Cell 0

from keras.utils import to_categorical
from keras_preprocessing.image import load_img
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
import os
import pandas as pd
import numpy as np

What the code could have been:

# Import libraries for image processing and neural network
from keras.preprocessing.image import load_img, img_to_array
from keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
import os
import pandas as pd
import numpy as np

# Constants
IMAGE_SIZE = (256, 256)  # resize image size
BATCH_SIZE = 32  # batch size for training
EPOCHS = 10  # number of epochs for training

# Load Image
def load_image(file_path):
    """
    Load image from file path and resize it to IMAGE_SIZE.
    
    Args:
    file_path (str): path to the image file.
    
    Returns:
    image (numpy array): resized image.
    """
    image = load_img(file_path, target_size=IMAGE_SIZE)
    return img_to_array(image)

# Load Dataset
def load_dataset(data_dir):
    """
    Load dataset from directory and convert image paths to numpy arrays.
    
    Args:
    data_dir (str): path to the dataset directory.
    
    Returns:
    images (numpy array): numpy array of image data.
    labels (numpy array): numpy array of labels.
    """
    images = []
    labels = []
    for category in os.listdir(data_dir):
        category_dir = os.path.join(data_dir, category)
        for file in os.listdir(category_dir):
            file_path = os.path.join(category_dir, file)
            image = load_image(file_path)
            images.append(image)
            labels.append(int(category))
    return np.array(images), np.array(labels)

# Preprocess Data
def preprocess_data(images, labels):
    """
    Convert image data to categorical labels and one-hot encode labels.
    
    Args:
    images (numpy array): numpy array of image data.
    labels (numpy array): numpy array of labels.
    
    Returns:
    images (numpy array): numpy array of image data.
    labels (numpy array): numpy array of one-hot encoded labels.
    """
    images = images / 255  # normalize pixel values
    labels = to_categorical(labels)  # one-hot encode labels
    return images, labels

# Build Model
def build_model():
    """
    Build neural network model using Sequential API.
    
    Returns:
    model (keras model): built neural network model.
    """
    model = Sequential()
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3)))
    model.add(MaxPooling2D((2, 2)))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(len(os.listdir(data_dir)), activation='softmax'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# Train Model
def train_model(model, images, labels):
    """
    Train neural network model on dataset.
    
    Args:
    model (keras model): built neural network model.
    images (numpy array): numpy array of image data.
    labels (numpy array): numpy array of one-hot encoded labels.
    """
    model.fit(images, labels, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_split=0.2)

# Main Function
def main():
    data_dir = 'path/to/dataset'  # replace with actual dataset path
    images, labels = load_dataset(data_dir)
    images, labels = preprocess_data(images, labels)
    model = build_model()
    train_model(model, images, labels)

if __name__ == '__main__':
    main()

from keras.utils import to_categorical
from keras_preprocessing.image import load_img
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D

import os
import pandas as pd
import numpy as np