Building the command line application Now that you've built and trained a deep neural network on the flower data set, it's time to convert it into an application that others can use. Your application should be a pair of Python scripts that run from the command line. For testing, you should use the checkpoint you saved in the first part. Specifications The project submission must include at least two files train.py and predict.py. The first file, train.py, will train a new network on a dataset and save the model as a checkpoint. The second file, predict.py, uses a trained network to predict the class for an input image. Feel free to create as many other files as you need. Our suggestion is to create a file just for functions and classes relating to the model and another one for utility functions like loading data and preprocessing images. Make sure to include all files necessary to run train.py and predict.py in your submission. Train a new network on a data set with train.py Basic usage: python train.py data_directory Prints out training loss, validation loss, and validation accuracy as the network trains Options: Set directory to save checkpoints: python train.py data_dir --save_dir save_directory Choose architecture: python train.py data_dir --arch "vgg13" Set hyperparameters: python train.py data_dir --learning_rate 0.01 --hidden_units 512 --epochs 20 Use GPU for training: python train.py data_dir --gpu Predict flower name from an image with predict.py along with the probability of that name. That is, you'll pass in a single image /path/to/image and return the flower name and class probability. Image-Classifier In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice, you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories.

Command Line Application Train a new network on a data set with train.py Basic Usage : python train.py data_directory Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains Options: Set direcotry to save checkpoints: python train.py data_dor --save_dir save_directory Choose arcitecture (densenet121 or vgg16 available): python train.py data_dir --arch "vgg16" Set hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_layer1 120 --epochs 20 Use GPU for training: python train.py data_dir --gpu gpu Output: A trained network ready with checkpoint saved for doing parsing of flower images and identifying the species. Predict flower name from an image with predict.py along with the probability of that name. That is you'll pass in a single image /path/to/image and return the flower name and class probability

Basic usage: python predict.py /path/to/image checkpoint Options: Return top K most likely classes: python predict.py input checkpoint ---top_k 3 Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_To_name.json Use GPU for inference: python predict.py input checkpoint --gpu Data The data used specifically for this assignment are a flower database(.json file). It is not provided in the repository as it's larger than what github allows. The data need to comprised of 3 folders:

test train validate Generally the proportions should be 70% training 10% validate and 20% test.

Inside the train, test and validate folders there should be folders bearing a specific number which corresponds to a specific category, clarified in the json file. For example if we have the image x.jpg and it is a lotus it could be in a path like this /test/5/x.jpg and json file

GPU/CPU As this project uses deep CNNs, for training of network you need to use a GPU. However after training you can always use normal CPU for the prediction phase.

Hyperparameters As you can see you have a wide selection of hyperparameters available and you can get even more by making small modifications to the code. Thus it may seem overly complicated to choose the right ones especially if the training needs at least 15 minutes to be completed. So here are some hints:

By increasing the number of epochs the accuracy of the network on the training set gets better and better but its timing consuming and saturates at higher levels. A big learning rate guarantees that the network will converge fast to a small error but it will constantly overshot A small learning rate guarantees that the network will reach greater accuracies but the learning processwe have done using vgg13 and vgg16 and by trying alexnet121 it was better.