abstraction for distributing your training across multiple processing units (GPUs, multiple machines, or TPUs)—with custom training loops. Layer representing preprocessing for the different observations. Transformers are deep neural networks that replace CNNs and RNNs with self-attention. An autoencoder is a special type of neural network that is trained to copy its input to its output. Here are instructions on how to do this. The reason why LSTMs have been used widely for this is because the model connects back to itself during a forward pass of your samples, and thus benefits Jun 30, 2019 · The function is available on github. r. (2017). . Part 3: TensorFlow Fundamentals Part 4: LSTMs + Tensorflow Tutorial. tf. We will use an embedding size of 300 and train over 50 epochs with mini-batches of size 256. Neural Network x 0 x 1 x n h 2 Input layer hidden layers output Jan 10, 2023 · To overcome the drawbacks encounters in RNN the scientist made an invention called “LONG SHORT TERM MEMORY”. In TensorFlow 2. Carga y prepara el conjunto de datos MNIST. 0 & Keras. Part 1: Neural Networks Overview. hidden_dim: the hidden dimension in LSTM. This tutorial shows you how to generate musical notes using a simple recurrent neural network (RNN). This guide will help you understand the basics of TimeSeries Forecasting. Given a sequence of notes, your model will learn to predict the next note in the sequence. build, where you know the shapes of the input tensors and can do the rest of the initialization. distribute. Evaluate the accuracy of the model. fit Jul 22, 2020 · Build our own BiLSTM model using tensorflow. Dec 8, 2019 · The input are sequences of words, output is one single class or label. A generator ("the artist") learns to create images that look real, while a discriminator ("the art critic") learns to tell real images apart from fakes. It may be helpful to add an additional weight + bias multiplication beneath the LSTM (e. With this change, the prior keras. Next Steps. model. How do we train a LSTM model? Training. Jul 25, 2016 · How to reduce overfitting in your LSTM models through the use of dropout; How to combine LSTM models with Convolutional Neural Networks that excel at learning spatial relationships; Kick-start your project with my new book Deep Learning for Natural Language Processing, including step-by-step tutorials and the Python source code files for all Jun 4, 2022 · Next, let us specify the batch size window and fold the data per batch. Set initial output and state. May 27, 2023 · Overview. How to tell if tensorflow is using gpu acceleration from inside python shell? 4. The main idea is that a deep learning model is usually a directed acyclic graph (DAG) of layers. The rest of the model looks like a regular regression model. You'll train a binary classifier to perform sentiment analysis on an IMDB dataset. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER Apr 12, 2024 · The Keras functional API is a way to create models that are more flexible than the keras. Layer class and implementing: __init__ , where you can do all input-independent initialization. 2, TensorFlow 1. Define the optimizer and the loss function. LSTM: it is our own LSTM Model in previous tutorial. This is an example of binary classification, an important and widely applicable kind of machine learning problem. This example shows how to train a DQN (Deep Q Networks) agent on the Cartpole environment using the TF-Agents library. This tutorial demonstrates how to preprocess audio files in the WAV format and build and train a basic automatic speech recognition (ASR) model for recognizing ten different words. Notes. These two are downloaded and converted from scratch, but it wouldn't be hard to convert the tutorial to use the caption datasets available in TensorFlow Datasets: Coco Captions and the full Conceptual Captions. To train word embeddings using Word2Vec algorithm, try the Word2Vec tutorial. TensorFlow创建LSTM ,现在我们要在TensorFlow中创建一个LSTM网络。. In this tutorial, you will discover how […] Apr 3, 2024 · Now, train the model in the usual way by calling Keras Model. Jan 7, 2021 · Long Short-Term Memory ( LSTM) based neural networks have played an important role in the field of Natural Language Processing. while_loop () is the main of lstm. Apr 3, 2024 · Each model in this tutorial will use the same training configuration. Padding comes from the need to encode sequence data into contiguous batches May 27, 2023 · This tutorial uses a small IMDb dataset for the purpose of demonstration. Mar 23, 2024 · This tutorial includes runnable code implemented using tf. Whereas, the idea of Bidirectional LSTMs (BiLSTM) is to aggregate input information in the past and future of a specific time step in LSTM models. Tutorials Learn how to use TensorFlow with end-to-end examples Guide Learn framework concepts and components Learn ML Educational resources to master your path with Run in a Notebook. We could also use a pre-trained embedding layer like word2vec or Glove. Oct 20, 2020 · Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. We’ll