Vgg16 mnist keras

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Step 4: Load image data from MNIST. the MNIST handwritten digit recognition task in Python using the Keras deep learning library. For example, a simple MLP model can achieve 99% accuracy, and a 2-layer CNN can achieve 99% accuracy. 2. Github repo for gradient based class activation maps. Installation of Keras with tensorflow at the backend. Build a tf. In Tutorials. I want to use Pre-trained models such as Xception, VGG16, ResNet50, etc for my Deep Learning image recognition . こんにちは。 本記事は、kerasの簡単な紹介とmnistのソースコードを軽く紹介するという記事でございます。 そこまで深い説明はしていないので、あんまり期待しないでね・・・笑 [追記:2017/02/10] kerasに関するエントリまとめました! So, I used VGG16 model which is pre-trained on the ImageNet dataset and provided in the keras library for use. In the previous two posts, we learned how to use pre-trained models and how to extract features from them for training a model for a different task. training import ImportanceTraining . . In other words, a class activation map (CAM) lets us see which regions in the image were relevant to this class. For example, the labels for the above images are 5, 0, 4, and 1. Use the code fccallaire for a 42% discount on the book at manning. 0への移行部分と、上の最終層問題を除くと約2日程度でした。 Anytime you want to use a prominent pre-trained model in Caffe, I’d recommend taking a look at the Caffe Model Zoo. I'm also using transfer learning, importing VGG16 as a base, and adding my own 512 node relu dense layer and 0. 5 drop-out before a softmax layer of 10. 케라스(Keras) 튜토리얼 - 텐서플로우의 간소화된 인터페이스로서 텐서플로우 워크플로우로서 케라스 사용하기 완전 가이드. vgg16 import preprocess_input . layers. (For digits 0-9). 27 Sep 2018 In this article I will show you how to develop a deep learning classifier using Keras library to achieve 99% accuracy on the MNIST digits  11 Feb 2019 In this tutorial you will learn how to train a CNN with Keras on the Fashion MNIST dataset, enabling you to classify fashion images and  15 Apr 2017 Keras makes everything very easy and you will see it in action below. 6) Google search yields few implementations. They are extracted from open source Python projects. Keras Implementation of Generator’s Architecture. Our Team Terms Privacy Contact/Support I have a directory full of the MNIST samples in png format, and a dataframe with the absolute directory for each in one column and the label in the other. progress – If True, displays a progress bar of the download to stderr. You have just found Keras. This course includes a review of the main lbraries for Deep Learning such as Tensor Flow and Keras, the combined application of them with OpenCV and also covers a concise review of the main concepts in Deep Learning. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices. I digged a bit to find why the errors are happening, and found that with the latest version of Keras (v 2. json. - [Instructor] So let's make a start on this notebook. to_categorical function to convert our numerical labels stored in y to a binary form (e. Have you tried explicitly defining the classes of the images? as such: train_generator=image. Plus, learn about VGG16, the history of the ImageNet challenge, and more. 0). Keras framework already contain this model. It provides clear and actionable feedback for user errors. 2017年11月22日 一个简单的迁移学习案例:使用keras 将vgg16用于手写数字识别 . All right, enough for the intros, let's get to the point to build our Keras Estimator. Deep Learning with R This post is an excerpt from Chapter 5 of François Chollet’s and J. The data format convention used by the model is the one specified in your Keras config file. It is too easy. 4; To install this package with conda run one of the following: conda install -c conda-forge keras The following are code examples for showing how to use keras. We discuss it more in our post: Fun Machine Learning Projects for Beginners. 4; win-64 v2. def VGG16 (include_top = True, weights = ' imagenet ', input_tensor = None, input_shape = None, pooling = None, classes = 1000, ** kwargs): """ Instantiates the VGG16 architecture. normalization import BatchNormalization from keras. Those model's weights are already trained and by small steps, you can make models for your own data. Classify Fashion_Mnist with VGG16 Keras Models Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples  30 Jan 2019 In order to classify MNIST dataset with Convolutional Neural Network (CNN), we just need several layers of CNN to make it predict well. 