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    Upvotes2edited May 4, 2018 at 7:33

    This is how I save and download model files from Collab.

    • Save only the trainable variables(These are my store/restore fns):

    code is as below :

    def store(sess_var, model_path):
    if model_path is not None:
    saver = tf.train.Saver(var_list=tf.trainable_variables())
    save_path = saver.save(sess_var, model_path)
    print("Model saved in path: %s" % save_path)
    else:
    print("Model path is None - Nothing to store")



    def restore(sess_var, model_path):
    if model_path is not None:
    if os.path.exists("{}.index".format(model_path)):
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  2. tensorflow/models: Models and examples built with TensorFlow

  3. tf-models-official · PyPI

  4. Models | TensorFlow Lite

  5. TensorFlow.js models

    WebTensorFlow (v2.16.1) Versions… TensorFlow.js TensorFlow Lite TFX LIBRARIES TensorFlow.js TensorFlow Lite TFX All libraries RESOURCES Models & datasets Tools Responsible AI Recommendation systems …

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  6. People also ask
    How do I start using TensorFlow Lite?You can start with using pre-trained models in TensorFlow Lite and move up to building custom models over time, as follows: Start developing machine learning features with already trained models. Modify existing TensorFlow Lite models using tools such as Model Maker. Build a custom model with TensorFlow tools and then convert it to TensorFlow Lite.
    What is a TensorFlow model?They are intended to be well-maintained, tested, and kept up to date with the latest TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read. These models are used as end-to-end tests, ensuring that the models run with the same or improved speed and performance with each new TensorFlow build.
    How many devices does TensorFlow run on?It's currently running on more than 4 billion devices! With TensorFlow 2.x, you can train a model with tf.Keras, easily convert a model to .tflite and deploy it; or you can download a pretrained TensorFlow Lite model from the model zoo.
    What's new in TensorFlow official models?This release of the Official Models targets TensorFlow 2.13.0. Note that Research/tutorial/sample models have been removed. New features: Checkpoints added for ResNet- (50, 101, 152), SpineNet- (49, 96, 143) and DeepLabV3+. Implementation of Perceiver IO: A General Architecture for Structured Inputs & Outputs.
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