Keras F1 Score Callback

We've been working on a cryptocurrency price movement prediction recurrent neural network, focusing mainly on the pre-processing that we've got to do. save_weights('race_model_single_batch. Estimator and use tf to export to inference graph. TensorFlow包含图像识别的特殊功能,这些图像存储在特定文件夹中。 出于安全目的,经常要识别相同的图像,这个逻辑很容易. It is a Python library for artificial neural network ML models which provides high level fronted to various deep learning frameworks with Tensorflow being the default one. 94787] Resnet50[Public Score = 0. How this bootcamp works ? Weekdays we will be having 3-4 Hrs of Algorithm classes & 3-4 Hrs of Development Classes. TensorFlow 2. How this bootcamp works ? Weekdays we will be having 3-4 Hrs of Algorithm classes & 3-4 Hrs of Development Classes. You can use callbacks to get a view on internal states and statistics of the model during training. The logs dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch. models import Sequential from keras. In this tutorial, we're going to be finishing up by building. kerasのmetricで[Keras] クラスごとのAccuracy, Precision, Recall, F-measureをmetricsを利用してTensorBoardで確認するを参考にしてたら、batch_sizeごとの計算になるせいで全然値が違って困ったので、毎エポック終了時に計算するCallbackを書きました。. The F1 Score or F-score is a weighted average of precision and recall. Add DDL Callbacks. As a continuation of my previous post on ASL Recognition using AlexNet — training from scratch, let us now consider how to solve this problem using the transfer learning technique. Next we define the keras model. optimize API and provide a high level interface to various pre-configured skopt. 如何保存 val data 上 f1-score 最高的模型. But for that case, you need to create a class and write some amount of code. You could easily switch from one model to another just by changing one line of code. In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true. If all inputs in the model are named, you can also pass a list mapping input names to data. We will have to use TimeDistributed to pass the output of RNN at each time step to a fully connected layer. It appears Precision, Recall and F1 metrics have been removed from metrics. Saat Kamu terus berjuang, Kamu sebenarnya seorang pekerja keras. 95 and not number of steps. Since CNTK 2. accuracy_score (y_true, y_pred, normalize=True, sample_weight=None) [source] ¶ Accuracy classification score. - Deep Sequential Neural Network. If you have ever used Keras to build a machine learning model, you’ve probably made a plot like this one before:. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results returned by the classifier, and r is the number of correct positive results divided by the. The correct way to implement these metrics is to write a callback function that calculates them at the end of each epoch over the validation data. In this blog post, we'll discover what TensorBoard is, what you can use it for, and how it works with Keras. By continuing to use this website you are consenting to our use of cookies. Using ConCORDe-Net, we obtained a cell detection F1 score of 0. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. layers import Dense, Input from keras. Precision, Recall, and the F1 Score Precision, recall, and the F1 Score are metrics related to classification. from tensorflow. utils import np_utils, print_summary import tensorflow as tf from keras. each document can belong to many classes) dataset. In Keras, we can easily create custom callbacks using keras. If you have ever used Keras to build a machine learning model, you've probably made a plot like this one before:. Next we define a callback for the model. They are extracted from open source Python projects. Keras • Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. callback_model_checkpoint is a callback that performs this task. This is a summary of the official Keras Documentation. In this post you will discover how you can check-point your deep learning models during training in Python using the Keras library. Tensorflow library provides the keras package as parts of its API, in order to use keras_metrics with Tensorflow Keras, you are advised to perform model training with initialized global variables: import numpy as np import keras_metrics as km import tensorflow as tf import tensorflow. These hyperparameters are set in the config. my score function can run correctly, even it's wrong, saved model should give me a same wrong result, you can see all my customer callback function and score function JZTrainCategory. 在 keras 原生支持的 metrics 里面,并不包括 f1-scores,但是在分类问题中,f1-scores 是一个很重要的评价指标。 