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"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from tqdm import tqdm\n",
"from tensorflow.keras.preprocessing import image\n",
"from sklearn.preprocessing import label_binarize\n",
"from sklearn.model_selection import train_test_split\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, Dropout\n",
"from keras.optimizers import Adam"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
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"outputs": [],
"source": [
"import tensorflow as tf\n",
"from keras.applications.resnet_v2 import ResNet50V2\n",
"from tensorflow import keras\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
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}
},
"outputs": [],
"source": [
"import os\n",
"from tensorflow.keras.preprocessing.image import load_img"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10222, 2)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>breed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000bec180eb18c7604dcecc8fe0dba07</td>\n",
" <td>boston_bull</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>001513dfcb2ffafc82cccf4d8bbaba97</td>\n",
" <td>dingo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>001cdf01b096e06d78e9e5112d419397</td>\n",
" <td>pekinese</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>00214f311d5d2247d5dfe4fe24b2303d</td>\n",
" <td>bluetick</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0021f9ceb3235effd7fcde7f7538ed62</td>\n",
" <td>golden_retriever</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id breed\n",
"0 000bec180eb18c7604dcecc8fe0dba07 boston_bull\n",
"1 001513dfcb2ffafc82cccf4d8bbaba97 dingo\n",
"2 001cdf01b096e06d78e9e5112d419397 pekinese\n",
"3 00214f311d5d2247d5dfe4fe24b2303d bluetick\n",
"4 0021f9ceb3235effd7fcde7f7538ed62 golden_retriever"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels_all = pd.read_csv('data_small/New folder/labels.csv')\n",
"print(labels_all.shape)\n",
"labels_all.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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},
"outputs": [],
"source": [
"breed_all = labels_all['breed']\n",
"breed_count = breed_all.value_counts()\n",
"breed_count.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>id</th>\n",
" <th>breed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9</td>\n",
" <td>0042188c895a2f14ef64a918ed9c7b64</td>\n",
" <td>scottish_deerhound</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>12</td>\n",
" <td>00693b8bc2470375cc744a6391d397ec</td>\n",
" <td>maltese_dog</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>79</td>\n",
" <td>01e787576c003930f96c966f9c3e1d44</td>\n",
" <td>scottish_deerhound</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80</td>\n",
" <td>01ee3c7ff9bcaba9874183135877670e</td>\n",
" <td>entlebucher</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>88</td>\n",
" <td>021b5a49189665c0442c19b5b33e8cf1</td>\n",
" <td>entlebucher</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index id breed\n",
"0 9 0042188c895a2f14ef64a918ed9c7b64 scottish_deerhound\n",
"1 12 00693b8bc2470375cc744a6391d397ec maltese_dog\n",
"2 79 01e787576c003930f96c966f9c3e1d44 scottish_deerhound\n",
"3 80 01ee3c7ff9bcaba9874183135877670e entlebucher\n",
"4 88 021b5a49189665c0442c19b5b33e8cf1 entlebucher"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CLASS_NAME = ['scottish_deerhound', 'maltese_dog', 'afghan_hound', 'entlebucher', 'bernese_mountain_dog']\n",
"labels = labels_all[(labels_all['breed'].isin(CLASS_NAME))]\n",
"labels = labels.reset_index()\n",
"labels.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
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"outputs": [],
"source": [
"train_path = 'data_small/New folder/train'\n",
"\n",
"\n",
"#reading dataset labels\n",
"train_labels = pd.read_csv('data_small/New folder/labels.csv')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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},
"outputs": [],
"source": [
"train_labels.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-31T12:01:44.672309Z",
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"outputs": [],
"source": [
