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--- a +++ b/README.md @@ -0,0 +1,2070 @@ +<div style="padding:10px; margin:0;font-family:newtimeroman;font-size:300%;text-align:center;border-radius: 30px 10px;overflow:hidden;font-weight:700;background-color:#272643; color:white"> + Blood Cell Cancer By Pytorch + +<div style="text-align:center;"> + <img src='https://i.postimg.cc/HxJRDmDz/blood-cancers.jpg'> + +<div style = 'border-radius: 10px; box-shadow: 0 2px 4px 0 rgba(0, 0, 0, 0.1);border:2px solid #90e0ef; background-color:##e3f6f5 ; ;padding:10px; font-size:130%'> +<p style="font-size:150%; font-weight:bold">About Dataset</p> + +<p>The definitive diagnosis of Acute Lymphoblastic Leukemia (ALL), as a highly prevalent cancer, requires invasive, expensive, and time-consuming diagnostic tests. ALL diagnosis using peripheral blood smear (PBS) images plays a vital role in the initial screening of cancer from non-cancer cases. The examination of these PBS images by laboratory users is riddled with problems such as diagnostic error because the non-specific nature of ALL signs and symptoms often leads to misdiagnosis.</p> + +<p>The images of this dataset were prepared in the bone marrow laboratory of Taleqani Hospital (Tehran, Iran). This dataset consisted of 3242 PBS images from 89 patients suspected of ALL, whose blood samples were prepared and stained by skilled laboratory staff. This dataset is divided into two classes benign and malignant. The former comprises hematogenous, and the latter is the ALL group with three subtypes of malignant lymphoblasts: Early Pre-B, Pre-B, and Pro-B ALL. All the images were taken by using a Zeiss camera in a microscope with a 100x magnification and saved as JPG files. A specialist using the flow cytometry tool made the definitive determination of the types and subtypes of these cells. + +<a id='tbl_content'></a> +<div style="background-color:#eef1fb; padding: 20px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1)"> + <ul> + <li><a href="#setup" style="font-size:24px; font-family:calibri; font-weight:bold"> Step 1 | Setup </a></li> + <ul> + <li><a href="#step11" style="font-size:18px; font-family:calibri"> Step 1.1 | Install required libraries </a></li> + <li><a href="#step12" style="font-size:18px; font-family:calibri"> Step 1.2 | Import required Libraries </a></li> + <li><a href="#step13" style="font-size:18px; font-family:calibri"> Step 1.3 | Configurations </a></li> + <li><a href="#step14" style="font-size:18px; font-family:calibri"> Step 1.4 | Device </a></li> + </ul> + <li><a href="#data" style="font-size:24px; font-family:calibri; font-weight:bold"> Step 2 | Data </a></li> + <ul> + <li><a href="#step21" style="font-size:18px; font-family:calibri" > Step 2.1 | Read Data </a></li> + <li><a href="#step22" style="font-size:18px; font-family:calibri" > Step 2.2 | Copy images to working dir </a></li> + <li><a href="#step23" style="font-size:18px; font-family:calibri" > Step 2.3 | Count Plot </a></li> + <li><a href="#step24" style="font-size:18px; font-family:calibri" > Step 2.4 | Plot Images </a></li> + <li><a href="#step25" style="font-size:18px; font-family:calibri" > Step 2.5 | Split images to Train-Valid-test folders </a></li> + </ul> + <li><a href="#aug" style="font-size:24px; font-family:calibri; font-weight:bold"> Step 3 | DATA AUGMENTATIONS </a></li> + <ul> + <li><a href="#step31" style="font-size:18px; font-family:calibri"> Step 3.1 | Blure </li> + <li><a href="#step32" style="font-size:18px; font-family:calibri"> Step 3.2 | Noise </li> + <li><a href="#step33" style="font-size:18px; font-family:calibri"> Step 3.3 | Flip </li> + <li><a href="#step34" style="font-size:18px; font-family:calibri"> Step 3.3 | Apply Augmantations </li> + </ul> + <li><a href="#dataset" style="font-size:24px; font-family:calibri; font-weight:bold"> Step 4 | DataSets and DataLoaders </a></li> + <ul> + <li><a href="#step41" style="font-size:18px; font-family:calibri"> Step 4.1 | Create Datasets and DataLoaders </a></li> + <li><a href="#step42" style="font-size:18px; font-family:calibri"> Step 4.2 | Data Shapes </a></li> + <li><a href="#step43" style="font-size:18px; font-family:calibri"> Step 4.3 | Model Compile </a></li> + <li><a href="#step43" style="font-size:18px; font-family:calibri"> Step 4.3 | Model Training </a></li> + <li><a href="#step44" style="font-size:18px; font-family:calibri"> Step 4.4 | Training Result </a></li> + <li><a href="#step45" style="font-size:18px; font-family:calibri"> Step 4.5 | Evaluation </a></li> + </ul> + <li><a href="#free" style="font-size:24px; font-family:calibri; font-weight:bold"> Step 5 | FreeUp some space in RAM and GPU </a></li> + <ul> + <li><a href="#step41" style="font-size:18px; font-family:calibri"> Step 5.1 | RAM </a></li> + <li><a href="#step42" style="font-size:18px; font-family:calibri"> Step 5.2 | GPU </a></li> + </ul> + <li><a href="#model" style="font-size:24px; font-family:calibri; font-weight:bold"> Step 6 | Model </a></li> + <ul> + <li><a href="#step41" style="font-size:18px; font-family:calibri"> Step 6.1 | PreTrained Model </a></li> + <li><a href="#step42" style="font-size:18px; font-family:calibri"> Step 6.2 | Change Last Layer (fc) </a></li> + <li><a href="#step43" style="font-size:18px; font-family:calibri"> Step 6.3 | Train the Model </a></li> + <li><a href="#step43" style="font-size:18px; font-family:calibri"> Step 6.4 | Evaluation </a></li> + <li><a href="#step44" style="font-size:18px; font-family:calibri"> Step 6.5 | Plot The Result </a></li> + <li><a href="#step45" style="font-size:18px; font-family:calibri"> Step 6.6 | Confusion Matrix </a></li> + </ul> + <li><a href="#author" style="font-size:24px; font-family:calibri; font-weight:bold"> Author </a></li> + </ul> + +</div> + +# <a id='setup'></a> +# <span style="background-color:#1d3461;background-size: cover;font-family:tahoma;font-size:180%;text-align:center;border-radius:15px 15px; padding:10px; border:solid 2px #09375b"><span style="color:red"><b> 1 | </b></span><span style="color:#ade8f4"><b> SETUP + +###### 🏠 [Tabel of Contents](#tbl_content) + +## <a id='step11'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">1.1 | Install required libraries + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 At the first step, Install requred python librasries with <code>pip install</code> command. + + +```python +# ! pip install -q split-folders +``` + +## <a id='step12'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">1.2 | Import required Libraries + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Then import nesseccary libraries with <code>import</code> command. + + +```python +import os # To work with main operating system commands +import gc # Its a 'Garbage collector' , to freeup spaces +import shutil # To copy and move files +import numpy as np # To work with arrays +import cv2 # Powerfull library to work with images +import random # To generate random