{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# WHALES AND DOLPHINS CNN ##\n\n*A CNN Model*\n\n## Overview\nAlthough this competition's goal was to find each individual, for this notebook our goal will be to identify species (instead of specific individuals). We do this to make computer vision classification using transfer learning more accessible by using fewer classes. In addition, being able to identify species can be extremely useful in real-life situations such as marine biology. For example, monitoring migration routes which can change due to human interaction and/or climate change. This study opens the door to further research on marine life. In this notebook, we will cover EDA as well as two different model architectures. Finally, we will cover our results at the end of the notebook. \n\nCompetition Link: https://www.kaggle.com/competitions/happy-whale-and-dolphin\n\nData Source: https://www.kaggle.com/competitions/happy-whale-and-dolphin/data \n\nFor direct data download: https://www.kaggle.com/competitions/happy-whale-and-dolphin/data#:~:text=get_app-,Download,-All or command line as >_ kaggle competitions download -c happy-whale-and-dolphin\n\nThis notebook has the follow layout:\n1. Brief Description of the Problem and Data\n2. Exploratory Data Analysis (EDA) and Preprocessing\n3. Model Construction and Architecture\n4. Results and Analysis\n5. Conclusion","metadata":{}},{"cell_type":"code","source":"#import libraries\nimport numpy as np \nimport pandas as pd \nimport cv2\nimport matplotlib.pyplot as plt \nimport seaborn as sns \nimport os \nfrom skimage import io\nfrom skimage.color import rgb2gray\nimport plotly.express as px\nimport random\nfrom sklearn.utils import shuffle\nimport shutil\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import OneHotEncoder\nimport tensorflow as tf\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import BatchNormalization\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import EarlyStopping","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-09-29T10:07:45.992297Z","iopub.execute_input":"2022-09-29T10:07:45.992584Z","iopub.status.idle":"2022-09-29T10:07:46.000321Z","shell.execute_reply.started":"2022-09-29T10:07:45.992553Z","shell.execute_reply":"2022-09-29T10:07:45.999299Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#check tensorflow verison\ntf.__version__","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.009192Z","iopub.execute_input":"2022-09-29T10:07:46.009513Z","iopub.status.idle":"2022-09-29T10:07:46.021839Z","shell.execute_reply.started":"2022-09-29T10:07:46.009476Z","shell.execute_reply":"2022-09-29T10:07:46.021072Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#get global path\nglobal_path = \"/kaggle/input/happy-whale-and-dolphin/\"\nos.listdir(global_path)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.026646Z","iopub.execute_input":"2022-09-29T10:07:46.027473Z","iopub.status.idle":"2022-09-29T10:07:46.035919Z","shell.execute_reply.started":"2022-09-29T10:07:46.027423Z","shell.execute_reply":"2022-09-29T10:07:46.035275Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#set paths\nsample_submission_path = \"/kaggle/input/happy-whale-and-dolphin/sample_submission.csv\"\ntrain_images_path = \"/kaggle/input/happy-whale-and-dolphin/train_images/\"\ntrain_path = \"/kaggle/input/happy-whale-and-dolphin/train.csv\"\ntest_images_path = \"/kaggle/input/happy-whale-and-dolphin/test_images/\"","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.049981Z","iopub.execute_input":"2022-09-29T10:07:46.050425Z","iopub.status.idle":"2022-09-29T10:07:46.054304Z","shell.execute_reply.started":"2022-09-29T10:07:46.050384Z","shell.execute_reply":"2022-09-29T10:07:46.053653Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 1. Brief Description of the Problem and Data\n\nWhales and dolphins are a key component for the marine ecosystem so being able to identify species could be helpful for other research projects. In this project, we are given 51033 images which are connected to the training dataframe, in which we are given the columns 'image id' (as jpg), 'species', and 'individual_id'. Again, we will not be trying to identify the individual_id column, instead we will try to identify the species columns based on computer vision techniques. We also are given testing images, which in our case we will not use since the testing data is just images with no way to know the species to compare. We also note that we do not have bounding boxes on the images and all the images are different shapes and resolutions.\n\nIn other words:\n* 51033 images of different sizes/dimensions. \n* Images have 3 color channels.\n* Train images are saved as jpg in a unique folder ('train_images') \n* Train dataframe (given as a CSV file) has 3 columns (image, species, individual_id) with 51033 entries.