Siamese | A Keras implementation of Siamese neural net | Machine Learning library
kandi X-RAY | Siamese Summary
kandi X-RAY | Siamese Summary
A Keras implementation of Siamese neural net is provide here as python scripts. Convolution and simple dense layer based networks are experimented with. Siamese architecture and cost function is based on the discussion found in this page: The AT&T face databased is used for testing and downloaded from here: This dataset was originally used in LeCunn's paper: Also MNIST data in csv form is used. Original source: (train.csv couldn't be uploaded due to memory limitation. change the code to test.csv and experiment with that).
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Top functions reviewed by kandi - BETA
- Train the model
- Create simple network
- Construct cnn block with max pool
- Visualize the results
- Draw a patch in 3d
- Cross validation
- Function to create aloo train and test set
- Generate training data for the LENet model
- Create a CNN
- Generate training data for convolution
- Read a PGM file
- Retrieve train data from the MNIST dataset
- Get training data for a given class
- Train the final model
- Generate data for classification
- Create pairwise pairwise pairwise pairs
- Creates train and test sets
- Create base network
- Calculates the Mahabian distance between two vectors
- Create aloo train and test set
- Generate training data for a new image
- Test on a given classifier
- Generate training data
- This function generates a data array that is used to create a new image
- Get training data for convolution
- Train model on dsea patch data
- Test if a segmentation model is on a semi - segmentation model
Siamese Key Features
Siamese Examples and Code Snippets
Community Discussions
Trending Discussions on Siamese
QUESTION
I'm trying to build a Siamese Neural Network to analyze the MNIST dataset, however when trying to fit the model to the dataset I encounter this problem according to which I have training data and labels shapes' mismatch. I tried changing the loss function as well as tried to squeeze the labels array, and neither of "solutions" worked.
Here are the train and labels arrays' shapes:
...ANSWER
Answered 2022-Mar-20 at 04:32I was not able to reproduce the error using the below code. I suspect that your labels shape is different than the one you reported or it does not contain strictly binary data (0s and 1s) only.
Also, you should use tf.keras.losses.BinaryCrossentropy instead of tf.keras.losses.CategoricalCrossentropy as your labels should be binary with the sigmoid activation in the last layer.
QUESTION
I was trying to create a matrix in PL/SQL using nested tables as such:
- firstly, I create a table type that is able to store varchars (that in my case is name lista_principala)
- secondly, using the previously declare table type lista_principala I try to create a table type that contains items of type lista_principala. I don't think that I'm wrong about my logic, but my PL/SQL implementation looks something like this:
ANSWER
Answered 2022-Mar-19 at 21:10You use lista.extend(20); to create a list with 20 items and these will all be initialised to NULL.
Then you set the values for the first 3 elements of the collection.
Then you loop through all 20 items and try to loop through the sub-list in each element; however, after the first 3, there is no sub-list contained in the element as the element is NULL.
Either:
- Just
lista.EXTEND(3);as you only want 3 elements to the array; or - Add a check if the list element is
NULLand then skip looping through the sub-list if it is.
The second option can be implemented as:
QUESTION
The errors I get are:
ZipFile requires mode 'r', 'w', 'x', or 'a'
or
ZipFile.__init__() got multiple values for argument 'mode'
or
TypeError: 'ZipFile' object is not callable
My Code:
...ANSWER
Answered 2022-Mar-16 at 08:13QUESTION
I am following this tutorial on how to train a siamese bert network:
https://keras.io/examples/nlp/semantic_similarity_with_bert/
all good, but I am not sure what is the best way to save the model after train it and save it. any suggestion?
I was trying with
model.save('models/bert_siamese_v1')
which creates a folder with save_model.bp keras_metadata.bp and two subfolders (variables and assets)
then I try to load it with:
...ANSWER
Answered 2022-Mar-08 at 16:13Try using tf.saved_model.save to save your model:
QUESTION
I am trying to build a Siamese neural network on characters-level using Keras, to learn if two names are similars or not.
So my two inputs X1 and X2 are a 3-D matrices:
X[number_of_cases, max_length_of_name, total_number_of_chars_in_DB]
In the real case:
- number_of_cases = 5000
- max_length_of_name = 50
- total_number_of_chars_in_DB = 38
I have one output binary matrix of size y[number_of_cases].
So for example:
print(X1[:3, :2])
Will give the following result:
...ANSWER
Answered 2022-Feb-17 at 11:32Your model inputs were [input_1, input_2] and outputs were predictions. But input_1 and input_2 were not connected to lstm1 and lstm2, so the input layers of the model was not connected to the output layer, that's why you are getting the error.
Try this instead:
QUESTION
Issue:
i have a successfully running nearest neighbour tensorflow model on colab, named top_classify. but when comes to saving, getting the error message below:
...ANSWER
Answered 2022-Jan-21 at 03:50After many detailed search, and trials, i found this forum post:
https://github.com/keras-team/keras/issues/15699 (Error when Saving model with data augmentation layer on Tensorflow 2.7 #15699). which states, data augmentation may create some save issues.
it wasn't stated in the question, but here is the details of the embedding_network, in my tf code:
QUESTION
I would like to implement the built in TensorFlow addons version of triplet loss with a tutorial here for a siamese network, however I can't seem to get it quite right. No matter how I wrangle the code another error pops up, currently
...ANSWER
Answered 2021-Nov-04 at 12:17I am not sure what exactly you are trying to do, but you also have to incorporate your labels into your training dataset when using the tfa.losses.TripletSemiHardLoss(). Here is a working example:
QUESTION
I am creating a database with MongoDB and using the Mongoose ODM. I'm using Node.js. I ran the code without the last block several times and it was fine, but when I wrote the last block in order to use the .find() method, it threw me an odd error.
This is the app.js file:
...ANSWER
Answered 2021-Aug-30 at 20:41Mongoose has just updated and in 6+ version we have to pass the object as first parameter followed by call back function with params of error and result of query!!
To GET ALL records just pass the empty object.
QUESTION
I'm trying to extend this model to include custom metrics. I added false positive count, true positive count, and accuracy. The accuracy step is failing and throwing errors. I've tried a bunch of modifications but was unable to get it working. This is my code:
...ANSWER
Answered 2021-Oct-20 at 11:31I think you have two minor issues, that can easily be fixed:
QUESTION
Is my classification correct?
LeNet-5: Image classification,
AlexNet: Image classification,
VGG-16: Image classification,
ResNet: Image classification,
Inception module: Image classification,
MobileNet: Image classification,
EfficientNet: Image classification,
Neural Style Transfer: Image generation,
Sliding Windows Detection algorithm: Object detection,
R-CNN: Object detection,
YOLO: Object detection,
Siamese network: Image recognition,
U-Net: Semantic segmentation
If wrong, please correct me. THANKS!
...ANSWER
Answered 2021-Sep-14 at 15:36Your classification is correct if the purpose is - why they were invented initially. However rather than the task based taxonomy, CNNs are better studied on the basis of what different they are doing. Initially CNNs were designed for image classification, but the same network works for Object detection with slight modifications in last layer. For example, Faster RCNN (designed for Object detection) can use any of the architecture designed for classification such as VGG, ResNet etc (link). Similarly Faster-RCNN can be modified to do segmentation task in Mask-RCNN architecture (link).
Here is a chart showing evolutionary history of deep CNNs showing architectural innovations (source)
Here is another taxonomy showing different categories based on architecture style.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Siamese
You can use Siamese 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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