MobileNetV2 | A Keras implementation of MobileNetV2 | Machine Learning library
kandi X-RAY | MobileNetV2 Summary
kandi X-RAY | MobileNetV2 Summary
A Keras implementation of MobileNetV2.
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Top functions reviewed by kandi - BETA
- Residual network
- Bottleneck bottleneck
- Inverse residual block
- A block of convolutional block
- Divide value into divisible by divisor
- Train model
- Generate training images
- Custom fine tuning function
- Convert to images
MobileNetV2 Key Features
MobileNetV2 Examples and Code Snippets
Lines of Code : 21License : Permissive (MIT License)
Copydef create_model(input_shape):
# load MobileNetV2
model = MobileNetV2(input_shape=input_shape)
# remove the last fully connected layer
model.layers.pop()
# freeze all the weights of the model except the last 4 layers
for layer def create_mobilenet_model(input_shape, output_shape):
model = MobileNetV2(input_shape=input_shape)
# remove the last layer
model.layers.pop()
# freeze all the weights of the model except for the last 4 layers
for layer in model.l Community Discussions
Trending Discussions on MobileNetV2
QUESTION
I used this repo : https://github.com/Turoad/lanedet to convert a pytorch model that use mobilenetv2 as backbone To ONNX but I didn't succeeded.
i got a Runtime error that says:
RuntimeError: Exporting the operator eye to ONNX opset version 12 is not supported. Please open a bug to request ONNX export support for the missing operator.
it's really disappointing, looking to the good result that this model gives and the quick performance that it provides,
is there any way that I can fix this bug? because I need to convert it to ONNX and then to TF lite model to use it in Android App I will provide the pretrained model that I have used and the way that I follow in converting..
Thank you so much for helping!
my colab notebook:
https://colab.research.google.com/drive/18udIh8tNJvti7jKmR4jRaRO-oYDgRmvA?usp=sharing
the pretrained model that I use:
https://drive.google.com/file/d/1o3-BgLIQesurIyDCKGliqbo2inUA5cPw/view?usp=sharing
...ANSWER
Answered 2022-Mar-15 at 06:45Use torch>=1.7.0 to convert the model, because operation Eye is added.
QUESTION
I'm again struggling with the usage of tensorflow datasets. I'm again loading my images via
...ANSWER
Answered 2022-Jan-06 at 16:38Maybe try using tf.data.Dataset.map:
QUESTION
After calling the fit function I can see that the model is converging in training but after I go to call the evaluate method it acts as if the model hasn't done the fitting at all. The best example is below where I use the training generator for train and validation and get different results.
...ANSWER
Answered 2021-Nov-24 at 11:43evaluate() function takes a validation dataset as an input to evaluate already trained model.
From the looks of it you are using a training dataset (train_gen) for validation_data and passing the same dataset as an input to model.evaluate()
QUESTION
I am used to work with tenserflow - keras but now I am forced to start working with Pytorch for flexibility issues. However, I don't seem to find a pytorch code that is focused on training only the classifciation layer of a model. Is that not a common practice ? Now I have to wait out the calculation of the feature extraction of the same data for every epoch. Is there a way to avoid that ?
...ANSWER
Answered 2021-Dec-05 at 23:11Assuming you already have the features ìn features_x, you can do something like this to create and train the model:
QUESTION
I have a model that works and fit correctly. But if I save the model after training, when I try to load it, it throws this error:
ValueError: Graph disconnected: cannot obtain value for tensor KerasTensor(type_spec=TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name='InputLucaSchifoso'), name='InputLucaSchifoso', description="created by layer 'InputLucaSchifoso'") at layer "conv2d_5LucaSchifoso". The following previous layers were accessed without issue: []
This is the creation of the model and its training that works whitout errors
...ANSWER
Answered 2021-Nov-25 at 13:55The problem was generated by the input layers inside the model, for some reason they don't create any problem during compiling and training of the model, but they do during loading.
QUESTION
I want to do a simple test of the SparseCategoricalCrossentropy function, to see what exactly it does to an output. For that I use the output of the last layer of a MobileNetV2.
...ANSWER
Answered 2021-Oct-15 at 09:42Since you are using the SparseCategoricalCrossentropy loss function, the shape of y_true should be [batch_size] and the shape of y_pred should be
[batch_size, num_classes]. Furthermore, y_true should consist of integer values. See the documentation. In your concrete example, you could try something like this:
QUESTION
I'm trying to redefine an enum when it fails, but then an error is raised.
My code looks like the following:
...ANSWER
Answered 2021-Oct-17 at 02:30The reason that is happening is:
_EnumDicttracks all used names_EnumDictthinksmy_exception_instanceshould be a member- Python clears the
asvariable when leaving theexceptclause- by assigning
Nonetomy_exception_instance(and then deleting the variable) - causing
_EnumDictto think a key is being reused
- by assigning
One workaround (as of Python 3.7) is to add my_exception_instance to an _ignore_1 attribute:
QUESTION
I have data inside 2 folders (Training with 10000 images and Validation with 1000 images) and in each of these folders I have 10 folders (the respective classes). I put all this data into a dataframe to use later. It turns out that some images in certain folders at the moment I use "flow_from_dataframe" in Tensorflow are assumed to have invalid names and are therefore ignored.
And I try to access any image outside Tensorflow, for example by simply making the image open and I still can't access certain files when the path is completely correct
...ANSWER
Answered 2021-Aug-11 at 13:32Your generators are incorrect. For example you have this code.
QUESTION
I'm trying to upgrade my model to tensorflow 2.4 but the network achieves lower accuracy after upgrade. I noticed that loss function for a single batch is different even though:
- I use
model = keras.models.load_model('path/to/model.h5')with the same path for both versions (this file was created using tf 1.12) - I check that weights match
- I check that batch used is the same
- I replicated this problem on both proprietary dataset and
keras.datasets.mnist.
I expect that if I manage to achieve the same loss on both versions I will also achieve the same accuracy after training.
Requirements tf 1.12 version
...ANSWER
Answered 2021-Aug-24 at 14:45This difference comes from the fact that MobileNet contains BatchNormalization layers. Their behaviour changed in Tensorflow 2.x. You can read more here. To recreate Tensorflow 1.x behaviour of BatchNormalization layers I added the following fragment to the model creation code.
QUESTION
I have trained a model using the Functional API and two different kind of pre-trained model: EfficientNet B5 and MobileNet V2. After tranining with the saved model, I'm running an application which uses that model to make some predictions.
I'm fronting a doubt relatated to what is the correct way to pass the images to "model.prediction()" arguments.
Model:
...ANSWER
Answered 2021-Aug-03 at 14:25I copied your code and then printed out the model summary as shown below
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install MobileNetV2
You can use MobileNetV2 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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