\n* We are given the test images but since we will not be modeling individual_id (we will model for species instead) we don't need the test_images folder. \n* 30 unique species\n* No missing entries in dataframe","metadata":{}},{"cell_type":"code","source":"#view data\ntrain_data = pd.read_csv(train_path)\ntrain_data.head()   ","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.069285Z","iopub.execute_input":"2022-09-29T10:07:46.069915Z","iopub.status.idle":"2022-09-29T10:07:46.208859Z","shell.execute_reply.started":"2022-09-29T10:07:46.069868Z","shell.execute_reply":"2022-09-29T10:07:46.207982Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"########### TEST ##########\n'''\nThis cell is used to do quick debugging and make sure \nall cells run, prior to running all the images. You can \nrun this notebook as such if you don't want to wait for \nall images. Simply uncomment below'''\n\n#train_data = train_data[0:100]","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.210599Z","iopub.execute_input":"2022-09-29T10:07:46.210907Z","iopub.status.idle":"2022-09-29T10:07:46.215751Z","shell.execute_reply.started":"2022-09-29T10:07:46.210866Z","shell.execute_reply":"2022-09-29T10:07:46.215087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#training data information and data types\ntrain_data.info()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.216680Z","iopub.execute_input":"2022-09-29T10:07:46.217283Z","iopub.status.idle":"2022-09-29T10:07:46.246629Z","shell.execute_reply.started":"2022-09-29T10:07:46.217231Z","shell.execute_reply":"2022-09-29T10:07:46.245626Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#number of missing data\ntrain_data.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.249149Z","iopub.execute_input":"2022-09-29T10:07:46.249690Z","iopub.status.idle":"2022-09-29T10:07:46.261061Z","shell.execute_reply.started":"2022-09-29T10:07:46.249644Z","shell.execute_reply":"2022-09-29T10:07:46.260399Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#describe data\ntrain_data.describe()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.261998Z","iopub.execute_input":"2022-09-29T10:07:46.262870Z","iopub.status.idle":"2022-09-29T10:07:46.287142Z","shell.execute_reply.started":"2022-09-29T10:07:46.262827Z","shell.execute_reply":"2022-09-29T10:07:46.286131Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# find unique species\nprint(train_data['species'].unique())","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.289115Z","iopub.execute_input":"2022-09-29T10:07:46.289456Z","iopub.status.idle":"2022-09-29T10:07:46.299468Z","shell.execute_reply.started":"2022-09-29T10:07:46.289424Z","shell.execute_reply":"2022-09-29T10:07:46.298484Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 2. Exploratory Data Analysis (EDA) and Preprocessing\n\nIn our exploration of data, we will plot the counts of species as well as visualize some of our images. We first must correct misspellings and groupings. In addition, using our knowledge of dolphins and whales, we will plot how many of each we have as well. During model construction, we will crop the images and we will normalize them as well which is part of the preprocessing step.\n\nWe see there are 30 unique species. However, there are also some misspellings. We will fix the misspelling errors. We also notice that 'globis' is a term for a pilot whale so we will also group those in one. It is also important to note that killer whales and pilot whales (including short-finned and long-finned pilot whales) are actually a type of dolphin.\n\nOn a discussion board in Kaggle, the competition host clarifies the species: \"long_finned_pilot_whale and short_finned_pilot_whale are very similar species where we would expect more variation between individuals than between species. pilot_whale and globis are both short_finned_pilot_whale and thus the three can be merged.\" https://www.kaggle.com/c/happy-whale-and-dolphin/discussion/305468\n\n\nNote that more **data preprocessing** for the images will actually be done during the batching and running of the model. Due to space limitations, if we were to edit each image and save it, we would waste time and most likely use up much valuable memory space. For our preprocessing, we will be using ImageDataGenerator which allows us to modify/augment images as we batch them. In that step, we will normalize the images and split into training and testing set. We also will randomly flip them vertically and/or rotate the images. We do this to allow the model to learn more image possiblities of what fin positions could be. We also will have all images resized to 64x64x3 so they are all standard for the model to learn. Finally, after much delibertion and sleepless nights for waiting for the model to train, we have decided to greatly reduce the number of species (and therefore images) that we train the model with. Additionally, the species are quite unbalanced so by reducing the image count, we are making the species counts more balanced which can result in stronger models. As for training speed, I'm not sure why (and if you can tell me in the comments I would appreciate it) but the model runs mostly on CPU and takes more than an hour to train each epoch in both models. For these two reasons (time to train and unbalanced sets), in this section we will also reduce the number of images to work with. \n\nSummary of preprocessing steps we will perform:\n* Fix misspellings\n* Properly group whales and dolphins\n* Normalize images (multiply by 1/255)\n* Crop images to 64x64 pixel and 3 channels (64, 64, 3) \n* Split into training-validation sets (80-20 split) \n* Randomly flip vertically (augment images) \n* Randomly rotate up to 45 degrees (augment images) ","metadata":{}},{"cell_type":"code","source":"# fix misspellings\ntrain_data['species'].replace({'bottlenose_dolpin':'bottlenose_dolphin',  #missing the 'h' in dolphin\n                               'kiler_whale': 'killer_whale',             #missing the 'l' in killer\n                               'globis':'pilot_whale',                    #correcting and merging species names\n                               'short_finned_pilot_whale': 'pilot_whale', #merge pilot whales species names\n                               