"# Create X_data as a pandas dataframe\n",
"X_data = pd.DataFrame(columns=['image'])\n",
"\n",
"# Loop over the images and load them into X_data\n",
"for i, file_path in enumerate(train_labels):\n",
" img = cv2.imread(file_path)\n",
" print(i, file_path, img)\n",
" x = np.expand_dims(img,axis=0)\n",
" print(i, x)\n",
" X_data.loc[i] = x[i] / 255.0\n",
"\n",
"# Create train_labels as a pandas dataframe\n",
"train_labels = pd.read_csv('/kaggle/input/dog-breeding/New folder/train')\n",
"\n",
"# Merge X_data and train_labels on the 'id' column\n",
"train_data = pd.merge(X_data, train_labels, on='id')\n",
"\n",
"# Print train image and one hot encode shape & size\n",
"print('\\nTrain Images shape:', X_data.shape, ' size: {:,}'.format(X_data.size))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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}
},
"outputs": [],
"source": [
"x = train_labels.breed.unique()\n",
"include = ['beagle', 'chihuahua', 'doberman','french_bulldog', 'golden_retriever', 'malamute', 'pug', 'saint_bernard', 'scottish_deerhound','tibetan_mastiff']\n",
"for i in x :\n",
" if i not in include:\n",
" train_labels = train_labels.drop(train_labels[ train_labels['breed'] == i ].index)\n",
"print(train_labels.shape)\n",
"train_labels.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-31T11:51:03.032746Z",
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 588/588 [00:00<00:00, 630.73it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Train Images shape: (3, 1, 224, 224, 3) size: 451,584\n",
"One-hot encoded output shape: (3, 5) size: 15\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# fix --- different data structure to store imgs and ids\n",
"# X_data = np.zeros((len(labels), 224, 224, 3), dtype='float32')\n",
"X_data = []\n",
"ids = []\n",
"valid_indices = []\n",
"\n",
"# One hot encoding\n",
"Y_data = label_binarize(labels['breed'], classes = CLASS_NAME)\n",
"\n",
"# Reading and converting image to numpy array and normalizing dataset\n",
"for i in tqdm(range(len(labels))):\n",
" try: # for fast reproducing and fixing purposes (because of sampled data)\n",
" img = image.load_img(f'data_small/New folder/train/{labels[\"id\"][i]}.jpg', target_size=(224, 224))\n",
" except FileNotFoundError:\n",
" continue\n",
" img = image.img_to_array(img)\n",
" \n",
" \n",
" x = np.expand_dims(img.copy(), axis=0)\n",
" \n",
" # fix --- different data structure to store imgs and ids\n",
" X_data.append(x / 255.0)\n",
" ids.append(labels.iloc[i]['id'])\n",
" valid_indices.append(i)\n",
"\n",
"X_data = np.array(X_data)\n",
"Y_data = Y_data[valid_indices]\n",
"\n",
"# Printing train image and one hot encode shape & size\n",
"print('\\nTrain Images shape: ',X_data.shape,' size: {:,}'.format(X_data.size))\n",
"print('One-hot encoded output shape: ',Y_data.shape,' size: {:,}'.format(Y_data.size))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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},
"outputs": [],
"source": [
"plt.figure(figsize=(18,4))\n",
"cp = sns.countplot(x = 'breed', data = train_labels)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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"outputs": [],
"source": [
"invalid_files = []\n",
"for filename in filenames:\n",
" try:\n",
" load_img(os.path.join(data_dir, filename))\n",
" except:\n",
" invalid_files.append(filename)\n",
"\n",
"print(f\"Number of invalid files: {len(invalid_files)}\")\n",
"print(\"Invalid files: \", invalid_files)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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"shell.execute_reply.started": "2023-05-31T11:51:04.769725Z"
}
},
"outputs": [],
"source": [
"train_datagen = ImageDataGenerator(\n",
" rescale = 1./255, \n",
" validation_split = 0.2,\n",
" shear_range = 0.2, \n",
" zoom_range = 0.2, horizontal_flip = True, \n",
" width_shift_range = 0.2,\n",
" height_shift_range = 0.2,\n",
" rotation_range = 20,\n",
" brightness_range=[0.2,1.0])\n",
"\n",
"train_set = train_datagen.flow_from_dataframe(dataframe = train_labels,\n",
" directory = train_path,\n",
" x_col = \"id\",\n",
" y_col = \"breed\",\n",
" batch_size = 16,\n",
" subset=\"training\",\n",
" class_mode = \"categorical\",\n",
" target_size = (224,224),\n",
