Here this is the code which I used to train my dataset to MobilenetV2 pretrained ML model

Backup_Mobilenet.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0f8a8071",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1857 images belonging to 2 classes.\n",
+      "Found 463 images belonging to 2 classes.\n",
+      "Epoch 1/10\n",
+      "59/59 [==============================] - 51s 778ms/step - loss: 0.2540 - accuracy: 0.9251 - val_loss: 0.1802 - val_accuracy: 1.0000\n",
+      "Epoch 2/10\n",
+      "59/59 [==============================] - 44s 739ms/step - loss: 0.0450 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 1.0000\n",
+      "Epoch 3/10\n",
+      "59/59 [==============================] - 44s 743ms/step - loss: 0.0229 - accuracy: 1.0000 - val_loss: 0.0968 - val_accuracy: 1.0000\n",
+      "Epoch 4/10\n",
+      "59/59 [==============================] - 44s 739ms/step - loss: 0.0145 - accuracy: 1.0000 - val_loss: 0.0778 - val_accuracy: 1.0000\n",
+      "Epoch 5/10\n",
+      "59/59 [==============================] - 44s 739ms/step - loss: 0.0103 - accuracy: 1.0000 - val_loss: 0.0658 - val_accuracy: 1.0000\n",
+      "Epoch 6/10\n",
+      "59/59 [==============================] - 44s 746ms/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.0571 - val_accuracy: 1.0000\n",
+      "Epoch 7/10\n",
+      "59/59 [==============================] - 44s 742ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 0.0476 - val_accuracy: 1.0000\n",
+      "Epoch 8/10\n",
+      "59/59 [==============================] - 44s 737ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.0420 - val_accuracy: 1.0000\n",
+      "Epoch 9/10\n",
+      "59/59 [==============================] - 44s 742ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.0386 - val_accuracy: 1.0000\n",
+      "Epoch 10/10\n",
+      "59/59 [==============================] - 44s 742ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.0379 - val_accuracy: 1.0000\n",
+      "15/15 [==============================] - 9s 567ms/step - loss: 0.0379 - accuracy: 1.0000\n",
+      "Validation accuracy: 100.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import tensorflow_hub as hub\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from PIL import Image\n",
+    "\n",
+    "# Define the paths to your dataset folders\n",
+    "dataset_path = r'C:\\Users\\A C E R\\Desktop\\ELBOW ANGLE'\n",
+    "correct_position_path = r'C:\\Users\\A C E R\\Desktop\\ELBOW ANGLE\\Corect Position'\n",
+    "wrong_position_path = r'C:\\Users\\A C E R\\Desktop\\ELBOW ANGLE\\Wrong Posision'\n",
+    "\n",
+    "# Parameters for training\n",
+    "batch_size = 32\n",
+    "image_size = (224, 224)\n",
+    "num_epochs = 10\n",
+    "learning_rate = 0.001\n",
+    "\n",
+    "# Create data generators for training and validation\n",
+    "datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)\n",
+    "\n",
+    "train_generator = datagen.flow_from_directory(\n",
+    "    dataset_path,\n",
+    "    target_size=image_size,\n",
+    "    batch_size=batch_size,\n",
+    "    class_mode='binary',\n",
+    "    subset='training'\n",
+    ")\n",
+    "\n",
+    "val_generator = datagen.flow_from_directory(\n",
+    "    dataset_path,\n",
+    "    target_size=image_size,\n",
+    "    batch_size=batch_size,\n",
+    "    class_mode='binary',\n",
+    "    subset='validation'\n",
+    ")\n",
+    "\n",
+    "# Load the MobileNet model from TensorFlow Hub\n",
+    "model_url = 'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4'\n",
+    "base_model = hub.KerasLayer(model_url, input_shape=image_size + (3,))\n",
+    "\n",
+    "# Freeze the base model\n",
+    "base_model.trainable = False\n",
+    "\n",
+    "# Build the model\n",
+    "model = tf.keras.Sequential([\n",
+    "    base_model,\n",
+    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
+    "])\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(\n",
+    "    optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),\n",
+    "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
+    "    metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "# Train the model\n",
+    "history = model.fit(\n",
+    "    train_generator,\n",
+    "    validation_data=val_generator,\n",
+    "    epochs=num_epochs\n",
+    ")\n",
+    "\n",
+    "# Evaluate the model on the validation set\n",
+    "_, accuracy = model.evaluate(val_generator)\n",
+    "print(f'Validation accuracy: {accuracy*100:.2f}%')\n",
+    "\n",
+    "# Load a test image and predict the class label\n",
+    "#test_image_path = 'C:\\\\Users\\\\A C E R\\\\Desktop\\\\image_2.jpeg'  # Provide the path to your test image\n",
+    "#test_image = Image.open(test_image_path)\n",
+    "#test_image = test_image.resize(image_size)  # Resize the image to match the input size of the model\n",
+    "#test_image = tf.keras.preprocessing.image.img_to_array(test_image)\n",
+    "#test_image = test_image / 255.0  # Normalize the image\n",
+    "\n",
+    "# Expand dimensions to create a batch of size 1\n",
+    "#test_image = tf.expand_dims(test_image, axis=0)\n",
+    "\n",
+    "# Predict the class probability\n",
+    "#prediction = model.predict(test_image)[0][0]\n",
+    "\n",
+    "# Determine the predicted class label based on the threshold of 0.5\n",
+    "#predicted_label = 'Correct Position' if prediction >= 0.5 else 'Incorrect Position'\n",
+    "\n",
+    "# Print the predicted class label and probability\n",
+    "#print(f'Test image prediction: {predicted_label}')\n",
+    "#print(f'Prediction probability: {prediction}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8037638f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 1s 530ms/step\n",
+      "Test image prediction: Correct Position\n",
+      "Prediction probability: 0.9298062324523926\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load a test image and predict the class label\n",
+    "test_image_path = 'C:\\\\Users\\\\A C E R\\\\Desktop\\\\image_2.jpeg'  # Provide the path to your test image\n",
+    "test_image = Image.open(test_image_path)\n",
+    "test_image = test_image.resize(image_size)  # Resize the image to match the input size of the model\n",
+    "test_image = tf.keras.preprocessing.image.img_to_array(test_image)\n",
+    "test_image = test_image / 255.0  # Normalize the image\n",
+    "\n",
+    "# Expand dimensions to create a batch of size 1\n",
+    "test_image = tf.expand_dims(test_image, axis=0)\n",
+    "\n",
+    "# Predict the class probability\n",
+    "prediction = model.predict(test_image)[0][0]\n",
+    "\n",
+    "# Determine the predicted class label based on the threshold of 0.5\n",
+    "predicted_label = 'Correct Position' if prediction >= 0.5 else 'Incorrect Position'\n",
+    "\n",
+    "# Print the predicted class label and probability\n",
+    "print(f'Test image prediction: {predicted_label}')\n",
+    "print(f'Prediction probability: {prediction}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ec84268",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd28c2e7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
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