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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "85dcc51c",
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import os\n",
"import cv2\n",
"import imghdr\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "945cdd4c",
"metadata": {},
"outputs": [],
"source": [
"#To create augmented dataset and store it locally\n",
"#DONOT RUN THIS NOW THAT THE AUGMENTED DATASET HAS ALREADY BEEN CREATED\n",
"ans=input(\"Are you sure you want to run this(y/n): \")\n",
"if(ans==\"y\"): \n",
" from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
" datagen = ImageDataGenerator(\n",
" rotation_range=45, \n",
" width_shift_range=0.2, \n",
" height_shift_range=0.2,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True,\n",
" fill_mode='nearest')\n",
"\n",
" i = 0\n",
" for batch in datagen.flow_from_directory(directory=\"pepe_folder\", \n",
" batch_size=20, \n",
" target_size=(256, 256),\n",
" color_mode=\"rgb\",\n",
" save_to_dir=r'data\\aug_pepe', \n",
" save_prefix='aug', \n",
" save_format='png'):\n",
" i += 1\n",
" if i > 60:\n",
" break\n",
"\n",
" i = 0\n",
" for batch in datagen.flow_from_directory(directory=\"notpepe_folder\", \n",
" batch_size=20, \n",
" target_size=(256, 256),\n",
" color_mode=\"rgb\",\n",
" save_to_dir=r'data\\aug_notpepe', \n",
" save_prefix='aug', \n",
" save_format='png'):\n",
" i += 1\n",
" if i > 60:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b27e514f",
"metadata": {},
"outputs": [],
"source": [
"#to retrieve augmented dataset and remove corrupted files from it\n",
"data_dir = 'data' \n",
"image_exts = ['jpeg','jpg', 'bmp', 'png']\n",
"for image_class in os.listdir(data_dir): \n",
" for image in os.listdir(os.path.join(data_dir, image_class)):\n",
" image_path = os.path.join(data_dir, image_class, image)\n",
" try: \n",
" img = cv2.imread(image_path)\n",
" tip = imghdr.what(image_path)\n",
" if tip not in image_exts: \n",
" print('Image not in ext list {}'.format(image_path))\n",
" os.remove(image_path)\n",
" except Exception as e: \n",
" print('Issue with image {}'.format(image_path))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "75b52f06",
"metadata": {},
"outputs": [],
"source": [
"predata = tf.keras.utils.image_dataset_from_directory('data')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1d8486d",
"metadata": {},
"outputs": [],
"source": [
"data_iterator = predata.as_numpy_iterator()\n",
"batch = data_iterator.next()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81675bd7",
"metadata": {},
"outputs": [],
"source": [
"#to view a sample batch and verify the labels\n",
"fig, ax = plt.subplots(ncols=4, figsize=(20,20))\n",
"for idx, img in enumerate(batch[0][:4]):\n",
" ax[idx].imshow(img.astype(int))\n",
" ax[idx].title.set_text(batch[1][idx])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e55af779",
"metadata": {},
"outputs": [],
"source": [
"#scaling the images\n",
"data = predata.map(lambda x,y: (x/255, y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "91ca596a",
"metadata": {},
"outputs": [],
"source": [
"#splitting the dataset into training, validation and testing\n",
"train_size = int(len(data)*.7)\n",
"val_size = int(len(data)*.2)+1\n",
"test_size = int(len(data)*.1)\n",
"train = data.take(train_size)\n",
"val = data.skip(train_size).take(val_size)\n",
"test = data.skip(train_size+val_size).take(test_size)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "75fd8b96",
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten,BatchNormalization,Input"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f2eb4c6d",
"metadata": {},
"outputs": [],
"source": [
"#building the model architecture\n",
"model = Sequential()\n",
"model.add(Input(shape=(256, 256, 3)))\n",
"model.add(Conv2D(16, (3, 3), strides=1, activation='relu'))\n",
"model.add(BatchNormalization())\n",
"model.add(MaxPooling2D())\n",
"\n",
"model.add(Conv2D(32, (3, 3), strides=1, activation='relu'))\n",
"model.add(BatchNormalization())\n",
"model.add(MaxPooling2D())\n",
"\n",