" seed = 42,\n",
" shuffle = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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"shell.execute_reply.started": "2023-05-31T11:51:04.771865Z"
}
},
"outputs": [],
"source": [
"validate_set = train_datagen.flow_from_dataframe(dataframe = train_labels,\n",
" directory = train_path,\n",
" x_col = \"id\",\n",
" y_col = \"breed\",\n",
" batch_size = 16,\n",
" subset=\"validation\",\n",
" class_mode = \"categorical\",\n",
" target_size = (224,224),\n",
" seed = 42,\n",
" shuffle = True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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}
},
"outputs": [],
"source": [
"train_set.batch_size"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-31T11:51:04.775557Z",
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}
},
"outputs": [],
"source": [
"resnet = ResNet50V2(input_shape = [224,224,3], weights = 'imagenet', include_top = False)\n",
"\n",
"for layer in resnet.layers:\n",
" layer.trainable = False\n",
"\n",
"x = keras.layers.Flatten()(resnet.output)\n",
"\n",
"x = keras.layers.Dropout(0.4)(x)\n",
"\n",
"pred = keras.layers.Dense(10, activation='softmax')(x)\n",
"\n",
"model = tf.keras.models.Model(inputs=resnet.input, outputs=pred)\n",
"\n",
"opt = tf.keras.optimizers.Adam(learning_rate = 1e-5)\n",
"model.compile(optimizer=opt,loss='categorical_crossentropy',metrics=['accuracy'])\n",
"\n",
"train_step = train_set.n//train_set.batch_size\n",
"validate_step = validate_set.n//validate_set.batch_size\n",
"\n",
"resnet50 = model.fit(train_set,validation_data = validate_set,epochs = 30,steps_per_epoch = train_step, validation_steps = validate_step)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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"shell.execute_reply": "2023-05-31T11:51:04.778252Z",
"shell.execute_reply.started": "2023-05-31T11:51:04.778229Z"
}
},
"outputs": [],
"source": [
"X_train_and_val, X_test, Y_train_and_val, Y_test = train_test_split(X_data, Y_data, test_size = 0.1)\n",
"\n",
"X_train, X_val, Y_train, Y_val = train_test_split(X_train_and_val, Y_train_and_val, test_size = 0.2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-31T11:51:04.779816Z",
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"shell.execute_reply.started": "2023-05-31T11:51:04.780355Z"
}
},
"outputs": [],
"source": [
"epochs = 50\n",
"batch_size = 62\n",
"\n",
"history = model.fit(X_train, Y_train, batch_size = batch_size, epochs = epochs, validation_data = (X_val, Y_val))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-31T11:51:04.781954Z",
"iopub.status.idle": "2023-05-31T11:51:04.782718Z",
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"shell.execute_reply.started": "2023-05-31T11:51:04.782484Z"
}
},
"outputs": [],
"source": [
"Y_pred = model.predict(X_test)\n",
"score = model.evaluate(X_test, Y_test)\n",
"print('Accuracy over the test set: \\n ', round((score[1]*100), 2), '%')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-31T11:51:04.784137Z",
"iopub.status.idle": "2023-05-31T11:51:04.784918Z",
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"shell.execute_reply.started": "2023-05-31T11:51:04.784681Z"
}
},
"outputs": [],
"source": [
"plt.figure(figsize=(12, 5))\n",
"plt.plot(history.history['accuracy'])\n",
"plt.plot(history.history['val_accuracy'])\n",
"plt.title('Model Accuracy')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Epochs')\n",
"plt.legend(['train', 'val'])\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.status.busy": "2023-05-31T11:51:04.786277Z",
"iopub.status.idle": "2023-05-31T11:51:04.787025Z",
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"shell.execute_reply.started": "2023-05-31T11:51:04.786787Z"
}
},
"outputs": [],
"source": [
"plt.imshow(X_test[1,:,:,:])\n",
"plt.show()\n",
"\n",
"# Finding max value from predition list and comaparing original value vs predicted\n",
"print(\"Originally : \",labels['breed'][np.argmax(Y_test[1])])\n",
"print(\"Predicted : \",labels['breed'][np.argmax(Y_pred[1])])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.12"
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},
"nbformat": 4,
"nbformat_minor": 4
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