deep-learning-models | Keras code and weights files | Machine Learning library
kandi X-RAY | deep-learning-models Summary
kandi X-RAY | deep-learning-models Summary
Keras code and weights files for popular deep learning models.
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Top functions reviewed by kandi - BETA
- Constructor for ResNet50
- Convolution block layer
- Compute identity block
- Create a Neural NetworkNet
- Depth - wise convolution block
- A block of convolutional block
- Preprocess an audio file
- Check if librosa is installed
- A ExampleV3
- 2D convolution layer
- Inception ResNet v2
- Resnet block
- VGG19
- VGG16
- Creates a MusicTagger CRNN layer
- Xception model
- Helper function for decoding predictions
deep-learning-models Key Features
deep-learning-models Examples and Code Snippets
import re
from gutenberg import acquire
from gutenberg import cleanup
from tensor2tensor.data_generators import problem
from tensor2tensor.data_generators import text_problems
from tensor2tensor.utils import registry
@registry.register_problem
cla
pip install tensor2tensor
# See what problems, models, and hyperparameter sets are available.
# You can easily swap between them (and add new ones).
t2t-trainer --registry_help
PROBLEM=translate_ende_wmt32k
MODEL=transformer
HPARAMS=transformer_bas
t2t-decoder --problem=languagemodel_multi_wiki_translate \
--model=transformer \
--hparams_set=transformer_tall_pretrain_lm_tpu_adafactor_large \
--decode_hparams='batch_size=1,multiproblem_task_id=64510' \
--hparams="" \
--outpu
def enable_tensor_float_32_execution(enabled):
"""Enable or disable the use of TensorFloat-32 on supported hardware.
[TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format),
or TF32 for short, is a math mode
Community Discussions
Trending Discussions on deep-learning-models
QUESTION
Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/
This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?
I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:
...ANSWER
Answered 2021-Aug-19 at 03:49This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference
update to
QUESTION
We were given an assignment in which we were supposed to implement our own neural network, and two other already developed Neural Networks. I have done that and however, this isn't the requirement of the assignment but I still would want to know that what are the steps/procedure I can follow to improve the accuracy of my Models?
I am fairly new to Deep Learning and Machine Learning as a whole so do not have much idea.
The given dataset contains a total of 15 classes (airplane, chair etc.) and we are provided with about 15 images of each class in training dataset. The testing dataset has 10 images of each class.
Complete github repository of my code can be found here (Jupyter Notebook file): https://github.com/hassanashas/Deep-Learning-Models
I tried it out with own CNN first (made one using Youtube tutorials). Code is as follows,
...ANSWER
Answered 2022-Jan-04 at 12:58Disclaimer: it's been a few years since I've played with CNNs myself, so I can only pass on some general advice and suggestions.
First of all, I would like to talk about the results you've gotten so far. The first two networks you've trained seem to at least learn something from the training data because they perform better than just randomly guessing.
However: the performance on the test data indicates that the network has not learned anything meaningful because those numbers suggest the network is as good as (or only marginally better than) a random guess.
As for the third network: high accuracy for training data combined with low accuracy for testing data means that your network has overfitted. This means that the network has memorized the training data but has not learned any meaningful patterns.
There's no point in continuing to train a network that has started overfitting. So once the training accuracy increases and testing accuracy decreases for a few epochs consecutively, you can stop training.
Increase the dataset sizeNeural networks rely on loads of good training data to learn patterns from. Your dataset contains 15 classes with 15 images each, that is very little training data.
Of course, it would be great if you could get hold of additional high-quality training data to expand your dataset, but that is not always feasible. So a different approach is to artificially expand your dataset. You can easily do this by applying a bunch of transformations to the original training data. Think about: mirroring, rotating, zooming, and cropping.
Remember to not just apply these transformations willy-nilly, they must make sense! For example, if you want a network to recognize a chair, do you also want it to recognize chairs that are upside down? Or for detecting road signs: mirroring them makes no sense because the text, numbers, and graphics will never appear mirrored in real life.
