Tensorflow Shuffle Buffer Size

If the user-defined function passed into the map transformation changes the size of the elements, then the ordering of the map transformation and the transformations that buffer elements affects the memory usage. Documentation for the TensorFlow for R interface. %>% dataset_shuffle (buffer. In this post, you will learn how to save a large amount of data (images) into a single TFRecords format file and load it batch-wise to train your network in tensorflow. So our goal has been to build a CNN. So for example, if you have 100,000 items in your dataset, but you set the buffer to a thousand. I have tried adjusting the following train_config settings:. batch_size: Number of images per batch. Reads CSV files into a dataset, where each element is a (features, labels) list that corresponds to a batch of CSV rows. The problem is , no matter how I change the model, no matter how I change the batch_size or even change the optimizer, and no matter how long I run the training I get always the same accuracy : arround 3%. Tensorflow生成TFRecord 4. 그런 다음, 각 줄에 decode_csv 를 적용합니다. A Dataset is a sequence of elements, which are themselves composed of tf. Historically, TensorFlow is considered the "industrial lathe" of machine learning frameworks: a powerful tool with intimidating complexity and a steep learning curve. shuffle(buffer_size=2325000) ' ,the cost of time to load image. Visualizing shuffledness. And it will be flattened into chunks in the size of our window_size + 1. load()` callers to specify `shuffle_files=True` when nec… 97ff263 Aug 20, 2019. Which gives us this output when we print, which now looks like a nice set of features and labels. data API: Easily construct a complex input pipeline. The first buffer_size elements are stored in memory. At the beginning of each epoch, shuffle the list of shard filenames. In latest Google I/O, 7 talks are represented and some TensorFlow new features and functions are released. Details of the shuffle is controlled by buffer_size. experimental. Once it's flattened, it's easy to shuffle it. Defaults to ``100``. map: Calls the decode_csv function with each element in the dataset as an argument (since we are using TextLineDataset, each element will be a line of CSV text). The dataset is then prefetched. For an image size of 299x299, the output will be of size (batch_size, 8, 8, 2048), that is, we are making use of 2048 feature maps. Nov 28 2018- POSTED BY Brijesh Comments Off on TensorFlow Text Classification using Attention Mechanism Spread the love In this tutorial, we're gonna to build a recurrent neural network that's able to classify reviews. 1 (CUDA 9, cuDNN 7), Python 3. 0 データ モジュール名 データセット モデル作成 モデルのインスタンス化と訓練準備 訓練関数 訓練 PyTorch import データ準備 モデル作成 モデルのインスタンス化と訓練準備 学習コード. shuffle(buffer_size=50000)). How to use TensorFlow tf. map, Dataset. batch(10) return dataset tf. 04 Mobile device (e. shuffle (buffer_size = 10000) (4)repeat repeat的功能就是将整个序列重复多次,主要用来处理机器学习中的epoch,假设原先的数据是一个epoch,使用repeat(5)就可以将之变成5个epoch:. py script that is bundled with the Inception TensorFlow model. This document discusses aspects of the Inception model and how they come together to make the model run efficiently on Cloud TPU. Other options. Randomly shuffles the elements of this dataset. 本文是"基于Tensorflow高阶API构建大规模分布式深度学习模型系列"文章的第三篇,由于标题太长所以修改为"构建分布式Tensorflow模型系列"。Tensorflow在1. map : Calls the decode_csv function with each element in the dataset as an argument (since we are using TextLineDataset, each element will be a line of CSV text). TensorFlow 2. shuffle(buffer_size=10000) dataset = dataset. buffer_size: An integer, representing the number of elements from this dataset from which the new dataset will sample. of records. shuffle(buffer_size This can be achived with TensorFlow Queues but than you can't train and validate you. To conclude, and answer your question, a smaller mini-batch size (not too small) usually leads not only to a smaller number of iterations of a training algorithm, than a large batch size, but also to a higher accuracy overall, i. x in the past, you know what I'm talking about. , Linux Ubuntu 16. Importancia de buffer_size en shuffle() Quería dar seguimiento a la respuesta anterior de @mrry para enfatizar la importancia de buffer_size en tf. I wanted to understand how the level of shuffledness changed as you fiddled with the buffer_size parameter. batch size 정의하기 tf. shuffle: Reads buffer_size records, then shuffles (randomizes) their order. TensorFlow documentation と、 tf. Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor. iPhone 8, Pixel 2, Samsung Gal. 04): Ubuntu 16. Clone via HTTPS Clone with Git or checkout with SVN using the repository's web address. Note: This post was written together with the awesome Julian Eisenschlos and was originally published on the TensorFlow blog. Earlier, I mentioned that the weights for determining attention for a particular word are a function of the hidden_state immediately before that timestep in. This is the companion code to the post "Attention-based Neural Machine Translation with Keras" on the TensorFlow for R blog. batch_size / 128. e, a neural network that performs better, in the same amount of training time, or less. seed (Optional) An integer, representing the random seed that will be used to create the distribution. Tensor to a given shape. Data files are hosted in a Google Cloud Storage (GCS) bucket which is why their address starts with gs:// Cell "Imports" All the necessary Python libraries are imported here, including TensorFlow and also matplotlib for visualizations. data는 이렇게 간단히 정의가 가능하다. Works with stock TensorFlow, Keras, PyTorch, and Apache MXNet. Kaggle Dog vs Cat dataset consists of the 25,000 color images of the dogs and the cats that we use for the training. I've implemented a self-organising map in Tensorflow's low-level API. string_input_producer to load data for 2 epochs, I used. Tensorflow高效流水线Pipeline 2. IMAGE_SIZE x InputReaderCifar10. It addresses the problem of MNIST being too easy for modern neural networks, along with some other issues. Bedeutung von buffer_size in shuffle() Ich wollte die vorherige Antwort von @mrry weiterverfolgen, um die Bedeutung von buffer_size in tf. Since the hidden is the hidden state of a timestep in the decoder, we add a dimension of size 1 to represent the timestep hence shape of hidden becomes (batch_size, time_step, hidden_size). TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models. I've already tried to set shuffle_buffer_size though (e. The first buffer_size elements are stored in memory. The shuffle buffer is filled before any elements are pulled from it. In this tutorial we will build a deep learning model to classify words. Can you help? Thank you. 1 (CUDA 9, cuDNN 7), Python 3. Download the Dataset. --hp_shuffle_buffer_size). shuffle: buffer_size 레코드를 읽은 후 순서를 셔플(무작위 배정)합니다. BUFFER_SIZE = 50000 BATCH_SIZE = 64 TAKE_SIZE = 5000. of records. How to use TensorFlow tf. Next, we shuffle the dataset and allow retrieving data from it until the specified number of epochs has been reached: dataset = dataset. Essentially, this fills the dataset with buffer_size elements, then randomly samples elements from this buffer. ly/tf-aiconf about k l a t o my t r to A this repo e d n i Rem navigate o how t Workshop GitHub repo: bit. Now, with integer outputs you could treat this as a multiclass classification problem, but you'd need to encode things properly for tensorflow to understand. Because we're now want our model to output labeled classes (0 for Trump, 1 for Hillary), I wrote a preprocessing script that checks whether the label value in our previous dataset (% for Hillary) is less than 0. It shows the step by step how to integrate Google Earth Engine and TensorFlow 2. , the number of bins on the frequency axis. # Scale the learning rate linearly with the batch size. e, a neural network that performs better, in the same amount of training time, or less. string_input_producer to produce several epochs data? - Stack Overflow. TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models. data는 이렇게 간단히 정의가 가능하다. Tensor to a given shape. To conclude, and answer your question, a smaller mini-batch size (not too small) usually leads not only to a smaller number of iterations of a training algorithm, than a large batch size, but also to a higher accuracy overall, i. shuffle_seed: Randomization seed to use for shuffling. So having a buffer size of 1 is like not shuffling, having a buffer of the length of your dataset is like a traditional shuffling. 先来看一下Tensorflow的数据读取机制吧. One big caveat when shuffling is to make sure that the buffer_size argument is big enough. Documentation for the TensorFlow for R interface. Importancia de buffer_size en shuffle() Quería dar seguimiento a la respuesta anterior de @mrry para enfatizar la importancia de buffer_size en tf. At the TensorFlow Dev Summit 2019, Google introduced the alpha version of TensorFlow 2. tensorflow中的数据集类Dataset有一个shuffle方法,用来打乱数据集中数据顺序,训练时非常常用。