How do I tune my ResNet50?

How do I tune my ResNet50?

Fine-tuning ResNet with Keras, TensorFlow, and Deep Learning

1. Implement our configuration file.
2. Create a Python script to build/organize our image dataset.
3. Implement a second Python script used to fine-tune ResNet with Keras and TensorFlow.
4. Execute the training script and fine-tune ResNet on our dataset.

How do you load a pre-trained model in TensorFlow?

Loading resnet First I download the inception_resnet_v2.py file. This file allows us to load the network structure into TF. If it’s not in the same path as your current path, you need to add its folder to your path. Next we can load the saved weights from the pretrained model.

How do you fine tune a keras model?

Fine-tuning in Keras

1. Load the pre-trained model. First, we will load a VGG model without the top layer ( which consists of fully connected layers ).
2. Freeze the required layers. In Keras, each layer has a parameter called “trainable”.
3. Create a new model.
4. Setup the data generators.
5. Train the model.
6. Check Performance.

When to fine tuning ResNet-50 for new dataset?

Join ResearchGate to ask questions, get input, and advance your work. I would say if the dataset consists of images which are different and small from pretrained model then one should go for the fine tuning the lower layers of pretrained models. If the problem lied in poor training accuracy, I would concur with Amin’s advice.

How to create a transfer learning model using resnet50?

We now create our model using Transfer Learning using Pre-trained ResNet50 by adding our own fully connected layer and the final classifier using sigmoid activation function. We see that the weights of ResNet50 are not trainable as we had frozen them.

How does resnet50 help to achieve high accuracy?

For ResNet50 what helped more to achieve a high accuracy was to resize the input from 32 x 32 to 224 x 224. This is because of how the model was constructed which in this sense was not compatible with the dataset but it was easy to solve by fitting it to the original size of the architecture.

How to use resnet50 as a feature extractor?

Weights of ResNet50 pre-trained model is used as feature extractor Weights of the pre-trained model are frozen and are not updated during the training We do not want to load the last fully connected layers which act as the classifier. We accomplish that by using “ include_top=False ”.