From the brief description of the classes you have (planes and chairs and whatnot...), I think mirroring horizontally could be the best transformation to apply initially. That will already double your training dataset size.
Also, keep in mind that an artificially inflated dataset is never as good as one of the same size that contains all authentic, real images. A mirrored image contains much of the same information as its original, we merely hope it will delay the network from overfitting and hope that it will learn the important patterns instead.
Lower the learning rateThis is a bit of side note, but try lowering the learning rate. Your network seems to overfit in only a few epochs which is very fast. Obviously, lowering the learning rate will not combat overfitting but it will happen more slowly. This means that you can hopefully find an epoch with better overall performance before overfitting takes place.
Note that a lower learning rate will never magically make a bad-performing network good. It's just one way to locate a set of parameters that performs a tad bit better.
Randomize the training data orderDuring training, the training data is presented in batches to the network. This often happens in a fixed order over all iterations. This may lead to certain biases in the network.
First of all, make sure that the training data is shuffled at least once. You do not want to present the classes one by one, for example first all plane images, then all chairs, etc... This could lead to the network unlearning much of the first class by the end of each epoch.
Also, reshuffle the training data between epochs. This will again avoid potential minor biases because of training data order.
Improve the network designYou've designed a convolutional neural network with only two convolution layers and two fully connected layers. Maybe this model is too shallow to learn to differentiate between the different classes.
Know that the convolution layers tend to first pick up small visual features and then tend to combine these in higher level patterns. So maybe adding a third convolution layer may help the network identify more meaningful patterns.
Obviously, network design is something you'll have to experiment with and making networks overly deep or complex is also a pitfall to watch out for!
QUESTION
In the above article, they are creating both JSON and HDF5 files,
...ANSWER
Answered 2021-Jun-30 at 22:27The article you linked is pretty old and does not use the best practices.
You can save the whole model (including architecture, weights, and even optimizer state) into a single HDF5 file using model.save('something.h5')
, and then load it using
QUESTION
I'm working on a deep learning project and I tried following a tutorial to evaluate my model with Cross-Validation.
I was looking at this tutorial: https://machinelearningmastery.com/use-keras-deep-learning-models-scikit-learn-python/
I started by first splitting my dataset into features and labels:
...ANSWER
Answered 2021-Apr-06 at 06:08model = KerasClassifier(build_fn=create_model(), ...)
QUESTION
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:
...ANSWER
Answered 2021-Feb-21 at 16:35Assuming my_data.dat
is a file containing binary data, the following code loads it into an ioBytesIO buffer that is seekable:
QUESTION
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
...ANSWER
Answered 2020-Dec-16 at 16:56Try this:
QUESTION
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:
...ANSWER
Answered 2020-Oct-27 at 15:51The error message is complaining about the fact that you have angle brackets in your bucket name, which are not considered numbers or letters. Make sure your bucket name is exactly what you see in the Cloud console.
QUESTION
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
...ANSWER
Answered 2020-Apr-29 at 23:25try to comment this lines:
QUESTION
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
...ANSWER
Answered 2020-Apr-02 at 08:34You are using the functionality for the sequential model import, but are creating the model using a functional API.
To import models created with the functional API you need to use a different importer. https://deeplearning4j.konduit.ai/keras-import/model-functional shows how to do that.
The TL;DR of it is that you have to use
KerasModelImport.importKerasModelAndWeights(simpleMlp);
instead of
KerasModelImport.importKerasSequentialModelAndWeights(simpleMlp);
QUESTION
For a somewhat old machine learning project (TensorFlow 1.4) that I'm reviving, the Inception V3 model is used (demo.py
):
ANSWER
Answered 2020-Mar-24 at 19:12Sure, you need to put the file inside ~/.keras/models
and Keras will pick it up automatically.
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Install deep-learning-models
You can use deep-learning-models 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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