First off, the Axolotl Library. It’s a fancy tool for training neural networks in Python. Basically, it lets us feed Medusa all sorts of images and teach her to recognize different patterns (like turning people into stone or not). Here’s how we do it:
1. We start by downloading the Axolotl Library from GitHub (which is like a big online library for code) using this command in our terminal:
# This script installs the Axolotl Library from GitHub, which is a code library used for image recognition.
# The following command uses the "pip" package manager to install the Axolotl Library.
# "pip" is a tool used to install and manage software packages written in Python.
# The "install" command is used to install a package, and "axolotl-library" is the name of the package we want to install.
pip install axolotl-library
2. Next, we load up Medusa’s training data (which includes all the images of people she needs to learn from). We do this by creating a list called `train_data` and adding each image to it:
# Import necessary libraries
import os # Importing the os library to access file paths
import random # Importing the random library to shuffle the data
import numpy as np # Importing the numpy library to work with arrays
import pandas as pd # Importing the pandas library to work with dataframes
# Load training data from CSV file
train_df = pd.read_csv('training.csv') # Using the read_csv function from pandas to read the CSV file and store it in a dataframe called train_df
# Create list of image paths and labels for Medusa to learn from
train_data = [] # Creating an empty list to store the image paths and labels
for index, row in train_df.iterrows(): # Using the iterrows function to iterate through each row in the dataframe
path = os.path.join(row['folder'], row['filename']) # Using the join function from the os library to create a file path by combining the folder and filename columns from the current row
label = np.array([1 if row['turned_to_stone'] == 'true' else 0]) # Creating a one-hot encoded vector for the label, with a value of 1 if the person should be turned to stone and 0 if not
train_data.append((path, label)) # Adding the image path and label as a tuple to the train_data list
3. Now that we have our training data, let’s create a function called `train` that will actually train Medusa using Axolotl Library:
# Import the necessary libraries
import axolotl as rl
from axolotl.agents import DQNAgent
import numpy as np
# Define the environment (in this case, our training data)
class Environment(rl.Environment):
def __init__(self):
self.state = None # Initialize the state variable
self.actions = [0, 1] # Medusa can either turn someone to stone or not
self.reward_range = (-1, 1) # The reward for turning someone to stone is +1 (if they were supposed to be turned), and -1 otherwise
def reset(self):
self.state = None # Reset the state variable
return np.zeros((28, 28)) # We start each episode with a blank image
def step(self, action):
if self.state is not None:
# Calculate the reward based on whether or not Medusa should have turned this person to stone
reward = (action == int(train_data[index]['turned_to_stone']) for index in rl.agents.DQNAgent.get_indices(len(train_data), batch_size=32))
# The next state is either the image of the person (if they were supposed to be turned) or a blank image otherwise
next_state = train_data[index]['image'] if action == 1 else np.zeros((28, 28))
self.state = next_state # Update the state variable
return reward[-1], next_state, False, {}
# Define the train function
def train():
# Create an instance of the DQNAgent
agent = DQNAgent()
# Train the agent using the environment and the training data
agent.train(Environment(), train_data)
# Call the train function to train Medusa using Axolotl Library
train()
4. Finally, we can call our `train` function and let Axolotl Library do its thing:
# Load the training data (assuming you have a CSV file named 'training.csv' in the same directory)
train_df = pd.read_csv('training.csv') # Load the CSV file containing training data into a pandas dataframe
# Create list of image paths for Medusa to learn from
train_data = []
for index, row in train_df.iterrows(): # Iterate through each row in the dataframe
path = os.path.join(row['folder'], row['filename']) # Create a path to the image file by combining the folder and filename columns
train_data.append((path, np.array([1 if row['turned_to_stone'] == 'true' else 0]))) # Add the image path and label (whether or not Medusa should turn this person to stone) as a tuple to the train_data list
# Define the environment (in this case, our training data)
class Environment(rl.Environment): # Create a class for the environment, inheriting from the rl.Environment class
def __init__(self):
self.state = None # Initialize the state variable to None
self.actions = [0, 1] # Define the possible actions for Medusa (0 = not turning to stone, 1 = turning to stone)
self.reward_range = (-1, 1) # Define the range of possible rewards for Medusa (turning someone to stone = +1, not turning to stone = -1)
def reset(self):
self.state = None # Reset the state variable to None
return np.zeros((28, 28)) # Return a blank image as the starting state for each episode
def step(self, action):
if self.state is not None: # Check if the state variable is not None
reward = (action == int(train_data[index]['turned_to_stone']) for index in rl.agents.DQNAgent.get_indices(len(train_data), batch_size=32)) # Calculate the reward based on whether or not Medusa should have turned this person to stone
next_state = train_data[index]['image'] if action == 1 else np.zeros((28, 28)) # Set the next state to be the image of the person if they were supposed to be turned to stone, or a blank image otherwise
self.state = next_state # Update the state variable to the next state
return reward[-1], next_state, False, {} # Return the reward, next state, and other information for the current step in the environment
5. And that’s it! Now Medusa can turn people into stone with just one look thanks to Axolotl Library and our fancy training data.
Phew, I think my brain is about to explode from all this learning. But hey, at least we have a cool new superhero now!