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@IkkiOcean
IkkiOcean committed Oct 6, 2024
1 parent 26e2381 commit 08f90f0
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119 changes: 87 additions & 32 deletions Deep_Learning/Dog Species Classification/Models/MobileNetV2.ipynb
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Expand Up @@ -40,19 +40,29 @@
},
"outputs": [],
"source": [
"# Import the OpenCV library for image processing\n",
"import cv2\n",
"\n",
"# Import NumPy for numerical operations and array manipulation\n",
"import numpy as np\n",
"\n",
"# Import Matplotlib for plotting and visualizing data\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Import the OS module for interacting with the operating system\n",
"import os\n",
"\n",
"# Import TensorFlow library for building and training machine learning models\n",
"import tensorflow as tf\n",
"\n",
"# Import ImageDataGenerator for data augmentation and image preprocessing\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.applications import MobileNetV2\n",
"from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Input, Lambda, GlobalAveragePooling2D, Dropout, Dense\n",
"\n",
"# Import the Adam optimizer for optimizing the model during training\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras.callbacks import ModelCheckpoint"
"\n",
"# Import ModelCheckpoint callback for saving the model during training\n",
"from tensorflow.keras.callbacks import ModelCheckpoint\n"
]
},
{
Expand Down Expand Up @@ -181,8 +191,10 @@
}
],
"source": [
"# Iterate through each class folder path in the list of class folder paths\n",
"for class_folder_path in class_folder_paths:\n",
" print('{0}:'.format(class_folder_path), ' ', len(os.listdir(class_folder_path)))"
" # Print the class folder path and the number of files (images) in that folder\n",
" print('{0}:'.format(class_folder_path), ' ', len(os.listdir(class_folder_path)))\n"
]
},
{
Expand Down Expand Up @@ -302,13 +314,25 @@
}
],
"source": [
"# Create a figure and a grid of subplots with 2 rows and 5 columns\n",
"fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(15, 7))\n",
"\n",
"# Iterate over the flattened array of axes and their corresponding indices\n",
"for i, ax in enumerate(axes.flat):\n",
" # Construct the directory path for each dog's training images\n",
" dir = TRAIN_DIR + \"/\" + train_dogs[i] + \"/\"\n",
" \n",
" # Open the first image in the directory and display it in the current subplot\n",
" ax.imshow(Image.open(dir + os.listdir(dir)[0]))\n",
" \n",
" # Set the title of the subplot to the dog's name\n",
" ax.set_title(train_dogs[i])\n",
"\n",
"# Adjust the layout to prevent overlap between subplots\n",
"plt.tight_layout()\n",
"plt.show()"
"\n",
"# Display the entire figure with the subplots\n",
"plt.show()\n"
]
},
{
Expand Down Expand Up @@ -361,35 +385,40 @@
}
],
"source": [
"train_data_gen = ImageDataGenerator(horizontal_flip = True,\n",
" rotation_range=20,\n",
" width_shift_range=0.1,\n",
" height_shift_range=0.1,\n",
" zoom_range=0.2)\n",
"# Import ImageDataGenerator for data augmentation and loading\n",
"train_data_gen = ImageDataGenerator(horizontal_flip=True, # Randomly flip images horizontally\n",
" rotation_range=20, # Randomly rotate images up to 20 degrees\n",
" width_shift_range=0.1, # Randomly shift images horizontally (10% of width)\n",
" height_shift_range=0.1, # Randomly shift images vertically (10% of height)\n",
" zoom_range=0.2) # Randomly zoom into images (up to 20%)\n",
"\n",
"train_generator = train_data_gen.flow_from_directory(TRAIN_DIR,\n",
" target_size = (224, 224),\n",
" color_mode = 'rgb',\n",
" batch_size = 32,\n",
" class_mode ='categorical',\n",
" shuffle = True)\n",
"# Create a training data generator that loads images from the specified directory (TRAIN_DIR)\n",
"train_generator = train_data_gen.flow_from_directory(TRAIN_DIR, # Directory with training images\n",
" target_size=(224, 224), # Resize all images to 224x224 pixels\n",
" color_mode='rgb', # Use RGB color (3 channels)\n",
" batch_size=32, # Process images in batches of 32\n",
" class_mode='categorical',# Labels are categorical (multi-class)\n",
" shuffle=True) # Shuffle the data at the start of each epoch\n",
"\n",
"# Create an image generator for validation data (no augmentation)\n",
"val_data_gen = ImageDataGenerator()\n",
"\n",
"val_generator = val_data_gen.flow_from_directory(VAL_DIR,\n",
" target_size = (224, 224),\n",
" color_mode = 'rgb',\n",
" batch_size = 32,\n",
" class_mode = 'categorical',\n",
" shuffle = False)\n",
"val_data_gen = ImageDataGenerator()\n",
"# Create a validation data generator from the specified directory (VAL_DIR)\n",
