{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.8.17","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"tpu1vmV38","dataSources":[{"sourceId":6799,"databundleVersionId":4225553,"sourceType":"competition"},{"sourceId":48576833,"sourceType":"kernelVersion"}],"dockerImageVersionId":30529,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# IMAGENET DATASET TRAINED ON RESNET ARCHITECTURE","metadata":{"execution":{"iopub.status.busy":"2023-10-14T16:06:48.494376Z","iopub.execute_input":"2023-10-14T16:06:48.494816Z","iopub.status.idle":"2023-10-14T16:06:48.500974Z","shell.execute_reply.started":"2023-10-14T16:06:48.494786Z","shell.execute_reply":"2023-10-14T16:06:48.499729Z"}}},{"cell_type":"markdown","source":"<img src=\"https://i2.wp.com/www.adeveloperdiary.com/wp-content/uploads/2019/09/How-to-prepare-Imagenet-dataset-for-Image-Classification-adeveloperdiary.com-7.jpg?resize=700%2C300&ssl=1\" width='700' height='500' />","metadata":{}},{"cell_type":"markdown","source":"## Description:\n***In this project the Imagenet dataset used in Imagenet challenge with 100 classes is trained and tested using one of the recently most accurate architecture *ResNet152V2* architecture in different ways.Project is diveded into 4 sections:***\n\n **1. Import libraries and download dataset**\n \n **2. Preprocess data**\n \n **3. Train model**\n \n **4. Test annd deployment**\n ","metadata":{}},{"cell_type":"markdown","source":"## In this challenge I will use Resnet 50 model to train the dataset in three ways. \n\n  **1. Just importing the model and directly train it** \n  \n  **2. Importing model and preprocess it by transfer-learning**\n  \n  **3. Import model andf using fine-tuning the model**","metadata":{}},{"cell_type":"markdown","source":"### Section 1","metadata":{}},{"cell_type":"code","source":"!pip install tqdm","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:15:58.215118Z","iopub.execute_input":"2024-02-28T09:15:58.215588Z","iopub.status.idle":"2024-02-28T09:16:02.678856Z","shell.execute_reply.started":"2024-02-28T09:15:58.215562Z","shell.execute_reply":"2024-02-28T09:16:02.677675Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install seaborn","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:16:02.680778Z","iopub.execute_input":"2024-02-28T09:16:02.681058Z","iopub.status.idle":"2024-02-28T09:16:06.926588Z","shell.execute_reply.started":"2024-02-28T09:16:02.68103Z","shell.execute_reply":"2024-02-28T09:16:06.925722Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf \nfrom tensorflow.keras.applications import ResNet50 \nimport numpy as np\nimport tensorflow_datasets as tfds\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nimport os\nimport random\nfrom glob import glob\nfrom keras.models import *\nfrom keras.layers import *\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras.optimizers import Adam\nfrom keras.applications.vgg16 import preprocess_input\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img, img_to_array,array_to_img\nfrom IPython.display import display\nfrom PIL import Image","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-02-28T09:16:06.927808Z","iopub.execute_input":"2024-02-28T09:16:06.928062Z","iopub.status.idle":"2024-02-28T09:16:48.966781Z","shell.execute_reply.started":"2024-02-28T09:16:06.928035Z","shell.execute_reply":"2024-02-28T09:16:48.965755Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mapping_path = '/kaggle/input/imagenet-object-localization-challenge/LOC_synset_mapping.txt'\n\n# Creating of mapping dictionaries to get the image classes\n\nclass_mapping_dict = {}\nclass_mapping_dict_number = {}\nmapping_class_to_number = {}\nmapping_number_to_class = {}\ni = 0\nfor line in open(mapping_path):\n    class_mapping_dict[line[:9].strip()] = line[9:].strip()\n    class_mapping_dict_number[i] = line[9:].strip()\n    mapping_class_to_number[line[:9].strip()] = i\n    mapping_number_to_class[i] = line[:9].strip()\n    