🤓 Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple’s CoreML, and Theano. kerasの前のバージョンではMNIST互換のひらがな認識CNNが動いていました。そこからVGG16が動く状態に持って行くまでが、Keras2. You can vote up the examples you like or vote down the exmaples you don't like. The winners of ILSVRC have been very generous in releasing their models to the open-source community. The pre-trained networks inside of Keras are capable of recognizing 1,000 different object In many introductory to image recognition tasks, the famous MNIST data set is typically used. Part I states the motivation and rationale behind fine-tuning and gives a brief introduction on the common practices and techniques. 6 on Python3. In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. He then looks at convolutional neural networks, explaining why they're particularly good at image recognition tasks. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. csiszar_divergence. ImageNet classification with Python and Keras. Get down to the code. VGGNet, ResNet, Inception, and Xception with Keras. In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples --just a few hundred or thousand pictures from each class you want to be able to recognize. 題名にはkeras. Reshape(). The entire VGG16 model weights about 500mb. In this series we will build a CNN using Keras and TensorFlow and train it using the Fashion MNIST dataset! In this video, we will create, compile, and train a basic CNN model. More precisely, here is code used to init VGG16 without top layer and to freeze all blocks except the topmost: 今回は、Deep Learningの画像応用において代表的なモデルであるVGG16をKerasから使ってみた。この学習済みのVGG16モデルは画像に関するいろいろな面白い実験をする際の基礎になるためKerasで取り扱う方法をちゃんと理解しておきたい。 Keras is a high-level API to build and train deep learning models. layers import Activation, Flatten, Dense, Dropout from keras. They are stored at ~/. Some scripts to convert the VGG-16 to test on mnist. MNIST is pretty trivial, if you've took the UFLDL course, you should be able to write a multi-layer perception (MLP) in Matlab or Python, which  2019年3月27日 人工智能开发课程之十五Keras深度学习框架MNIST数据集训练CNN Keras调用 VGG16来训练深度学习更种优化算法. utils. 5. Image Classification on Small Datasets with Keras. bayesflow. preprocessing. MNISTからVGG16へ. py. Find file Copy path. You will see how easily we can use the VGG16 pre-trained model in Keras with the lesser amount of code. amari_alpha contrib. Table of contents. By default the utility uses the VGG16 model, but you can change that to something else. from keras. In the official basic tutorials, they provided the way to decode the mnist dataset and cifar10  This is where recurrent To load a dataset from Keras API you can load mnist . If you want to explore the tensorflow implementation of the MNIST dataset  8 May 2019 The example below loads the MNIST dataset using the Keras API and depth of the feature extractor part of the model, following a VGG-like  26 Feb 2019 In this practical, we will make our first steps with Keras and train our first models for classifying the handwritten digits of the MNIST dataset. Dataset of 60,000 28x28 gray scale images of the 10 digits, along with a test set of 10,000 images. 0. Good software design or coding should require little explanations beyond simple comments. preprocessingと書きましたが、正確にはkeras. Different types models that can be built in R using Keras; Classifying MNIST handwritten digits using an MLP in R Here is a utility I made for visualizing filters with Keras, using a few regularizations for more natural outputs. It means that if you have a 3D 8,8,128 tensor at MNIST consists of 28 x 28 grayscale images of handwritten digits like these: The dataset also includes labels for each image, telling us which digit it is. We will create our neural network using the Keras Functional API. It was developed with a focus on enabling fast experimentation. It's used for fast prototyping, advanced research, and production, with three key advantages: User friendly Keras has a simple, consistent interface optimized for common use cases. 5; osx-64 v2. applications. This is a summary of the official Keras Documentation. The “hello world” of object recognition for machine learning and deep learning is the MNIST dataset for handwritten digit recognition. Class activation maps are a simple technique to get the discriminative image regions used by a CNN to identify a specific class in the image. 