diff --git a/Project_Notebook.ipynb b/Project_Notebook.ipynb
index 3fc1bd65bfdec8fd2ad51a749562de169624eab0..8a90228efaa7d813d1a15b075a72da550f07b07c 100644
--- a/Project_Notebook.ipynb
+++ b/Project_Notebook.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"id": "d7e56e0e-7eec-429d-940b-c3337db4b4dc",
"metadata": {},
"outputs": [],
@@ -17,6 +17,7 @@
"from tqdm import tqdm\n",
"from data_processing import load_sort_data, transform\n",
"from EDA import find_mean_img, eigenimages, plot_pca\n",
+ "from tensorflow.keras.preprocessing import image_dataset_from_directory\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n"
]
@@ -90,7 +91,41 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
+ "id": "52d44e91",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_meta = pd.read_csv('./data/ISIC2018_Task3_Training_GroundTruth.csv')\n",
+ "test_meta = pd.read_csv('./data/ISIC2018_Task3_Validation_GroundTruth.csv')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b25db3d1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Process metadata and decision labelslabels\n",
+ "lab = ['MEL', 'NV', 'BCC', 'AKIEC', 'BKL', 'DF', 'VASC']\n",
+ "\n",
+ "#the original metadata was one-hot encoded so we must reverse\n",
+ "train_labels = train_meta[lab].idxmax(axis=1)\n",
+ "test_labels = test_meta[lab].idxmax(axis=1)\n",
+ "\n",
+ "#combine the reversed one hot encoded with the image file name\n",
+ "ptrainmeta = pd.concat([train_meta['image'], train_labels],keys=['image_id', 'dx'], axis=1)\n",
+ "ptestmeta = pd.concat([test_meta['image'], test_labels],keys=['image_id', 'dx'], axis=1)\n",
+ "\n",
+ "#save off .csv's that contain decision labels and image names\n",
+ "ptrainmeta.to_csv('./data/train_metadata.csv')\n",
+ "ptestmeta.to_csv('./data/test_metadata.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
"id": "b8c4f292",
"metadata": {},
"outputs": [
@@ -98,35 +133,60 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 1099/1099 [00:22<00:00, 48.04it/s]\n",
- "100%|██████████| 6705/6705 [02:20<00:00, 47.65it/s]\n",
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- "100%|██████████| 7/7 [03:32<00:00, 30.35s/it]\n"
+ "100%|██████████| 6705/6705 [01:09<00:00, 97.14it/s]\n",
+ "100%|██████████| 1113/1113 [00:12<00:00, 92.28it/s]\n",
+ "100%|██████████| 1099/1099 [00:10<00:00, 100.47it/s]\n",
+ "100%|██████████| 115/115 [00:01<00:00, 99.05it/s]\n",
+ "100%|██████████| 327/327 [00:02<00:00, 110.73it/s]\n",
+ "100%|██████████| 514/514 [00:04<00:00, 112.84it/s]\n",
+ "100%|██████████| 142/142 [00:01<00:00, 106.61it/s]\n",
+ "100%|██████████| 7/7 [01:46<00:00, 15.18s/it]\n"
]
}
],
"source": [
"# function takes 3 parameters: metadata filename, the folder of the raw images, and the desired name of the destination directory. \n",
- "metadata, dest_dir = load_sort_data('HAM10000_metadata', 'ISIC2018_Task3_Training_Input', 'Training_Images_')"
+ "metadata, dest_dir = load_sort_data('train_metadata.csv', 'ISIC2018_Task3_Training_Input', 'training/')"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 20,
+ "id": "87405f6d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 123/123 [00:01<00:00, 86.44it/s]\n",
+ "100%|██████████| 15/15 [00:00<00:00, 94.34it/s]\n",
+ "100%|██████████| 22/22 [00:00<00:00, 92.05it/s]\n",
+ "100%|██████████| 21/21 [00:00<00:00, 114.75it/s]\n",
+ "100%|██████████| 8/8 [00:00<00:00, 142.86it/s]\n",
+ "100%|██████████| 1/1 [00:00<00:00, 100.00it/s]\n",
+ "100%|██████████| 3/3 [00:00<00:00, 85.72it/s]\n",
+ "100%|██████████| 7/7 [00:02<00:00, 3.11it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "metadata, dest_dir = load_sort_data('test_metadata.csv', 'ISIC2018_Task3_Validation_Input', 'test/')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
"id": "7e9702c3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'h:\\\\School\\\\Winter 2022\\\\DS Projects\\\\2018\\\\hvm-image-clf/data/Training_Images_'"
+ "'h:\\\\School\\\\Winter 2022\\\\DS Projects\\\\2018\\\\hvm-image-clf/data/training/Training_Images_'"
]
},
- "execution_count": 3,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -148,7 +208,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 26,
"id": "0ba9148a",
"metadata": {},
"outputs": [
@@ -188,7 +248,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 27,
"id": "e6d378d5",
"metadata": {},
"outputs": [
@@ -220,7 +280,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "<ipython-input-5-f268d13f0828>:5: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
+ "<ipython-input-27-f268d13f0828>:5: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
" metadata.drop('dx',1).isna().groupby(\n"
]
}
@@ -240,7 +300,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 28,
"id": "91aa284b",
"metadata": {},
"outputs": [
@@ -286,7 +346,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 29,