i+=1\ntrain_path = '/kaggle/input/imagenet-object-localization-challenge/ILSVRC/Data/CLS-LOC/train'\n\n# Creation of dataset_array and true_classes\n\ndataset_array = []\ntrue_classes = []\nimages_array = []\nfor train_class in tqdm(os.listdir(train_path)):\n    i = 0\n    for el in os.listdir(train_path + '/' + train_class):\n        if i < 10:\n            path = train_path + '/' + train_class + '/' + el\n            image = load_img(path,target_size=(224,224,3))\n            image_array = img_to_array(image).astype(np.uint8)\n            images_array.append(image_array)\n            true_class = class_mapping_dict[path.split('/')[-2]]\n            true_classes.append(true_class)\n            i+=1\n        else:\n            break\nimages_array = np.array(images_array)\ntrue_classes = np.array(true_classes)\nprint('Preprocessing in progress')\ndataset_array = preprocess_input(images_array)\nprint('FINISH')","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:16:48.968874Z","iopub.execute_input":"2024-02-28T09:16:48.969675Z","iopub.status.idle":"2024-02-28T09:23:33.904746Z","shell.execute_reply.started":"2024-02-28T09:16:48.969626Z","shell.execute_reply":"2024-02-28T09:23:33.9036Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"rand_indices = random.sample(range(0, 1000),5)\nplt.figure(figsize=(20, 20))\nplt.suptitle('Before preprocess',x = 0.5,y = 0.6)\nfor i in range(5):\n    ax = plt.subplot(1,5,i+1)\n    ax.imshow(images_array[rand_indices[i]])\nplt.figure(figsize=(20, 20))\nplt.suptitle('After preprocess',x = 0.5,y = 0.6)\nfor i in range(5):\n    ax = plt.subplot(1,5,i+1)\n    ax.imshow(dataset_array[rand_indices[i]])","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:23:33.905984Z","iopub.execute_input":"2024-02-28T09:23:33.906374Z","iopub.status.idle":"2024-02-28T09:23:35.870955Z","shell.execute_reply.started":"2024-02-28T09:23:33.906324Z","shell.execute_reply":"2024-02-28T09:23:35.869995Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model building\n\n# 1.Original ResNet50 from the scratch \n**ResNet-50** is a convolutional neural network (CNN) architecture that is part of the **ResNet** (*Residual Network*) family, developed by Microsoft Research. It is known for its deep structure and effectiveness in image recognition tasks, particularly in the context of the **ImageNet Large Scale Visual Recognition Challenge (ILSVRC)**.\n\nHere's a brief overview of the ResNet-50 architecture:\n\nInput Layer: Accepts input images typically of size 224x224 pixels.\n\nConvolutional Layers: The network begins with a series of convolutional layers that extract features from the input image. ResNet-50 has 50 convolutional layers, hence the name.\n\nResidual Blocks: The distinctive feature of ResNet is its use of residual blocks. These blocks contain skip connections (also known as shortcut connections) that allow the gradient to flow more easily during training, mitigating the vanishing gradient problem in very deep networks. ResNet-50 consists of several stacked residual blocks.\n\nPooling Layers: After certain convolutional stages, max-pooling layers are employed to downsample the feature maps, reducing their spatial dimensions while retaining important information.\n\nFully Connected Layers: Towards the end of the network, there are fully connected layers that perform classification based on the extracted features. In ResNet-50, these layers are typically followed by a softmax activation function to output class probabilities.\n\nOutput Layer: The output layer produces the final classification predictions. For classification tasks like ImageNet, this layer typically has 1000 nodes corresponding to the 1000 classes in the ImageNet dataset.\n\nOverall, ResNet-50 is effective due to its deep architecture, which allows it to capture increasingly complex patterns in the input images. The use of residual blocks enables training of very deep networks without encountering the degradation problem, leading to improved accuracy in image recognition tasks.