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mnist-gan.py
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mnist-gan.py
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from numpy import expand_dims
from numpy import zeros
from numpy import ones
from numpy import vstack
from numpy.random import randn
from numpy.random import randint
from tensorflow.keras.datasets.mnist import load_data
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Reshape
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Conv2DTranspose
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.layers import Dropout
from matplotlib import pyplot
import numpy as np
from os import listdir
from PIL import Image
# load an image as an rgb numpy array
def load_image(filename):
# load image from file
image = Image.open(filename)
# convert to RGB, if needed
image = image.convert('RGB')
# convert to array
pixels = np.asarray(image)
return pixels
# load images and extract faces for all images in a directory
def load_faces(directory, n_faces):
faces = list()
# enumerate files
for filename in listdir(directory):
# load the image
pixels = load_image(directory + filename)
# store
faces.append(pixels)
# stop once we have enough
if len(faces) >= n_faces:
break
return np.asarray(faces)
# define the standalone discriminator model
def define_discriminator(in_shape=(28,28,1)):
model = Sequential()
model.add(Conv2D(64, (3,3), strides=(2, 2), padding='same', input_shape=in_shape))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.4))
model.add(Conv2D(64, (3,3), strides=(2, 2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
# compile model
opt = Adam(lr=0.0002, beta_1=0.5)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
return model
# define the standalone generator model
def define_generator(latent_dim):
model = Sequential()
# foundation for 7x7 image
n_nodes = 128 * 7 * 7
model.add(Dense(n_nodes, input_dim=latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(Reshape((7, 7, 128)))
# upsample to 14x14
model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
# upsample to 28x28
model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
model.add(Conv2D(1, (7,7), activation='sigmoid', padding='same'))
return model
# define the combined generator and discriminator model, for updating the generator
def define_gan(g_model, d_model):
# make weights in the discriminator not trainable
d_model.trainable = False
# connect them
model = Sequential()
# add generator
model.add(g_model)
# add the discriminator
model.add(d_model)
# compile model
opt = Adam(lr=0.0002, beta_1=0.5)
model.compile(loss='binary_crossentropy', optimizer=opt)
return model
# load and prepare mnist training images
def load_real_samples():
# load mnist dataset
(trainX, _), (_, _) = load_data()
# expand to 3d, e.g. add channels dimension
X = expand_dims(trainX, axis=-1)
# convert from unsigned ints to floats
X = X.astype('float32')
# scale from [0,255] to [0,1]
X = X / 255.0
return X
# select real samples
def generate_real_samples(dataset, n_samples):
# choose random instances
ix = randint(0, dataset.shape[0], n_samples)
# retrieve selected images
X = dataset[ix]
# generate 'real' class labels (1)
y = ones((n_samples, 1))
return X, y
# generate points in latent space as input for the generator
def generate_latent_points(latent_dim, n_samples):
# generate points in the latent space
x_input = randn(latent_dim * n_samples)
# reshape into a batch of inputs for the network
x_input = x_input.reshape(n_samples, latent_dim)
return x_input
# use the generator to generate n fake examples, with class labels
def generate_fake_samples(g_model, latent_dim, n_samples):
# generate points in latent space
x_input = generate_latent_points(latent_dim, n_samples)
# predict outputs
X = g_model.predict(x_input)
# create 'fake' class labels (0)
y = zeros((n_samples, 1))
return X, y
# create and save a plot of generated images (reversed grayscale)
def save_plot(examples, epoch, n=10):
# plot images
for i in range(n * n):
# define subplot
pyplot.subplot(n, n, 1 + i)
# turn off axis
pyplot.axis('off')
# plot raw pixel data
pyplot.imshow(examples[i, :, :, 0], cmap='gray_r')
# save plot to file
filename = 'models/mnist/generated_plot_e%03d.png' % (epoch+1)
pyplot.savefig(filename)
pyplot.close()
# evaluate the discriminator, plot generated images, save generator model
def summarize_performance(epoch, g_model, d_model, dataset, latent_dim, n_samples=100):
# prepare real samples
X_real, y_real = generate_real_samples(dataset, n_samples)
# evaluate discriminator on real examples
_, acc_real = d_model.evaluate(X_real, y_real, verbose=0)
# prepare fake examples
x_fake, y_fake = generate_fake_samples(g_model, latent_dim, n_samples)
# evaluate discriminator on fake examples
_, acc_fake = d_model.evaluate(x_fake, y_fake, verbose=0)
# summarize discriminator performance
print('>Accuracy real: %.0f%%, fake: %.0f%%' % (acc_real*100, acc_fake*100))
# save plot
save_plot(x_fake, epoch)
# save the generator model tile file
filename = 'generator_model_%03d.h5' % (epoch + 1)
g_model.save(filename)
# train the generator and discriminator
def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=100, n_batch=256):
bat_per_epo = int(dataset.shape[0] / n_batch)
half_batch = int(n_batch / 2)
# manually enumerate epochs
for i in range(n_epochs):
# enumerate batches over the training set
for j in range(bat_per_epo):
# get randomly selected 'real' samples
X_real, y_real = generate_real_samples(dataset, half_batch)
# generate 'fake' examples
X_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)
# create training set for the discriminator
X, y = vstack((X_real, X_fake)), vstack((y_real, y_fake))
# update discriminator model weights
d_loss, _ = d_model.train_on_batch(X, y)
# prepare points in latent space as input for the generator
X_gan = generate_latent_points(latent_dim, n_batch)
# create inverted labels for the fake samples
y_gan = ones((n_batch, 1))
# update the generator via the discriminator's error
g_loss = gan_model.train_on_batch(X_gan, y_gan)
# summarize loss on this batch
print('>%d, %d/%d, d=%.3f, g=%.3f' % (i+1, j+1, bat_per_epo, d_loss, g_loss))
# evaluate the model performance, sometimes
if (i+1) % 10 == 0:
summarize_performance(i, g_model, d_model, dataset, latent_dim)
# save model
# size of the latent space
latent_dim = 100
# create the discriminator
d_model = define_discriminator()
# create the generator
g_model = define_generator(latent_dim)
# create the gan
gan_model = define_gan(g_model, d_model)
# load image data
dataset = load_real_samples()
# train model
train(g_model, d_model, gan_model, dataset, latent_dim)