Most life forms evolved initially in marine habitats. By volume, oceans provide about 90% of the living space on the planet. The earliest vertebrates appeared in the form of fish, which live exclusively in water. Some of these evolved into amphibians, which spend portions of their lives in water and portions on land. One group of amphibians evolved into reptiles and mammals and a few subsets of each returned to the ocean as sea snakes, sea turtles, seals, manatees, and whales. Plant forms such as kelp and other algae grow in the water and are the basis for some underwater ecosystems. Plankton forms the general foundation of the ocean animal chain, particularly phytoplankton which are key primary producers.Source
\n","\n","\n","# ❗Author's Note:\n","Make sure to run the cells from top to bottom with a GPU accelerator. There are some linux commands present in some cells so this is important to take into account. Also, any suggestions, comments and recommendations to improve the notebook will be highly appreciated. Cheers!
\n","\n"]},{"cell_type":"markdown","metadata":{"id":"kQELj5Dwf3on"},"source":["# 🏗️Import Necessary Libraries"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:39:27.280347Z","iopub.status.busy":"2024-03-21T12:39:27.279685Z","iopub.status.idle":"2024-03-21T12:39:33.681073Z","shell.execute_reply":"2024-03-21T12:39:33.680188Z","shell.execute_reply.started":"2024-03-21T12:39:27.279953Z"},"id":"lEzJXgjDf5y8","trusted":true},"outputs":[],"source":["# Import Data Science Libraries\n","import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","from sklearn.model_selection import train_test_split\n","import itertools\n","import random\n","\n","# Import visualization libraries\n","import matplotlib.pyplot as plt\n","import matplotlib.cm as cm\n","import cv2\n","import seaborn as sns\n","\n","# Tensorflow Libraries\n","from tensorflow import keras\n","from tensorflow.keras import layers,models\n","from keras_preprocessing.image import ImageDataGenerator\n","from keras.layers import Dense, Dropout\n","from tensorflow.keras.callbacks import Callback, EarlyStopping,ModelCheckpoint\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.applications import MobileNetV2\n","from tensorflow.keras import Model\n","from tensorflow.keras.layers.experimental import preprocessing\n","\n","# System libraries\n","from pathlib import Path\n","import os.path\n","\n","# Metrics\n","from sklearn.metrics import classification_report, confusion_matrix\n","sns.set_style('darkgrid')"]},{"cell_type":"markdown","metadata":{"id":"D_SnZPRah_9D"},"source":["# 🤙Create helper functions"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:39:33.683189Z","iopub.status.busy":"2024-03-21T12:39:33.682655Z","iopub.status.idle":"2024-03-21T12:39:35.103207Z","shell.execute_reply":"2024-03-21T12:39:35.102237Z","shell.execute_reply.started":"2024-03-21T12:39:33.683158Z"},"id":"F8ReVC2MiBRZ","outputId":"c53e6082-d883-478d-d42f-887eb8399678","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["--2024-03-21 12:39:34-- https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 10246 (10K) [text/plain]\n","Saving to: ‘helper_functions.py’\n","\n","helper_functions.py 100%[===================>] 10.01K --.-KB/s in 0s \n","\n","2024-03-21 12:39:34 (68.7 MB/s) - ‘helper_functions.py’ saved [10246/10246]\n","\n"]}],"source":["!