曾经看到 stack-overflow 上面的一个回答 How to calculate F1 Macro in Keras?. activation_model = tf. py script or via command-line-interface. • Being able to go from idea to result with the least possible delay is key to doing good research. utils import np_utils, print_summary import tensorflow as tf from keras. You will get training and validation F1 score after each epoch. In this blogger, I use Keras API of customizing layer to fulfill the Swish Beta function mentioned in paper Swish Beta Function In Keras | Ching-Chuan Chen's Blogger Ching-Chuan Chen's Blogger. The tone was set at the start of the evening, when Styles, Luke Gallows and Karl Anderson. 最近开始做音频分类,在此做一个总结。音频分类本质上可以看成是一个图像分类的任务,用深度学习进行音频分类,主要分为两步:1. kerasで1epochごとに各クラスのprecision,recall,f1のグラフを描画する. Both Keras and tflearn make it simpler to deal with TF. I want to have a metric that's correctly aggregating the values out of the differen. Who This Book Is For If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep. You'll see the test score now equals the evaluated metrics of the best epoch. Note: this article originally appeared in Towards Data Science. % pylab inline import os import keras import numpy as np import pandas as pd import keras. Welcome to the next tutorial covering deep learning with Python, Tensorflow, and Keras. hdf5") race_model. Your on data are not likely built into Keras. Keras used to implement the f1 score in its metrics; however, the developers decided to remove it in Keras 2. F1 score on Keras(Correct version). Keras allows us to access the model during training via a Callback function, on which we can extend to compute the desired quantities. Simple Image Classification using Convolutional Neural Network — Deep Learning in python. That way the Keras system calculates an average on the batch results. The performance (here I am using F1-score) drops sometimes considerably after a longer increase period, so it seems the adaption overshoots at this point, although I am using the adam optimzer. Notice that there are a ton of Keras related imports. fbeta_score fbeta_score(y_true, y_pred, beta=1) Computes the F score. predict(X_test) y_pred = (y_pred > 0. It is a Python library for artificial neural network ML models which provides high level fronted to various deep learning frameworks with Tensorflow being the default one. cluster import KMeans from keras import callbacks from keras. layers import Conv2D, MaxPooling2D from keras import backend as K batch_size = 128 num_classes = 10 epochs = 12 # input image dimensions img_rows, img_cols = 28, 28 # the data, split between train and. keras is TensorFlow’s implementation of this API. metrics import f1_score, classification_report class F1Metrics (Callback):. Classifying the Iris Data Set with Keras 04 Aug 2018. Metrics for training fastai models are simply functions that take input and target tensors, and return some metric of interest for training. MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique to measure performance. Event Map Team Project. All this makes Keras easy to learn and easy to use. The formula for the F1 score is. This is Part 2 of a MNIST digit classification notebook. A callback is a set of functions to be applied at given stages of the training procedure. Note that the metrics are prefixed with ‘val_’ for the validation. See Wikipedia's page for more information on the AUROC. Here's my actual code: # Split dataset in train and test data X_train, X_. The Far-Reaching Impact of MATLAB and Simulink Explore the wide range of product capabilities, and find the solution that is right for your application or industry. In pattern recognition, information retrieval and classification (machine learning), precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of the total amount of relevant instances that were actually retrieved. Using the checkpoint callback in Keras In Chapter 2 , Using Deep Learning to Solve Regression Problems , we saw the. callback_model_checkpoint is a callback that performs this task. AUROC creates a Callback for computing the AUC score for the ROC curve with auc_roc_score at the end of each epoch, given that averaging over batches is incorrect in case of the AUROC. I think it is much more closer to Keras than this library since it has callbacks and the flexibility that comes with them. By default, save_weights_only is set to false, which means the complete model is being saved - including architecture and configuration. callbacks. 95600] Xception[Public Score = 0. Services offered by F1 Plumbing, working as Plumber in Chelmsford, Ongar, London, Essex, Brentwood Checkatrade. You could easily switch from one model to another just by changing one line of code. monitor tells Keras which metric is used for evaluation, mode='max' tells keras to use keep the model with the maximum score and with period we can define how often the model is evaluated. Model()クラスもつ属性(attribute)である"summary()"を使う. To use keras bundled with tensorflow you must use from tensorflow import keras instead of import keras and import horovod. Callbacks are functions that can be applied at certain stages of the training process, such as at the end of each epoch. This callback, which is automatically applied to each Keras model, records the loss and additional metrics that can be added in the. Here's a callback that you can use for integrating Training Metrics: Here's a callback that you can use for integrating Training Metrics: import keras from sklearn. DDLCallback() as the first callback in the callback list. A callback has access to its associated model through the class property self. The measure of correctness is expressed by F1-score as it is an excellent score for classification problems with even imbalance class size and finds a good balance between precision and recall. text import Tokenizer, sequence from keras. 