Example 1: IMAGE Recognition/Classification¶

Train an AI model using medicalai's numpy dataset processor¶

import os
import medicalai as ai

Download sample Dataset¶

datasetDWLD = ai.getFile('https://github.com/aibharata/covid19-dataset/archive/v1.0.zip', subDir='dataset')
datasetFolderPath = datasetDWLD+'/covid19-dataset-1.0/chest-xray-pnumonia-covid19/'

Define the hyperparameters of Dataset Processor¶

A. Specify the dimensions of image to be fed to network

IMG_HEIGHT = 64
IMG_WIDTH = 64
OUTPUT_CLASSES = 3

Process your dataset using Numpy based `datasetFromFolder` class.¶

The datasetFromFolder class takes a folder path where your dataset is located. This folder should have test and train folders. Each of the folder should have the class sub-folder for your classification problem.

trainSet,testSet,labelNames =ai.datasetFromFolder(datasetFolderPath, targetDim = (IMG_WIDTH,IMG_WIDTH)).load_dataset()

# Print shapes of the loaded test and train data
print('TrainSet Data Shape: {:}; TrainSet Labels Shape:{:}'.format(testSet.data.shape,testSet.labels.shape))
print('TrainSet Data Shape: {:}; TrainSet Labels Shape:{:}'.format(testSet.data.shape,testSet.labels.shape))

Define the hyperparameters of Training¶

A. Specify training hyperparamters

batch_size = 32
epochs = 10
learning_rate = 0.0001

B. Specify the model name to save/retrain

MODEL_SAVE_NAME = 'medicalai_test_model_1'

C. Choose from the prebuilt networks from Medicalai Library. You can also pass a class with your own custom network to AI_NAME parameter.

AI_NAME = 'tinyMedNet'

Initialize TRAIN_ENGINE and Start Training¶

trainer = ai.TRAIN_ENGINE()
trainer.train_and_save_model(AI_NAME=AI_NAME,
                             MODEL_SAVE_NAME = MODEL_SAVE_NAME, 
                             trainSet=trainGen, testSet=testGen,
                             OUTPUT_CLASSES=OUTPUT_CLASSES, 
                             RETRAIN_MODEL= True,
                             BATCH_SIZE= batch_size,
                             EPOCHS= epochs, 
                             LEARNING_RATE= learning_rate,
                             SAVE_BEST_MODEL = True,
                             showModel = True # Set this True if you want to see model summary
                             )

View and Save Training Stats¶

Plot training accuracy and loss w.r.t to epochs

trainer.plot_train_acc_loss()

Generate evaluation report for Trained Model¶

trainer.generate_evaluation_report(testSet)

Explain the model for a input sample¶

trainer.explain(testSet.data[0:1], layer_to_explain='CNN3', classNames = labelNames)