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n","\n","# Import series of helper functions for our notebook\n","from helper_functions import create_tensorboard_callback, plot_loss_curves, unzip_data, compare_historys, walk_through_dir, pred_and_plot"]},{"cell_type":"markdown","metadata":{"id":"Hb_XPhOwiCNY"},"source":["# 📥Load and Transform Data"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:39:35.104841Z","iopub.status.busy":"2024-03-21T12:39:35.104546Z","iopub.status.idle":"2024-03-21T12:39:35.110177Z","shell.execute_reply":"2024-03-21T12:39:35.109049Z","shell.execute_reply.started":"2024-03-21T12:39:35.104813Z"},"id":"8nD56d7Xxmc3","trusted":true},"outputs":[],"source":["BATCH_SIZE = 32\n","TARGET_SIZE = (224, 224)"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:39:35.112745Z","iopub.status.busy":"2024-03-21T12:39:35.112426Z","iopub.status.idle":"2024-03-21T12:39:38.184520Z","shell.execute_reply":"2024-03-21T12:39:38.183509Z","shell.execute_reply.started":"2024-03-21T12:39:35.112718Z"},"id":"5kXkjadNxsNI","outputId":"478c212a-b21e-4e08-e472-9b101759173e","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["There are 23 directories and 0 images in '../input/sea-animals-image-dataste'.\n","There are 0 directories and 482 images in '../input/sea-animals-image-dataste/Penguin'.\n","There are 0 directories and 497 images in '../input/sea-animals-image-dataste/Clams'.\n","There are 0 directories and 499 images in '../input/sea-animals-image-dataste/Lobster'.\n","There are 0 directories and 500 images in '../input/sea-animals-image-dataste/Otter'.\n","There are 0 directories and 497 images in '../input/sea-animals-image-dataste/Eel'.\n","There are 0 directories and 500 images in '../input/sea-animals-image-dataste/Corals'.\n","There are 0 directories and 531 images in '../input/sea-animals-image-dataste/Puffers'.\n","There are 0 directories and 483 images in '../input/sea-animals-image-dataste/Squid'.\n","There are 0 directories and 572 images in '../input/sea-animals-image-dataste/Whale'.\n","There are 0 directories and 579 images in '../input/sea-animals-image-dataste/Sea Urchins'.\n","There are 0 directories and 499 images in '../input/sea-animals-image-dataste/Crabs'.\n","There are 0 directories and 499 images in '../input/sea-animals-image-dataste/Starfish'.\n","There are 0 directories and 414 images in '../input/sea-animals-image-dataste/Seal'.\n","There are 0 directories and 562 images in '../input/sea-animals-image-dataste/Octopus'.\n","There are 0 directories and 488 images in '../input/sea-animals-image-dataste/Shrimp'.\n","There are 0 directories and 590 images in '../input/sea-animals-image-dataste/Sharks'.\n","There are 0 directories and 517 images in '../input/sea-animals-image-dataste/Sea Rays'.\n","There are 0 directories and 494 images in '../input/sea-animals-image-dataste/Fish'.\n","There are 0 directories and 478 images in '../input/sea-animals-image-dataste/Seahorse'.\n","There are 0 directories and 500 images in '../input/sea-animals-image-dataste/Nudibranchs'.\n","There are 0 directories and 782 images in '../input/sea-animals-image-dataste/Dolphin'.\n","There are 0 directories and 1903 images in '../input/sea-animals-image-dataste/Turtle_Tortoise'.\n","There are 0 directories and 845 images in '../input/sea-animals-image-dataste/Jelly Fish'.