'long_finned_pilot_whale':'pilot_whale'},  #merge merge pilot whales\n                              inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.300600Z","iopub.execute_input":"2022-09-29T10:07:46.300835Z","iopub.status.idle":"2022-09-29T10:07:46.311905Z","shell.execute_reply.started":"2022-09-29T10:07:46.300806Z","shell.execute_reply":"2022-09-29T10:07:46.311258Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# actual number of unique speceis\nprint(f'Number of Species: {train_data[\"species\"].nunique()}')","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2022-09-29T10:07:46.312844Z","iopub.execute_input":"2022-09-29T10:07:46.313495Z","iopub.status.idle":"2022-09-29T10:07:46.328334Z","shell.execute_reply.started":"2022-09-29T10:07:46.313452Z","shell.execute_reply":"2022-09-29T10:07:46.327401Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"After fixing the mispelling and labels in species, we actually have 26 different species we're working with. We see that some species have many more recordings than others. Specifically, the bottlenose dolphin has the most photos while the fraisers dolphin has the least. Since the bottlenose dolphin has so many more counts than the rest, we may run into unbalanced dataset training difficulties. which we will fix later in this section.","metadata":{}},{"cell_type":"code","source":"#species counts\ntrain_data['species'].value_counts()","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2022-09-29T10:07:46.331988Z","iopub.execute_input":"2022-09-29T10:07:46.332225Z","iopub.status.idle":"2022-09-29T10:07:46.343587Z","shell.execute_reply.started":"2022-09-29T10:07:46.332196Z","shell.execute_reply":"2022-09-29T10:07:46.342697Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#plot species counts so that its easier to visualize\nfig = plt.figure(figsize=(16, 5))\nsns.countplot(x=train_data['species'],\n            order=train_data['species'].value_counts().index).set(title='Species Counts')\nplt.xticks(rotation=90);","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2022-09-29T10:07:46.344521Z","iopub.execute_input":"2022-09-29T10:07:46.345044Z","iopub.status.idle":"2022-09-29T10:07:46.748680Z","shell.execute_reply.started":"2022-09-29T10:07:46.345013Z","shell.execute_reply":"2022-09-29T10:07:46.747746Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now let's visualize whales and dolphin and compare the spread. It is important to note that beluga's are whales and that the following animals are actually part of the dolphin family:\n1. Killer whale\n2. False killer whale\n3. Pigmy killer whale\n4. Melon-headed whale\n5. Southern Right whale\n6. Short finned pilot whale\n7. Long finned pilot whale\n\nTherefore, we will create a new column and categorize the animals. Then we will visualize them.","metadata":{}},{"cell_type":"code","source":"#declare dolphins and whales\ndolphins = ['melon_headed_whale', 'false_killer_whale',\n            'bottlenose_dolphin', 'southern_right_whale',\n            'common_dolphin', 'killer_whale',\n            'dusky_dolphin', 'pilot_whale', 'sei_whale',\n            'spinner_dolphin', 'spotted_dolphin',\n            'commersons_dolphin', 'white_sided_dolphin',\n            'rough_toothed_dolphin', 'pantropic_spotted_dolphin',\n            'pygmy_killer_whale', 'frasiers_dolphin']\n\nwhales = ['humpback_whale','beluga', 'minke_whale', 'fin_whale','blue_whale', 'gray_whale',\n         'cuviers_beaked_whale', 'brydes_whale']\n\n#add new column to dataframe with correct classification (whale or dolphin)\ntrain_data['family'] = 'dolphin'\nfor index in range(len(train_data)):\n    if train_data.species[index] in whales:\n        train_data.family[index] = 'whale'","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.749954Z","iopub.execute_input":"2022-09-29T10:07:46.750289Z","iopub.status.idle":"2022-09-29T10:07:46.778126Z","shell.execute_reply.started":"2022-09-29T10:07:46.750256Z","shell.execute_reply":"2022-09-29T10:07:46.777266Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#visualize dolphins and whale counts\nfig = plt.figure(figsize=(16, 5))\nsns.countplot(x=train_data['species'],\n              order=train_data['species'].value_counts().index, \n              hue=train_data['family'],\n              dodge=False).set(title='Species Counts by Family')\nplt.xticks(rotation=90);","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:46.779655Z","iopub.execute_input":"2022-09-29T10:07:46.779955Z","iopub.status.idle":"2022-09-29T10:07:47.249169Z","shell.execute_reply.started":"2022-09-29T10:07:46.779914Z","shell.execute_reply":"2022-09-29T10:07:47.248192Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Great! Above we see we have alot of bottlenose dolphin images and very few of the rough-toothed dolphin and fraisers dolphin. These values are quite unbalanced!\n\nBelow we see we have 47.4% whales and 52.6% dolphins which is quite close; our dataset is quite balance with regards to whales vs. dolphins. Too bad we were are not just classifying into two groups! But, not to worry because we have 25 groups to classify later on.  ","metadata":{}},{"cell_type":"code","source":"# visualize as a pie chart\nfig = px.pie(train_data, values = train_data['family'].value_counts().values, names = train_data['family'].unique())\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:47.251332Z","iopub.execute_input":"2022-09-29T10:07:47.251669Z","iopub.status.idle":"2022-09-29T10:07:48.483617Z","shell.execute_reply.started":"2022-09-29T10:07:47.251624Z","shell.execute_reply":"2022-09-29T10:07:48.482751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now that we have a better idea of what kind of data we have to identify the animals, let's take a look at the images we are given. However, before that let's take care of our uneven/unbalanced datasets. Since we have very uneven class sizes, we will delete the species that appear >900 times. In addition, we will randomly delete a percentage of the species which have many many more images than the others (for example, bottlenose dolphins).","metadata":{}},{"cell_type":"code","source":"#delete species with <900 images\nrare_species = ['brydes_whale', 'pantropic_spotted_dolphin', 'commersons_dolphin', 'pygmy_killer_whale', \n                'rough_toothed_dolphin', 'frasiers_dolphin', 'white_sided_dolphin', 'cuviers_beaked_whale',\n                'common_dolphin', 'sei_whale', 'spotted_dolphin', 'southern_right_whale','pilot_whale', \n                'gray_whale', 'fin_whale', 'minke_whale', 'melon_headed_whale', 'spinner_dolphin']\nprint(f'We will delete these species: {rare_species}')\n\n#delete the rare species\nfor species in rare_species: \n    train_data = train_data[train_data['species'] != species]","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:48.486326Z","iopub.execute_input":"2022-09-29T10:07:48.486642Z","iopub.status.idle":"2022-09-29T10:07:48.502506Z","shell.execute_reply.started":"2022-09-29T10:07:48.486611Z","shell.execute_reply":"2022-09-29T10:07:48.501501Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"We have way too many of these species: bottlenose_dolphin, beluga, humpback_whale.\")\n\n#randomly delete 50% (about 5000 total) of bottlenose dolphin images\ntrain_data = train_data.drop(train_data[train_data['species'] == 'bottlenose_dolphin'].sample(frac=.5).index)\n\n#randomly delete 30% \ntrain_data = train_data.drop(train_data[train_data['species'] == 'beluga'].sample(frac=.3).index)\ntrain_data = train_data.drop(train_data[train_data['species'] == 'humpback_whale'].sample(frac=.3).index)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:48.503971Z","iopub.execute_input":"2022-09-29T10:07:48.504318Z","iopub.status.idle":"2022-09-29T10:07:48.520660Z","shell.execute_reply.started":"2022-09-29T10:07:48.504273Z","shell.execute_reply":"2022-09-29T10:07:48.519741Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#reset index after deleting rows\ntrain_data = train_data.reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:48.521847Z","iopub.execute_input":"2022-09-29T10:07:48.522668Z","iopub.status.idle":"2022-09-29T10:07:48.527690Z","shell.execute_reply.started":"2022-09-29T10:07:48.522624Z","shell.execute_reply":"2022-09-29T10:07:48.526865Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#double check new species counts\ntrain_data['species'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:48.528999Z","iopub.execute_input":"2022-09-29T10:07:48.529397Z","iopub.status.idle":"2022-09-29T10:07:48.544417Z","shell.execute_reply.started":"2022-09-29T10:07:48.529353Z","shell.execute_reply":"2022-09-29T10:07:48.543660Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#visualize species for model\nfig = plt.figure(figsize=(16, 4))\nsns.countplot(x=train_data['species'],\n            order=train_data['species'].value_counts().index).set(title='Species Counts for Modeling')\nplt.xticks(rotation=45);","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:48.545687Z","iopub.execute_input":"2022-09-29T10:07:48.546490Z","iopub.status.idle":"2022-09-29T10:07:48.801169Z","shell.execute_reply.started":"2022-09-29T10:07:48.546442Z","shell.execute_reply":"2022-09-29T10:07:48.800291Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Great! It looks like we have 7 unique species and a much more balanced dataset! Now let's go ahead by checking the image shapes and visualize some of these animals.","metadata":{}},{"cell_type":"code","source":"#show array of first 5 images\nfor i in range(5):\n    image = train_data.image[i]\n    image = io.imread(train_images_path+image, cv2.IMREAD_GRAYSCALE)\n    print(f'Shape for image {i+1}: {image.shape}')","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:48.802640Z","iopub.execute_input":"2022-09-29T10:07:48.802888Z","iopub.status.idle":"2022-09-29T10:07:49.691503Z","shell.execute_reply.started":"2022-09-29T10:07:48.802859Z","shell.execute_reply":"2022-09-29T10:07:49.690578Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#view the first 25 images\nfig, ax = plt.subplots(5, 5, figsize=(10, 10))\n\nfor i, axi in enumerate(ax.flat):\n    file = train_data.image[i]\n    image = io.imread(train_images_path+file)\n    axi.imshow(image)\n    axi.set(xticks=[], yticks=[], xlabel = train_data.species[i]);\n    cv2.waitKey(0)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:07:49.693181Z","iopub.execute_input":"2022-09-29T10:07:49.693480Z","iopub.status.idle":"2022-09-29T10:08:01.944208Z","shell.execute_reply.started":"2022-09-29T10:07:49.693440Z","shell.execute_reply":"2022-09-29T10:08:01.943484Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Those are some good looking dolphins and whales! Now that our images and species are taken care of, let's remove the columns in the dataframe we will not need for training, just to clean up our space a bit.","metadata":{}},{"cell_type":"code","source":"#delete columns we don't need\ntrain_data = train_data.drop(axis=1, columns=['individual_id', 'family'])","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:01.945562Z","iopub.execute_input":"2022-09-29T10:08:01.946008Z","iopub.status.idle":"2022-09-29T10:08:01.951465Z","shell.execute_reply.started":"2022-09-29T10:08:01.945973Z","shell.execute_reply":"2022-09-29T10:08:01.950335Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#one last look at final dataframe\ntrain_data.