\n\n![](https://github.com/Mukhriddin19980901/Imagenet_dataset/blob/main/images/resnet50.jpg?raw=true)","metadata":{}},{"cell_type":"code","source":"model_resnet = ResNet50(weights='imagenet',include_top=True,input_shape=(224,224,3))\nmodel_resnet.summary()","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:23:35.872381Z","iopub.execute_input":"2024-02-28T09:23:35.872797Z","iopub.status.idle":"2024-02-28T09:23:42.284116Z","shell.execute_reply.started":"2024-02-28T09:23:35.872762Z","shell.execute_reply":"2024-02-28T09:23:42.28331Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Model check()","metadata":{}},{"cell_type":"code","source":"model_resnet.get_weights()","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:23:42.285121Z","iopub.execute_input":"2024-02-28T09:23:42.285381Z","iopub.status.idle":"2024-02-28T09:23:42.804769Z","shell.execute_reply.started":"2024-02-28T09:23:42.285358Z","shell.execute_reply":"2024-02-28T09:23:42.803966Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### In my first attempt I use the original pre-trained ResNet50 model to get the maximum result","metadata":{}},{"cell_type":"code","source":"classes = 1000\n\nprint('Prediction in progress')\npredictions_array = model_resnet.predict(dataset_array[:classes])\nprint('Predicted')\n\npredict_classes = []\n\nfor i in tqdm(range(classes)):\n    arg_max = predictions_array[i].argmax()\n    predict_class = class_mapping_dict_number[arg_max]\n    predict_classes.append(predict_class)\n    \npredict_classes = np.array(predict_classes)","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:23:42.80571Z","iopub.execute_input":"2024-02-28T09:23:42.805955Z","iopub.status.idle":"2024-02-28T09:24:04.015524Z","shell.execute_reply.started":"2024-02-28T09:23:42.805933Z","shell.execute_reply":"2024-02-28T09:24:04.014473Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Predictions ","metadata":{}},{"cell_type":"code","source":"random_index = random.sample(range(0, classes), 5)\n\nplt.figure(figsize=(25, 25))\nplt.suptitle('Predict classes', x = 0.5, y = 0.6)\nfor i in range(5):\n    ax = plt.subplot(1, 5, i + 1)\n    ax.imshow(images_array[random_index[i]])\n    if predict_classes[random_index[i]] == true_classes[random_index[i]]:\n        plt.title(predict_classes[random_index[i]], color='green', size=10)\n    else:\n        plt.title(predict_classes[random_index[i]], color='red', size=10)\nplt.figure(figsize=(25, 25))\nplt.suptitle('True classes', x = 0.5, y = 0.6)\nfor i in range(5):\n    ax = plt.subplot(1, 5, i + 1)\n    ax.imshow(images_array[random_index[i]])\n    if predict_classes[random_index[i]] == true_classes[random_index[i]]:\n        plt.title(true_classes[random_index[i]], color='green', size=10)\n    else:\n        plt.title(true_classes[random_index[i]], color='red', size=10)","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:04.016745Z","iopub.execute_input":"2024-02-28T09:24:04.017059Z","iopub.status.idle":"2024-02-28T09:24:06.619174Z","shell.execute_reply.started":"2024-02-28T09:24:04.01703Z","shell.execute_reply":"2024-02-28T09:24:06.618338Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy = 0\nfor i in range(classes):\n    if predict_classes[i] == true_classes[i]:\n        accuracy+=1\naccuracy /= classes\nprint('accuracy : ' + str(accuracy))","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:06.621776Z","iopub.execute_input":"2024-02-28T09:24:06.622027Z","iopub.status.idle":"2024-02-28T09:24:06.628334Z","shell.execute_reply.started":"2024-02-28T09:24:06.622005Z","shell.execute_reply":"2024-02-28T09:24:06.627644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### *The training of model without any changes to the architecture shows that accuracy rate ***79%*** which is a little bit high but not enought for deploying so I am going to try model transfer learning and compare both sides.