001, momentum = 0. from tensorflow. Keras learning rate schedules and decay. This article uses a Keras implementation of that model whose definition was taken from the Keras-OpenFace project. Tensorboard. It is helpful to know that the F1/F Score is a measure of how accurate a model is by using Precision and Recall following the formula of: F1_Score = 2 * ((Precision * Recall) / (Precision + Recall)) Precision is commonly called positive predictive value. Since CNTK 2. layers import Dense, Input from keras. Model()クラスもつ属性(attribute)である"summary()"を使う. optimizers import SGD from sklearn. As the title suggest, this post approaches building a basic Keras neural network using the Sequential model API. layers import Dense, Dropout, Activation, Flatten from keras. import Libraries: import keras import numpy as np from pandas import read_csv from keras. 0 (Sequential, Functional, and Model subclassing) In the first half of this tutorial, you will learn how to implement sequential, functional, and model subclassing architectures using Keras and TensorFlow 2. 1 Tested on Python Version : 3. 95 and not number of steps. datasets import cifar10 from keras. Fortunately, Keras allows us to access the validation data during training via a Callback function, on which we can extend to compute the desired quantities. Model(inputs = model. This rather quick and dirty notebook showing how to get started on segmenting nuclei using a neural network in Keras. The formula for the F1 score is. input, outputs = layer_outputs). KerasCallback() batch_size = 128 num_classes = 10 epochs = 12 // … Finally, let Keras use our. The relevant methods of the callbacks will then be called at each stage of the training. # Predicting the Test set results y_pred = classifier. Keras learning rate schedules and decay. A callback is a set of functions to be applied at given stages of the training procedure. TensorFlow and Keras TensorFlow • Open Source • Low level, you can do everything! • Complete documentation • Deep learning research, complex networks • Was developed by theGoogle Brainteam • Written mostly in C++ and CUDA and Python Keras • Open source • High level, less flexible • Easy to learn • Perfect for quick. You can then restore the model as outlined in the previous paragraph. After training for 500 iterations, the resulting model scores 99. As stated in this article, CNTK supports parallel training on multi-GPU and multi-machine. 0, precision and recall were removed from the master branch because they were batch-wise so the value may or may not be correct. Here is a sample code to compute and. In this short notebook we will take a quick look on how to use Keras with the familiar Iris data set. Maintain backwards compatibility: the existing Gluon way to train a model will be supported and maintained - it is needed for complex models and full imperative control by the user. When you want to do some tasks every time a training/epoch/batch, that’s when you need to define your own callback. You can then restore the model as outlined in the previous paragraph. As the starting point, I took the blog post by Dr. predict(X_test) y_pred = (y_pred > 0. callbacks. The hope is that [callbacks][keras-callbacks] can be used, but there is no way to tell inside a callback what split the use at the moment. keras import models from tensorflow. Keras learning rate schedules and decay. - Kernel Density Estimation (KDE). See Wikipedia's page for more information on the AUROC. Keras • Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. models import Sequential from keras. Model()クラスもつ属性(attribute)である"summary()"を使う. [Update: The post was written for Keras 1. for true positive) the first column is the ground truth vector, the second the actual prediction and the third is kind of a label-helper column, that contains in the case of true positive only ones. By continuing to use this website you are consenting to our use of cookies. - Deep Sequential Neural Network. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results returned by the classifier, and r is the number of correct positive results divided by the. Dense layer to maximize class output, you tend to get better results with 'linear' activation as opposed to 'softmax'. In this codelab, you'll learn about how to use convolutional neural Networks to improve your image classification models. # -*- coding: utf-8 -*-from __future__ import print_function from __future__ import absolute_import import warnings import copy import time import numpy as np import multiprocessing import threading import six try: import queue except ImportError: import Queue as queue from. seqeval is a Python framework for sequence labeling evaluation. Convolutional Neural Networks are very popular in Deep Learning applications. How will you select one best mo. F1 (name='f1', output_names=None, label_names=None, average='macro') [source] ¶ Bases: mxnet. initializers import VarianceScaling from. optimizers import SGD from keras. I want to compute the precision, recall and F1-score for my binary KerasClassifier model, but don't find any solution. Our simple model was able to predict the time-series tag with 23% confidence and predicts a higher confidence for r , which is also the most frequent tag in our training set. Using the F1 score as the evaluation metric for all the models, XGBoost is the best performer at 0. metrics import accuracy_score, f1_score from datetime import datetime. 所以Keras作者意识到这个问题,在2. To ensure that metrics used for early stopping and other hyper parameter tuning remain in sync throughout training, we have to add ddl. Build a convolutional neural network in keras using the latest Tensorflow 2 API. The code used for this article is on GitHub. Here's a simple example saving a list of losses over each batch during training:. 2 or downgrade to Keras 2. kerasのpre-traindedモデルにもあるVGG16をkerasで実装しました。 単純にVGG16を使うだけならpre-traindedモデルを使えばいいのですが、自分でネットワーク構造をいじりたいときに不便+実装の勉強がしたかったので実装してみました。. pyの簡素化 from keras. The probability that the unknown item is a forgery is only 0. core import Dense, Dropout, Activation from keras. 0, precision and recall were removed from the master branch because they were batch-wise so the value may or may not be correct. 0 (Sequential, Functional, and Model subclassing) In the first half of this tutorial, you will learn how to implement sequential, functional, and model subclassing architectures using Keras and TensorFlow 2. This document describes the available hyperparameters used for training NMT-Keras. The specific task herein is a common one (training a classifier on the MNIST dataset), but this can be considered an example of a template for approaching any such similar task. Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models. You can pass a list of callbacks (as the keyword argument callbacks ) to the fit() function. callbacks import TensorBoard autoencoder. Flexible Data Ingestion. object: Model object to evaluate. If you are optimizing final keras. Supervised Deep Learning is widely used for machine learning, i. Kerasのkeras. Our simple model was able to predict the time-series tag with 23% confidence and predicts a higher confidence for r , which is also the most frequent tag in our training set. By default, save_weights_only is set to false, which means the complete model is being saved - including architecture and configuration. 3 ways to create a Keras model with TensorFlow 2. metrics import precision_score, recall_score model = keras. layers import Dense, Dropout ; from keras. config import _astroNN_MODEL_NAME from astroNN. save() method, that allowed us to save our Keras model after we were done training. In this post, we will build a multiclass classifier using Deep Learning with Keras. The relative contribution of precision and recall to the F1 score are equal. scikit_learn import KerasClassifier import keras. By default, f1 score is not part of keras metrics and hence we can’t just directly write f1-score in metrics while compiling model and get results. The accuracy was around 0. Using the checkpoint callback in Keras In Chapter 2 , Using Deep Learning to Solve Regression Problems , we saw the. The formula for the F1 score is. Increased average F1 score from 79% to 90%. # For now, anaGo's best score is 90. F1 2019 Career Mode - Part 2 How I got my Credit Score from 0 to 792 - Beginner Guide - Duration:. preprocessing. Here is an well explained example, Building ML models is hard. The accuracy was around 0. In this blogger, I use Keras API of customizing layer to fulfill the Swish Beta function mentioned in paper Swish Beta Function In Keras | Ching-Chuan Chen's Blogger Ching-Chuan Chen's Blogger. However this metric is available in scikit-learn, which is not suitable for deep learning. layers import Dense, Dropout from keras. from __future__ import print_function import keras from keras. Model must be compiled first. kerasで1epochごとに各クラスのprecision,recall,f1のグラフを描画する. There are wrappers for classifiers and regressors, depending upon. MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique to measure performance. A blog about software products and computer programming. callbacks import Callback from seqeval. callbacks import Callback,ModelCheckpoint from keras. layers import Dense from tensorflow. Pre-trained models and datasets built by Google and the community. utils import shuffle from keras. • It was developed with a focus on enabling fast experimentation. View Wen Zhang's profile on LinkedIn, the world's largest professional community. serving or just tf) apply optimizations (freezing, quantitization etc) Theoretically you could even train as Keras Model, convert to tf. You define and use a callback when you want to automate some tasks after every training/epoch that help you. The F1 Score or F-score is a weighted average of precision and recall. You can pass a list of callbacks (as the keyword argument callbacks ) to the fit() function. 03 [keras] 색상있는 이미지 분류하기1 (0) 2018. As Python Developer worked on writing scripts to automate the recursive work using Numpy, Pandas, Scheduling, Multi threading and various other python libraries for multiple Business cases. org/stable/modules/generated/sklearn. com uses cookies to make sure you get the best browsing experience. predict(X_test) y_pred = (y_pred > 0. It expects integer indices. If the run is stopped unexpectedly, you can lose a lot of work. Estimator and use tf to export to inference graph. Compute Precision, Recall, F1 score for each epoch. # -*- coding: utf-8 -*-from __future__ import print_function from __future__ import absolute_import import warnings import copy import time import numpy as np import multiprocessing import threading import six try: import queue except ImportError: import Queue as queue from. While these toolboxes should be preferred by. By default, save_weights_only is set to false, which means the complete model is being saved - including architecture and configuration. CNTK Multi-GPU Support with Keras. code:: model. They are extracted from open source Python projects. The next line of code involves creating a Keras callback - callbacks are certain functions which Keras can optionally call, usually after the end of a training epoch. training import ImportanceTraining, BiasedImportanceTraining # assuming model is a Keras model wrapped_model = ImportanceTraining(model) wrapped_model = BiasedImportanceTraining(model, k=1. callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping import matplotlib. If all inputs in the model are named, you can also pass a list mapping input names to data. The first two parts of the tutorial walk through training a model on AI Platform using prewritten Keras code, deploying the trained model to AI Platform, and serving online predictions from the deployed model. Tensorboard support is provided via the tensorflow. Keras allows you to observe the current state of your model at particular points in time, known as callbacks. 05 or accuracy > 0. there are multiple classes), multi-label (e. Here, I show you some examples to get a feel for what Callbacks are. 95 for all cases except for the Dictyostelium phenotyping, which resulted in an F1 score around 0. layers import GRU ; import keras ; from keras. Thanks for the response. models import Sequential from keras. In the first part of this guide, we’ll discuss why the learning rate is the most important hyperparameter when it comes to training your own deep neural networks. This makes it easy to run the example, but hard to abstract the example to your own data. Fortunately, Keras allows us to access the validation data during training via a Callback class. TensorBoardの利用 tf. Serendeputy is a newsfeed engine for the open web, creating your newsfeed from tweeters, topics and sites you follow. Note that the metrics are prefixed with 'val_' for the validation. Evaluating Keras neural network performance using Yellowbrick visualizations. Keywords: Tensorflow, Keras, OpenCV, Variational Autoencoder, Logistic Regression, Kernel SVM, Recurrent Neural Networks (LSTM) and Human Pose Estimation (Openpose). Wen has 5 jobs listed on their profile. It is helpful to know that the F1/F Score is a measure of how accurate a model is by using Precision and Recall following the formula of: F1_Score = 2 * ((Precision * Recall) / (Precision + Recall)) Precision is commonly called positive predictive value. score(x_test, y_test) 0. callbacks import Callback from sklearn. keras import models from tensorflow. clone_metric(metric) Returns a clone of the metric if stateful, otherwise returns it as is. activation_model = tf. Using the sub-folder structure as below allows us to compare between multiple models or multiple optimizations of the same model. Estimator and use tf to export to inference graph. Things have been changed little, but the the repo is up-to-date for Keras 2. 