其中shuffle方法有一个参数buffer_size,非常令人费解,文档的解释如下: buffer_size: A tf. I've already tried to set shuffle_buffer_size though (e. Powerful and simple online compiler, IDE, interpreter, and REPL. [input_data] Image 데이터로. estimator API in TensorFlow to solve a binary classification problem: Given census data about a person such as age, education, marital status, and occupation (the features), we will try to predict whether or not the person earns more than 50,000 dollars a year (the target label). Tensor to a given shape. batch(10) return dataset tf. We will obtain other quantities that will be useful for spectrogram creation, like the number of chunks and the FFT size, i. Setting up the cluster. We also can shuffle the data before feeding it to a network. batch_size / 128. Tensor components. Using Keras (a high-level API for TensorFlow) we can directly download Fashion MNIST with a single function call. Can you help? Thank you. You can also pre-encode all your sequences and store their encodings to a TFRecord file, then later load it to build a tf. NOTE: If the number of elements (N) in this dataset is not an exact multiple of batch_size, the final batch contain smaller tensors with shape N % batch_size in the batch dimension. So our goal has been to build a CNN. 0a on a laptop 1050ti 4gig I increased the data set from ~4500 to ~20000 and my network no longer trains. shuffle(buffer=10000) to shuffle dataset. Achieving peak performance requires an efficient input pipeline that delivers data for the next step before the current step has finished. shuffle: Reads buffer_size records, then shuffles (randomizes) their order. prefetch_buffer_size: An int specifying the number of feature batches to prefetch for performance improvement. shuffle(buffer_size=10000) dataset = dataset. Given an input tensor, returns a new tensor with the same values as the input tensor with shape shape. shuffle_and_repeat(). py Find file Copy path tensorflower-gardener Update `tfds. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "K1y4OHpGgss7" }, "source": [ "This tutorial demonstrates how to classify structured data. Tensorflowのtf. IMAGE_SIZE x InputReaderCifar10. Now, with integer outputs you could treat this as a multiclass classification problem, but you'd need to encode things properly for tensorflow to understand. string_input_producer to load data for 2 epochs, I used. This document discusses aspects of the Inception model and how they come together to make the model run efficiently on Cloud TPU. I know we can ues dataset. 04): Ubuntu 16. TensorFlow documentation と、 tf. Replica zero is the input pipeline (reading data happens on the CPU). So for example, if you have 100,000 items in your dataset, but you set the buffer to a thousand. Tensor components. at the beginning of a TensorFlow graph. 0 in the same pipeline (EE->Tensorflow->EE). prefetch() e l'argomento output_buffer_size in tf. A template for a custom tensorflow estimator and experiment with python3 typings for desired parameter types - tf-experiment-template. Historically, TensorFlow is considered the "industrial lathe" of machine learning frameworks: a powerful tool with intimidating complexity and a steep learning curve. train_size (int): Size of the dataset; will be used for train, train eval and test datasets. Each image is a different size of the pixel intensities, represented as the [0, 255] integer values in the RGB color space. map : Calls the decode_csv function with each element in the dataset as an argument (since we are using TextLineDataset, each element will be a line of CSV text). optional uint32 shuffle_buffer_size = 11 [default = 2048] the default value is 2048, it's too big for batch_size=1 , should be modified accordingly, it consumes a lot of RAM in my opinion. Use the model to predict the presence of heart disease from patient data. dataset = dataset. Given an input tensor, returns a new tensor with the same values as the input tensor with shape shape. IMAGE_SIZE x InputReaderCifar10. Using a shuffle buffer speeds things up a bit. I'd guess (without really knowing your data, mind) that you'd be better off with regression (so no cross-entropy either). repeat(), causing another wait for the shuffle-buffer to be filled. The great thing about TensorFlow Estimators is that we barely have to change any of our code to use an entirely new type of model. In order to make sure everything happens using parallel calls, set tf. We can use a buffer_size with the same size as the dataset for a full shuffle. shuffle¶ creme. Now, with integer outputs you could treat this as a multiclass classification problem, but you'd need to encode things properly for tensorflow to understand. With numbers similar to my use case, 5 epochs of training take about 16 seconds with the standard feed_dict approach, 12-20 seconds with the TensorFlow Dataset API, and 8 seconds with a custom TensorFlow control-flow construct. What's New in TensorFlow 2. The guidelines given here to run linear and logistic regression with TensorFlow in a distributed setting are specific to the Urika-GX platform. python import debug as tf_debug sess = tf_debug. The first buffer_size elements are stored in memory. Many of our machine learning algorithms are written on top of TensorFlow, an open-source dataflow software library originally release by Google. %>% dataset_shuffle (buffer. In practice. Datasets can work with out-of-memory sources (not needed in this case) by streaming them record by record, and the shuffle method uses a buffer_size to continuously sample from fixed sized set without loading the entire thing into memory. shuffle(buffer_size=50000)). The only real issue I have is a low GPU usage during training reported by GPU-Z (27%). string_input_producer to load data for 2 epochs, I used. tensorflow Significado de buffer_size em Dataset. In this post, we will continue our journey to leverage Tensorflow TFRecord to reduce the training time by 21%. map : Calls the decode_csv function with each element in the dataset as an argument (since we are using TextLineDataset, each element will be a line of CSV text). You call a shuffle and you pass it the shuffle buffer. buffer_size: An integer, representing the number of elements from this dataset from which the new dataset will sample. train_dataset = train_dataset. 1: Data flow in TensorFlow Dataset API. There is a lot of datasets available on the internet. So a large buffer_size may cause a delay when your Dataset is starting. With Safari, you learn the way you learn best. To visualize the graph, just run command tensorboard --logdir /your/dir/ if tensorflow is installed. Tensorflowのtf. map(augmentation). Reshapes a tf. Earlier, I mentioned that the weights for determining attention for a particular word are a function of the hidden_state immediately before that timestep in. map: 데이터세트의 각 요소를 인수로 삼아 decode_csv 함수를 호출합니다(TextLineDataset를 사용하므로 각 요소가 CSV 텍스트의 한 줄이 됨). 知乎专栏-何之源:十图详解tensorflow数据读取机制; 这一篇文章对于 tensorflow的数据读取机制 讲解得很不错,大噶可以先看一下,有一个了解。 Dataset API是怎么用的呢. 0 comes with Keras packaged inside, there is no need to import Keras as a separate module (although you can do this if you need). 0で行っています。 ドキュメントに. There are four methods of getting data into a TensorFlow program: tf. data 를 사용하지 않을 땐 여러 방법들로 batch 를 만들었지만 tf. This fills a buffer with buffer_size elements, then randomly samples elements from this buffer, replacing the selected elements with new elements. shuffle(buffer_size=10000) (4)repeat repeat的功能就是将整个序列重复多次,主要用来处理机器学习中的epoch,假设原先的数据是一个epoch,使用. With Safari, you learn the way you learn best. map() provide a way to tune the performance of your input pipeline: both arguments tell TensorFlow to create a buffer of at most buffer_size elements, and a background thread to fill that buffer in the background. BUFFER_SIZE = 50000 BATCH_SIZE = 64 TAKE_SIZE = 5000. This is the companion code to the post "Generating digits with Keras and TensorFlow eager execution" on the TensorFlow for R blog. Nov 28 2018- POSTED BY Brijesh Comments Off on TensorFlow Text Classification using Attention Mechanism Spread the love In this tutorial, we're gonna to build a recurrent neural network that's able to classify reviews. For some reason it seems that there are two buffers build up. estimator API in TensorFlow to solve a binary classification problem: Given census data about a person such as age, education, marital status, and occupation (the features), we will try to predict whether or not the person earns more than 50,000 dollars a year (the target label). At the TensorFlow Dev Summit 2019, Google introduced the alpha version of TensorFlow 2. 