*  ","metadata":{}},{"cell_type":"markdown","source":"# 2.Resnet with transfer learning \n\n *Transfer learning with ResNet-50 involves taking a pre-trained ResNet-50 model (which has been trained on a large dataset, typically ImageNet) and fine-tuning it on a new dataset or task. Instead of training the model from scratch, transfer learning leverages the knowledge learned from the original task (e.g., ImageNet classification) and applies it to a new, possibly related task.*","metadata":{}},{"cell_type":"markdown","source":"![trf.png](attachment:0d50d366-f296-45f9-92a9-622bab511f19.png)","metadata":{},"attachments":{"0d50d366-f296-45f9-92a9-622bab511f19.png":{"image/png":"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"}}},{"cell_type":"code","source":"Resnet_tf_learn=ResNet50(weights='imagenet',include_top=False,input_shape=(224,224,3))\nResnet_tf_learn.summary()","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:06.629199Z","iopub.execute_input":"2024-02-28T09:24:06.629442Z","iopub.status.idle":"2024-02-28T09:24:08.94046Z","shell.execute_reply.started":"2024-02-28T09:24:06.629405Z","shell.execute_reply":"2024-02-28T09:24:08.939362Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in Resnet_tf_learn.layers:\n    i.trainable=False\nResnet_tf_learn.summary()","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:08.941781Z","iopub.execute_input":"2024-02-28T09:24:08.942071Z","iopub.status.idle":"2024-02-28T09:24:09.257135Z","shell.execute_reply.started":"2024-02-28T09:24:08.942047Z","shell.execute_reply":"2024-02-28T09:24:09.256022Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class Resnet50(tf.keras.Model):\n    def __init__(self):\n        super(Resnet50,self).__init__()\n        self.resnet = Resnet_tf_learn\n        self.flatten1 = tf.keras.layers.Flatten()\n        self.dense_1 = tf.keras.layers.Dense(2048,activation='relu',kernel_regularizer=\"l1\")\n        self.drop = tf.keras.layers.Dropout(0.2)\n        self.dense_2 = tf.keras.layers.Dense(2048,activation='relu')\n        self.dense_3 = tf.keras.layers.Dense(classes,activation='softmax')\n    def call(self,x):\n        x = self.resnet(x)\n        x = self.flatten1(x)\n        x = self.dense_1(x)\n        x = self.drop(x)\n        x = self.dense_2(x)\n        return self.dense_3(x)\nresnet_tf_learn = Resnet50()\nresnet_tf_learn.build((None, 224, 224, 3))","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:09.258263Z","iopub.execute_input":"2024-02-28T09:24:09.258542Z","iopub.status.idle":"2024-02-28T09:24:10.108923Z","shell.execute_reply.started":"2024-02-28T09:24:09.258518Z","shell.execute_reply":"2024-02-28T09:24:10.107593Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Resnet_tf_learn.get_weights()","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:10.110309Z","iopub.execute_input":"2024-02-28T09:24:10.110593Z","iopub.status.idle":"2024-02-28T09:24:10.63148Z","shell.execute_reply.started":"2024-02-28T09:24:10.11057Z","shell.execute_reply":"2024-02-28T09:24:10.630349Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Prediction in progress')\npredictions_array_tr= resnet_tf_learn.predict(dataset_array[:classes])\nprint('Predicted')\n\npredict_classes_tr = []\n\nfor i in tqdm(range(classes)):\n    arg_max_tr = predictions_array_tr[i].argmax()\n    predict_class_tr = class_mapping_dict_number[arg_max_tr]\n    predict_classes_tr.append(predict_class_tr)\npredict_classes_tr = np.array(predict_classes_tr)","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:10.632631Z","iopub.execute_input":"2024-02-28T09:24:10.632907Z","iopub.status.idle":"2024-02-28T09:24:32.406153Z","shell.execute_reply.started":"2024-02-28T09:24:10.632883Z","shell.execute_reply":"2024-02-28T09:24:32.404911Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"random_index = random.sample(range(0, classes), 5)\n\nplt.figure(figsize=(25, 25))\nplt.suptitle('Predicted