t81_558_class_09_2_popular_transfer.ipynb

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    "# T81-558: Applications of Deep Neural Networks\n",
    "**Module 9: Transfer Learning**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
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    "# Module 9 Material\n",
    "\n",
    "* Part 9.1: Introduction to Keras Transfer Learning [[Video]](https://www.youtube.com/watch?v=WLlP6S-Z8Xs&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_09_1_keras_transfer.ipynb)\n",
    "* **Part 9.2: Popular Pretrained Neural Networks for Keras** [[Video]](https://www.youtube.com/watch?v=ctVA1_46YEE&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_09_2_popular_transfer.ipynb)\n",
    "* Part 9.3: Transfer Learning for Computer Vision and Keras [[Video]](https://www.youtube.com/watch?v=61vMUm_XBMI&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_09_3_transfer_cv.ipynb)\n",
    "* Part 9.4: Transfer Learning for Languages and Keras [[Video]](https://www.youtube.com/watch?v=ajmAAg9FxXA&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_09_4_transfer_nlp.ipynb)\n",
    "* Part 9.5: Transfer Learning for Keras Feature Engineering [[Video]](https://www.youtube.com/watch?v=Dttxsm8zpL8&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_09_5_transfer_feature_eng.ipynb)"
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    "# Part 9.2: Popular Pretrained Neural Networks for Keras\n",
    "\n",
    "The following two sites, among others, can be great starting points to find pretrained models for use in your projects:\n",
    "\n",
    "* [TensorFlow Model Zoo](https://modelzoo.co/)\n",
    "* [Papers with Code](https://paperswithcode.com/)\n",
    "\n",
    "Keras contains built-in support for several pretrained models.  You can find the [complete list](https://keras.io/applications/) in the Keras documentation.\n",
    "\n",
    "### DenseNet\n",
    "\n",
    "The [Dense Convolutional Network (DenseNet)](https://arxiv.org/abs/1608.06993) model is [provided by keras](https://keras.io/applications/#densenet). Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, while requiring less computation to achieve high performance. Code and pre-trained models are available in Keras.\n",
    "\n",
    "### InceptionResNetV2 and InceptionV3\n",
    "\n",
    "The [Inception ResNet V2](https://arxiv.org/pdf/1512.00567v3.pdf) model is [provided by Keras](https://keras.io/applications/#inceptionresnetv2). A convolutional neural network (CNN) achieves a new state of the art in terms of accuracy on the ILSVRC image classification benchmark. Inception-ResNet-v2 is a variation of our earlier Inception V3 model, which borrows some ideas from Microsoft's ResNet papers.\n",
    "\n",
    "### MobileNet\n",
    "\n",
    "The [MobileNet](https://arxiv.org/abs/1704.04861) model is [provided by Keras].(https://keras.io/applications/#mobilenetv2). Created for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build lightweight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade-off between latency and accuracy. These hyper-parameters allow the model builder to choose the right-sized model for their application based on the problem's constraints. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, fine-grain classification, face attributes, and large scale geo-localization.\n",
    "\n",
    "\n",
    "### MobileNetV2\n",
    "\n",
    "The [MobileNet V2](https://arxiv.org/abs/1801.04381) model is [provided by Keras](https://keras.io/applications/#mobilenetv2). Improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. We also demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3, which we call Mobile DeepLabv3. \n",
    "\n",
    "### NASNet\n",
    "\n",
    "The [NASNet](https://arxiv.org/abs/1707.07012) model is [provided by Keras](https://keras.io/applications/#nasnet). Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose searching for an architectural building block on a small dataset and then transferring it to a larger dataset. The key contribution of this work is the design of a new search space (the \"NASNet search space\"), which enables transferability. In our experiments, we search for the best convolutional layer (or \"cell\") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named \"NASNet architecture.\" We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, NASNet achieves a 2.4% error rate, which is state-of-the-art. On ImageNet, NASNet achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures. It has 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, NASNets' accuracy exceeds those of the state-of-the-art human-designed models. For instance, a small version of NASNet achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset.\n",
    "\n",
    "### ResNet, ResNetV2, ResNeXt\n",
    "\n",
    "The [ResNet](https://arxiv.org/abs/1512.03385), [ResNetV2](https://arxiv.org/abs/1603.05027), and [ResNeXt](https://arxiv.org/abs/1611.05431) models are [provided by Keras](https://keras.io/applications/#resnet). Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62% error) and CIFAR-100, and a 200-layer ResNet on ImageNet. \n",
    "\n",
    "### VGG16 and VGG19\n",
    "\n",
    "The [VGG16 and VGG19](https://arxiv.org/abs/1409.1556) models are [provided by Keras](https://keras.io/applications/#vgg16). In this work, we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with tiny (3x3) convolution filters. This shows that a significant improvement in the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localization and classification tracks, respectively. We also show that our representations generalize well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.\n",
    "\n",
    "### Xception\n",
    "\n",
    "The [Xception](https://arxiv.org/abs/1610.02357) model is [provided by Keras](https://keras.io/applications/#xception). We present an interpretation of Inception modules in convolutional neural networks as an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for). It significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to more efficient use of model parameters.\n"
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