0 in the same pipeline (EE->Tensorflow->EE). Qiita is a technical knowledge sharing and collaboration platform for programmers. MNIST classification with TensorFlow's Dataset API. If one component of shape is the special value -1, the size of that dimension is computed so that the total size remains constant. train_size (int): Size of the dataset; will be used for train, train eval and test datasets. The following are code examples for showing how to use tensorflow. Tensor to a given shape. The best practice is usually. shuffle tensorflow-gpu tensorflow-datasets (4) Conforme a documentation TensorFlow, os métodos de prefetch - prefetch e map da classe tf. The buffer_size argument specifies the size of the dataset from which shuffle will randomly sample. com/rstudio/keras/blob/master/vignettes/examples/eager_pix2pix. L'argomento buffer_size in tf. Data files are hosted in a Google Cloud Storage (GCS) bucket which is why their address starts with gs:// Cell "Imports" All the necessary Python libraries are imported here, including TensorFlow and also matplotlib for visualizations. tensorflow-gpu tensorflow-datasets (4). This is the companion code to the post "Attention-based Neural Machine Translation with Keras" on the TensorFlow for R blog. I have been waiting for more than 10 mins and this isn't still complete. If one component of shape is the special value -1, the size of that dimension is computed so that the total size remains constant. How to write into and read from a TFRecords file in TensorFlow. Kaggle Dog vs Cat dataset consists of the 25,000 color images of the dogs and the cats that we use for the training. There is a lot of datasets available on the internet. In practice. Quoting from their API page: TensorFlow has APIs available in several languages both for constructing and executing a TensorFlow graph. NUM_CHANNELS and the `decoded_label` is the label of that image, a vector of size InputReaderCifar10. What's the reason?. I was not able to find the place in the code which would set that. The function we are going to use to compute the spectrogram doesn't allow us to change the FFT size and instead by default uses the first power of 2 greater than the window size. TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. L'argomento buffer_size in tf. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "BwpJ5IffzRG6" }, "source": [ "This tutorial demonstrates how to generate text using a. At the beginning of each epoch, shuffle the list of shard filenames. From the overview page. It will be removed in a future version. And this is possible using the shuffle method. shuffle(buffer_size=10000) dataset = dataset. experimental. Our latest find is Dataset API. buffer_size is greater than the number of examples in the Dataset, ensuring that the data is. txt and write to it the names of our classes: squares triangles Now, to convert our images to TensorFlow TFRecord format, we are going to just use the build_image_data. batch(BATCH_SIZE, drop_remainder=True). "TensorFlow - Importing data" Nov 21, 2017. Dataset , ambos possuem um parâmetro chamado buffer_size. Combine these labeled datasets into a single dataset, and shuffle it. Tensor components. I am new to TensorFlow and I would really appreciate if someone could look at my code to see whether things are done efficiently and suggest improvements. We also can shuffle the data before feeding it to a network. The buffer_size argument is the number of elements to be "pre-fetched" before randomization. dataset_shuffle: Randomly shuffles the elements of this dataset. はじめに TensorFlow 2. 그런 다음, 각 줄에 decode_csv 를 적용합니다. %>% dataset_shuffle (buffer_size. If one component of shape is the special value -1, the size of that dimension is computed so that the total size remains constant. Deep Learning Frameworks Speed Comparison When we want to work on Deep Learning projects, we have quite a few frameworks to choose from nowadays. 一方で、Tensorflowにはミニバッチを使った学習を簡単に行うための、tf. It enables you to build complex input pipelines from simple, reusable pieces. string_input_producer to load data for 2 epochs, I used. So your elements aren't in any particular order. Tensorflow is in my opinion the most powerful framework for deep learning application and for any kind of graph computation really. We randomly shuffle the dataset. Tensorflow Dataset API initialiser hook fix. Ask Question 4. Reshapes a tf. The problem is , no matter how I change the model, no matter how I change the batch_size or even change the optimizer, and no matter how long I run the training I get always the same accuracy : arround 3%. The buffer_size defines how many records are prefetched, which is usually the mini batch_size of the job. Simple Audio Classification with Keras. Chained along together with this zip method is first the shuffle() dataset method. TensorFlow Hub was announced at TensorFlow Dev Summit 2018 and promises to reduce the effort required to use existing machine learning models and weights in your own custom model. shuffle (stream, buffer_size, seed=None) [source] ¶ Shuffles a stream of data. 0で行っています。 