class names', x = 0.5, y = 0.6)\nfor i in range(5):\n    ax = plt.subplot(1, 5, i + 1)\n    ax.imshow(images_array[random_index[i]])\n    if predict_classes_tr[random_index[i]] == true_classes[random_index[i]]:\n        plt.title(predict_classes_tr[random_index[i]], color='green', size=10)\n    else:\n        plt.title(predict_classes_tr[random_index[i]], color='red', size=10)\nplt.figure(figsize=(25, 25))\nplt.suptitle('Original class names', x = 0.5, y = 0.6)\nfor i in range(5):\n    ax = plt.subplot(1, 5, i + 1)\n    ax.imshow(images_array[random_index[i]])\n    if predict_classes_tr[random_index[i]] == true_classes[random_index[i]]:\n        plt.title(true_classes[random_index[i]], color='green', size=10)\n    else:\n        plt.title(true_classes[random_index[i]], color='red', size=10)","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:32.407458Z","iopub.execute_input":"2024-02-28T09:24:32.407824Z","iopub.status.idle":"2024-02-28T09:24:35.512832Z","shell.execute_reply.started":"2024-02-28T09:24:32.40779Z","shell.execute_reply":"2024-02-28T09:24:35.51172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_tr = 0\nfor i in range(classes):\n    if predict_classes_tr[i] == true_classes[i]:\n        accuracy_tr+=1\naccuracy_tr /= classes\nprint('accuracy : ' + str(accuracy_tr))","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:35.513983Z","iopub.execute_input":"2024-02-28T09:24:35.514253Z","iopub.status.idle":"2024-02-28T09:24:35.521778Z","shell.execute_reply.started":"2024-02-28T09:24:35.514228Z","shell.execute_reply":"2024-02-28T09:24:35.520996Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Mission failed!**","metadata":{}},{"cell_type":"markdown","source":"**I have reviewed my model above and changed the output layers and their values  and checked the result about 15 times to get the maximum accuracy. But the result I got is too low.The wieghts of original pre-trained ResNet50 model and  the weights after applying transfer learning process are too different. There might be several resons :\n1.The original pre-trained ResNet50 model might be well-suited for the ImageNet dataset due to its similarity to the dataset it was trained on. If the new task or dataset you're applying transfer learning to is significantly different from ImageNet, the features learned by the pre-trained model may not be as relevant or useful.\n2.Transfer learning tends to perform best when the new dataset is small.The ImageNet dataset is large and diverse,there might not be as much benefit from transfer learning compared to training the model from scratch on the the dataset.** ","metadata":{}},{"cell_type":"markdown","source":"# 3.Fine-tuning model","metadata":{}},{"cell_type":"code","source":"resnet_fine_tuning = ResNet50(weights='imagenet',include_top=False,input_shape=(224,224,3))","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:35.522776Z","iopub.execute_input":"2024-02-28T09:24:35.523033Z","iopub.status.idle":"2024-02-28T09:24:36.98087Z","shell.execute_reply.started":"2024-02-28T09:24:35.523009Z","shell.execute_reply":"2024-02-28T09:24:36.979846Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for layer in resnet_fine_tuning.layers[-10:]:\n    layer.trainable = True\nresnet_fine_tuning.summary()","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:36.982039Z","iopub.execute_input":"2024-02-28T09:24:36.982327Z","iopub.status.idle":"2024-02-28T09:24:37.262526Z","shell.execute_reply.started":"2024-02-28T09:24:36.982302Z","shell.execute_reply":"2024-02-28T09:24:37.25379Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class My_model(tf.keras.Model):  # creating a model consists of the last convolutional and fully connected layers\n    def __init__(self):\n        super(My_model,self).