Deep-Learning-Models | Deep Learning Models implemented in python | Machine Learning library

I tried converting a MATLAB model to PyTorch using ONNX, like proposed here by Andrew Naguib:

How to import deep learning models from MATLAB to PyTorch?

I tried running the model using the following code:

How do I add Keras dropout layer? Unfortunately, I don't know where exactly I would have to add this layer. I looked at 2 links:

• https://keras.io/api/layers/regularization_layers/dropout/
• https://machinelearningmastery.com/dropout-regularization-deep-learning-models-keras/

For example, I've seen this

I am trying to load a tensorflow model in SavedModel format from my google cloud bucket into my cloud function. I am using this tutorial: https://cloud.google.com/blog/products/ai-machine-learning/how-to-serve-deep-learning-models-using-tensorflow-2-0-with-cloud-functions

The cloud function compiles correctly. However when I send an http request to the cloud function it gives this error:

Traceback (most recent call last):

File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker_v2.py", line 402, in run_http_function result = _function_handler.invoke_user_function(flask.request) File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker_v2.py", line 268, in invoke_user_function return call_user_function(request_or_event) File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker_v2.py", line 261, in call_user_function return self._user_function(request_or_event) File "/user_code/main.py", line 29, in predict download_blob('', 'firstModel/saved_model.pb', '/tmp/model/saved_model.pb') File "/user_code/main.py", line 17, in download_blob bucket = storage_client.get_bucket(bucket_name) File "/env/local/lib/python3.7/site-packages/google/cloud/storage/client.py", line 356, in get_bucket bucket = self._bucket_arg_to_bucket(bucket_or_name) File "/env/local/lib/python3.7/site-packages/google/cloud/storage/client.py", line 225, in _bucket_arg_to_bucket bucket = Bucket(self, name=bucket_or_name) File "/env/local/lib/python3.7/site-packages/google/cloud/storage/bucket.py", line 581, in init name = _validate_name(name) File "/env/local/lib/python3.7/site-packages/google/cloud/storage/_helpers.py", line 67, in _validate_name raise ValueError("Bucket names must start and end with a number or letter.") ValueError: Bucket names must start and end with a number or letter.

I am confused because my buckets' title is a string of letters around 20 characters long without any punctuation/special characters.

This is some of the code that I am running:

I have made a Convolutional Neural Network to classify cats and dogs images. The dataset, as well as the code, was available online. I used Python as my programming language. But now I need to deploy this model on a server and need to access it using REST API.

I have saved my model using HDF5 format. example "model.h5" For reference: https://machinelearningmastery.com/save-load-keras-deep-learning-models/

We can convert it into PMML file as well but CNN is not supported yet by PMML file.

We can use flask library to convert the model into restful web service like this: "https://www.linode.com/docs/applications/big-data/how-to-move-machine-learning-model-to-production/"

But I would prefer java.

I prefer making a microservice using Spring Boot. But I didn't get any step by step article on how to do it.

Can anyone help me out, how can we achieve accessing model via REST API using Java. Or any other method to deploy and access using REST API.

Any help would be appreciated.

I have a model that I've trained for 40 epochs. I kept checkpoints for each epochs, and I have also saved the model with model.save(). The code for training is:

Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(None, 299, 299, 3), dtype=float32) at layer "input_1". The following previous layers were accessed without issue: []

How can I eliminate the error? I am trying to build inceptionv3 network and call but model is not getting compiled. I believe the input layer is not at all getting inputs but i don't understand why

I trained my model in python keras. I am trying to load that in java code but getting following error How to fix this problem.

Ref:

https://towardsdatascience.com/deploying-keras-deep-learning-models-with-java-62d80464f34a

https://deeplearning4j.konduit.ai/keras-import/overview

For a somewhat old machine learning project (TensorFlow 1.4) that I'm reviving, the Inception V3 model is used (demo.py):

I have seen other posts that say just add extra dimensions as it expects. Firstly, I don't know how to do that exactly, but most importantly, I want to know why my dimensions are changing so I can figure this out on my own for future models.

Note, I am restricted to only MLP for training. This means only Fully Connected layers. No Convolutional Layers nor feedbacks are allowed (LSTM or any RNN architecture). No pre-trained models are allowed such as (resnet, densenet, ...). I can use other operations in-between layers, like Dropout, Batch Normalization or other types of layer input/output manipulation. I expect I will have to supply my entire code to get the help I need. Please forgive all my comments in my code, which I have as a reminder to me as to what everything does. I do know I will need data augmentation, but need this to work first.