number and random choices +import matplotlib.pyplot as plt # To visualization +import seaborn as sns # To visualization +import splitfolders # To splite images to [train, validation, test] +from PIL import Image # To read images +from tqdm.notebook import tqdm # Beautifull progress-bar +from termcolor import colored # To colorfull output +from warnings import filterwarnings # to avoid python warnings + +import torch # Pytorch framework +import torchvision.transforms as transforms # to apply some functions befor create a dataset +from torchvision.datasets import ImageFolder # To create dataset from images on local drive +from torch.utils.data import DataLoader # Create DataLoader +from torchvision.models import googlenet, GoogLeNet_Weights # Pre-trained model with its weights +import torch.nn as nn # Neural-Networs function +from datetime import datetime # To calculate time and duration +from sklearn.metrics import confusion_matrix, classification_report # To calculate and plot Confusion Matrix +``` + +## <a id='step13'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">1.3 | Configurations + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Apply above libraries configs to better performances. + + +```python +# Add a style to seaborn plots for better visualization +sns.set_style('darkgrid') + +# To avoide Python warniongs +filterwarnings('ignore') +``` + + +```python +# Initialization values + +img_size = (128, 128) + +batch_size = 64 + +num_epochs = 30 +``` + + +```python +# Show all colors used in this notebook +colors_dark = ['#1d3461', '#eef1fb', '#ade8f4', 'red', 'black', 'orange', 'navy', '#fbf8cc'] + +sns.palplot(colors_dark) +``` + + + + + + + +## <a id='step14'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">1.4 | Device + + +```python +device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + +if device.type == 'cuda' : + print(colored(' GPU is available ', 'green', 'on_white', attrs=['bold'])) +else : + print(colored(' You are using CPU ', 'red', 'on_white', attrs=['bold'])) +``` + + [1m[107m[32m GPU is available [0m + + +# <a id='data'></a> +# <span style="background-color:#1d3461;background-size: cover;font-family:tahoma;font-size:180%;text-align:center;border-radius:15px 15px; padding:10px; border:solid 2px #09375b"><span style="color:red"><b> 2 | </b></span><span style="color:#ade8f4"><b> DATA + +##### 🏠 [Tabel of Contents](#tbl_content) + +## <a id='step21'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight:900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">2.1 | Read Data + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Set path of Dataset on <b>kaggle</b> or your <b>local</b> drive. + + +```python +# Path of main dataset +base_dir = 'C:\\envs\\DataSets\\Blood cell Cancer [ALL]' + +# Path of working directory +working_dir = 'C:\\envs\\Working\\Blood_Cell_Cancer' +``` + +## <a id='step22'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">2.2 | Copy images to working dir + +<div style = 'border : 3px solid non; background-color:#dce9f5 ; ;padding:10px; color:black'> +<b>Target is<b> :<br> + + working/ + ├── images/ + │ ├── Benign + │ │ ├── image-1.jpg + │ │ ├── image-2.jpg + │ │ ├── ... + │ │ + │ ├── Early_Pre_B + │ │ ├── image-1.jpg + │ │ ├── image-2.jpg + │ │ ├── ... + │ │ + │ ├── Pre_B + │ │ ├── image-1.jpg + │ │ ├── image-2.jpg + │ │ ├── ... + │ │ + │ ├── Pro_B + │ ├── image-1.jpg + │ ├── image-2.jpg + │ ├── ... + │ + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Base in above diagram, we should do below steps : + </p> + <ul style="font-size:15px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 1. Create a <b>Folder</b> in working-directory. </ul> + <ul style="font-size:15px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 2. Create a Folder for <b>each class</b> . </ul> + <ul style="font-size:15px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 3. <b>Copy</b> images from dataset to this folder. + + +```python +# Create 'Image' folder in working directory (step 1) +Images = os.path.join(working_dir, 'Images') +if not os.path.exists(Images) : + os.mkdir(Images) +``` + + +```python +# For each class, create a folder in Images folder (step 2) +Benign = os.path.join(Images, 'Benign') +Early_Pre_B = os.path.join(Images, 'Early_Pre_B') +Pre_B = os.path.join(Images, 'Pre_B') +Pro_B = os.path.join(Images, 'Pro_B') + +os.mkdir(Benign) +os.mkdir(Early_Pre_B) +os.mkdir(Pre_B) +os.mkdir(Pro_B) +``` + + +```python +# Copy images from dataset to working-dir/Images + +for folder in os.listdir(base_dir) : + folder_path = os.path.join(base_dir, folder) + for img in tqdm(os.listdir(folder_path)) : + src = os.path.join(folder_path, img) + + match folder : + case 'Benign' : + shutil.copy(src, os.path.join(Benign, img)) + + case '[Malignant] early Pre-B' : + shutil.copy(src, os.path.join(Early_Pre_B, img)) + + case '[Malignant] Pre-B' : + shutil.copy(src, os.path.join(Pre_B, img)) + + case '[Malignant] Pro-B' : + shutil.copy(src, os.path.join(Pro_B, img)) + +print(colored('All images copied to working directory', 'green')) +``` + + + 0%| | 0/512 [00:00<?, ?it/s] + + + + 0%| | 0/979 [00:00<?, ?it/s] + + + + 0%| | 0/955 [00:00<?, ?it/s] + + + + 0%| | 0/796 [00:00<?, ?it/s] + + + [32mAll images copied to working directory[0m + + + +```python +# Read and show classes + +classes = os.listdir(Images) +num_classes = len(classes) +print(classes) +print(f'Number of classes : {num_classes}') +``` + + ['Benign', 'Early_Pre_B', 'Pre_B', 'Pro_B'] + Number of classes : 4 + + +## <a id='step23'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">2.3 | Count Plot + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Show a number of samples in each class by countplot . + + +```python +# A variable to store values +counts = [] + +# Loop over class names +for class_name in classes : + class_path = os.path.join(Images, class_name) + counts.append(len(os.listdir(class_path))) + +# Plot the result +plt.figure(figsize=(13, 4), dpi=400) +ax = sns.barplot(x=counts, y=classes, palette='Set1', hue=classes) +for i in range(len(classes)) : + ax.bar_label(ax.containers[i]) +plt.title('Number of images in each class', fontsize=20, fontweight='bold', c='navy') +ax.set_xlim(0, 1200) +ax.set_xlabel('Counts', fontweight='bold') +ax.set_ylabel('Classes', fontweight='bold') +plt.show() +``` + + + + + + + +## <a id='step24'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">2.4 | Plot Images + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Now plot some images in each class + + +```python +# A loop to iterate below codes for each class +for class_name in classes : + # To create a plot with 1 row and 6 column + fig, ax = plt.subplots(1, 6, figsize=(15, 2)) + # Define a variable for each class_name's path by joining base_directory and each class_name + class_path = os.path.join(Images, class_name) + # Files is a list of all image names in each folder (class) + files = os.listdir(class_path) + # Choose 