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What is epoch in CNN?
One Epoch is when an ENTIRE dataset is passed forward and backward through the neural network only ONCE. Since one epoch is too big to feed to the computer at once we divide it in several smaller batches.
Why fine tuning increases the accuracy in a CNN?
Applying fine-tuning allows us to utilize pre-trained networks to recognize classes they were not originally trained on. And furthermore, this method can lead to higher accuracy than transfer learning via feature extraction.
How do I increase the accuracy of my resnet50?
Pick one pre-trained model that you think it gives the best performance with your hyper-parameters (say ResNet-50 layers). After you obtained the optimal hyper parameters, just select the same but more layers net (say ResNet-101 or ResNet-152 layers) to increase the accuracy.
What is the difference between iteration and epoch?
An epoch is defined as the number of times an algorithm visits the data set . Iteration is defined as the number of times a batch of data has passed through the algorithm.In other words, it is the number of passes, one pass consists of one forward and one backward pass.
How many convolutional blocks are in a VGG network?
VGG network has many variants but we shall be using VGG-16 which is made up of 5 convolutional blocks and 2 fully connected layers after that. See below: Input to the network is 224 *224 and network is: There are two steps to our training methodology Freeze, Pre-train and Finetune (FPT):
Where can I find the Keras vgg16 implementation?
Since, keras has provided a VGG16 implementation, we shall reuse that. This code is in VGG16.py file in the network folder. If we specify include_top as True, then we will have the exact same implementation as that of Imagenet pretraining with 1000 output classes.
How to calculate the weights of a VGG network?
Another important thing to note here is we normalize by dividing each pixel value in all the images by 255, since we are using 8bit images so each pixel value is now between 0 and 1. This is also loosely called pre-processing of input images for VGG network. Our imagenet weights have also been obtained using the same normalization.
Is there a way to increase accuracy with fine tuning?
Well, not sure if it is a right solution, but I was able to increase accuracy at least up to 70% with this code (probably the main reason is decreased learning rate and more epochs): I guess there is a way to achieve much better results with fine-tuning (up to 98%), but I wasn’t able to achieve it with provided code.