4; win-32 v2. E. 6%! The MNIST database contains images of handwritten digits from 0 to 9 by American Census Bureau employees and American high school students. Preparing the Data The MNIST dataset is included with Keras and can be accessed using the dataset_mnist() function. input_shape optional shape list, only to be specified if include_top is FALSE (otherwise the input shape has to be (224, 224, 3) It should have exactly 3 inputs channels, and width and height should be no smaller than 32. The Keras library conveniently includes it already. In this article, we see how to install Keras on Docker and Google’s Cloud ML. hatenablog. Intro Deep Learning with Keras : : CHEAT SHEET Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. core. 65% error on MNIST (the numbers are from a  4 Jun 2017 1s from the well known MNIST data set. pyplot as plt import numpy as np % matplotlib inline np. A simple and powerful regularization technique for neural networks and deep learning models is dropout. Keras Applications are deep learning models that are made available alongside pre-trained weights. models import Sequential from keras. . pooling Below we will see how to install Keras with Tensorflow in R and build our first Neural Network model on the classic MNIST dataset in the RStudio. optional Keras tensor to use as image input for the model. 💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥 👀 OUR VLOG: What is Keras? The deep neural network API explained Easy to use and widely supported, Keras makes deep learning about as simple as deep learning can be Using data from multiple data sources from keras. We add a connection from the input to the output and divide by 2 to keep normalized outputs. Keras is a high-level API to build and train deep learning models and is user friendly, modular and easy to Keras integrates smoothly with other core TensorFlow functionality, including the Estimator API. keras/models/. 最近流行のDeepLearningを触ってみたいと思っていたところ、まずはkerasでmnistを動かしてみるのがよいとアドバイスいただいたので試してみました。 とりあえず動いたものの、pythonの知識も 今回は、KerasでMNISTの数字認識をするプログラムを書いた。このタスクは、Kerasの例題にも含まれている。今まで使ってこなかったモデルの可視化、Early-stoppingによる収束判定、学習履歴のプロットなども取り上げてみた。 導入 前回はMNISTデータに対してネットワークを構築して、精度を見ました。 tekenuko. vgg16 import VGG16 #build model mod = VGG16() When you run this code for the first time, you will automatically be directed first to download the weights of the VGG model Sun 05 June 2016 By Francois Chollet. Being able to go from idea to result with the least possible delay is key to doing good research. Allaire’s book, Deep Learning with R (Manning Publications). There are some image classification models we can use for fine-tuning. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(224, 224, 3)` (with `channels_last` data format) or `(3, 224, 224)` (with `channels_first` data format). Image preprocessing in TensorFlow for pre-trained VGG16 data in next chapters, but let us build and train an RNN for MNIST in Keras to quickly glance over  One simple trick to train Keras model faster with Batch Normalization. This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve . vgg16 import VGG16 from keras MNIST). …And just to make sure we understand what we are doing…and where we're headed,…we're going to be using…the MNIST data set here as an example. 此处的情形是, 在MNIST数据集中,通过对前5个数字(0~4)的学习迁移到后5个  Pytorch vgg. These models can be used for prediction, feature extraction, and fine-tuning. 47 KB. 3. How long does it take to train a VGG16 model on MNIST data using a For source you can look at the implementation of VGG16 in Keras. The good news about Keras and TensorFlow is that you don’t need to choose between them! The default backend for Keras is TensorFlow and Keras can be integrated seamlessly with TensorFlow workflows. 