6 random image from each class to show in plot + random_images = random.choices(files, k=6) + # A loop to iterate in each 6 random images + for i in range(6) : + # print class_name as suptitle for each class + plt.suptitle(class_name, fontsize=20, fontweight='bold') + # variable img is path of image, by joining class_path and image file name + img = os.path.join(class_path ,random_images[i]) + # load image in img variable using keras.utils.load_img(image_path) + img = Image.open(img) + # Plot image + ax[i].imshow(img) + # Turn axis off + ax[i].axis('off') + # Make plots to become nearer to each other + plt.tight_layout() +``` + + + + + + + + + + + + + + + + + + + + + + + + + +## <a id='step25'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">2.5 | Split images to Train-Valid-test folders + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 In this step, split images to 3 part, <b>Train, Validation and Test</b> by ratio <b>70%, 15%, 15%</b> of whole images. + + +```python +# create folder for train and validation and test +train_valid = os.path.join(working_dir, 'train_valid') + +splitfolders.ratio( + input=Images, output=train_valid, seed=42, ratio=(0.7, 0.15, 0.15) +) + +print(colored(f' All images splited to TRAIN / VALIDATION / TEST folders. ', 'white', 'on_green', attrs=['bold'])) +``` + + Copying files: 3242 files [00:28, 114.30 files/s] + + [1m[42m[97m All images splited to TRAIN / VALIDATION / TEST folders. [0m + + + + + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Count Images in each folder + + +```python +# list of folders +folders = os.listdir(train_valid) + +print(colored('Number of samples in each folder : ', 'green', attrs=['bold'])) +for folder in folders : + # A variable to store count of images in each part + counts = 0 + folder_path = os.path.join(train_valid, folder) + for class_name in os.listdir(folder_path) : + class_path = os.path.join(folder_path, class_name) + counts += len(os.listdir(class_path)) + print(colored(f'{folder} : {counts}', 'blue',attrs=['bold'])) +``` + + [1m[32mNumber of samples in each folder : [0m + [1m[34mtest : 490[0m + [1m[34mtrain : 2268[0m + [1m[34mval : 484[0m + + +# <a id='aug'></a> +# <span style="background-color:#1d3461;background-size: cover;font-family:tahoma;font-size:180%;text-align:center;border-radius:15px 15px; padding:10px; border:solid 2px #09375b"><span style="color:red"><b> 3 | </b></span><span style="color:#ade8f4"><b> DATA AUGMENTATIONS + +###### 🏠 [Tabel of Contents](#tbl_content) + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Data augmentation is the process of artificially generating new data from existing data, primarily to train new machine learning (ML) models. Data augmentation can address a variety of challenges when training a CNN model, such as limited or imbalanced data, overfitting, and variation and complexity. This technique can increase the size of the dataset and balance the classes by applying different transformations + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Here, choose a sample image to plot with each Augmentation function to represent changes. + + +```python +sample_image = os.path.join(Benign, 'Sap_013 (1).jpg') +``` + +## <a id='step31'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">3.1 | Blure + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Blurring an image is a process that makes the image less sharp and reduces its level of detail. It distorts the detail of an image which makes it less clear. The most common use of image blurriness is to remove noise from the image; the other is to get the most detailed part of the image and smooth out the less detailed ones. Image blur is also called image smoothing. + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 <b>We use 3 kind of bluring : </b></p> + <ul style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 1. opencv blur (smoothing) </ul> + <ul style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 2. Gausian blur </ul> + <ul style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 3. Meidan blur </ul> + + +```python +def Blure_Filter(img, filter_type ="blur", kernel=13): + ''' + ### Filtering ### + img: image + filter_type: {blur: blur, gaussian: gaussian, median: median} + ''' + if filter_type == "blur": + return cv2.blur(img,(kernel,kernel)) + + elif filter_type == "gaussian": + return cv2.GaussianBlur(img, (kernel, kernel), 0) + + elif filter_type == "median": + return cv2.medianBlur(img, kernel) +``` + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Represent <b>blur function</b> on sample image. + + +```python +plt.figure(figsize=(10, 2.25), dpi=400) +plt.suptitle('Blured samples', fontweight='bold', fontsize=15) +# Original image +plt.subplot(1, 4, 1) +img = cv2.imread(sample_image) +img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) +plt.imshow(img) +plt.axis('off') +plt.title('Original', fontweight='bold') + # Blurs + # List of filters +filters = ['blur', 'gaussian', 'median'] +for filter in filters : + indx = filters.index(filter) + plt.subplot(1, 4, indx+2) + filtered_img = Blure_Filter(img, filter_type=filter, kernel=13) + plt.imshow(filtered_img) + plt.axis('off') + plt.title(filter, fontweight='bold') +``` + + + + + + + +## <a id='step32'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">3.2 | Noise + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Noise is deliberately altering pixels to be different than what they may should have represented. Old-fashioned films are famous for having speckles black and white pixels present where they should not be. This is noise! + Noise is one kind of imperfection that can be particularly frustrating for machines versus human understanding. While humans can easily ignore noise (or fit it within appropriate context), algorithms struggle. This is the root of so-called adversarial attacks where small, human-imperceptible pixel changes can dramatically alter a neural network's ability to make an accurate prediction. + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 <b>We use 3 kind of Noise adding : </b></p> + <ul style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 1. Gaussian noise </ul> + <ul style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 2. sp noise </ul> + + +```python +def Add_Noise(img, noise_type="gauss"): + ''' + ### Adding Noise ### + img: image + cj_type: {gauss: gaussian, sp: salt & pepper} + ''' + if noise_type == "gauss": + mean=0 + st=0.5 + gauss = np.random.normal(mean,st,img.shape) + gauss = gauss.astype('uint8') + image = cv2.add(img,gauss) + return image + + elif noise_type == "sp": + prob = 0.01 + black = np.array([0, 0, 0], dtype='uint8') + white = np.array([255, 255, 255], dtype='uint8') + + probs = np.random.random(img.shape[:2]) + img[probs < (prob / 2)] = black + img[probs > 1 - (prob / 2)] = white + return img +``` + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Represent <b>Noise adding