Keras

Deep Learning for humans (by keras-team)

Keras Alternatives

Similar projects and alternatives to Keras

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better Keras alternative or higher similarity.

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Keras reviews and mentions

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 2024-06-13.
  • Using Google Magika to build an AI-powered file type detector
    4 projects | dev.to | 13 Jun 2024
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models.
  • Side Quest #3: maybe the real Deepfakes were the friends we made along the way
    3 projects | dev.to | 20 May 2024
    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
  • Library for Machine learning and quantum computing
    4 projects | dev.to | 27 Apr 2024
    Keras
  • My Favorite DevTools to Build AI/ML Applications!
    9 projects | dev.to | 23 Apr 2024
    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
    1 project | news.ycombinator.com | 22 Apr 2024
  • Getting Started with Gemma Models
    4 projects | dev.to | 15 Apr 2024
    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.
  • Keras 3.0
    4 projects | news.ycombinator.com | 28 Nov 2023
    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
    1 project | news.ycombinator.com | 27 Nov 2023
  • Can someone explain how keras code gets into the Tensorflow package?
    2 projects | /r/tensorflow | 24 Jul 2023
    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?
  • How popular are libraries in each technology
    21 projects | dev.to | 21 Jun 2023
    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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    www.influxdata.com | 22 Jun 2024
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keras-team/keras is an open source project licensed under Apache License 2.0 which is an OSI approved license.

The primary programming language of Keras is Python.


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