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Building a Generative Adversarial Network using Keras
Generative Adversarial Networks (GANs) are a powerful type of neural network model that can be used to generate new data samples. In this tutorial, we will walk you through the process of building a GAN using the Keras library in Python.
Step 1: Import necessary libraries
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Reshape, Flatten
from keras.layers import Conv2D, Conv2DTranspose
Step 2: Define the generator model
def build_generator():
model = Sequential()
model.add(Dense(128 * 7 * 7, input_dim=100))
model.add(Reshape((7, 7, 128)))
model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
model.add(Conv2D(1, (7,7), activation='sigmoid', padding='same'))
return model
Step 3: Define the discriminator model
def build_discriminator():
model = Sequential()
model.add(Conv2D(64, (3,3), strides=(2,2), padding='same', input_shape=(28, 28, 1)))
model.add(Conv2D(128, (3,3), strides=(2,2), padding='same'))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
return model
Step 4: Combine the generator and discriminator models
generator = build_generator()
discriminator = build_discriminator()
gan = Sequential()
gan.add(generator)
gan.add(discriminator)
Step 5: Compile and train the GAN model
discriminator.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
discriminator.trainable = False
gan.compile(loss='binary_crossentropy', optimizer='adam')
gan.fit(X_train, y_train, epochs=100, batch_size=128)
By following these steps, you can build a Generative Adversarial Network using the Keras library in Python. GANs have a wide range of applications in image generation, text generation, and more. Experiment with different architectures and hyperparameters to generate more realistic and diverse samples.