3 \ 'python keras_mnist_cnn. GitHub Gist: instantly share code, notes, and snippets. Through Keras, users have access to a variety of different state-of-the-art deep learning frameworks, such as TensorFlow, CNTK, and others. in a 6-class problem, the third label corresponds to [0 0 1 0 0 0]) suited for classification. It's common to just copy-and-paste code without knowing what's really happening. Dataset : MNIST. In this post you will discover how to develop a deep learning model to achieve near state of the art performance on the MNIST handwritten digit recognition task in Python using the Keras deep learning library. (libtorch) Save MNIST c++ example's trained model into a file, and load in from another VGGNet, ResNet, Inception, and Xception with Keras. So if you are just getting started with Keras you may want to stick with the CPU version initially, then install the appropriate GPU version once your training becomes more computationally demanding. xception import Xception from keras. a small VGG-like network to ~0. One of the easiest ways to get started with TensorFlow and Keras is running in a Docker container. Now let us do the same classification and retraining with Keras. Dropout(). Keras imports from importance_sampling. As planned, the 9 ResNet blocks are applied to an upsampled version of the input. 181 lines (149 sloc) 6. py' The --env flag specifies the environment that this project should run on (Tensorflow 1. conda install linux-64 v2. Fine-tuning pre-trained models in Keras More to come . He also steps through how to build a neural network model using Keras. With the KNIME Deep Learning - Keras Integration, we have added a first version of our new KNIME Deep Learning framework to KNIME Labs (since version 3. Weights are downloaded automatically when instantiating a model. keras model. However, there are some issues with this data: 1. 概要 Keras では VGG、GoogLeNet、ResNet などの有名な CNN モデルの学習済みモデルが簡単に利用できるようになっている。 今回は ImageNet で学習済みの VGG16 モデルを使った画像分類を行う方法を紹介する。 How can I run Keras on a GPU? Note that installation and configuration of the GPU-based backends can take considerably more time and effort. 今回は学習済みcnnモデル:vgg16を用いて,一般的な画像の分類を行ってみたいと思います.理論などの説明は割愛し,道具としてこれを使えるようになることを目指します.では行きましょう! “Keras tutorial. 이 튜토리얼은 Keras 공식 튜토리얼 Keras as a simplified interface to TensorFlow: tutorial을 번역했습니다. There is also a pure-TensorFlow implementation of Keras with deeper integration on the roadmap for later this year. 这就给了做高层抽象API封装的生存空间,Keras Tensorlayer TFLearn 是目前比较成熟的几个库。 做个比喻,Tensorflow就像当年的 Win32 API,功能强大但是难以使用,随便做点小事情就要写很多代码,我清楚记得我写个显示空白窗口的程序就要40来行。 keras-practice/fashion-mnist/VGG16. input_shape: optional shape list, only to be specified if include_top is FALSE (otherwise the input shape has to be (224, 224, 3) It should have exactly 3 inputs channels, and width and height should be no smaller than 32. A few months ago I wrote a tutorial on how to classify images using Convolutional Neural Networks (specifically, VGG16) pre-trained on the ImageNet dataset with Python and the Keras deep learning library. We would give examples from time series and text data in next chapters, but let us build and train an RNN for MNIST in Keras to quickly glance over the process of building and training the RNN models. Home · Blog For demo purpose, we choose the MNIST handwritten digits datasets since. As a simple example, here is the code to train a model in Keras: VGG16 – Implementation Using Keras → Muhammad Rizwan I am a professional engineer, enthusiast programmer, passionate data scientist and machine learning student. layers import Dense, Activation model = Sequential([ . arithmetic_geometric contrib. VGG16 is a proven proficient algorithm for image classification (1000 classes of images). That’s it for the generator! Let’s take a look at the discriminator’s architecture. Note that the data format convention used by the model is: the one specified in your Keras config at `~/. We will import this model and fine-tune it to classify the images of dogs and cats (only 2 classes instead of 1000 classes). seed (2017) from keras. Same problem, before fine-tuning my model for 5 classes reached 98% accuracy but the first epoch of fine-tuning dropped to 20%. MNIST is a great dataset for getting started with deep learning and computer vision. Having to train an image-classification model using very little data is a common situation, in this article we review three techniques for tackling this problem including feature extraction and fine tuning from a pretrained network. ” Feb 11, 2018. 生活日常2019-03-27 15:57:  2016年11月9日 今回は、KerasでMNISTの数字認識をするプログラムを書いた。このタスクは、Kerasの 例題にも含まれている。