Tensorflow/Keras Plug-N-Play Deep Learning Models Compilation

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Deep Learningdeepbay
Overview

made-with-python Development Status GitHub version Last Commit

DeepBay

This project was created with the objective of compile Machine Learning Architectures created using Tensorflow or Keras. The architectures must be provided as a ready-to-use Plug-and-Play module that can be easily integrated into any existing project or architecture design.

Installation

You can use pip for install this from PyPi:

pip install deepbay

Quick Start

You can use any architecture inside deepbay as an self-contained model ready to be trained:

import tensorflow as tf
import deepbay

denseblock = deepbay.DenseBlock(units=1)

Or you can integrate it to any existing architecture, just use it as any other keras layer:

import tensorflow as tf
import deepbay

model = tf.keras.models.Sequential()
model.add(deepbay.DenseBlock(units=1))

Keep an eye on input/output shapes, you can found them in the class documentation inside every module

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Comments
  • WARNING:tensorflow:Gradients do not exist for variables

    WARNING:tensorflow:Gradients do not exist for variables

    When Training a DenseBlock instance, it output this warning:

    WARNING:tensorflow:Gradients do not exist for variables ['dense_block_1/batch_normalization_1/gamma:0', 'dense_block_1/batch_normalization_1/beta:0'] when minimizing 
the loss.

    It is caused by the Bath Normalization layer, check what is going on.

    bug 
    opened by elpapi42 0
Releases(v0.8.3)
Owner
Whitman Bohorquez
Backend, APIs, Microservices, DDD, Clean Architectures, DevOps, Data Science and Deep Learning. All that stuff you already know exists, Mixed.
Whitman Bohorquez
GitHub Repository https://pypi.org/project/deepbay/
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