\n"]}],"source":["# Walk through each directory\n","dataset = \"../input/sea-animals-image-dataste\"\n","walk_through_dir(dataset)"]},{"cell_type":"markdown","metadata":{"id":"MLAnhGlf1hmo"},"source":["# 📅Placing data into a Dataframe\n","The first column `filepaths` contains the file path location of each individual images. The second column `labels`, on the other hand, contains the class label of the corresponding image from the file path"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:39:38.186444Z","iopub.status.busy":"2024-03-21T12:39:38.186021Z","iopub.status.idle":"2024-03-21T12:39:38.612165Z","shell.execute_reply":"2024-03-21T12:39:38.611141Z","shell.execute_reply.started":"2024-03-21T12:39:38.186403Z"},"id":"s14XOEp01m_s","trusted":true},"outputs":[],"source":["image_dir = Path(dataset)\n","\n","# Get filepaths and labels\n","filepaths = list(image_dir.glob(r'**/*.JPG')) + list(image_dir.glob(r'**/*.jpg')) + list(image_dir.glob(r'**/*.png')) + list(image_dir.glob(r'**/*.PNG'))\n","\n","labels = list(map(lambda x: os.path.split(os.path.split(x)[0])[1], filepaths))\n","\n","filepaths = pd.Series(filepaths, name='Filepath').astype(str)\n","labels = pd.Series(labels, name='Label')\n","\n","# Concatenate filepaths and labels\n","image_df = pd.concat([filepaths, labels], axis=1)"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:39:38.613864Z","iopub.status.busy":"2024-03-21T12:39:38.613477Z","iopub.status.idle":"2024-03-21T12:40:29.478533Z","shell.execute_reply":"2024-03-21T12:40:29.477241Z","shell.execute_reply.started":"2024-03-21T12:39:38.613825Z"},"trusted":true},"outputs":[],"source":["import PIL\n","from pathlib import Path\n","from PIL import UnidentifiedImageError\n","\n","path = Path(\"../input/sea-animals-image-dataste\").rglob(\"*.jpg\")\n","for img_p in path:\n"," try:\n"," img = PIL.Image.open(img_p)\n"," except PIL.UnidentifiedImageError:\n"," print(img_p)"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:40:29.480626Z","iopub.status.busy":"2024-03-21T12:40:29.480140Z","iopub.status.idle":"2024-03-21T12:40:30.024259Z","shell.execute_reply":"2024-03-21T12:40:30.023254Z","shell.execute_reply.started":"2024-03-21T12:40:29.480581Z"},"id":"d3-uoP4n1oqK","outputId":"4af0c4a5-c87d-42eb-aa1b-fe89f1fe8876","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["The data will be split into three different categories: Training, Validation and Testing. The training data will be used to train the deep learning CNN model and its parameters will be fine tuned with the validation data. Finally, the performance of the data will be evaluated using the test data(data the model has not previously seen).
"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:40:33.152774Z","iopub.status.busy":"2024-03-21T12:40:33.152464Z","iopub.status.idle":"2024-03-21T12:40:33.160717Z","shell.execute_reply":"2024-03-21T12:40:33.159695Z","shell.execute_reply.started":"2024-03-21T12:40:33.152745Z"},"id":"xaJlHTlz2K4M","trusted":true},"outputs":[],"source":["# Separate in train and test data\n","train_df, test_df = train_test_split(image_df, test_size=0.2, shuffle=True, random_state=42)"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:40:33.162432Z","iopub.status.busy":"2024-03-21T12:40:33.162077Z","iopub.status.idle":"2024-03-21T12:40:33.170105Z","shell.execute_reply":"2024-03-21T12:40:33.169165Z","shell.execute_reply.started":"2024-03-21T12:40:33.162400Z"},"id":"3puUVDwl2Mcz","trusted":true},"outputs":[],"source":["train_generator = ImageDataGenerator(\n"," preprocessing_function=tf.keras.applications.mobilenet_v3.preprocess_input,\n"," validation_split=0.2\n",")\n","\n","test_generator = ImageDataGenerator(\n"," preprocessing_function=tf.keras.applications.mobilenet_v3.preprocess_input\n",")"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:40:33.171486Z","iopub.status.busy":"2024-03-21T12:40:33.171167Z","iopub.status.idle":"2024-03-21T12:40:38.197499Z","shell.execute_reply":"2024-03-21T12:40:38.196446Z","shell.execute_reply.started":"2024-03-21T12:40:33.171460Z"},"id":"CsftNShQ2PaK","outputId":"ee3c99b5-3932-4187-f600-1e86cf334026","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 7559 validated image filenames belonging to 23 classes.