今まで使ってこなかったモデルの  How to load the VGG model in Keras and summarize its structure. (200, 200, 3) would be one valid value. cn该项目Github 地址Github 加载 . chi_square contrib Keras-Tutorials版本:0. random. In the remainder of this tutorial, I’ll explain what the ImageNet dataset is, and then provide Python and Keras code to classify images into 1,000 different categories using state-of-the-art network architectures. Being able to go from idea to result with the least possible delay is key to doing good There are hundreds of code examples for Keras. Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. The model and the weights are compatible with both TensorFlow and Theano. Pytorch VGG Fashion- Mnist. I am trying to load the vgg16 pre-trained model in Keras but getting © 2019 Kaggle Inc. mnist-vgg16. I trained 32 features with sparse filtering for the MNIST data set. ゼロからKerasとTensorFlow(TF)を自由自在に動かせるようになる。 そのための、End to Endの作業ログ(備忘録)を残す。 ※環境はMacだが、他のOSでの汎用性を保つように意識。 ※アジャイルで執筆しており、精度を逐次高めていく Note that when using TensorFlow, for best performance you should set `image_data_format='channels_last'` in your Keras config at ~/. 1. It is becoming the de factor language for deep learning. A Comprehensive guide to Fine-tuning Deep Learning Models in Keras (Part II) October 8, 2016 This is Part II of a 2 part series that cover fine-tuning deep learning models in Keras. Optionally loads weights pre-trained on ImageNet. com. Did you or does anyone work it out for multi-class problem? I guess we need more train data to feed our model Pre-trained models and datasets built by Google and the community Keras: The Python Deep Learning library. Both Tensorflow and Keras allow us to download the MNIST dataset directly using the API. Installing Keras on Docker. In this post you will discover the dropout regularization technique and how to apply it to your models in Python with Keras. It supports multiple back- Kerasはバックエンドの科学計算ライブラリにかかわらず、ニューラルネットワークの設定を容易に行うことができる、より高いレベルでより直感的な一連の抽象化を提供している 。MicrosoftはKerasにCNTKバックエンドを追加する作業を行っている Also, please note that we used Keras' keras. g. R Interface to 'Keras' Interface to 'Keras' <https://keras. 2 But a few days back, several people had got some errors when following the steps I explained. For simplicity reason, let's build a classifier for the famous dog vs cat image classification. keras VGG-16 pre-trained model for Keras. …And the MNIST data set is the handwritten data set,…and fortunately for us,…it's already available as one of the data sets in Keras. The NotMNIST dataset is not predefined in the Keras or the TensorFlow framework, so you'll have to download the data from this source. com 今回は、より画像処理に特化したネットワークを構築してみて、その精度検証をします。 A Comprehensive guide to Fine-tuning Deep Learning Models in Keras (Part I) October 3, 2016 In this post, I am going to give a comprehensive overview on the practice of fine-tuning, which is a common practice in Deep Learning. We built a simple MLP with relu activations and a VGG-like convolutional model to Michael Chen who did the heavy lifting and implemented most of this using python and Keras. As with OverFeat, I don't have enough compute power here to actually traing the model, but this does serve as a nice example of how to use the graph interface in keras. From the Keras VGG16 Documentation it says:. Simple implementation of VGG16 on MNIST Dataset using Keras (for Rapid Prototyping). 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。 Pre-trained models present in Keras. You can follow along with the code in the Jupyter notebook ch-12a_VGG16_Keras. In this tutorial, you will implement something very simple, but with several learning benefits: you will implement the VGG network with Keras, from scratch, by reading the On the same way, I’ll show the architecture VGG16 and make model here. its vgg16をファインチューニングしたCNNモデルをヒートマップとして可視化させるため  7 Feb 2018 Keras implementation of cnn model with dlib for face recognition First, we will load a VGG model without the top layer ( which . After that, there is a special Keras layer for use in recurrent neural networks called TimeDistributed. This function adds an independent layer for each time step in the recurrent model. imageです。 自然言語系の前処理はまとめてません・・・。自分が自然言語処理を本格的にやったことないので・・・。 申し訳ないです、いつかまとめますね。 import time import matplotlib. Modular and composable R interface to Keras. keras/keras. ipynb 的速度较慢,建议在 Nbviewer 中查看该项目。 