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:01.952595Z","iopub.execute_input":"2022-09-29T10:08:01.952825Z","iopub.status.idle":"2022-09-29T10:08:01.972523Z","shell.execute_reply.started":"2022-09-29T10:08:01.952795Z","shell.execute_reply":"2022-09-29T10:08:01.971673Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#number of species\nprint(f\"We have {train_data['species'].nunique()} unique species to send to the model.\")","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:01.974114Z","iopub.execute_input":"2022-09-29T10:08:01.974392Z","iopub.status.idle":"2022-09-29T10:08:01.980669Z","shell.execute_reply.started":"2022-09-29T10:08:01.974354Z","shell.execute_reply":"2022-09-29T10:08:01.979738Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#check shape of dataframe\nprint(f'We have {train_data.shape[0]} entries and {train_data.shape[1]} columns.')","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:01.982178Z","iopub.execute_input":"2022-09-29T10:08:01.982480Z","iopub.status.idle":"2022-09-29T10:08:01.991214Z","shell.execute_reply.started":"2022-09-29T10:08:01.982438Z","shell.execute_reply":"2022-09-29T10:08:01.990320Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 3. Model Construction and Architecture\n\nDuring both model trainings, we will shuffle the data, normalize the images, and crop them to 64x64 pixels. In addition, we will augment the images during batching by randomly rotating the images and/or randomly flipping them vertically. These steps can help make the model more robust and learn better because the images are all standardized and shown in more positions. We will  be using Tensorflow layers to construct our models.\n\nFor this project we will use one very simple model and one transfer leanring model. Both models will have five layers after its \"base\" layer. We call it a \"base\" layer so that we may more easily compare the two models later on. Each model will output to produce the 7 different category options. In addition, each model's input will be with a shape of (64, 64, 3). The five layers after the 'base' layer will be as such:\n1. Global Average Pooling\n2. Dense layer with 64 filters and activation function ReLU\n3. Dropout layer with 0.4 dropout\n4. Dense layer with 128 filters and activation function ReLU\n5. Output layer using Dense with 7 categories and activation function as 'softmax'\n\nWe use ReLU as our activation function because it usually works well with computer vision models and has produced good results in the past. We use dropout which is an effective way to prevent overfitting and reduce the over-generalization error. Our output is 7 since we have 7 species to choose from. Finally, we use softmax because it is able to bring the logistic regression concept into a multi-class setting and has been shown to produce good results. During the making of this notebook, we tried different values for dropout (including 0.2 and 0.8) and we decided to stay with 0.4. In addition, we will use categorical crossentropy for loss measurement and categorical accuracy. We will use an Adam as our optimizer with a learning rate of 0.0001.  \n\n**SIMPLE MODEL:** Our simple model will have a \"base\" of one Conv2D layer. This is to make it the simplist we can to be able to compare the models later. This Conv2D layer will have 32 filters, a kernel size of 2x2, stride of 1, and activation as ReLU. We use 32 filters because we know that our next dense layer will be 64 filters, we can easily step it up to continue training. After our simple base layer, we have our 5 layers mentioned above. Although we could have used different hyperparameters (such as filter size, stride and activation) we decided to stick with the values presented as they are our go-to when constructing a simple model.\n\n**RESNET50 BASE MODEL:** ResNet50, otherwise known as Residual Networks 50, and which has 50 layers in its neural network, is the base transfer learning model we will use to compare. ResNet50 overcomes the vanishing gradient problem during back propagation by using skip connections throughout its structure. ReNet50 uses two types of blocks, the identity block and convolutional block. For more information on ResNet50: https://blog.devgenius.io/resnet50-6b42934db431 and https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet50/ResNet50. We will not be using the last layer of the ResNet50 model, and instead we will use the 5 layers mentioned above to finish the architecture. Lastly, we will use the ImageNet base verison of ResNet50 which was trained on the ImageNet data.