"val_generator = val_data_gen.flow_from_directory(VAL_DIR, # Directory with validation images\n",
" target_size=(224, 224), # Resize all images to 224x224 pixels\n",
" color_mode='rgb', # Use RGB color (3 channels)\n",
" batch_size=32, # Process images in batches of 32\n",
" class_mode='categorical',# Labels are categorical (multi-class)\n",
" shuffle=False) # Do not shuffle data (to maintain order)\n",
"\n",
"\n",
"test_generator = val_data_gen.flow_from_directory(TEST_DIR,\n",
" target_size = (224, 224),\n",
" color_mode = 'rgb',\n",
" batch_size = 32,\n",
" class_mode = 'categorical',\n",
" shuffle = False)"
"# Create a test data generator from the specified directory (TEST_DIR)\n",
"test_generator = val_data_gen.flow_from_directory(TEST_DIR, # Directory with test images\n",
" target_size=(224, 224), # Resize all images to 224x224 pixels\n",
" color_mode='rgb', # Use RGB color (3 channels)\n",
" batch_size=32, # Process images in batches of 32\n",
" class_mode='categorical',# Labels are categorical (multi-class)\n",
" shuffle=False) # Do not shuffle data (to maintain order)"
]
},
{
Expand Down Expand Up @@ -550,15 +579,29 @@
}
],
"source": [
"# 'before_mobilenet' initializes a Sequential model.\n",
"# It takes images of shape (224, 224, 3) as input and \n",
"# applies the preprocessing function from MobileNetV2.\n",
"before_mobilenet = Sequential([Input((224,224,3)),\n",
" Lambda(preprocess_input)])\n",
"\n",
"# 'mobilenet' loads the pre-trained MobileNetV2 model.\n",
"# The input shape is specified as (224, 224, 3) with RGB channels.\n",
"# 'include_top=False' means that we are not including the top classification \n",
"# layer of the pre-trained model, allowing us to customize the output for our task.\n",
"mobilenet = MobileNetV2(input_shape = (224,224,3), include_top = False)\n",
"\n",
"# 'after_mobilenet' is another Sequential model that takes the features \n",
"# from MobileNetV2, applies global average pooling, followed by a dropout \n",
"# layer (to prevent overfitting), and then a Dense layer to classify \n",
"# the output into 70 classes (for the dog breeds).\n",
"after_mobilenet = Sequential([GlobalAveragePooling2D(),\n",
" Dropout(0.2),\n",
" Dense(70, activation = 'softmax')])\n",
"\n",
"# Finally, the full model is built by chaining 'before_mobilenet',\n",
"# 'mobilenet', and 'after_mobilenet' sequentially. \n",
"# This creates an end-to-end model for dog breed classification.\n",
"model = Sequential([before_mobilenet, mobilenet, after_mobilenet])"
]
},
Expand All @@ -585,8 +628,12 @@
},
"outputs": [],
"source": [
"# Setting the optimizer as Adam with a learning rate of 0.00001\n",
"opt = Adam(learning_rate=0.00001)\n",
"model.compile(optimizer = opt, loss = 'categorical_crossentropy', metrics = ['accuracy'])"
"\n",
"# Compiling the model with the Adam optimizer, categorical crossentropy loss (used for multi-class classification problems),\n",
"# and accuracy as the metric to evaluate model performance.\n",
"model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])"
]
},
{
Expand Down Expand Up @@ -969,10 +1016,18 @@
}
],
"source": [
"# Build the model with input shape (None, 224, 224, 3)\n",
"# 'None' indicates that the model can take a variable batch size, \n",
"# while '224, 224, 3' is the shape of each input image (224x224 RGB).\n",
"model.build(((None, 224, 224, 3)))\n",
"\n",
"# Display the summary of the model before MobileNet integration\n",
"before_mobilenet.summary()\n",
"\n",
"# Display the summary of the MobileNet architecture\n",
"mobilenet.summary()\n",
"\n",
"# Display the summary of the model after integrating MobileNet\n",
"after_mobilenet.summary()"
]
},
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91 changes: 63 additions & 28 deletions Deep_Learning/Dog Species Classification/Models/ResNet50V2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -40,19 +40,29 @@
},
"outputs": [],
"source": [
"# Import the OpenCV library for image processing\n",
"import cv2\n",
"\n",
"# Import NumPy for numerical operations and array manipulation\n",
"import numpy as np\n",
"\n",
"# Import Matplotlib for plotting and visualizing data\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Import the OS module for interacting with the operating system\n",
"import os\n",
"\n",
"# Import TensorFlow library for building and training machine learning models\n",
"import tensorflow as tf\n",
"\n",
"# Import ImageDataGenerator for data augmentation and image preprocessing\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.applications.resnet_v2 import ResNet50V2, preprocess_input\n",
"\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Input, Lambda, GlobalAveragePooling2D, Dropout, Dense\n",