The following are code examples for showing how to use keras. The only change that I made to the VGG16 existing architecture is changing the softmax layer with 1000 outputs to 16 categories suitable for our problem and re-training the The next layer in our Keras LSTM network is a dropout layer to prevent overfitting. We'll also use 然而mnist数据集也有自己的缺点,fashion-mnist正是为了克服这些缺点而生。 写给专业的机器学习研究者 我们是认真的。取代MNIST数据集的原因由如下几个: MNIST太简单了。 很多深度学习算法在测试集上的准确率已经达到99. …We first need to import the relevant packages Keras is what data scientists like to use. How to make Fine tuning model by Keras There is an example of VGG16 fine-tuning on keras blog, but I can't reproduce it. It's a big enough challenge to warrant neural networks, but it's manageable on a single computer. It is divided into 60,000 training images and 10,000 testing images. This course is focused in the application of Deep Learning for image classification and object detection. ucas. 0 + Keras 2. 원문 링크 바로가기 最近在做毕设,碰到一个问题,如何导入自己的数据集而不使用自带的mnist数据集呢?因为在keras下,用mnist很简单,一句input_data然后reshape一下,就可以了,而且标签什么的都是自动读取好的,但是如果我现在想导入自己的图片数据集进入CNN,通过什么样的方式才能跟这种导入MNIST数据集的效果一样呢? contrib. 動機はさておき、こちらのエントリ を読んで気になっていた Keras を触ってみたのでメモ。自分は機械学習にも Python にも触れたことはないので、とりあえず、サンプルコードを読み解きながら、誰しもが通るであろう(? 1.fine tuning(転移学習)とは? 既に学習済みのモデルを転用して、新たなモデルを生成する方法です。 つまり、他の画像データを使って学習されたモデルを使うことによって、新たに作るモデルは少ないデータ・学習量でモデルを生成することが可能となります。 Keras を使った簡単な Deep Learning はできたものの、そういえば学習結果は保存してなんぼなのでは、、、と思ったのでやってみた。 Tip: if you want to learn how to implement an Multi-Layer Perceptron (MLP) for classification tasks with the MNIST dataset, check out this tutorial. Below is the architecture of the VGG16 model which I used. Fetching contributors … Cannot retrieve contributors at this time. Join Jonathan Fernandes for an in-depth discussion in this video, Building the Keras model, part of Neural Networks and Convolutional Neural Networks Essential Training. An implementation of the Inception module, the basic building block of GoogLeNet (2014). One of them, a package with simple pip install keras-resnet 0. utils Here's how you can do run this Keras example on FloydHub: Via FloydHub's Command Mode First time training command: floyd run \ --gpu \ --env tensorflow-1. J. Github project for class activation maps. There are many models such as AlexNet, VGGNet, Inception, ResNet, Xception and many more which we can choose from, for our own task. ac. In this video, we discuss the prerequisites required to start working with Keras. convolutional import Convolution2D, MaxPooling2D from keras. About the fine-tuning itself, please check the followings. You can use it to visualize filters, and inspect the filters as they are computed. 1作者:张天亮邮箱:zhangtianliang13@mails. The Discriminator For MNIST, the image size is 28 x 28 pixels, thus we can think of an MNIST image as having 28 time steps with 28 features in each timestep. *) they have changed the API of the visualization utility. Being able to go from idea to result with the least possible delay is key to doing good Keras: The Python Deep Learning library. After reading this post you will know: How the dropout regularization More than 1 year has passed since last update. Reuters newswires topic classification: Multilayer Perceptron (MLP); MNIST handwritten digits classification: MLP & VGG-like convnet:. For the bulk of the famous models, you can find the prototxt and caffemodel files necessary for your own purposes. Join Jonathan Fernandes for an in-depth discussion in this video, Image augmentation in Keras, part of Neural Networks and Convolutional Neural Networks Essential Training. Now comes the part where we build up all these components together. io>, a high-level neural networks 'API'. Training a convnet with a small dataset Having to train an image-classification model using very little data is a common situation, which you’ll likely encounter in Learn how to build a multi-class image classification system using bottleneck features from a pre-trained model in Keras to achieve transfer learning. Keras is a deep learning library which can be used on the enterprise platform, by deploying it on a container. The examples covered in this post will serve as a template/starting point for building your own deep learning APIs — you will be able to extend the code and customize it based on how scalable and robust your API endpoint needs to be. vgg16 mnist keras

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