Wasserstein GANs with Gradient Penalty (WGAN-GP) Python Code Example

Here is a Python code example that demonstrates the use of gradient penalty in Wasserstein GANs with Gradient Penalty (WGAN-GP):

python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np

# Define the critic model
critic = keras.Sequential([
    layers.Dense(256, activation='relu', input_shape=(28*28,)),
    layers.Dense(1)
])

# Define the gradient penalty function
def gradient_penalty(critic, real_data, fake_data, epsilon):
    # Calculate the critic outputs for real and fake data
    real_outputs = critic(real_data)
    fake_outputs = critic(fake_data)

    # Calculate the gradients of the critic outputs with respect to the inputs
    real_gradients = tf.gradients(critic(real_data), real_data)
    fake_gradients = tf.gradients(critic(fake_data), fake_data)

    # Calculate the gradient penalty
    gradient_penalty = tf.reduce_mean((tf.norm(real_gradients, axis=1) - 1) ** 2 + (tf.norm(fake_gradients, axis=1) - 1) ** 2)

    return gradient_penalty

# Define the WGAN-GP loss function
def wgan_gp_loss(critic, real_data, fake_data, epsilon):
    # Calculate the critic outputs for real and fake data
    real_outputs = critic(real_data)
    fake_outputs = critic(fake_data)

    # Calculate the gradient penalty
    gradient_penalty = gradient_penalty(critic, real_data, fake_data, epsilon)

    # Calculate the WGAN-GP loss
    wgan_gp_loss = tf.reduce_mean(real_outputs) - tf.reduce_mean(fake_outputs) + 10 * gradient_penalty

    return wgan_gp_loss

# Compile the critic model
critic.compile(optimizer='adam', loss=wgan_gp_loss)

# Generate some sample data
real_data = np.random.rand(100, 28*28)
fake_data = np.random.rand(100, 28*28)

# Train the critic model
critic.fit([real_data, fake_data], epochs=100)

In this example, the `gradient_penalty` function calculates the gradient penalty by computing the norm of the gradients of the critic outputs with respect to the inputs for both real and fake data. The `wgan_gp_loss` function calculates the WGAN-GP loss by subtracting the mean of the fake outputs from the mean of the real outputs and adding the gradient penalty. The critic model is then compiled with the WGAN-GP loss function and trained on sample data.

can you provide a Python code example that demonstrates the use of gradient penalty in WGAN-GP