"id": "05398a91",
"metadata": {},
"outputs": [
@@ -294,7 +354,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 327/327 [00:02<00:00, 139.03it/s]\n"
+ "100%|██████████| 327/327 [00:03<00:00, 92.87it/s] \n"
]
}
],
@@ -305,7 +365,7 @@
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 21,
"id": "e8642d8d",
"metadata": {},
"outputs": [
@@ -313,7 +373,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 514/514 [00:06<00:00, 76.53it/s] \n"
+ "100%|██████████| 15/15 [00:00<00:00, 93.75it/s]\n"
]
}
],
@@ -323,7 +383,34 @@
},
{
"cell_type": "code",
- "execution_count": 74,
+ "execution_count": 22,
+ "id": "2060e363",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[179., 185., 183., ..., 197., 192., 190.],\n",
+ " [163., 167., 168., ..., 135., 128., 119.],\n",
+ " [170., 168., 167., ..., 179., 178., 182.],\n",
+ " ...,\n",
+ " [198., 199., 195., ..., 175., 176., 176.],\n",
+ " [153., 150., 153., ..., 123., 115., 106.],\n",
+ " [179., 182., 184., ..., 178., 178., 180.]], dtype=float32)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bcc_images"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
"id": "5312b5de",
"metadata": {},
"outputs": [
@@ -331,7 +418,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 1099/1099 [00:21<00:00, 51.53it/s]\n"
+ "100%|██████████| 1099/1099 [00:24<00:00, 45.15it/s]\n"
]
}
],
@@ -341,7 +428,7 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 32,
"id": "49338970",
"metadata": {},
"outputs": [
@@ -349,7 +436,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 115/115 [00:01<00:00, 111.11it/s]\n"
+ "100%|██████████| 115/115 [00:01<00:00, 107.58it/s]\n"
]
}
],
@@ -359,7 +446,7 @@
},
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": 33,
"id": "784d69cd",
"metadata": {},
"outputs": [
@@ -367,7 +454,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 1113/1113 [00:21<00:00, 50.65it/s]\n"
+ "100%|██████████| 1113/1113 [00:24<00:00, 46.00it/s]\n"
]
}
],
@@ -377,7 +464,7 @@
},
{
"cell_type": "code",
- "execution_count": 77,
+ "execution_count": 34,
"id": "6cd167a7",
"metadata": {},
"outputs": [
@@ -385,7 +472,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 6705/6705 [08:49<00:00, 12.65it/s]\n"
+ "100%|██████████| 6705/6705 [09:36<00:00, 11.63it/s]\n"
]
}
],
@@ -396,7 +483,7 @@
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 35,
"id": "4de5cec3",
"metadata": {},
"outputs": [
@@ -404,7 +491,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 142/142 [00:01<00:00, 103.95it/s]\n"
+ "100%|██████████| 142/142 [00:01<00:00, 92.27it/s]\n"
]
}
],
@@ -414,23 +501,23 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 36,
"id": "d92158fa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([[168., 164., 163., ..., 141., 140., 139.],\n",
- " [139., 145., 146., ..., 160., 160., 160.],\n",
- " [171., 174., 172., ..., 184., 188., 189.],\n",
+ "array([[171., 170., 166., ..., 163., 170., 171.],\n",
+ " [158., 163., 163., ..., 189., 187., 174.],\n",
+ " [173., 177., 176., ..., 184., 185., 183.],\n",
" ...,\n",
- " [104., 101., 98., ..., 34., 31., 29.],\n",
- " [ 84., 84., 88., ..., 10., 8., 9.],\n",
- " [157., 157., 154., ..., 156., 157., 155.]], dtype=float32)"
+ " [113., 112., 111., ..., 99., 104., 103.],\n",
+ " [118., 120., 128., ..., 162., 148., 149.],\n",
+ " [157., 153., 153., ..., 172., 175., 175.]], dtype=float32)"
]
},
- "execution_count": 37,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
@@ -1182,17 +1269,313 @@
"id": "3157d03a",
"metadata": {},
"source": [
- "# 4. Data Processing for Model Ingestion"
+ "# 5. Model Creation"
]
},
{
- "cell_type": "markdown",
- "id": "64adf033",
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "5f0f70da",
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 10015 files belonging to 7 classes.\n"
+ ]
+ }
+ ],
"source": [
- "# 5. Model Creation"
+ "#make test set\n",
+ "seed = 12345\n",
+ "training_data = image_dataset_from_directory(\n",
+ " directory = './data/training/',\n",
+ " labels = 'inferred',\n",
+ " label_mode = 'categorical',\n",
+ " class_names = lab,\n",
+ " batch_size = 32,\n",
+ " image_size = (600,450),\n",
+ " shuffle = True,\n",
+ " seed = seed,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "b8213cb9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found 193 files belonging to 7 classes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_data = image_dataset_from_directory(\n",
+ " directory = './data/test/',\n",
+ " labels = 'inferred',\n",
+ " label_mode = 'categorical',\n",
+ " class_names = lab,\n",
+ " batch_size = 32,\n",
+ " image_size = (600,450),\n",
+ " shuffle = True,\n",
