How to increase the accuracy of CNN models?
Increase model capacity >> Increasing model depth (more number of layers) and width (number of filters in each convolution layer). Increase image resolution (progressive resizing) >> From 128 x 128 x 3 to 256 x 256 x 3 or to even higher size. Random image rotations >> Change orientation of the image.
Is it possible to get 80% accuracy?
There is always a possibility that the model is over-fitting after certain number of epoch or accuracy may stop improving, the already saved weights will be helpful. This is how the training will progress and you can see we achieved 80% training as well as validation accuracy.
How to compute accuracy of CNN in TensorFlow?
My classifier has 5 convolutional layers followed by 2 fully connected layers. The final FC layer has an output dimension of 2 for which I have used: Now you can just specify you want it in the metrics parameter in model.compile. This post is from 3.6 years ago when tensorflow was still in version 1.
Which is the best code for CNN models?
This was one of… The best performing code implemented using ImageDataGenerator, flow_from_dataframe, various data augmentation techniques, L2 regularization, batch normalization, one hot encoding on a subset of data considering the class weights in Keras. Lets take a look at all the performance improvement techniques.
Why do we need callbacks for CNN models?
Callbacks will help us retain the best weights and biases we trained so far, since we will be training with multiple epochs and each epoch will go through entire training set. There is always a possibility that the model is over-fitting after certain number of epoch or accuracy may stop improving, the already saved weights will be helpful.
Is it better to fine tune a pre trained model?
It is often better to fine-tune the pre-trained model for two reasons: Our fine-tuned model can generate the output in the correct format. Generally speaking, in a neural network, while the bottom and mid-level layers usually represent general features, the top layers represent the problem-specific features.