Category: Misc

Packt has Published “The TensorFlow Workshop ”

Grab your digital copy now if you feel you are interested.

As part of our marketing activities, we are offering free digital copies of the book in return for unbiased feedback in the form of a reader review.

Get started with TensorFlow fundamentals to build and train deep learning models with real-world data, practical exercises, and challenging activities.

Here is what you will learn from the book:

1. Get to grips with TensorFlow’s mathematical operations
2. Pre-process a wide variety of tabular, sequential, and image data
3. Understand the purpose and usage of different deep learning layers
4. Perform hyperparameter-tuning to prevent overfitting of training data
5. Use pre-trained models to speed up the development of learning models
6. Generate new data based on existing patterns using generative models

Key Features

• Understand the fundamentals of tensors, neural networks, and deep learning
• Discover how to implement and fine-tune deep learning models for real-world datasets
• Build your experience and confidence with hands-on exercises and activities

Please comment below or DM me for more details

I have a somewhat unique issue that I cannot solve because nothing on Google comes up.

My data are one-hot encoded DNA sequences of VARYING length. This is easily stored in a jagged NumPy array (4 x n x m), where n = number of samples, m = length of sequence (may vary.) However, the size requirements after zero-padding the entire array (padded by max sequence length) is insane and I need to avoid doing that.

The solution I have thought up is as follows:

1. Generated jagged numpy array (varying input lengths)
2. Extract k sequences from this large array where k = batch size
3. Zero-pad the batch
4. Pass to model
5. Repeat from step 2

Any help would be greatly appreciated. Thanks!

Hi,
I’m working on a project that uses Edge TPU and I need to use appropriate models[1] converted from Tensorflow form. I need a face recognition and decided to use FaceNet implementation and took the model from here: [2]. I have it working on my PC, but when I tried to convert the model to tflite and compile it for edgetpu (using steps presented in [3]), I ended up with all of the resulting embeddings (output tensors) to be the same. They are in proper form (128D vector with uint8 values (as opposed to float32 values of TF model)), but they are all the same.

Does anyone has any idea what might be the reason of that? Is such conversion impossible on already pre-trained model or am I missing something obvious?

References:

[1] https://coral.ai/docs/edgetpu/models-intro/

[2] https://github.com/nyoki-mtl/keras-facenet

[3] https://www.tensorflow.org/lite/performance/post_training_integer_quant

Here in this example: Using pre-trained word embeddings | Keras, we can see that by providing pre-trained word embedding in embedding layer initialization, we can boost the performance of the model. But before doing that, they are removing the tokens which are not available in the current data-set. But I wonder, if it is helpful or not. If we kept all the tokens wouldn’t it be more helpful to classify text? As in that case the unknown words would also get some representations and which would be helpful in further classification process. Why we are not taking this advantage? Correct me if I am mistaken.