+ " seed = seed,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "f1be00cd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<BatchDataset element_spec=(TensorSpec(shape=(None, 600, 450, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 7), dtype=tf.float32, name=None))>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "f8524747",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow.keras import datasets,layers, models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "25f8bceb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = models.Sequential()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "17a95bfb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(600,450,3)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "2c9a3491",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.add(layers.AveragePooling2D(2,2))\n",
+ "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
+ "model.add(layers.AveragePooling2D((2, 2)))\n",
+ "model.add(layers.Conv2D(64, (3, 3), activation='relu'))"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "d668b5de",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " conv2d (Conv2D) (None, 598, 448, 32) 896 \n",
+ " \n",
+ " average_pooling2d (AverageP (None, 299, 224, 32) 0 \n",
+ " ooling2D) \n",
+ " \n",
+ " conv2d_1 (Conv2D) (None, 297, 222, 64) 18496 \n",
+ " \n",
+ " average_pooling2d_1 (Averag (None, 148, 111, 64) 0 \n",
+ " ePooling2D) \n",
+ " \n",
+ " conv2d_2 (Conv2D) (None, 146, 109, 64) 36928 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 56,320\n",
+ "Trainable params: 56,320\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "9fef7a20",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.add(layers.Flatten())\n",
+ "model.add(layers.Dense(64, activation='relu'))\n",
+ "model.add(layers.Dense(10))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "b2c2de4d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " conv2d (Conv2D) (None, 598, 448, 32) 896 \n",
+ " \n",
+ " average_pooling2d (AverageP (None, 299, 224, 32) 0 \n",
+ " ooling2D) \n",
+ " \n",
+ " conv2d_1 (Conv2D) (None, 297, 222, 64) 18496 \n",
+ " \n",
+ " average_pooling2d_1 (Averag (None, 148, 111, 64) 0 \n",
+ " ePooling2D) \n",
+ " \n",
+ " conv2d_2 (Conv2D) (None, 146, 109, 64) 36928 \n",
+ " \n",
+ " flatten (Flatten) (None, 1018496) 0 \n",
+ " \n",
+ " dense (Dense) (None, 64) 65183808 \n",
+ " \n",
+ " dense_1 (Dense) (None, 10) 650 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 65,240,778\n",
+ "Trainable params: 65,240,778\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "25c98f80",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.compile(optimizer='adam', \n",
+ " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
+ " metrics=['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "acd6773b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " conv2d (Conv2D) (None, 598, 448, 32) 896 \n",
+ " \n",
+ " average_pooling2d (AverageP (None, 299, 224, 32) 0 \n",
+ " ooling2D) \n",
+ " \n",
+ " conv2d_1 (Conv2D) (None, 297, 222, 64) 18496 \n",
+ " \n",
+ " average_pooling2d_1 (Averag (None, 148, 111, 64) 0 \n",
+ " ePooling2D) \n",
+ " \n",
+ " conv2d_2 (Conv2D) (None, 146, 109, 64) 36928 \n",
+ " \n",
+ " flatten (Flatten) (None, 1018496) 0 \n",
+ " \n",
+ " dense (Dense) (None, 64) 65183808 \n",
+ " \n",
+ " dense_1 (Dense) (None, 10) 650 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 65,240,778\n",
+ "Trainable params: 65,240,778\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "ffe382a0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n"
+ ]
+ }
+ ],
+ "source": [
+ "history = model.fit(training_data, epochs=10, validation_data = (test_data))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1fc861d9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "history"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64adf033",
+ "metadata": {},
+ "source": []
+ },
{
"cell_type": "markdown",
"id": "c3114115",
@@ -1229,7 +1612,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.2"
+ "version": "3.9.0"
}
},
"nbformat": 4,
diff --git a/data_processing.py b/data_processing.py
index 37f6849180c78aa89a518560466468d0eb11e2c6..cd831cd2ad7e369b68b175a30cd2216ef4630e3b 100644
--- a/data_processing.py
+++ b/data_processing.py
@@ -9,19 +9,19 @@ import numpy as np
from tqdm import tqdm
-def load_sort_data(meta_filename = str, image_folder = str, output_folder = str):
+def load_sort_data(meta_filename = str, image_folder = str,sub_dir= str):
"""[summary]
Args:
meta_filename ([type], optional): [description]. Defaults to str.
image_folder ([type], optional): [description]. Defaults to str.
- output_folder ([type], optional): [description]. Defaults to str.
+ sub_dir (str,optional): Parent directory of the output folders.
Returns:
[type]: [description]
"""
data_dir = os.getcwd() + "/data/"
- dest_dir = data_dir + output_folder
+ dest_dir = data_dir + sub_dir
metadata = pd.read_csv(data_dir + '/' + meta_filename)
labels = metadata['dx'].unique()
label_images = []