__init__(name='ResNet_fine_tuning')\n        self.resnet = resnet_fine_tuning\n        self.aver_pool = tf.keras.layers.AveragePooling2D(pool_size=(7,7))\n        self.flatten1 = tf.keras.layers.Flatten()\n        self.dense_1 = tf.keras.layers.Dense(2048,activation='relu')\n        self.dense_2 = tf.keras.layers.Dense(classes,activation='softmax')\n    def call(self,x):\n        x = self.resnet(x)\n        x = self.aver_pool(x)\n        x = self.flatten1(x)\n        x = self.dense_1(x)\n        return self.dense_2(x)\nResnet_fine_tuning = My_model()\nResnet_fine_tuning.build((None, 224, 224, 3))\nResnet_fine_tuning.summary()","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:37.263588Z","iopub.execute_input":"2024-02-28T09:24:37.26388Z","iopub.status.idle":"2024-02-28T09:24:37.735591Z","shell.execute_reply.started":"2024-02-28T09:24:37.263854Z","shell.execute_reply":"2024-02-28T09:24:37.734708Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Resnet_fine_tuning.get_weights()","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:37.736807Z","iopub.execute_input":"2024-02-28T09:24:37.737112Z","iopub.status.idle":"2024-02-28T09:24:38.228362Z","shell.execute_reply.started":"2024-02-28T09:24:37.737085Z","shell.execute_reply":"2024-02-28T09:24:38.227368Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Prediction in progress')\npredictions_array_ft= Resnet_fine_tuning.predict(dataset_array[:classes])\nprint('Predicted')\n\npredict_classes_ft = []\n\nfor i in tqdm(range(classes)):\n    arg_max_ft = predictions_array_ft[i].argmax()\n    predict_class_ft = class_mapping_dict_number[arg_max_ft]\n    predict_classes_ft.append(predict_class_ft)\npredict_classes_ft = np.array(predict_classes_ft)","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:38.229422Z","iopub.execute_input":"2024-02-28T09:24:38.229698Z","iopub.status.idle":"2024-02-28T09:24:58.989031Z","shell.execute_reply.started":"2024-02-28T09:24:38.229673Z","shell.execute_reply":"2024-02-28T09:24:58.987955Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"random_index = random.sample(range(0, classes), 5)\n\nplt.figure(figsize=(25, 25))\nplt.suptitle('Predicted class names', x = 0.5, y = 0.6)\nfor i in range(5):\n    ax = plt.subplot(1, 5, i + 1)\n    ax.imshow(images_array[random_index[i]])\n    if predict_classes_ft[random_index[i]] == true_classes[random_index[i]]:\n        plt.title(predict_classes_ft[random_index[i]], color='green', size=10)\n    else:\n        plt.title(predict_classes_ft[random_index[i]], color='red', size=10)\nplt.figure(figsize=(25, 25))\nplt.suptitle('Original class names', x = 0.5, y = 0.6)\nfor i in range(5):\n    ax = plt.subplot(1, 5, i + 1)\n    ax.imshow(images_array[random_index[i]])\n    if predict_classes_ft[random_index[i]] == true_classes[random_index[i]]:\n        plt.title(true_classes[random_index[i]], color='green', size=10)\n    else:\n        plt.title(true_classes[random_index[i]], color='red', size=10)","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:24:58.990403Z","iopub.execute_input":"2024-02-28T09:24:58.991335Z","iopub.status.idle":"2024-02-28T09:25:01.850995Z","shell.execute_reply.started":"2024-02-28T09:24:58.991298Z","shell.execute_reply":"2024-02-28T09:25:01.849924Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_ft = 0\nfor i in range(classes):\n    if predict_classes_ft[i] == true_classes[i]:\n        accuracy_ft+=1\naccuracy_ft /= classes\nprint('accuracy : ' + str(accuracy_ft))","metadata":{"execution":{"iopub.status.busy":"2024-02-28T09:25:01.852157Z","iopub.execute_input":"2024-02-28T09:25:01.852436Z","iopub.status.idle":"2024-02-28T09:25:01.859073Z","shell.execute_reply.started":"2024-02-28T09:25:01.852412Z","shell.execute_reply":"2024-02-28T09:25:01.858194Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Fine-tuning a model requires a substantial amount of data to adapt the pre-trained features to the new task. If the ImageNet data used for fine-tuning is limited or not representative of the entire dataset, the model may not generalize well to the original ImageNet data.**","metadata":{}}]}