Kaggle Vgg16

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Noteeach Keras Application expects a specific kind of input preprocessing.For VGG16, call keras.applications.vgg16.preprocess_input on yourinputs before passing them to the model.vgg16.preprocess_input will convert the input images from RGB to BGR,then will zero-center each color channel with respect to the ImageNetdataset, without scaling.

Note: each Keras Application expects a specific kind of input preprocessing.For VGG19, call keras.applications.vgg19.preprocess_input on yourinputs before passing them to the model.vgg19.preprocess_input will convert the input images from RGB to BGR,then will zero-center each color channel with respect to the ImageNetdataset, without scaling.

In this tutorial, we are going to see the Keras implementation of VGG16 architecture from scratch. VGG16 is a convolutional neural network architecture that was the runners up in the 2014 ImageNet challenge (ILSVR) with 92.7% top-5 test accuracy over a dataset of 14 million images belonging to 1000 classes. Although it finished runners up it went on to become quite a popular mainstream image classification model and is considered as one of the best image classification architecture.

Now that we have set up the Google Colab let us start with the actual building of the Keras implementation of VGG16 image classification architecture.VGG16 Keras ImplementationVGG16 Transfer Learning ApproachDeep Convolutional Neural networks may take days to train and require lots of computational resources. So to overcome this we will use Transfer Learning for implementing VGG16 with Keras.Transfer learning is a technique whereby a deep neural network model that was trained earlier on a similar problem is leveraged to create a new model at hand. One or more layers from the already trained model are used in the new model. We will go through more details in a subsequent section below.

Here we have defined a function and have implemented the VGG16 architecture using Keras framework. We have performed some changes in the dense layer. In our model, we have replaced it with our own three dense layers of dimension 256128 with ReLU activation and finally 1 with sigmoid activation.

As we discussed in the above section that we will use Transfer Learning for implementing VGG16 with Keras. So we will reuse the model weights from pre-trained models that were developed for standard computer vision benchmark datasets like ImageNet. We have downloaded pre-trained weights that do not have top layers weights. As you see above we have replaced the last three layers by our own layer and pre-trained weights do not contain the weights of newly three dense layers. So that is why we have to download pre-trained layer without top. (Link to download these weights are given at the bottom of the article)Since we only have to initialize the weight to the last convolutional that is why we have called model and pass input as model input and output as the last convolutional blockIn [11]:Vgg16 = Model(inputs=model.input, outputs=model.get_layer('vgg16').output)Now load the weights using the function load_weights of Keras

So to overcome this problem the concept of Early Stoping is used.In this technique, we can specify an arbitrarily large number of training epochs and stop training once the model performance stops improving on a hold out validation dataset. Keras supports the early stopping of training via a callback called EarlyStopping.Below are various arguments in EarlyStopping.

The EarlyStopping callback will stop training once triggered, but the model at the end of training may not be the model with the best performance on the validation dataset.An additional callback is required that will save the best model observed during training for later use. This is known as the ModelCheckpoint callback.

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