\n\n**REASONING:** We chose to compare a very simple model to a transfer learning model to really demonstrate how transfer learning can change your performance and time to train. We specifically chose ResNet50 because it uses skip connections which help avoid the vanishing weight problem (which you can read more about [here](https://towardsdatascience.com/the-vanishing-gradient-problem-69bf08b15484). We chose the hyperparameters we did because they have proven to perform well in other models. \n\nNote, data preprocessing for the images will be done during the batching and running of the model. Due to space limitations, if we were to edit each image and save it, we would waste time and most likely use up much valuable memory space. For our preprocessing, we will be using ImageDataGenerator which allows us to modify/augment images as we batch them. In that step, we will normalize the images and split into training and testing set. We also will randomly flip them vertically and/or rotate the images. We do this to allow the model to learn more image possiblities of what fin positions could be. We also will have all images resized to 64x64x3 so they are all standard for the model to learn. \n\n**ResNet50 Architecture**\n\n![image.png](attachment:ddca6062-f68e-4dc4-8362-1b4eddc07028.png)\nhttps://www.researchgate.net/figure/Left-ResNet50-architecture-Blocks-with-dotted-line-represents-modules-that-might-be_fig3_331364877\n\n","metadata":{},"attachments":{"ddca6062-f68e-4dc4-8362-1b4eddc07028.png":{"image/png":"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"}}},{"cell_type":"code","source":"#set globals\nBATCH_SIZE = 256\nRANDOM_STATE = 49\nEPOCHS = 6\nNUM_SPECIES = train_data[\"species\"].nunique() #7 classes\npixels = 64\nTARGET_SIZE = (pixels, pixels) \nINPUT_SHAPE = (pixels, pixels, 3)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:01.992395Z","iopub.execute_input":"2022-09-29T10:08:01.993286Z","iopub.status.idle":"2022-09-29T10:08:02.004149Z","shell.execute_reply.started":"2022-09-29T10:08:01.993228Z","shell.execute_reply":"2022-09-29T10:08:02.003222Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#shuffle data\ntrain_data = shuffle(train_data, random_state=RANDOM_STATE)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:02.005522Z","iopub.execute_input":"2022-09-29T10:08:02.005901Z","iopub.status.idle":"2022-09-29T10:08:02.018583Z","shell.execute_reply.started":"2022-09-29T10:08:02.005870Z","shell.execute_reply":"2022-09-29T10:08:02.017579Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Setup GPU accelerator - configure Strategy\ntpu = None\ntry:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.TPUStrategy(tpu) #Assume TPU\nexcept ValueError:\n    strategy = tf.distribute.get_strategy() #if not TPU, set default for GPU/CPU","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:02.022045Z","iopub.execute_input":"2022-09-29T10:08:02.022425Z","iopub.status.idle":"2022-09-29T10:08:02.043180Z","shell.execute_reply.started":"2022-09-29T10:08:02.022388Z","shell.execute_reply":"2022-09-29T10:08:02.042329Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#set up image generator to split into 80/20 train-validation groups\ndatagen = ImageDataGenerator(validation_split=0.2) \n\n#set up training batching\ntrain_generator = datagen.flow_from_dataframe(\n    dataframe=train_data,\n    directory=train_images_path,\n    x_col=\"image\",\n    y_col=\"species\",\n    subset=\"training\",\n    batch_size=BATCH_SIZE,\n    seed=RANDOM_STATE,\n    class_mode=\"categorical\",\n    target_size=TARGET_SIZE) #resize images to 64x64x3\n\n#set up valid data batching\nvalid_generator = datagen.flow_from_dataframe(\n    dataframe=train_data,\n    directory=train_images_path,\n    x_col=\"image\",\n    y_col=\"species\",\n    subset=\"validation\",\n    batch_size=BATCH_SIZE,\n    seed=RANDOM_STATE,\n    class_mode=\"categorical\",\n    target_size=TARGET_SIZE) #resize images to 64x64x3","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:02.045369Z","iopub.execute_input":"2022-09-29T10:08:02.046421Z","iopub.status.idle":"2022-09-29T10:08:02.273343Z","shell.execute_reply.started":"2022-09-29T10:08:02.046378Z","shell.execute_reply":"2022-09-29T10:08:02.272443Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Build and Train Simple Model","metadata":{}},{"cell_type":"code","source":"with strategy.scope(): #use TPU/GPU strategy\n\n    #define input image size\n    input_ = tf.keras.Input(shape=INPUT_SHAPE) #64x64x3\n\n    #image augmentation\n    x = layers.Rescaling(1./255.0)(input_) #normalize\n    x = layers.RandomFlip('vertical')(x)   #randomly flip\n    x = layers.RandomRotation(0.3)(x)      #randomly rotate up to 30 degrees\n\n    #set up base model (simple base)\n    x = layers.Conv2D(32, kernel_size=2, strides=(1, 1), activation='relu')(x)\n\n    #set up model which will be the same in each \n    x = layers.GlobalAveragePooling2D()(x)\n    x = layers.Dense(64, activation='relu')(x) \n    x = layers.Dropout(0.4)(x) \n    x = layers.Dense(128, activation='relu')(x) \n    output_ = layers.Dense(NUM_SPECIES, activation='softmax')(x) #output 7 categories/classes\n\n    #make model\n    model_1 = tf.keras.models.Model(inputs = input_, \n                                    outputs=output_)\n\n    #compile model\n    model_1.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = 0.0001), #set up optimizer with small lr\n                    loss = tf.keras.losses.CategoricalCrossentropy(), #set up loss function\n                    metrics = [tf.keras.metrics.CategoricalAccuracy()]) #set up performance metric","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-09-29T10:08:02.274746Z","iopub.execute_input":"2022-09-29T10:08:02.275411Z","iopub.status.idle":"2022-09-29T10:08:02.543413Z","shell.execute_reply.started":"2022-09-29T10:08:02.275363Z","shell.execute_reply":"2022-09-29T10:08:02.542384Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#view model summary/structure\nmodel_1.summary()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-09-29T10:08:02.544550Z","iopub.execute_input":"2022-09-29T10:08:02.544802Z","iopub.status.idle":"2022-09-29T10:08:02.556237Z","shell.execute_reply.started":"2022-09-29T10:08:02.544764Z","shell.execute_reply":"2022-09-29T10:08:02.555322Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# train