Image Classification Using TensorFlow

This project demonstrates how to create an image classification model using TensorFlow in a Jupyter Notebook to classify images based on emotions.

Steps Involved:

1. Setup and Import Libraries:

   • Import TensorFlow, Keras, NumPy, and Matplotlib.

2. Load and Preprocess the Dataset:

   • Load an emotion-labeled image dataset.
   • Split the dataset into training and validation sets.

3. Build the Model:

   • Create a Sequential model with Conv2D, MaxPooling2D, Flatten, Dense, and Dropout layers.

4. Compile the Model:

   • Use the Adam optimizer and Sparse Categorical Crossentropy loss function.

5. Train the Model:

   • Train the model using the training dataset and validate it with the validation dataset.