\n","Found 1889 validated image filenames belonging to 23 classes.\n","Found 2363 validated image filenames belonging to 23 classes.\n"]}],"source":["# Split the data into three categories.\n","train_images = train_generator.flow_from_dataframe(\n"," dataframe=train_df,\n"," x_col='Filepath',\n"," y_col='Label',\n"," target_size=TARGET_SIZE,\n"," color_mode='rgb',\n"," class_mode='categorical',\n"," batch_size=BATCH_SIZE,\n"," shuffle=True,\n"," seed=42,\n"," subset='training'\n",")\n","\n","val_images = train_generator.flow_from_dataframe(\n"," dataframe=train_df,\n"," x_col='Filepath',\n"," y_col='Label',\n"," target_size=TARGET_SIZE,\n"," color_mode='rgb',\n"," class_mode='categorical',\n"," batch_size=BATCH_SIZE,\n"," shuffle=True,\n"," seed=42,\n"," subset='validation'\n",")\n","\n","test_images = test_generator.flow_from_dataframe(\n"," dataframe=test_df,\n"," x_col='Filepath',\n"," y_col='Label',\n"," target_size=TARGET_SIZE,\n"," color_mode='rgb',\n"," class_mode='categorical',\n"," batch_size=BATCH_SIZE,\n"," shuffle=False\n",")"]},{"cell_type":"code","execution_count":17,"metadata":{"execution":{"iopub.execute_input":"2024-03-21T12:40:38.199510Z","iopub.status.busy":"2024-03-21T12:40:38.198839Z","iopub.status.idle":"2024-03-21T12:40:38.204906Z","shell.execute_reply":"2024-03-21T12:40:38.203816Z","shell.execute_reply.started":"2024-03-21T12:40:38.199468Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["['Clams', 'Corals', 'Crabs', 'Dolphin', 'Eel', 'Fish', 'Jelly Fish', 'Lobster', 'Nudibranchs', 'Octopus', 'Otter', 'Penguin', 'Puffers', 'Sea Rays', 'Sea Urchins', 'Seahorse', 'Seal', 'Sharks', 'Shrimp', 'Squid', 'Starfish', 'Turtle_Tortoise', 'Whale']\n"]}],"source":["class_labels = list(test_images.class_indices.keys())\n","print(class_labels)"]},{"cell_type":"code","execution_count":18,"metadata":{"_kg_hide-output":true,"execution":{"iopub.execute_input":"2024-03-21T12:40:38.207238Z","iopub.status.busy":"2024-03-21T12:40:38.206339Z","iopub.status.idle":"2024-03-21T12:40:40.930890Z","shell.execute_reply":"2024-03-21T12:40:40.930059Z","shell.execute_reply.started":"2024-03-21T12:40:38.207208Z"},"id":"sLbR4WtD2RPg","trusted":true},"outputs":[],"source":["# Data Augmentation Step\n","augment = tf.keras.Sequential([\n"," layers.experimental.preprocessing.Resizing(224,224),\n"," layers.experimental.preprocessing.Rescaling(1./255),\n"," layers.experimental.preprocessing.RandomFlip(\"horizontal\"),\n"," layers.experimental.preprocessing.RandomRotation(0.1),\n"," layers.experimental.preprocessing.RandomZoom(0.1),\n"," layers.experimental.preprocessing.RandomContrast(0.1),\n"," #layers.experimental.preprocessing.RandomCrop(0.1, 0.1),\n"," layers.experimental.preprocessing.RandomHeight(0.1),\n"," layers.experimental.preprocessing.RandomWidth(0.1),\n","])"]},{"cell_type":"markdown","metadata":{"id":"xSEhK2w02Uk-"},"source":["# 🤹Training the model\n","The model images will be subjected to a pre-trained CNN model called MobileNetV2. Three callbacks will be utilized to monitor the training. These are: Model Checkpoint, Early Stopping, Tensorboard callback. The summary of the model hyperparameter is shown as follows:
\n","\n","\n"," Batch size: 32
\n"," Epochs: 100
\n"," Input Shape: (224, 224, 3)
\n"," Output layer: 23\n","
\n"," Thanks for viewing my work. If you like it, consider sharing it to others or give feedback to improve the notebook. Have a beautiful day my friend.\n","
\n","