"model.add(Conv2D(16, (3, 3), strides=1, activation='relu'))\n",
"model.add(MaxPooling2D())\n",
"\n",
"model.add(Flatten())\n",
"model.add(Dense(256, activation='relu'))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"\n",
"from tensorflow.keras.optimizers import Adam\n",
"\n",
"learning_rate = 0.0006\n",
"optimizer = Adam(learning_rate=learning_rate)\n",
"\n",
"model.compile(optimizer=optimizer, loss=tf.losses.BinaryCrossentropy(), metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c639b2c",
"metadata": {},
"outputs": [],
"source": [
"#to view the model summary\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99a49621",
"metadata": {},
"outputs": [],
"source": [
"#saving logs\n",
"logdir='logs'\n",
"tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c550f6f",
"metadata": {},
"outputs": [],
"source": [
"hist = model.fit(train, epochs=10, validation_data=val, callbacks=[tensorboard_callback])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d19ccb13",
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.metrics import Precision, Recall, BinaryAccuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d6237387",
"metadata": {},
"outputs": [],
"source": [
"#calculating precision, recall and binary accuracy\n",
"pre = Precision()\n",
"re = Recall()\n",
"acc = BinaryAccuracy()\n",
"for batch in test.as_numpy_iterator(): \n",
" X, y = batch\n",
" yhat = model.predict(X)\n",
" pre.update_state(y, yhat)\n",
" re.update_state(y, yhat)\n",
" acc.update_state(y, yhat)\n",
"print(pre.result(), re.result(), acc.result())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2db17093",
"metadata": {},
"outputs": [],
"source": [
"#calculating the F1 score and test accuracy\n",
"from sklearn.metrics import f1_score\n",
"test_images = []\n",
"test_labels = []\n",
"\n",
"for image, label in test:\n",
" test_images.append(image)\n",
" test_labels.append(label)\n",
"\n",
"test_images = tf.concat(test_images, axis=0)\n",
"test_labels = tf.concat(test_labels, axis=0)\n",
"\n",
"\n",
"y_pred = model.predict(test_images)\n",
"y_pred_binary = (y_pred > 0.5).astype(int) \n",
"y_true = test_labels \n",
"\n",
"\n",
"f1 = f1_score(y_true, y_pred_binary)\n",
"\n",
"print(\"F1 Score:\", f1)\n",
"test_loss = model.evaluate(test)\n",
"print(\"Test Loss:\", test_loss)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b937369d",
"metadata": {},
"outputs": [],
"source": [
"#plotting the losses\n",
"fig = plt.figure()\n",
"plt.plot(hist.history['loss'], color='teal', label='Training Loss')\n",
"plt.plot(hist.history['val_loss'], color='orange', label='Validation Loss')\n",
"\n",
"\n",
"plt.axhline(y=test_loss[0], color='red', linestyle='--', label='Test Loss')\n",
"\n",
"fig.suptitle('Loss', fontsize=20)\n",
"plt.legend(loc=\"upper left\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "618e8ed4",
"metadata": {},
"outputs": [],
"source": [
"for i in range(13):\n",
" img = cv2.imread(f\"C:/Users/91896/Desktop/not/OIP ({i}).jpeg\")\n",
" plt.imshow(img)\n",
" resize = tf.image.resize(img, (256,256))\n",
" plt.imshow(resize.numpy().astype(int))\n",
" plt.show()\n",
" yhat = model.predict(np.expand_dims(resize/255, 0))\n",
" yhat\n",
" if yhat > 0.5: \n",
" print(f'Pepe Found')\n",
" else:\n",
" print(f'Pepe Not Found')"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a453a1ec",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[1], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_model\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Saving the model\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m model\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimageclassifier.keras\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [
"from tensorflow.keras.models import load_model\n",
"\n",
"# Saving the model\n",
"model.save('imageclassifier.keras')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9df6180d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "aaac6285",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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