function</b> on sample image. + + +```python +plt.figure(figsize=(10, 2.75), dpi=400) +plt.suptitle('Noised samples', fontweight='bold', fontsize=15) +plt.subplot(1, 3, 1) +img = cv2.imread(sample_image) +img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) +plt.imshow(img) +plt.axis('off') +plt.title('Original', fontweight='bold') + +noises = ['gauss', 'sp'] +for noise in noises : + indx = noises.index(noise) + plt.subplot(1, 3, indx+2) + noised_img = Add_Noise(img, noise_type=noise) + plt.imshow(noised_img) + plt.axis('off') + plt.title(noise, fontweight='bold') +``` + + + + + + + +## <a id='step33'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">3.3 | Flip + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Flipping an image (and its annotations) is a deceivingly simple technique that can improve model performance in substantial ways. + +Our models are learning what collection of pixels and the relationship between those collections of pixels denote an object is in-frame. But machine learning models (like convolutional neural networks) have a tendency to be quite brittle: they might memorize a specific ordering of pixels describes an object, but if that same object is mirrored across the image, our models may struggle to recognize it. + +Consider the orientation of your face when you are taking a selfie versus using the backwards lens on your camera: one interpretation may be mirrored while the other is not, yet they are still both your face. This mirroring of orientation is what we call flipping an image. + +By creating several versions of our images in various orientations, we give our deep learning model more information to learn from without having to go through the time consuming process of collecting and labeling more training data. + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 <b>We use 3 kind of Fliping : </b></p> + <ul style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 1. X axis </ul> + <ul style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 2. Y axis </ul> + <ul style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;"> 3. X & Y </ul> + + +```python +def Flip(img, flip_code) : + flipped_img = cv2.flip(img, flip_code) + return flipped_img +``` + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Represent <b>Flip function</b> on sample image. + + +```python +plt.figure(figsize=(10, 2.75), dpi=400) +plt.suptitle('Flip a sample', fontweight='bold', fontsize=15) + +plt.subplot(1, 4, 1) +img = cv2.imread(sample_image) +img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) +plt.imshow(img) +plt.axis('off') +plt.title('Original', fontweight='bold') + +plt.subplot(1, 4, 2) +fliped = Flip(img, flip_code=0) +plt.imshow(fliped) +plt.axis('off') +plt.title('Horizontal Flip', fontweight='bold') + +plt.subplot(1, 4, 3) +fliped = Flip(img, flip_code=1) +plt.imshow(fliped) +plt.axis('off') +plt.title('Vertical Flip', fontweight='bold') + +plt.subplot(1, 4, 4) +fliped = Flip(img, flip_code=-1) +plt.imshow(fliped) +plt.axis('off') +plt.title('X&Y Flip', fontweight='bold') +plt.show() +``` + + + + + + + +## <a id='step34'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">3.4 | Apply Augmantations + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 OK ! Its time to apply above functions to <b>Train images</b> . Do this by define a function to choose randomly between 3 kind of augs and apply them to images. At last return a <b>dictionary</b> with <b>key</b> of new image name and <b>value</b> of augmented images. + + +```python +def Apply_Augmentations(img) : + ''' Apply random choice of augmentation functions on images ''' + + returned_augs = dict() + + AUGS = ['Blure', 'Noise', 'Flip'] + + # How many of Augs choosen ? + random_num = random.randint(1, 3) + random_choice = random.choices(AUGS, k=random_num) + # To avoid repeatations : + random_choice = list(set(random_choice)) + + for choice in random_choice : + if choice == 'Blure' : + filters = ['blur', 'gaussian', 'median'] + kernels = [5, 7, 9, 11] + random_filter = random.choices(filters, k=1)[0] + random_kernel = random.choices(kernels, k=1)[0] + blured_img = Blure_Filter(img, filter_type=random_filter, kernel=random_kernel) + new_name = '_blured' + returned_augs[new_name] = blured_img + + + elif choice == 'Noise' : + noises = ['gauss', 'sp'] + random_noise = random.choices(noises, k=1)[0] + noised_img = Add_Noise(img, noise_type=random_noise) + new_name = '_noised' + returned_augs[new_name] = noised_img + + + elif choice == 'Flip' : + flip_codes = [-1, 0, 1] + random_code = random.choices(flip_codes, k=1)[0] + flipped_img = Flip(img, flip_code=random_code) + new_name = '_fliped' + returned_augs[new_name] = flipped_img + + return returned_augs +``` + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Count images in train folder beforeand after of augmentation to find out how many images added to train folder. + + +```python +train_dir = os.path.join(train_valid, 'train') +num_samples_befor_aug = 0 + +for folder in os.listdir(train_dir) : + folder_path = os.path.join(train_dir, folder) + num_samples_befor_aug += len(os.listdir(folder_path)) + +print(colored(f' Number of samples in TRAIN folder befor Augmentation : {num_samples_befor_aug} ', 'black', 'on_white', attrs=['bold'])) +``` + + [1m[107m[30m Number of samples in TRAIN folder befor Augmentation : 2268 [0m + + + +```python +for folder in os.listdir(train_dir) : + folder_path = os.path.join(train_dir, folder) + for img_name in tqdm(os.listdir(folder_path)) : + img_path = os.path.join(folder_path, img_name) + img = cv2.imread(img_path) + returned = Apply_Augmentations(img) + + for exported_name, exported_image in returned.items() : + # 1_left.jpg ---TO---> 1_lef_blured.jpg + new_name = img_name.split('.')[0] + exported_name + '.' + img_name.split('.')[-1] + new_path = os.path.join(folder_path, new_name) + + # Save new image + cv2.imwrite(new_path, exported_image) + + +print(colored(f' Augmentation Completed. ', 'white', 'on_green', attrs=['bold'])) +``` + + + 0%| | 0/358 [00:00<?, ?it/s] + + + + 0%| | 0/685 [00:00<?, ?it/s] + + + + 0%| | 0/668 [00:00<?, ?it/s] + + + + 0%| | 0/557 [00:00<?, ?it/s] + + + [1m[42m[97m Augmentation Completed. [0m + + + +```python +num_samples_after_aug = 0 + +for folder in os.listdir(train_dir) : + folder_path = os.path.join(train_dir, folder) + num_samples_after_aug += len(os.listdir(folder_path)) + +print(colored(f' Number of samples in TRAIN folder after Augmentation : {num_samples_after_aug} ', 'black', 'on_white', attrs=['bold'])) +``` + + [1m[107m[30m Number of samples in TRAIN folder after Augmentation : 5917 [0m + + + +```python +print(colored(f' {num_samples_after_aug-num_samples_befor_aug} images added to train directory. ', 'white', 'on_blue', attrs=['bold'])) +``` + + [1m[44m[97m 3649 images added to train directory. [0m + + +# <a id='dataset'></a> +# <span style="background-color:#1d3461;background-size: cover;font-family:tahoma;font-size:180%;text-align:center;border-radius:15px 15px; padding:10px; border:solid 2px #09375b"><span style="color:red"><b> 4 | </b></span><span style="color:#ade8f4"><b> DataSets and DataLoaders + +###### 🏠 [Tabel of Contents](#tbl_content) + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Now, its time to create a dataset of images by some transforms and after that create DataLoader for each dataset. + +## <a id='step41'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">4.1 | Create Datasets and DataLoaders + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Torchvision supports common computer vision transformations in the torchvision.transforms and torchvision.transforms.v2 modules. Transforms can be used to transform or augment data for training or inference of different tasks (image classification, detection, segmentation, video classification). + + +```python +transform = transforms.Compose( + [ + transforms.Resize(img_size), + transforms.ToTensor() + ] +) +``` + + +```python +############################# TRAIN ############################# +# Dataset +train_ds = ImageFolder(root=os.path.join(train_valid, 'train'), transform=transform) + +# DataLoader +train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True) + +print(colored(f'TRAIN Folder :\n', 'green', attrs=['bold'])) +print(train_ds) + +############################# VALIDATION ############################# +# Dataset +valid_ds = ImageFolder(root=os.path.join(train_valid, 'val'), transform=transform) + +# DataLoader +valid_loader = DataLoader(valid_ds, batch_size=batch_size, shuffle=True) + +print(colored(f'VALID Folder :\n', 'green', attrs=['bold'])) +print(valid_ds) + +############################# TEST ############################# +# Dataset +test_ds = ImageFolder(root=os.path.join(train_valid, 'test'), transform=transform) + +# DataLoader +test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=True) + +print(colored(f'TEST Folder :\n', 'green', attrs=['bold'])) +print(test_ds) +``` + + [1m[32mTRAIN Folder : + [0m + Dataset ImageFolder + Number of datapoints: 5917 + Root location: C:\envs\Working\Blood_Cell_Cancer\train_valid\train + StandardTransform + Transform: Compose( + Resize(size=(128, 128), interpolation=bilinear, max_size=None, antialias=True) + ToTensor() + ) + [1m[32mVALID Folder : + [0m + Dataset ImageFolder + Number of datapoints: 484 + Root location: C:\envs\Working\Blood_Cell_Cancer\train_valid\val + StandardTransform + Transform: Compose( + Resize(size=(128, 128), interpolation=bilinear, max_size=None, antialias=True) + ToTensor() + ) + [1m[32mTEST Folder : + [0m + Dataset ImageFolder + Number of datapoints: 490 + Root location: C:\envs\Working\Blood_Cell_Cancer\train_valid\test + StandardTransform + Transform: Compose( + Resize(size=(128, 128), interpolation=bilinear, max_size=None, antialias=True) + ToTensor() + ) + + +## <a id='step42'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">4.2 | Data Shapes + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Read a batch of data from each loaders(train_loader, valid_loader, test_loader), to represet shape of bach and its data type. + + +```python +# print shape of dataset for each set +for key, value in {'Train': train_loader, "Validation": valid_loader, 'Test': test_loader}.items(): + for X, y in value: + print(colored(f'{key}:', 'white','on_green', attrs=['bold'])) + print(f"Shape of images [Batch_size, Channels, Height, Width]: {X.shape}") + print(f"Shape of y: {y.shape} {y.dtype}\n") + print('-'*45) + break +``` + + [1m[42m[97mTrain:[0m + Shape of images [Batch_size, Channels, Height, Width]: torch.Size([64, 3, 128, 128]) + Shape of y: torch.Size([64]) torch.int64 + + --------------------------------------------- + [1m[42m[97mValidation:[0m + Shape of images [Batch_size, Channels, Height, Width]: torch.Size([64, 3, 128, 128]) + Shape of y: torch.Size([64]) torch.int64 + + --------------------------------------------- + [1m[42m[97mTest:[0m + Shape of images [Batch_size, Channels, Height, Width]: torch.Size([64, 3, 128, 128]) + Shape of y: torch.Size([64]) torch.int64 + + --------------------------------------------- + + +# <a id='free'></a> +# <span style="background-color:#1d3461;background-size: cover;font-family:tahoma;font-size:180%;text-align:center;border-radius:15px 15px; padding:10px; border:solid 2px #09375b"><span style="color:red"><b> 5 | </b></span><span style="color:#ade8f4"><b> FreeUp some space in RAM and GPU + +###### 🏠 [Tabel of Contents](#tbl_content) + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Because of defining lots of variables and functions, our RAM may filled with unneccessary data and our GPU may be filled too. In this part by <b>Deleting</b> unneccessary variabels and using <code>gc.collect</code> function for RAM and <code>torch.cuda</code> for GPU cache we can free up some space to better performances. + +## <a id='step51'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">5.1 | RAM + + +```python +del [ax, base_dir, Benign, class_name, class_path, counts, colors_dark, exported_image, exported_name, Early_Pre_B, fig, files, filter, filtered_img, filters, fliped, folder, folder_path] +del [folders, i, Images, img, indx, key, noise, noised_img, noises, num_classes, num_samples_after_aug, num_samples_befor_aug, Pre_B, Pro_B, random_images] +del [sample_image, train_dir, value, working_dir, X, y, returned, src] +del [img_name, img_path, img_size, new_name, new_path, ] + +gc.collect() +``` + + + + + 72827 + + + +## <a id='step52'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">5.2 | GPU + + +```python +torch.cuda.empty_cache() +``` + +# <a id='model'></a> +# <span style="background-color:#1d3461;background-size: cover;font-family:tahoma;font-size:180%;text-align:center;border-radius:15px 15px; padding:10px; border:solid 2px #09375b"><span style="color:red"><b> 6 | </b></span><span style="color:#ade8f4"><b> Model + +###### 🏠 [Tabel of Contents](#tbl_content) + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 10px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Instead of define a new model form scatch , i prefer to use a <b>Pre-Trained</b> model, <b>GoogleNet</b> with its trained wights <b>GoogLeNet_Weights</b> . + Google Net (or Inception V1) was proposed by research at Google (with the collaboration of various universities) in 2014 in the research paper titled “Going Deeper with Convolutions”. This architecture was the winner at the ILSVRC 2014 image classification challenge. It has provided a significant decrease in error rate as compared to previous winners AlexNet (Winner of ILSVRC 2012) and ZF-Net (Winner of ILSVRC 2013) and significantly less error rate than VGG (2014 runner up). This architecture uses techniques such as 1×1 convolutions in the middle of the architecture and global average pooling. + +<img src='https://i.postimg.cc/x1tJbp5V/Xqv0n.jpg'> + +## <a id='step61'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">6.1 | PreTrained Model + + +```python +model = googlenet(weights=GoogLeNet_Weights) +model +``` + + + + + GoogLeNet( + (conv1): BasicConv2d( + (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (conv2): BasicConv2d( + (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (conv3): BasicConv2d( + (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (inception3a): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception3b): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (inception4a): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception4b): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception4c): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception4d): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception4e): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True) + (inception5a): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception5b): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (aux1): None + (aux2): None + (avgpool): AdaptiveAvgPool2d(output_size=(1, 1)) + (dropout): Dropout(p=0.2, inplace=False) + (fc): Linear(in_features=1024, out_features=1000, bias=True) + ) + + + +## <a id='step62'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">6.2 | Change Last Layer (fc) + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Out-feature of GoogleNet has 1000 Neuron, but in this case, our model should have 4 neuron, Length of classes. So change <b>fc</b> part of GoogleNet and replace it with a Sequential of fully connected network. + + +```python +model.fc = nn.Sequential( + nn.Linear(in_features=1024, out_features=512), + nn.ReLU(), + nn.Dropout(0.2), + nn.Linear(in_features=512, out_features=128), + nn.ReLU(), + nn.Dropout(0.2), + nn.Linear(in_features=128, out_features=64), + nn.ReLU(), + nn.Dropout(0.2), + nn.Linear(in_features=64, out_features=4) +) +``` + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Its time to first use of GPU ! Move the model to GPU to accelerate process. + + +```python +model.to(device) +``` + + + + + GoogLeNet( + (conv1): BasicConv2d( + (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (conv2): BasicConv2d( + (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (conv3): BasicConv2d( + (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (inception3a): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception3b): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (inception4a): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception4b): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception4c): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception4d): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception4e): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True) + (inception5a): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (inception5b): Inception( + (branch1): BasicConv2d( + (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (branch2): Sequential( + (0): BasicConv2d( + (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch3): Sequential( + (0): BasicConv2d( + (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + (1): BasicConv2d( + (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + (branch4): Sequential( + (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True) + (1): BasicConv2d( + (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) + ) + ) + ) + (aux1): None + (aux2): None + (avgpool): AdaptiveAvgPool2d(output_size=(1, 1)) + (dropout): Dropout(p=0.2, inplace=False) + (fc): Sequential( + (0): Linear(in_features=1024, out_features=512, bias=True) + (1): ReLU() + (2): Dropout(p=0.2, inplace=False) + (3): Linear(in_features=512, out_features=128, bias=True) + (4): ReLU() + (5): Dropout(p=0.2, inplace=False) + (6): Linear(in_features=128, out_features=64, bias=True) + (7): ReLU() + (8): Dropout(p=0.2, inplace=False) + (9): Linear(in_features=64, out_features=4, bias=True) + ) + ) + + + +## <a id='step63'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">6.3 | Train the Model + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 As the first step in this part, define some functions to make output more beautifull and better undrestand. + + +```python +def DeltaTime(dt) : + '''A Function to apply strftime manualy on delta.datetime class''' + h = dt.seconds // 3600 + dh = dt.seconds % 3600 + + m = dh // 60 + s = dh % 60 + + if h<10 : h='0'+str(h) + else : h = str(h) + + if m<10 : m='0'+str(m) + else : m = str(m) + + if s<10 : s='0'+str(s) + else : s = str(s) + + return( h + ':' + m + ':' + s) +``` + + +```python +def Beauty_epoch(epoch) : + ''' Return epochs in 2 digits - like (01 or 08) ''' + if epoch<10 : + return '0' + str(epoch) + else : + return str(epoch) +``` + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Lets Train the model with train data and evaluate with validations. + + +```python +# Create Loss_function and Optimizer +Learning_Rate = 0.001 + +criterion = nn.CrossEntropyLoss() +optimizer = torch.optim.Adam(model.parameters(), lr=Learning_Rate) + +# Some variables to store loss and accuracy to plot them +train_losses = np.zeros(num_epochs) +train_accs = np.zeros(num_epochs) +valid_losses = np.zeros(num_epochs) +valid_accs = np.zeros(num_epochs) + +print(colored('Training Starts ... ', 'blue', 'on_white', attrs=['bold'])) +for epoch in range(num_epochs) : + # Set the mode to TRAIN + model.train() + + # Current time to calculate duration of epoch + t0 = datetime.now() + + # Some variables to store data + train_loss = [] + train_acc = [] + valid_loss = [] + valid_acc = [] + n_correct = 0 + n_total = 0 + + ############### + #### Train #### + ############### + + # Read Images and Labels from TrainLoader + for images, labels in train_loader : + # Move Data to GPU + images = images.to(device) + labels = labels.to(device) + + # Reshape labels to [Batch-Size, 1] + # labels = torch.reshape(labels, (-1, 1)) + + # Zero Grad Optimizer + optimizer.zero_grad() + + # Forward Pass + y_pred = model(images) + loss = criterion(y_pred, labels) + + # Backward pass + loss.backward() + optimizer.step() + + # Train Loss + train_loss.append(loss.item()) + + # Train Accuracy + _, prediction = torch.max(y_pred, 1) + n_correct += (prediction==labels).sum().item() + n_total += labels.shape[0] + + train_losses[epoch] = np.mean(train_loss) + train_accs[epoch] = n_correct / n_total + + #################### + #### Validation #### + #################### + + n_correct = 0 + n_total = 0 + + # Read Images and Labels from ValidLoader + for images, labels in valid_loader : + # Move Data to GPU + images = images.to(device) + labels = labels.to(device) + + # Reshape labels to [Batch-Size, 1] + # labels = torch.reshape(labels, (-1, 1)) + + # Forward pass + y_pred = model(images) + loss = criterion(y_pred, labels) + + # Validation Loss + valid_loss.append(loss.item()) + + # val Accuracy + _, prediction = torch.max(y_pred, 1) + n_correct += (prediction==labels).sum().item() + n_total += labels.shape[0] + + valid_losses[epoch] = np.mean(valid_loss) + valid_accs[epoch] = n_correct / n_total + + + + ############################### Duration ############################### + + dt = datetime.now() - t0 + + ############################### BEAUTIFULL OUTPUT ############################### + EPOCH = colored(f' Epoch [{Beauty_epoch(epoch+1)}/{num_epochs}] ', 'black', 'on_white', attrs=['bold']) + TRAIN_LOSS = colored(f' Train Loss:{train_losses[epoch]:.4f} ', 'white', 'on_green', attrs=['bold']) + TRAIN_ACC = colored(f' Train Acc:{train_accs[epoch]:.4f} ', 'white', 'on_blue', attrs=['bold']) + VAL_LOSS = colored(f' Val Loss:{valid_losses[epoch]:.4f} ', 'white', 'on_green', attrs=['bold']) + VAL_ACC = colored(f' Val Acc:{valid_accs[epoch]:.4f} ', 'white', 'on_blue', attrs=['bold']) + DURATION = colored(f' Duration : {DeltaTime(dt)} ', 'white', 'on_dark_grey', attrs=['bold']) + LR = colored(f' lr = {Learning_Rate} ', 'black', 'on_cyan', attrs=['bold']) + + + # Print the result of each epochs + print(f'{EPOCH} -> {TRAIN_LOSS}{TRAIN_ACC} {VAL_LOSS}{VAL_ACC} {DURATION} {LR}') + + +print(colored('Training Finished ...', 'blue', 'on_white', attrs=['bold'])) +``` + + [1m[107m[34mTraining Starts ... [0m + [1m[107m[30m Epoch [01/30] [0m -> [1m[42m[97m Train Loss:0.2178 [0m[1m[44m[97m Train Acc:0.9280 [0m [1m[42m[97m Val Loss:0.0504 [0m[1m[44m[97m Val Acc:0.9814 [0m [1m[100m[97m Duration : 00:01:49 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [02/30] [0m -> [1m[42m[97m Train Loss:0.0515 [0m[1m[44m[97m Train Acc:0.9865 [0m [1m[42m[97m Val Loss:0.0952 [0m[1m[44m[97m Val Acc:0.9814 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [03/30] [0m -> [1m[42m[97m Train Loss:0.0389 [0m[1m[44m[97m Train Acc:0.9910 [0m [1m[42m[97m Val Loss:0.0138 [0m[1m[44m[97m Val Acc:0.9959 [0m [1m[100m[97m Duration : 00:01:29 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [04/30] [0m -> [1m[42m[97m Train Loss:0.0232 [0m[1m[44m[97m Train Acc:0.9932 [0m [1m[42m[97m Val Loss:0.0269 [0m[1m[44m[97m Val Acc:0.9897 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [05/30] [0m -> [1m[42m[97m Train Loss:0.0059 [0m[1m[44m[97m Train Acc:0.9983 [0m [1m[42m[97m Val Loss:0.0217 [0m[1m[44m[97m Val Acc:0.9917 [0m [1m[100m[97m Duration : 00:01:28 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [06/30] [0m -> [1m[42m[97m Train Loss:0.0244 [0m[1m[44m[97m Train Acc:0.9943 [0m [1m[42m[97m Val Loss:0.1601 [0m[1m[44m[97m Val Acc:0.9793 [0m [1m[100m[97m Duration : 00:01:28 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [07/30] [0m -> [1m[42m[97m Train Loss:0.0123 [0m[1m[44m[97m Train Acc:0.9971 [0m [1m[42m[97m Val Loss:0.0147 [0m[1m[44m[97m Val Acc:0.9917 [0m [1m[100m[97m Duration : 00:01:30 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [08/30] [0m -> [1m[42m[97m Train Loss:0.0026 [0m[1m[44m[97m Train Acc:0.9993 [0m [1m[42m[97m Val Loss:0.1443 [0m[1m[44m[97m Val Acc:0.9814 [0m [1m[100m[97m Duration : 00:01:31 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [09/30] [0m -> [1m[42m[97m Train Loss:0.0355 [0m[1m[44m[97m Train Acc:0.9927 [0m [1m[42m[97m Val Loss:0.0460 [0m[1m[44m[97m Val Acc:0.9897 [0m [1m[100m[97m Duration : 00:01:30 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [10/30] [0m -> [1m[42m[97m Train Loss:0.0089 [0m[1m[44m[97m Train Acc:0.9980 [0m [1m[42m[97m Val Loss:0.0095 [0m[1m[44m[97m Val Acc:0.9959 [0m [1m[100m[97m Duration : 00:01:32 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [11/30] [0m -> [1m[42m[97m Train Loss:0.0205 [0m[1m[44m[97m Train Acc:0.9958 [0m [1m[42m[97m Val Loss:0.0523 [0m[1m[44m[97m Val Acc:0.9876 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [12/30] [0m -> [1m[42m[97m Train Loss:0.0185 [0m[1m[44m[97m Train Acc:0.9961 [0m [1m[42m[97m Val Loss:0.0065 [0m[1m[44m[97m Val Acc:0.9959 [0m [1m[100m[97m Duration : 00:01:32 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [13/30] [0m -> [1m[42m[97m Train Loss:0.0075 [0m[1m[44m[97m Train Acc:0.9983 [0m [1m[42m[97m Val Loss:0.1034 [0m[1m[44m[97m Val Acc:0.9835 [0m [1m[100m[97m Duration : 00:01:32 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [14/30] [0m -> [1m[42m[97m Train Loss:0.0217 [0m[1m[44m[97m Train Acc:0.9961 [0m [1m[42m[97m Val Loss:0.1034 [0m[1m[44m[97m Val Acc:0.9814 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [15/30] [0m -> [1m[42m[97m Train Loss:0.0213 [0m[1m[44m[97m Train Acc:0.9963 [0m [1m[42m[97m Val Loss:0.0368 [0m[1m[44m[97m Val Acc:0.9876 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [16/30] [0m -> [1m[42m[97m Train Loss:0.0017 [0m[1m[44m[97m Train Acc:0.9998 [0m [1m[42m[97m Val Loss:0.0424 [0m[1m[44m[97m Val Acc:0.9917 [0m [1m[100m[97m Duration : 00:01:32 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [17/30] [0m -> [1m[42m[97m Train Loss:0.0066 [0m[1m[44m[97m Train Acc:0.9990 [0m [1m[42m[97m Val Loss:0.0770 [0m[1m[44m[97m Val Acc:0.9897 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [18/30] [0m -> [1m[42m[97m Train Loss:0.0241 [0m[1m[44m[97m Train Acc:0.9946 [0m [1m[42m[97m Val Loss:0.0921 [0m[1m[44m[97m Val Acc:0.9814 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [19/30] [0m -> [1m[42m[97m Train Loss:0.0381 [0m[1m[44m[97m Train Acc:0.9919 [0m [1m[42m[97m Val Loss:0.0295 [0m[1m[44m[97m Val Acc:0.9917 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [20/30] [0m -> [1m[42m[97m Train Loss:0.0072 [0m[1m[44m[97m Train Acc:0.9983 [0m [1m[42m[97m Val Loss:0.0251 [0m[1m[44m[97m Val Acc:0.9938 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [21/30] [0m -> [1m[42m[97m Train Loss:0.0099 [0m[1m[44m[97m Train Acc:0.9976 [0m [1m[42m[97m Val Loss:0.0414 [0m[1m[44m[97m Val Acc:0.9938 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [22/30] [0m -> [1m[42m[97m Train Loss:0.0068 [0m[1m[44m[97m Train Acc:0.9985 [0m [1m[42m[97m Val Loss:0.0537 [0m[1m[44m[97m Val Acc:0.9917 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [23/30] [0m -> [1m[42m[97m Train Loss:0.0064 [0m[1m[44m[97m Train Acc:0.9992 [0m [1m[42m[97m Val Loss:0.1647 [0m[1m[44m[97m Val Acc:0.9917 [0m [1m[100m[97m Duration : 00:01:32 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [24/30] [0m -> [1m[42m[97m Train Loss:0.0005 [0m[1m[44m[97m Train Acc:1.0000 [0m [1m[42m[97m Val Loss:0.0403 [0m[1m[44m[97m Val Acc:0.9917 [0m [1m[100m[97m Duration : 00:01:32 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [25/30] [0m -> [1m[42m[97m Train Loss:0.0023 [0m[1m[44m[97m Train Acc:0.9995 [0m [1m[42m[97m Val Loss:0.0523 [0m[1m[44m[97m Val Acc:0.9876 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [26/30] [0m -> [1m[42m[97m Train Loss:0.0130 [0m[1m[44m[97m Train Acc:0.9970 [0m [1m[42m[97m Val Loss:0.0338 [0m[1m[44m[97m Val Acc:0.9897 [0m [1m[100m[97m Duration : 00:01:34 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [27/30] [0m -> [1m[42m[97m Train Loss:0.0013 [0m[1m[44m[97m Train Acc:0.9997 [0m [1m[42m[97m Val Loss:0.0267 [0m[1m[44m[97m Val Acc:0.9938 [0m [1m[100m[97m Duration : 00:01:33 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [28/30] [0m -> [1m[42m[97m Train Loss:0.0204 [0m[1m[44m[97m Train Acc:0.9968 [0m [1m[42m[97m Val Loss:0.0510 [0m[1m[44m[97m Val Acc:0.9917 [0m [1m[100m[97m Duration : 00:01:34 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [29/30] [0m -> [1m[42m[97m Train Loss:0.0110 [0m[1m[44m[97m Train Acc:0.9973 [0m [1m[42m[97m Val Loss:0.0458 [0m[1m[44m[97m Val Acc:0.9917 [0m [1m[100m[97m Duration : 00:01:32 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[30m Epoch [30/30] [0m -> [1m[42m[97m Train Loss:0.0091 [0m[1m[44m[97m Train Acc:0.9980 [0m [1m[42m[97m Val Loss:0.1245 [0m[1m[44m[97m Val Acc:0.9917 [0m [1m[100m[97m Duration : 00:01:32 [0m [1m[46m[30m lr = 0.001 [0m + [1m[107m[34mTraining Finished ...