I want to customize the ResNet50 pre-trained deep learning model but the problem is I don't understand the aspect I should look at carefully. Simply put, if I want to add extra layers or any parameters, how can these be done?
Is there anyone who can help me share the experience or steps needed to create a model that mimics or customized the ResNet50 model?
Dear Elias,
With pre-trained algorithms like ResNet, I have had the most success with modifying dropout layers within the residual blocks to modulate overfitting. I also recommend tailoring the initial and final blocks to suit your particular data and output classifications. If the convolutional kernel itself is modified, then the benefits of pre-training could become lost.
Please take a look at this paper, where I modify and implement a ResNet algorithm:
https://www.science.org/doi/10.1126/sciadv.abl4146
Hi Elias Hossain !
You should look into basic transfer learning techniques, which usually remove the last layers of the pre-trained ResNet, freeze the remaining layers (since the learned features should be appropriate for your dataset!), and add new layers (which will be trained from scratch). These new layers will be the one that will "specialize" for your new dataset.
Depending on your library, there are a lot of easy tutorials to do all of these things with a few lines of code.
For example, in keras (using VGG instead of ResNet) you would do something like
# load model
model = VGG16(weights = 'imagenet', include_top = False, input_shape = (224, 224, 3))
# freeze layers for layer in model.layers: layer.trainable = False
# add new layers
x = Flatten()(model.output)
x = Dense(32)(x)
predictions = Dense(1, activation="sigmoid")(x)
new_model = Model(model.input, predictions)
# compile model
new_model.compile(optimizer="Adam", loss="binary_crossentropy", metrics=["accuracy"])
# train model
history = new_model.fit(x_train_images, x_train_labels, batch_size=1, epochs=100)
I hope I've been helpful!
Elias Hossain Take the following steps:
1. The seven stages to creating a machine learning model.
2. Recognize the business challenge (and define success)
3. Recognize and identify data
4. Gather and prepare data.
5. Determine the model's characteristics and train it.
6. Assess the model's performance and set benchmarks.
Qamar Ul Islam Thank you for the information. I was determined to customize a pre-trained model from scratch. As you mentioned, I have been following the steps while working on ML; nevertheless, Raoul Raftopoulos clarified it very nicely, and I'm following his steps. Again, thank you so much for taking the time and providing your feedback.
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