app-micropython
Micropython on Unikraft (by unikraft)
Keras
Deep Learning for humans (by keras-team)
| app-micropython | Keras | |
|---|---|---|
| - | 79 | |
| 2 | 61,137 | |
| - | 0.3% | |
| 0.0 | 9.9 | |
| 8 months ago | 6 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
app-micropython
Posts with mentions or reviews of app-micropython.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning app-micropython yet.
Tracking mentions began in Dec 2020.
Keras
Posts with mentions or reviews of Keras.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-05-20.
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Side Quest #3: maybe the real Deepfakes were the friends we made along the way
def batcher_from_directory(batch_size:int, dataset_path:str, shuffle=False,seed=None) -> tf.data.Dataset: """ Return a tensorflow Dataset object that returns images and spectrograms as required. Partly inspired by https://github.com/keras-team/keras/blob/v3.3.3/keras/src/utils/image_dataset_utils.py Args: batch_size: The batch size. dataset_path: The path to the dataset folder which must contain the image folder and audio folder. shuffle: Whether to shuffle the dataset. Default to False. seed: The seed for the shuffle. Default to None. """ image_dataset_path = os.path.join(dataset_path, "image") # create the foundation datasets og_dataset = tf.data.Dataset.from_generator(lambda: original_image_path_gen(image_dataset_path), output_signature=tf.TensorSpec(shape=(), dtype=tf.string)) og_dataset = og_dataset.repeat(None) # repeat indefinitely ref_dataset = tf.data.Dataset.from_generator(lambda: ref_image_path_gen(image_dataset_path), output_signature=(tf.TensorSpec(shape=(), dtype=tf.string), tf.TensorSpec(shape=(), dtype=tf.bool))) ref_dataset = ref_dataset.repeat(None) # repeat indefinitely # create the input datasets og_image_dataset = og_dataset.map(lambda x: tf.py_function(load_image, [x, tf.convert_to_tensor(False, dtype=tf.bool)], tf.float32), num_parallel_calls=tf.data.AUTOTUNE) masked_image_dataset = og_image_dataset.map(lambda x: tf.py_function(load_masked_image, [x], tf.float32), num_parallel_calls=tf.data.AUTOTUNE) ref_image_dataset = ref_dataset.map(lambda x, y: tf.py_function(load_image, [x, y], tf.float32), num_parallel_calls=tf.data.AUTOTUNE) audio_spec_dataset = og_dataset.map(lambda x: tf.py_function(load_audio_data, [x, dataset_path], tf.float64), num_parallel_calls=tf.data.AUTOTUNE) unsync_spec_dataset = ref_dataset.map(lambda x, _: tf.py_function(load_audio_data, [x, dataset_path], tf.float64), num_parallel_calls=tf.data.AUTOTUNE) # ensure shape as tensorflow does not accept unknown shapes og_image_dataset = og_image_dataset.map(lambda x: tf.ensure_shape(x, IMAGE_SHAPE)) masked_image_dataset = masked_image_dataset.map(lambda x: tf.ensure_shape(x, MASKED_IMAGE_SHAPE)) ref_image_dataset = ref_image_dataset.map(lambda x: tf.ensure_shape(x, IMAGE_SHAPE)) audio_spec_dataset = audio_spec_dataset.map(lambda x: tf.ensure_shape(x, AUDIO_SPECTROGRAM_SHAPE)) unsync_spec_dataset = unsync_spec_dataset.map(lambda x: tf.ensure_shape(x, AUDIO_SPECTROGRAM_SHAPE)) # multi input using https://discuss.tensorflow.org/t/train-a-model-on-multiple-input-dataset/17829/4 full_dataset = tf.data.Dataset.zip((masked_image_dataset, ref_image_dataset, audio_spec_dataset, unsync_spec_dataset), og_image_dataset) # if shuffle: # full_dataset = full_dataset.shuffle(buffer_size=batch_size * 8, seed=seed) # not sure why buffer size is such # batch full_dataset = full_dataset.batch(batch_size=batch_size) return full_dataset
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Library for Machine learning and quantum computing
Keras
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My Favorite DevTools to Build AI/ML Applications!
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.
- Release: Keras 3.3.0
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Getting Started with Gemma Models
After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.
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Keras 3.0
All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467
You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/
- Keras 3: A new multi-back end Keras
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Can someone explain how keras code gets into the Tensorflow package?
I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?
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How popular are libraries in each technology
Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.
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List of AI-Models
Click to Learn more...
What are some alternatives?
When comparing app-micropython and Keras you can also consider the following projects:
Django - The Web framework for perfectionists with deadlines.
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
Flask - The Python micro framework for building web applications.
scikit-learn - scikit-learn: machine learning in Python
TheAlgorithms - All Algorithms implemented in Python
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
app-nginx - Nginx on Unikraft
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
thefuck - Magnificent app which corrects your previous console command.
tensorflow - An Open Source Machine Learning Framework for Everyone
awesome-python - An opinionated list of awesome Python frameworks, libraries, software and resources.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.