ドキュメントに. %>% dataset_shuffle (buffer. Once it's flattened, it's easy to shuffle it. You can also pre-encode all your sequences and store their encodings to a TFRecord file, then later load it to build a tf. Works with stock TensorFlow, Keras, PyTorch, and Apache MXNet. You can view and change this field later by using GCP Console. Based on the available hardware tensorflow will automatically set the number of parallel threads. The shuffled dataset doesn't report the end of a dataset until the shuffle-buffer is completely empty. experimental. Example of TensorFlows new Input Pipeline is the. MNIST classification with TensorFlow's Dataset API. If shape is 1-D or higher, then the operation returns a tensor with shape shape filled with the values of tensor. How to use TensorFlow tf. Download the Dataset. L'argomento buffer_size in tf. NOTE: If the number of elements (N) in this dataset is not an exact multiple of batch_size, the final batch contain smaller tensors with shape N % batch_size in the batch dimension. optional uint32 shuffle_buffer_size = 11 [default = 2048] the default value is 2048, it's too big for batch_size=1 , should be modified accordingly, it consumes a lot of RAM in my opinion. dataset=dataset. buffer_size is the number of batches that should be prefetched. shuffle(buffer_size=10000) dataset = dataset. The dataset is again shuffled. shuffle_and_repeat(). Can you help? Thank you. Documentation for the TensorFlow for R interface. There is a lot of datasets available on the internet. buffer_size: Number of images used as buffer for TensorFlows. shuffle(BUFFER_SIZE) dataset = dataset. What's the reason?. BUFFER_SIZE = 50000 BATCH_SIZE = 64 TAKE_SIZE = 5000. In particular, a shape of [-1] flattens into 1-D. We randomly shuffle the dataset. 先来看一下Tensorflow的数据读取机制吧. Tensorflow高效流水线Pipeline 2. If shape is 1-D or higher, then the operation returns a tensor with shape shape filled with the values of tensor. So a large buffer_size may cause a delay when your Dataset is starting. Replica zero is the input pipeline (reading data happens on the CPU). OBS: I will assume reader are already familiar with the basic concepts of Machine Learning and Convolutional Networks. 0 データ モジュール名 データセット モデル作成 モデルのインスタンス化と訓練準備 訓練関数 訓練 PyTorch import データ準備 モデル作成 モデルのインスタンス化と訓練準備 学習コード. Dataset , ambos possuem um parâmetro chamado buffer_size. Mar 05 2019- POSTED BY Brijesh Comments Off on How to use TensorFlow Dataset API for NLP input pipeline Spread the love NLP models use text to produce a basic form of natural language understanding. The great thing about TensorFlow Estimators is that we barely have to change any of our code to use an entirely new type of model. txt and write to it the names of our classes: squares triangles Now, to convert our images to TensorFlow TFRecord format, we are going to just use the build_image_data. dataset = dataset. I think this could be treated as supplementary for Tfdev-summit 2018 in last March. The buffer_size argument specifies the size of the dataset from which shuffle will randomly sample. train_and_evaluate函数,…. The buffer_size argument is the number of elements to be "pre-fetched" before randomization. Note that, if ``batch_size`` is not a divider of the dataset size (``1000`` for train and test) the remainder is dropped in each epoch (after shuffling). Note there's a buffer size argument (newest tflow) in the case that your dataset doesn't fit in memory. shuffle(buffer_size This can be achived with TensorFlow Queues but than you can't train and validate you. Then we apply decode_csv to each of the lines. 可以使用 tensorflow 提供的 batch generator, 首先把数据送入队列中 queue, 然后需要一种 tensorflow reader (tf 有好几种reader), 然后从队列中读取数据, 然后使用 tf. 知乎专栏-何之源:十图详解tensorflow数据读取机制; 这一篇文章对于 tensorflow的数据读取机制 讲解得很不错,大噶可以先看一下,有一个了解。 Dataset API是怎么用的呢. OBS: I will assume reader are already familiar with the basic concepts of Machine Learning and Convolutional Networks. batch() would shuffle the order of the batches, but not shuffle the items across batches. We can use a buffer_size with the same size as the dataset for a full shuffle. TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. shuffle(1000). %>% dataset_shuffle (buffer. The first buffer_size elements are stored in memory. string_input_producer to load data for 2 epochs, I used. Recommended value is the.