--- a
+++ b/Classifier/AllPytorch2020.py
@@ -0,0 +1,141 @@
+import os
+import random
+
+import numpy as np
+import pandas as pd
+import torch
+import torch.optim as optim
+import tqdm
+from sklearn.metrics import classification_report, confusion_matrix
+from sklearn.model_selection import train_test_split
+from torch import nn
+from torch.optim.adam import Adam
+from torch.utils.data import DataLoader
+from torch.utils.data.sampler import SubsetRandomSampler
+
+from Classes.Data import LeukemiaDataset, augmentation
+from Classes.Helpers import Helpers
+from Classes.interpretability import interpret_model
+from Classes.Model_2020 import LuekemiaNet, train_model
+from Classes.model_api import (confusion_matrix2, get_predictions,
+                               plot_training_history)
+
+SEED = 323
+
+
+def seed_everything(seed=SEED):
+    random.seed(seed)
+    os.environ['PYHTONHASHSEED'] = str(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+
+
+image_path = '/home/allen/Drive C/Peter Moss AML Leukemia Research/ALL-PyTorch-2020/Classifier/Model/Data/Test/Im047_0.jpg'
+image_path2 = '/home/allen/Drive C/Peter Moss AML Leukemia Research/ALL-PyTorch-2020/Classifier/Model/Data/Test/Im006_1.jpg'
+label_idx = '/home/allen/Drive C/Peter Moss AML Leukemia Research/Dataset/pred_label.json'
+
+seed_everything(SEED)
+# helper class
+helper = Helpers("Test Model", False)
+# train data directory
+train_dir = '/home/allen/Drive C/Peter Moss AML Leukemia Research/Dataset/all_train/'
+# train label directoy
+train_csv = '/home/allen/Drive C/Peter Moss AML Leukemia Research/Dataset/train.csv'
+# labels
+class_name = ["zero", "one"]
+
+# training batch size
+batch_size = helper.config["classifier"]["train"]["batch"]
+# accuracy and loss save directory
+acc_loss_png = helper.config["classifier"]["model_params"]["plot_loss_and_acc"]
+# confusion matrix save directory
+confusion_png = helper.config["classifier"]["model_params"]["confusion_matrix"]
+# number of epoch
+epochs = helper.config["classifier"]["train"]["epochs"]
+# learning rate
+learn_rate = helper.config["classifier"]["train"]["learning_rate_adam"]
+# decay
+decay = helper.config["classifier"]["train"]["decay_adam"]
+# read train CSV file
+labels = pd.read_csv(train_csv)
+# print label count
+labels_count = labels.label.value_counts()
+print(labels_count)
+# print 5 label header
+print(labels.head())
+# splitting data into training and validation set
+train, valid = train_test_split(labels, stratify=labels.label, test_size=0.2, shuffle=True)
+print(len(train), len(valid))
+
+# data augmentation
+training_transforms, validation_transforms = augmentation()
+
+# Read Acute Lymphoblastic Leukemia dataset from disk
+trainset = LeukemiaDataset(df_data=train, data_dir=train_dir, transform=training_transforms)
+validset = LeukemiaDataset(df_data=valid, data_dir=train_dir, transform=validation_transforms)
+
+train_size, valid_size = len(trainset), len(validset)
+print(train_size, valid_size)
+
+train_sampler = SubsetRandomSampler(list(train.index))
+valid_sampler = SubsetRandomSampler(list(valid.index))
+
+# Prepare dataset for neural networks
+train_data_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
+valid_data_loader = DataLoader(validset, batch_size=batch_size, shuffle=False)
+
+# Checking the dataset
+print('Training Set:\n')
+for images, labels in train_data_loader:
+    print('Image batch dimensions:', images.size())
+    print('Image label dimensions:', labels.size())
+    break
+print("\n")
+
+#
+for images, labels in valid_data_loader:
+    print("The labels: ", labels)
+
+# Define model
+model = LuekemiaNet()
+# check if CUDA is available, else use CPU
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+# Move our model to available hardware
+model.to(device)
+
+# Cross entropy loss function
+criterion = nn.CrossEntropyLoss()
+# specify optimizer (stochastic gradient descent) and learning rate = 0.001
+optimizer = Adam(params=model.parameters(), lr=learn_rate, weight_decay=decay)
+# scheduler = CyclicLR(optimizer, base_lr=lr, max_lr=0.01, step_size=5, mode='triangular2')
+scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=7,
+                                      gamma=helper.config["classifier"]["model_params"]["gamma"])
+
+# begin training
+real_model, history = train_model(model, train_data_loader,
+                                  valid_data_loader,
+                                  optimizer, scheduler,
+                                  criterion, train_size,
+                                  valid_size,
+                                  device=device, n_epochs=epochs)
+# plot model loss and accuracy
+plot_training_history(history, save_path=acc_loss_png)
+# Get model prediction
+y_pred, y_test = get_predictions(real_model, valid_data_loader, device)
+# Get model precision, recall and f1_score
+helper.logger.info(classification_report(y_test, y_pred, target_names=class_name))
+# Get model confusion matrix
+cm = confusion_matrix(y_test, y_pred)
+confusion_matrix2(cm, class_name, save_path=confusion_png)
+
+interpret_model(real_model, validation_transforms, image_path, label_idx, use_cpu=True,
+                interpret_type="integrated gradients")
+interpret_model(real_model, validation_transforms, image_path, label_idx, use_cpu=True, interpret_type="gradient shap")
+
+interpret_model(real_model, validation_transforms, image_path2, label_idx, use_cpu=True,
+                interpret_type="integrated gradients")
+interpret_model(real_model, validation_transforms, image_path2, label_idx, use_cpu=True, interpret_type="gradient shap")
+
+