I have used the "microsoft/resnet-50" for a vision task but I want to access the resnet layers inside the base model for Evaluation/Explainability purposes.
Code:
from transformers import AutoFeatureExtractor, TFResNetModel
model = TFResNetModel.from_pretrained("microsoft/resnet-50")
And model.summary() gives:
Since the resnet model is of type "TFResNetMainLayer", how can I access the layers inside this? Is it possible or are there any workarounds?
Related
I have a pretrained model trained in Keras.
I am trying to use that model in another task, but I don't need all the layers, but only the first 4 conv layers.
I have the model saved in "keras_pretrained_model.h5"
Is it possible to initialize the first 4 conv layers of the new model using the weights of the first 4 conv layers of the pretrained model from the '.h5' file?
Is loading the whole pretrained model first always necessary??
The pretrained model actually takes up a lot of space and I am not sure how to delete the pretrained model after I initializing the new model with the weights. As far as I understand, using tf.keras.backend.clear_session() will clear the new model created along with the old one.
So, my question is
Is there any way to initialize the weights in the new model layers without loading the whole pretrained model?
If I have to load the whole pretrained model, how to delete only the pretrained model without harming the new model in any way?
I have thought of two processes
If the names of the layers of the first 4 layers of the new model is same as the 4 layers in the pre-trained model, then
new_model.load_weights(path_to_old_model_file, by_name = True)
If the names don't mathc, then we can do layer wise weight initialization by taking the weights from the corresponding layers in the old model h5 file and setting the weights using set_weights() method.
I have written a code, which I have uploaded to github, here.
I would be very grateful, if anyone seeing this gives a feedback on this!!
I followed a blog on how to implement a vgg16-model from scratch and want to do the same with the pretrained model from Keras. I looked up some other blogs but can't find a fitting solution I think. My task is to classify integrated circuit images into defect or non defects.
I have seen on a paper that they used pretrained imagenet model of vgg16 for fabric defect detection, where they freezed the first seven layers and fine tuned the last nine for their own problem.
(Source: https://journals.sagepub.com/doi/full/10.1177/1558925019897396)
I have already seen examples on how to freeze all layers except the fully connected layers, but how can I try the example with freezing first x layers and fine tune the others for my problem?
The VGG16 is fairly easy to implement from scratch but for models like resnet or xception it is getting a little trickier.
It is not necessary to implement a model from scratch to freeze a few layers. You can do this on pre-trained models as well. In keras, you'd use trainable = False.
For example, let's say you want to use the pre-trained Xception model from keras and want to freeze the first x layers:
#In your includes
from keras.applications import Xception
#Since you're using the model for a different task, you'd want to remove the top
base_model = Xception(weights='imagenet', include_top=False)
#Freeze layers 0 to x
for layer in base_model.layers[0:x]:
layer.trainable = False
#To see all the layers in detail and to check trainable parameters
base_model.summary()
Ideally you'd want to add another layer on top of this model with the output as your classes. For more details, you can check this keras guide: https://keras.io/guides/transfer_learning/
A lot of times the pre-trained weights can be very useful in other classification tasks but in case you want to train a model from scratch on your dataset, you can load the model without the imagenet weights. Or better, load the weights but don't freeze any layers. This will retrain every layer taking imagenet weights as an initialization.
I hope I've answered your question.
I have a model that contains two-part encoder and decoder.
Each of them is a PyTorch model.
I inference them as below.
features = encoder(input)
output = decoder(features)
and I want to convert them into one ONNX model instead of two ONNX models.
How can I do it?
In general you should build a new class which inherits nn.Module that combines the multiple models. In this case you have a simple feed-forward network so we can use the nn.Sequential convenience class.
E.g.
model = torch.nn.Sequential([encoder, decoder])
# inference example
model.eval()
output = model(input)
# save model to onnx file ...
I am implementing a Machine Learning module that should run in a Raspberry Pi that at the moment is shared among different services.
My idea is to store in the device only the code in charge of retrieving the inputs of the ML module and performing the prediction, together with the file containing the Neural Network model already fitted using Keras.
In other words, I would like to avoid to install all the Keras/Tensorflow packages and dependencies if my purpose is only to perform the prediction on a trained model, and not to train a new model.
Is there a way to do that? Are there any lightweight libraries that allow to load the model of a Neural Network (with all the weights and biases settings) and perform a prediction, given the inputs?
What I am able to do now is to load in the Raspberry Pi a ".h5" file containing the model, weights and biases, but still I have to declare the building function of the model through Keras.
from tensorflow.keras.models import load_model
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
def NN_model():
'''
Definition of the Neural Network model
'''
model = Sequential()
model.add(Dense(7, input_dim=6, kernel_initializer='normal', activation='relu'))
model.add(Dense(15, kernel_initializer='normal', activation='relu'))
model.add(Dense(24, kernel_initializer='normal'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
'''
Load NN model and use it to predict the radiation values
for the next 24 hours, hour by hour
'''
regr = KerasRegressor(build_fn=NN_model, epochs=1000, batch_size=5, verbose=0)
regr.model = load_model('saved_model.h5')
pred=regr.predict(input_row)
Since a fitted Neural Network is just a matter of weights and biases (and activation functions), I would expect that, once these parameters are determined, I wouldn't need the whole Tensforflow and Keras environment to map an output to the inputs I give to the NN.
What I would like to have is just something like:
import lightweight_module as lm
regression_model = lm.load_model('saved_model.h5')
prediction=regression_model.predict(inputs)
What you can do is, prune your neural network while retaining the same accuracy. It removes all the unwanted connections between different neurons that does not learn anything significant. It not only reduces complexity of your NN, also drastically reduces the storage space required & also reduces the inference time. In Keras I don't know of any such module (though I think people have made their own version), but modules like pytorch & caffe have some implementation of AlexNets & VGGNets they can reduce the size of your NN model by even 49x times. You can find one such implementation here.
https://github.com/felzek/AlexNet-A-Practical-Implementation/blob/master/testModel.py
I'm messing around with the Keras api in tensorflow, attempting to implement an autoencoder. The sequential model works, but I want to be able to use the encoder (first two layers) and the decoder (last two layers) separately, but using the weights of my already trained model. Is there a way to do this? Do I have to make a custom model?
model = keras.Sequential()
model.add(encoder_1)
model.add(leaky_relu)
model.add(encoder_2)
model.add(leaky_relu2)
model.add(decoder_1)
model.add(leaky_relu3)
model.add(decoder_2)
encoder_model = keras.Sequential()
encoder_model.add(encoder_1)
encoder_model.add(leaky_relu)
encoder_model.add(encoder_2)
encoder_model.add(leaky_relu2)
decoder_model = keras.Sequential()
decoder_model.add(decoder_1)
model.add(leaky_relu3)
decoder_model.add(decoder_2)
I define my models like this but trying to run predict on either the encoder or decoder outputs
'Sequential' object has no attribute '_feed_input_names'
Yes, you should wrap the encoding and decoding layers in separate Model instances that you call separately. The Keras blogporst on autoencoders should contain everything you need to know: https://blog.keras.io/building-autoencoders-in-keras.html