0] I decided to look into Keras callbacks. It was developed with a focus on enabling fast experimentation. Keras is a high-level neural networks API, written in Python, and can run on top of TensorFlow, CNTK, or Theano. Run deep learning experiments on hundreds of machines, on and off the cloud, manage huge data sets and gain unprecedented visibility into your experiments. I have and LSTM sequence tagger in Keras which I use for highly unbalanced data. Since CNTK 2. How will you select one best mo. monitor tells Keras which metric is used for evaluation, mode='max' tells keras to use keep the model with the maximum score and with period we can define how often the model is evaluated. It is written in Python and can run on top of other low level neural network frameworks for numerical computations like TensorFlow, Theano, and CNTK etc. metrics import f1_score class FloydhubTrainigMetricsCallback ( keras. Heads-up: If you're using a GPU, do not use multithreading (i. weights_path (str) - path to the pre-trained weights file (if None, then it will be initialized according to params). F1 (name='f1', output_names=None, label_names=None, average='macro') [source] ¶ Bases: mxnet. keras as hvd in the import statements. If you are optimizing final keras. In this post we describe our attempt to re-implement a neural architecture for automated question answering called R-NET, which is developed by the Natural Language Computing Group of Microsoft Research Asia. compile (optimizer = sgd, loss = 'categorical_crossentropy', metrics =['accuracy']) class Metrics (keras. Estimator and use tf to export to inference graph. Keras Cheat Sheet: Neural Networks in Python Make your own neural networks with this Keras cheat sheet to deep learning in Python for beginners, with code samples. py as of today but I couldn't find any reference to their removal in the commit logs. You can also use it to create checkpoints which saves the model at different stages in training to help you avoid work loss in case your poor overworked computer decides to crash. I'm researching classification of imbalanced data, and I use the F1 score a lot in scikit-learn. input, outputs = layer_outputs). callbacks[/code] helps you to stop the training when a monitored quantity has stopped improving. Our simple model was able to predict the time-series tag with 23% confidence and predicts a higher confidence for r , which is also the most frequent tag in our training set. " Feb 11, 2018. How this bootcamp works ? Weekdays we will be having 3-4 Hrs of Algorithm classes & 3-4 Hrs of Development Classes. One thing that motivated me to write this code is that the available implementations are in Tensorflow or Theano and I found that both are hard to understand (not intuitive). At the end of the epoch, the average of the scores are determined and also stored in the history. They are extracted from open source Python projects. Abstract base class used to build new callbacks. It is a Python library for artificial neural network ML models which provides high level fronted to various deep learning frameworks with Tensorflow being the default one. The specific task herein is a common one (training a classifier on the MNIST dataset), but this can be considered an example of a template for approaching any such similar task. You can vote up the examples you like or vote down the ones you don't like. I could not answer his question. metrics import f1_score, precision_score, recall_score from keras. The following are code examples for showing how to use keras. Jul 25, 2017 · The reason for this is that the metric function is called at each batch step at validation. 所以Keras作者意识到这个问题,在2. Yes, you can achieve this by using the Keras Callbacks. keras:f1_score作为metric,存储模型, 加载带有自定义函数的模型(2/2) 阿里云双11来了! 从本博客参与阿里云,服务器最低只要86元/年!. The relevant methods of the callbacks will then be called at each stage of the training. keras里面如何计算f1-score ### 以下链接里面的code import numpy as np from keras. Under such situation, using F1 score could be a better metric. Precision, which we’ll denote p for convenience, is defined as [math]p = \frac{tp}{tp+fp}[/math] where tp and fp are true positives and false positives respectively. Keras has Scikit-learn API. 95 or loss < 0. There are wrappers for classifiers and regressors, depending upon. backend as K from tensorflow. If a set amount of epochs elapses without showing improvement, it automatically stops the training. callbacks import EarlyStopping ; from sklearn. layers import MaxPooling2D, Dropout from keras. The callback takes a couple of arguments to configure checkpointing. Deep learning models can take hours, days or even weeks to train. That way the Keras system calculates an average on the batch results. import os import tempfile import numpy as np import tensorflow as tf from matplotlib import pyplot as plt from tensorflow. preprocessing. layers import Dense, Dropout, Activation, Flatten from keras.