Photograph captures computer screen displaying Google Colaboratory (Colab) environment, specifically open notebook titled GFPGAN_inference.ipynb. Interface is divided into left sidebar file explorer and right main coding output area.

In left pane, folder hierarchy is shown. Root directory contains folder labeled “GFPGAN” and subfolder “samples.” Cursor hovers over “GFPGAN,” with tooltip label confirming selection. Sidebar includes navigation controls for file management, typical of Colab’s hosted environment linked to Google Drive.

Main pane on right displays execution logs from active cell. Terminal-style output shows download progress of image file “10047_00.png” from external URL. Processing status indicates tiled inference, with four tiles sequentially processed (Tile 1/4 through Tile 4/4). Log confirms that results are saved in “results” folder with filename “10047_00.png.”

Section header “4. Visualize” is visible beneath output, marking transition to visualization phase of workflow. Notebook toolbar at top provides controls for code, text, runtime, and tools, along with options to save or copy to Google Drive. Status message “Cannot save changes” appears at upper center, possibly due to limited editing permissions or temporary runtime mode.

Browser tabs are visible along top margin, including “stop motion for kids,” “curriculum development,” and “artificial intelligence.” Current active tab shows Colab URL referencing notebook execution session.

Overall, screenshot documents machine learning workflow within Colab environment, specifically applying GFPGAN (Generative Facial Prior-Generative Adversarial Network) for image restoration. The interface demonstrates file structure, execution process, and system outputs characteristic of deep-learning notebook pipelines.

Screenshot captures Visual Studio Code (VS Code) editor environment in dark theme. Central pane shows Python script containing imports, function definitions, and loop structures. Syntax highlighting is applied: keywords in purple, variables in white, strings in orange, and functions in blue-green.

Script begins with imports: import numpy as np, import tensorflow as tf, along with supporting libraries. Code defines function create_dataset which loads and normalizes data, shuffles, batches, and returns prepared dataset. Function employs TensorFlow dataset API (tf.data.Dataset.from_tensor_slices) and pipeline transformations such as shuffle, batch, and prefetch.

Subsequent section defines neural network model using Keras Sequential API. Layers include Dense layers with ReLU activations and final output layer with softmax activation. Optimizer is Adam, loss function is categorical crossentropy, and metrics include accuracy. Model is compiled and prepared for training.

Training loop uses .fit() method, specifying dataset, number of epochs, and validation data. Log outputs such as loss and accuracy are set to display per epoch.

Lower portion of script contains evaluation and prediction routines, including call to model.evaluate on test dataset and model.predict on new data samples. Code includes conditional if __name__ == "__main__": block, standard in Python scripts for main execution.

VS Code interface displays file path in tab labeled deep_learning_model.py. Explorer panel on left reveals workspace directory structure with src, data, and config folders. Top bar shows open command palette with options for Python interpreter selection.

Overall, screenshot demonstrates workflow of deep learning implementation in Python using TensorFlow, organized within modular script inside modern IDE environment.