then implement ShallowNet, which as the name suggests, is a very shallow CNN with only a single CONV layer. For any small CSV dataset the simplest way to train a TensorFlow model on it is to load it into memory as a pandas DataFrame or a NumPy array. Overview. Let’s get started. The functional API can handle models with non-linear topology, shared layers, and even multiple inputs or outputs. keras. For a given dataset of sequences, an encoder-decoder LSTM is configured to read the input sequence, encode it, decode it, and recreate it. Learn more in the setting up TF_CONFIG section of this document. CuDNNLSTM/CuDNNGRU layers have been deprecated, and you can build your model without worrying about the hardware it will run on. We show an example of such a model in Figure 2. n_batch = 2. As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. Having followed the online tutorial here, I decided to use data at time (t-2) and (t-1) to predict the value of var2 at time step t. Mar 23, 2024 · Download notebook. Sep 19, 2023 · Tutorials | TensorFlow Core. preprocessing_combiner. while_loop (), you can read: Understand TensorFlow tf. You will work with the Credit Card Fraud Detection dataset hosted on Kaggle. You switched accounts on another tab or window. Restore the latest checkpoint and test. Masking is a way to tell sequence-processing layers that certain timesteps in an input are missing, and thus should be skipped when processing the data. By default, an LSTM cell returns the hidden state for a single time Apr 3, 2024 · Each model in this tutorial will use the same training configuration. process the data in training and validation sets Jul 20, 2020 · There are some poits you should notice: 1. This tutorial has shown you how to train and visualize word embeddings from scratch on a small dataset. This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection. h (t −1) — previous hidden state. Pytorch is a dynamic neural network kit. Flickr8k Para ejecutar todas las celdas de este notebook: Selecciona Runtime > Run all. At the top of each tutorial, you'll see a Run in Google Colab button. Nov 18, 2021 · A model that makes use of multiple input variables may be referred to as a multivariate multi-step time series forecasting model. To reduce the logging noise use the tfdocs. g. Apr 13, 2024 · import pandas as pd import numpy as np # Make numpy values easier to read. Apr 3, 2024 · This is an example of binary —or two-class—classification, an important and widely applicable kind of machine learning problem. Apr 28, 2024 · This tutorial demonstrates how to use tf. Sequential(. 虽然现在依然还是理解得不够，但是比之前掌握的知识应该多了不少，希望能够整理成一个更好的教程。. It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. The tutorial demonstrates the basic application of transfer learning with TensorFlow Hub and Keras. GitHub でソースを表示. The following article serves a good introduction to LSTM, GRU and BiLSTM. 0). generate synthetic data to simulate a time series with different characteristics. It was proposed in 1997 by Sepp Hochreiter and Jurgen schmidhuber. Magnitude-based weight pruning gradually zeroes out model weights during the training process to achieve model sparsity. Also, I assign return_sequence Mar 23, 2024 · The 6 lines of code below define the convolutional base using a common pattern: a stack of Conv2D and MaxPooling2D layers. 4+) installed with either the TensorFlow (v1. Now we are going to solve a BBC news document classification problem with LSTM using TensorFlow 2. Aug 7, 2022 · How to manually manage state in an LSTM network for stateful prediction; Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples. Mar 23, 2024 · Basic regression: Predict fuel efficiency. Jul 9, 2018 · In this tutorial, we will implement an LSTM which will generate new stories after training on a dataset of folk stories. 1. Descarga e installa el paquete TensorFlow 2. Explore and run machine learning code with Kaggle Notebooks | Using data from Household Electric Power Consumption Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. We could increase the number of LSTM layers in the model, instead of just one layer in the encoder and one layer in the decoder. Add and tf. So set these up in a reusable way, starting with the list of callbacks. This article is extracted from the book Natural Language Processing with Tensorflow by Thushan Ganegedara. As sample data table shows, I am using the Feb 12, 2019 · @ Vaibhav gusain Both! I do think I have a pretty good understanding of the concept behind it from older questions I read. An LSTM module (or cell) has 5 essential components which allows it to model both long-term and short-term data. Contrast this with a classification problem, where the aim is to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is Nov 20, 2020 · I welcome you to the Beginner’s Series in Deep Learning with TensorFlow and Keras. Cell — Every unit of the LSTM network is known as a “cell”. 