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Merge pull request #4 from ZidanMusk/master
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README.md

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@@ -10,15 +10,15 @@ The main idea is that there's much stuff you do every time you start your tensor
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- [Project architecture](#project-architecture)
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- [Folder structure](#folder-structure)
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- [ Main Components](#main-components)
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- [Model](#model)
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- [Models](#models)
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- [Trainer](#trainer)
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- [Data Loader](#data-loader)
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- [Logger](#logger)
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- [Configration](#configuration)
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- [Configuration](#configuration)
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- [Main](#main)
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- [Future Work](#future-work)
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- [Contributing](#Contributing)
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- [Contributing](#acknowledgments)
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- [Contributing](#contributing)
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- [Acknowledgments](#acknowledgments)
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# In a Nutshell
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In a nutshell here's how to use this template, so **for example** assume you want to implement VGG model so you should do the following:
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pass
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def init_saver(self):
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#here you initalize the tensorflow saver that will be used in saving the checkpoints.
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# here you initalize the tensorflow saver that will be used in saving the checkpoints.
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self.saver = tf.train.Saver(max_to_keep=self.config.max_to_keep)
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```
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```
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├── base
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│ ├── base_model.py - this file contains the abstract class of the model.
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│ └── base_train.py - this file contains the abstract class of the trainer.
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│ └── base_train.py - this file contains the abstract class of the trainer.
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├── model -This folder contains any model of your project.
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├── model - this folder contains any model of your project.
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│ └── example_model.py
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├── trainer -this folder contains trainers of your project.
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├── trainer - this folder contains trainers of your project.
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│ └── example_trainer.py
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├── mains - here's the main/s of your project (you may need more than one main.
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├── mains - here's the main(s) of your project (you may need more than one main).
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├── data _loader
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│ └── data_generator.py - here's the data_generator that responsible for all data handling.
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│ └── data_generator.py - here's the data_generator that is responsible for all data handling.
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└── utils
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├── logger.py
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The base model contains:
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- ***Save*** -This function to save a checkpoint to the desk.
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- ***Load*** -This function to load a checkpoint from the desk.
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- ***Cur_epoch, Global_step counters*** -These variables to keep track of the curerent epoch and global step.
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- ***Init_Saver*** An abstract function to inialize the saver used for saving and loading the checkpoint, ***Note***: override this function in the model you want to implement.
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- ***Cur_epoch, Global_step counters*** -These variables to keep track of the current epoch and global step.
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- ***Init_Saver*** An abstract function to initialize the saver used for saving and loading the checkpoint, ***Note***: override this function in the model you want to implement.
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- ***Build_model*** Here's an abstract function to define the model, ***Note***: override this function in the model you want to implement.
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- #### **Your model**
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Here's where you implement your model.
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So you should :
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- Create your model class and Inherit the base_model class
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- Create your model class and inherit the base_model class
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- override "build_model" where you write the tensorflow model you want
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- override "init_save" where you create a tensorflow saver to use it to save and load checkpoint
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- call the "build_model" and "init_saver" in the initalizer.
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- call the "build_model" and "init_saver" in the initializer.
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### Trainer
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--------------
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- #### **Base trainer**
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Base trainer is an abstract class that just wrap the training process.
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- #### **Your trainer**
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Here's what you shoulf implement in your trainer.
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1. Create your trainer class and Inherit the base_trainer class.
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Here's what you should implement in your trainer.
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1. Create your trainer class and inherit the base_trainer class.
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2. override these two functions "train_step", "train_epoch" where you implement the training process of each step and each epoch.
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### Data Loader
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These class is responsible for all data handling and processing and provide an easy interface that can be used by the trainer.
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This class is responsible for all data handling and processing and provide an easy interface that can be used by the trainer.
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### Logger
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This class is responsible for the tensorboard summary, in your trainer create a dictionary of all tensorflow variables you want to summarize then pass this dictionary to logger.summarize().
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### Configuration

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