"# Import the Adam optimizer for optimizing the model during training\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras.callbacks import ModelCheckpoint"
"\n",
"# Import ModelCheckpoint callback for saving the model during training\n",
"from tensorflow.keras.callbacks import ModelCheckpoint\n"
]
},
{
Expand Down Expand Up @@ -302,13 +312,25 @@
}
],
"source": [
"# Create a figure and a grid of subplots with 2 rows and 5 columns\n",
"fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(15, 7))\n",
"\n",
"# Iterate over the flattened array of axes and their corresponding indices\n",
"for i, ax in enumerate(axes.flat):\n",
" # Construct the directory path for each dog's training images\n",
" dir = TRAIN_DIR + \"/\" + train_dogs[i] + \"/\"\n",
" \n",
" # Open the first image in the directory and display it in the current subplot\n",
" ax.imshow(Image.open(dir + os.listdir(dir)[0]))\n",
" \n",
" # Set the title of the subplot to the dog's name\n",
" ax.set_title(train_dogs[i])\n",
"\n",
"# Adjust the layout to prevent overlap between subplots\n",
"plt.tight_layout()\n",
"plt.show()"
"\n",
"# Display the entire figure with the subplots\n",
"plt.show()\n"
]
},
{
Expand Down Expand Up @@ -361,35 +383,41 @@
}
],
"source": [
"train_data_gen = ImageDataGenerator(horizontal_flip = True,\n",
" rotation_range=20,\n",
" width_shift_range=0.1,\n",
" height_shift_range=0.1,\n",
" zoom_range=0.2)\n",
"# Create an instance of ImageDataGenerator for training data with data augmentation\n",
"train_data_gen = ImageDataGenerator(horizontal_flip=True, # Randomly flip images horizontally\n",
" rotation_range=20, # Randomly rotate images by up to 20 degrees\n",
" width_shift_range=0.1, # Randomly shift images horizontally by 10%\n",
" height_shift_range=0.1, # Randomly shift images vertically by 10%\n",
" zoom_range=0.2) # Randomly zoom into images by up to 20%\n",
"\n",
"# Create a training data generator that reads images from the specified directory\n",
"train_generator = train_data_gen.flow_from_directory(TRAIN_DIR,\n",
" target_size = (224, 224),\n",
" color_mode = 'rgb',\n",
" batch_size = 32,\n",
" class_mode ='categorical',\n",
" shuffle = True)\n",
" target_size=(224, 224), # Resize images to 224x224\n",
" color_mode='rgb', # Use RGB color mode\n",
" batch_size=32, # Set the batch size to 32\n",
" class_mode='categorical', # Use categorical labels for multi-class classification\n",
" shuffle=True) # Shuffle the data at each epoch\n",
"\n",
"# Create an instance of ImageDataGenerator for validation data without augmentation\n",
"val_data_gen = ImageDataGenerator()\n",
"\n",
"# Create a validation data generator that reads images from the specified directory\n",
"val_generator = val_data_gen.flow_from_directory(VAL_DIR,\n",
" target_size = (224, 224),\n",
" color_mode = 'rgb',\n",
" batch_size = 32,\n",
" class_mode = 'categorical',\n",
" shuffle = False)\n",
"val_data_gen = ImageDataGenerator()\n",
" target_size=(224, 224), # Resize images to 224x224\n",
" color_mode='rgb', # Use RGB color mode\n",
" batch_size=32, # Set the batch size to 32\n",
" class_mode='categorical', # Use categorical labels for multi-class classification\n",
" shuffle=False) # Do not shuffle the data for validation\n",
"\n",
"\n",
"\n",
"# Create a test data generator that reads images from the specified directory\n",
"test_generator = val_data_gen.flow_from_directory(TEST_DIR,\n",
" target_size = (224, 224),\n",
" color_mode = 'rgb',\n",
" batch_size = 32,\n",
" class_mode = 'categorical',\n",
" shuffle = False)"
" target_size=(224, 224), # Resize images to 224x224\n",
" color_mode='rgb', # Use RGB color mode\n",
" batch_size=32, # Set the batch size to 32\n",
" class_mode='categorical', # Use categorical labels for multi-class classification\n",
" shuffle=False) # Do not shuffle the data for testing\n"
]
},
{
Expand Down Expand Up @@ -495,9 +523,14 @@
}
],
"source": [
"# Retrieve the class indices (mapping of class labels to numeric indices) from the training generator\n",
"labels = train_generator.class_indices\n",
"class_mapping = dict((v,k) for k,v in labels.items())\n",
"class_mapping"
"\n",
"# Create a dictionary that maps numeric indices back to class labels\n",
"class_mapping = dict((v, k) for k, v in labels.items())\n",
"\n",
"# Display the class mapping dictionary\n",
"class_mapping\n"
]
},
{
Expand Down Expand Up @@ -1419,6 +1452,8 @@
}
],
"source": [
"# Evaluate the performance of the trained model on the test dataset,\n",
"# which results in an accuracy of 96.28 percent\n",
"model.evaluate(test_generator)"
]
}
Expand Down
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