model_1\nhistory_model_1 = model_1.fit(train_generator,\n                              epochs = EPOCHS,\n                              validation_data = valid_generator)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:02.557627Z","iopub.execute_input":"2022-09-29T10:08:02.557986Z","iopub.status.idle":"2022-09-29T10:08:37.227340Z","shell.execute_reply.started":"2022-09-29T10:08:02.557949Z","shell.execute_reply":"2022-09-29T10:08:37.226543Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#check keys before plotting\nhistory_model_1.history.keys()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:37.229126Z","iopub.execute_input":"2022-09-29T10:08:37.229632Z","iopub.status.idle":"2022-09-29T10:08:37.236807Z","shell.execute_reply.started":"2022-09-29T10:08:37.229589Z","shell.execute_reply":"2022-09-29T10:08:37.236003Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# graph accuracy\nplt.plot(history_model_1.history['categorical_accuracy'])\nplt.plot(history_model_1.history['val_categorical_accuracy'])\nplt.title('Model 1 Accuracy')\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.legend(['train', 'validate'], loc='upper left')\nplt.show();\n\n#graph loss\nplt.plot(history_model_1.history['loss'])\nplt.plot(history_model_1.history['val_loss'])\nplt.title('Model 1 Loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'validate'], loc='upper left')\nplt.show();","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:37.238308Z","iopub.execute_input":"2022-09-29T10:08:37.238765Z","iopub.status.idle":"2022-09-29T10:08:37.863627Z","shell.execute_reply.started":"2022-09-29T10:08:37.238722Z","shell.execute_reply":"2022-09-29T10:08:37.862737Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#clear memory to make more room\nmodel_1 = None\nhistory_model_1 = None","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:08:37.864984Z","iopub.execute_input":"2022-09-29T10:08:37.865313Z","iopub.status.idle":"2022-09-29T10:08:37.869690Z","shell.execute_reply.started":"2022-09-29T10:08:37.865266Z","shell.execute_reply":"2022-09-29T10:08:37.868902Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Transfer Learning Model\nRedo model with transfer learning from ImageNet using ResNet50","metadata":{}},{"cell_type":"code","source":"with strategy.scope(): #use TPU/GPU\n    \n    #set up resnet50 base \n    base_model = tf.keras.applications.resnet50.ResNet50(input_shape=INPUT_SHAPE, \n                                                         include_top=False, \n                                                         weights='imagenet') \n    \n    #we set the ResNet base as untrainable so we don't reset/change \n    #its learned weights (the whole reason we are using it)\n    base_model.trainable = False\n    \n    #set input\n    input_ = tf.keras.Input(shape=INPUT_SHAPE) #input shape is 64x64x3\n    \n    #image augmentation\n    x = layers.Rescaling(1./255.0)(input_)    #normalize\n    x = layers.RandomFlip('vertical')(x)  #randomly flip\n    x = layers.RandomRotation(0.3)(x)     #randomly rotate\n\n    #set base model (ResNet50)\n    x = base_model(x, training=False) \n\n    #repeat same top layers as before\n    x = layers.GlobalAveragePooling2D()(x)\n    x = layers.Dense(64, activation='relu')(x) \n    x = layers.Dropout(0.4)(x) \n    x = layers.Dense(128, activation='relu')(x) \n    output_ = layers.Dense(NUM_SPECIES, activation='softmax')(x)  #output 7 classes\n\n    #make model\n    model_2 = tf.keras.models.Model(inputs = input_, \n                                    outputs=output_)\n    \n    #compile the model \n    model_2.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = 0.0001), #set optimizer with small lr\n                    loss = tf.keras.losses.CategoricalCrossentropy(), #set loss function\n                    metrics = [tf.keras.metrics.CategoricalAccuracy()]) #set performance metric","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:09:45.671439Z","iopub.execute_input":"2022-09-29T10:09:45.672307Z","iopub.status.idle":"2022-09-29T10:09:47.886715Z","shell.execute_reply.started":"2022-09-29T10:09:45.672257Z","shell.execute_reply":"2022-09-29T10:09:47.885833Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#quick look at model\nmodel_2.summary()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-09-29T10:09:49.856968Z","iopub.execute_input":"2022-09-29T10:09:49.857298Z","iopub.status.idle":"2022-09-29T10:09:49.876813Z","shell.execute_reply.started":"2022-09-29T10:09:49.857259Z","shell.execute_reply":"2022-09-29T10:09:49.876035Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# train model_2\nhistory_model_2 = model_2.fit(train_generator,\n                              epochs = EPOCHS,\n                              validation_data = valid_generator)","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:09:53.567347Z","iopub.execute_input":"2022-09-29T10:09:53.568449Z","iopub.status.idle":"2022-09-29T10:10:23.973715Z","shell.execute_reply.started":"2022-09-29T10:09:53.568404Z","shell.execute_reply":"2022-09-29T10:10:23.972790Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#model_2 keys\nhistory_model_2.history.keys()","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:09:12.610162Z","iopub.execute_input":"2022-09-29T10:09:12.610469Z","iopub.status.idle":"2022-09-29T10:09:12.616812Z","shell.execute_reply.started":"2022-09-29T10:09:12.610436Z","shell.execute_reply":"2022-09-29T10:09:12.615773Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# graph