[0m + + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 Plot the result of training. + + +```python +plt.figure(figsize=(12, 3), dpi=400) +plt.subplot(1, 2, 1) +sns.lineplot(train_accs, label='Train Accuracy') +sns.lineplot(valid_accs, label='Valid Accuracy') +plt.title('Accuracy') + +plt.subplot(1, 2, 2) +sns.lineplot(train_losses, label='Train Loss') +sns.lineplot(valid_losses, label='Validation Loss') +plt.title('Loss') + +plt.show() +``` + + + + + + + +## <a id='step64'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">6.4 | Evaluation + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 After finishing training, we should test the model with never unseen images to final evaluate the model. + + +```python +with torch.no_grad() : + model.eval() + t0 = datetime.now() + test_loss = [] + val_loss = [] + n_correct = 0 + n_total = 0 + + for images, labels in test_loader : + # Move input data to GPU + images = images.to(device) + labels = labels.to(device) + + # Forward pass + y_pred = model(images) + loss = criterion(y_pred, labels) + + # Train Loss + test_loss.append(loss.item()) + + # Train Accuracy + _, prediction = torch.max(y_pred, 1) + n_correct += (prediction==labels).sum().item() + n_total += labels.shape[0] + + test_loss = np.mean(train_loss) + train_acc = n_correct / n_total + dt = datetime.now() - t0 + print(colored(f'Loss:{test_loss:.4f}\nAccuracy:{train_acc:.4f}\nDuration:{dt}', 'green', attrs=['bold'])) +``` + + [1m[32mLoss:0.0091 + Accuracy:0.9939 + Duration:0:00:10.539431[0m + + +## <a id='step65'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">6.5 | Plot The Result + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 And now, plot some images with <b>real labels</b> and <b>predicted labels</b> .</p> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 To do this, we should create a Dictionary called label_map, a dictionary with indexes as keys and class_names as values. </p> + + +```python +# Create a label_map to show True and Predicted labels in below plot +classes.sort() +classes +labels_map = {} + +for index, label in enumerate(classes) : + labels_map[index] = label + +labels_map +``` + + + + + {0: 'Benign', 1: 'Early_Pre_B', 2: 'Pre_B', 3: 'Pro_B'} + + + + +```python +# Move model to CPU +cpu_model = model.cpu() + +# Get 1 batch of test_loader +for imgs, labels in test_loader : + break + +# Plot 1 batch of test_loader images with True and Predicted label +plt.subplots(4, 8, figsize=(16, 12)) +plt.suptitle('Rice images in 1 Batch', fontsize=25, fontweight='bold') +for i in range(32) : + ax = plt.subplot(4, 8, i+1) + img = torch.permute(imgs[i], (1, 2, 0)) + plt.imshow(img) + label = labels_map[int(labels[i])] + img = img[i].unsqueeze(0) + img = imgs[i].unsqueeze(0) + out = cpu_model(img) + predict = labels_map[int(out.argmax())] + plt.title(f'True :{label}\nPredict :{predict}') + plt.axis('off') + +plt.show() +``` + + + + + + + +## <a id='step66'></a> +## <span style="background-color:orange ;background-size: cover;font-family:tahoma;font-size:70%; font-weight: 900; text-align:left;border-radius:25px 25px; padding:10px; border:solid 2px #09375b"><span style="color:navy">6.6 | Confusion Matrix + +<div style="background-color:#fbf8cc; padding: 10px 10px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:left"> + <p style="font-size:16px; font-family:tahoma; line-height: 2em; text-indent: 20px;">🔵 And the final step is ploting <b>Confusion Matrix</b> by <code>sklearn</code> library. + + +```python +# Get out 2 list include y_true and y_pred for use in confusion_matrix +model = model.to(device) + +y_true = [] +y_pred = [] +for images, labels in test_loader: + images = images.to(device) + labels = labels.numpy() + outputs = model(images) + _, pred = torch.max(outputs.data, 1) + pred = pred.detach().cpu().numpy() + + y_true = np.append(y_true, labels) + y_pred = np.append(y_pred, pred) +``` + + +```python +classes = labels_map.values() + +print(classification_report(y_true, y_pred)) + +def plot_confusion_matrix(y_test, y_prediction): + '''Plotting Confusion Matrix''' + cm = confusion_matrix(y_true, y_pred) + ax = plt.figure(figsize=(8, 6)) + ax = sns.heatmap(cm, annot=True, fmt='', cmap="Blues") + ax.set_xlabel('Prediced labels', fontsize=18) + ax.set_ylabel('True labels', fontsize=18) + ax.set_title('Confusion Matrix', fontsize=25) + ax.xaxis.set_ticklabels(classes) + ax.yaxis.set_ticklabels(classes) + plt.show() + + +plot_confusion_matrix(y_true, y_pred) +``` + + precision recall f1-score support + + 0.0 0.99 1.00 0.99 78 + 1.0 1.00 0.99 0.99 148 + 2.0 0.99 1.00 1.00 144 + 3.0 0.99 0.99 0.99 120 + + accuracy 0.99 490 + macro avg 0.99 0.99 0.99 490 + weighted avg 0.99 0.99 0.99 490 + + + + + + + + + +<a id="author"></a> +<div style="border:3px solid navy; border-radius:30px; padding: 15px; background-size: cover; font-size:120%; text-align:left; background-image: url(https://i.postimg.cc/sXwGWcwC/download.jpg); background-size: cover"> + +<h4 align="left"><span style="font-weight:700; font-size:150%"><font color=#d10202>Author:</font><font color=navy> Nima Pourmoradi</font></span></h4> +<h6 align="left"><font color=#ff6200><a href='https://github.com/NimaPourmoradi'>github: https://github.com/NimaPourmoradi</font></h6> +<h6 align="left"><font color=#ff6200><a href='https://www.kaggle.com/nimapourmoradi'>kaggle : https://www.kaggle.com/nimapourmoradi</a></font></h6> +<h6 align="left"><font color=#ff6200><a href='https://www.linkedin.com/in/nima-pourmoradi-081949288/'>linkedin : www.linkedin.com/in/nima-pourmoradi</a></font></h6> +<h6 align="left"><font color=#ff6200><a href='https://t.me/Nima_Pourmoradi'>Telegram : https://t.me/Nima_Pourmoradi</a></font></h6> + +<div style="background-color:#c5d8d1; padding: 25px 0px 10px 0px; border-radius: 10px; box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.1);border:0px solid #0A2342; text-align:center"> + <p style="font-size:18px; font-family:tahoma; line-height: 2em; text-indent: 20px;"><b>✅ If you like my notebook, please upvote it ✅ + </b></p> +</div> + +<img src="https://i.postimg.cc/t4b3WtCy/1000-F-291522205-Xkrm-S421-Fj-SGTMR.jpg"> + +##### [🏠 Tabel of Contents](#tbl_content)