0. This tutorial demonstrates how to create and train a sequence-to-sequence Transformer model to translate Portuguese into English. You’ll learn how to pre-process TimeSeries Data and build a simple LSTM model, train it, and use it for forecasting. Introduction The code below has the aim to quick introduce Deep Learning analysis with TensorFlow using the Keras layers can be adjusted above 1 to create a stacked LSTM network. The latter just implement a Long Short Term Memory (LSTM) model (an instance of a Recurrent Neural Network which avoids the vanishing gradient problem). We will use an initial learning rate of 0. Two models are trained simultaneously by an adversarial process. This notebook demonstrates how to train a Variational Autoencoder (VAE) ( 1, 2) on the MNIST dataset. Either Flickr8k or a small slice of the Conceptual Captions dataset. The core runtime just fits in 16 KB on an Arm Cortex M3 and can run many basic models. 将使用的文本数据集是 Penn Tree Bank (PTB) 数据集，它是常用的基准测试语料库。. nn. layers. 畳み込みおよび回帰ニューラルネットワーク（CNN および RNN）を含む様々なスタイルのモデルを構築します。. II: Using Keras models with TensorFlow. Aug 27, 2020 · An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. Importa TensorFlow en tu programa: # Installa TensorFlow. This tutorial aims to describe how to carry out a… Jul 12, 2023 · Define a NMTDataset class with necessary functions to follow Step 1 to Step 4. The most important thing to remember when training Time Series models is to not shuffle the data (the order of the data matters). III: Multi-GPU and distributed training. Apr 24, 2016 · Note that this tutorial assumes that you have configured Keras to use the TensorFlow backend (instead of Theano). I assign input_width = 24 as the batch size window for perhaps a time period of 24 hours. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems. Jul/2016: First published; Update Mar/2017: Updated example for Keras 2. EPOCHS = 12. Mar 10, 2024 · This tutorial is set up to give a choice of datasets. A nest of tf. Sep 2, 2020 · Remember that in an LSTM, there are 2 data states that are being maintained — the “Cell State” and the “Hidden State”. 2. To run this code live, click the 'Run in Google Colab' link above. You can use the TensorFlow library do to numerical computations, which in In this tutorial, we're going to cover how to code a Recurrent Neural Network model with an LSTM in TensorFlow. Hidden state (h t) - This is output state information calculated w. May 23, 2019 · In this post, we will demonstrate how to build a Transformer chatbot. 0 version. Specifically, I have two variables (var1 and var2) for each time step originally. You can generate longer sequences of Mar 23, 2024 · Simple audio recognition: Recognizing keywords. from tensorflow. May 23, 2023 · TensorFlow Lite for Microcontrollers is designed to run machine learning models on microcontrollers and other devices with only a few kilobytes of memory. This tutorial demonstrates how to classify a highly imbalanced dataset in which the number of examples in one class greatly outnumbers the examples in another. Jul 24, 2023 · Introduction. If you see an example in Dynet, it will probably help you implement it in Pytorch). mnist import input_data. 和之前的版本不同，之前我是作为一个入门菜鸟一遍学一边做笔记。. The best way to implement your own layer is extending the tf. This tutorial demonstrates text classification starting from plain text files stored on disk. The two architectures that we will be using to use CNN along with LSTM are: ConvLSTM. keras import layers In memory data. Forget Gate. keras and eager execution. the next 12 months of Sales, or a radio signal value for the next 1 hour. We will be using a CNN to extract spatial features at a given time step in the input sequence (video) and then an LSTM to identify temporal relations between frames. Jan 6, 2023 · Having seen how to implement the scaled dot-product attention and integrate it within the multi-head attention of the Transformer model, let’s progress one step further toward implementing a complete Transformer model by applying its encoder. You can use KerasNLP components with their out-of-the-box configuration. This tutorial is a Google Colaboratory notebook. (Optional. 3. Nov 22, 2022 · Now, before going in-depth, let me introduce a few crucial LSTM specific terms to you-. e each individual bit of the string). All of these layers must not be already built. ) A keras layer that takes a flat list of tensors and combines them. Optional: Set the correct initial bias. The TensorFlow tutorials are written as Jupyter notebooks and run directly in Google Colab—a hosted notebook environment that requires no setup. Padding is a special form of masking where the masked steps are at the start or the end of a sequence. Mar 23, 2024 · The 'TF_CONFIG' environment variable is the standard way in TensorFlow to specify the cluster configuration to each worker that is part of the cluster. Apr 3, 2024 · Implementing custom layers. tutorials. Another example of a dynamic kit is Dynet (I mention this because working with Pytorch and Dynet is similar. t. Dec 22, 2023 · Introduction. TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models. cu jz yn vm nu vs ha yh kd ot