accuracy\nplt.plot(history_model_2.history['categorical_accuracy'])\nplt.plot(history_model_2.history['val_categorical_accuracy'])\nplt.title('Model 2 Accuracy')\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.legend(['train', 'validate'], loc='upper left')\nplt.show();\n\n#graph loss\nplt.plot(history_model_2.history['loss'])\nplt.plot(history_model_2.history['val_loss'])\nplt.title('Model 2 Loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'validate'], loc='upper left')\nplt.show();","metadata":{"execution":{"iopub.status.busy":"2022-09-29T10:09:12.618345Z","iopub.execute_input":"2022-09-29T10:09:12.618736Z","iopub.status.idle":"2022-09-29T10:09:13.032539Z","shell.execute_reply.started":"2022-09-29T10:09:12.618693Z","shell.execute_reply":"2022-09-29T10:09:13.031532Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Results\n\nWe have successfully trained our two models, the simple model (model 1) and the transfer learning model (model 2). The first thing we can see very quickly is the big difference in loss and accuracy between the simple and transfer learning model; we see that the simple model is learning but with only 6 epochs, it gets ~0.25 accuracy and down to ~1.85 loss. In comparision, the second model learns much faster - it after 6 epochs it has a failry high accuracy and the loss is much lower. Another thing to note is that the validation (testing) set in the second model seemed to perform much better every epoch than did the first model. \n\nThese differences can be summed up to faster learning and a better model with model 2. This is becasue the second model has already learned basic concepts because it was trained on ImageNet. In addition, the second model is much more complex (more than 50 layers!).This is a great example of how transfer learning can help models learn faster and better. \n\nSomethings to consider belong transfer learning are the hyperparameters. In our simple model, we used a Conv2D layer as our base layer. We tried different hyperparameters (including filters=64, strides=3, and kernel_size=3,3) but it seemed to perform well enough with the parameters we set above. We know in the ResNet50 model, there are many more hyperparameters which were used, however, since it is transfer learning, we are taking that model as is (without changing its hyperparameters) because we want the weights it has already learned. Lastly, we used a fairly small learning rate of 0.0001. If we modified that (larger or smaller) that would probably impact our simple model more than our larger model. We presume that a larger learning rate (0.01 for example) would allow the model to converge faster and perhaps have a better accuracy with only 6 epochs. ","metadata":{}},{"cell_type":"markdown","source":"# Conclusion\n\nOverall, we are quite happy with our transfer learning model. We were able to demonstrate how transfer learning can shorten training time and increase performance. Our second model out performed the simple model very quickly. This study was able to complete its goal by successfully identifying our seven species of dolphins and whales based on images of their fins.\n\nTo achieve such results, we first visualized the data and cleaned up the dataframe containing the information. We also decreased the number of images by removing species which had few images as well and removing some of the inputs for those species which had too many images. We did this for faster model training as well as to make the dataset more balanced (which helps produce a more performant model). During training, we preprocessed the images by normalizing and augmenting the images in batches. We then trained two models, our simple model and the transfer learning model using ResNet50. Finally, we discussed the results and hyperparameters in the *Results* section. \n\nIn addition to hyperparameter modification (discussed in the *Results* section above), we could have done a few modifications to improve our model(s). First, we could have used more images which would expose the model to more possiblities, positions, shapes, sizes and colors. Image size also played a part in training. We used fairly small images (64x64x3) but the images we were given generally were much larger. We could have used larger image sizes during training such as 100x100, 256x256 or even 500x500. We tried with 256x256 and 100x100 but training was very slow. We also could have used more epochs (such as 10, 20, 50, etc). Because this notebook is for demonstration purposes, we only used a few epochs. However, if you increase the epochs to much, you could have run into overfitting by training it for too long. Some ways to avoid such behavior would be to use early stopping ([Tensorflow link](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping)). Lastly, we could have added more layers to either model which may have resulted in better performance as well. \n\nThis study gives way to possible future projects. By using transfer learning in such a way, we can train models to identify a wide variety of things, not just whales and dolphins. Here are a few interesting papers on transfer learning which readers may find useful:\n1. https://link.springer.com/chapter/10.1007/978-3-030-01424-7_27\n2. https://ieeexplore.ieee.org/abstract/document/8432110\n3. https://academic.oup.com/imaiai/article-abstract/5/2/159/2363463?login=false\n4. https://www.sciencedirect.com/science/article/abs/pii/S1389041718310933\n\nI hope you enjoyed this notebook! Thank you!\n\n-----End-----","metadata":{}}]}