Face-Generating GAN
Using a good face-classifier to teach generators how to make nonexistent people.
| UPDATED
UPDATE 08/14/2020: The code is now on GitHub.
This notebook creates a Genrative Adversarial Network (GAN). The GAN is designed to produce realistic human faces after being trained on a large data set of human faces. Prior to this, it’s functionality is tested by training on a database of handwriting sample and then producing realistic images of had-drawn alphanumeric characters.
Getting started is relatively straightforward. All that’s really needed are the python packages os, glob, matplotlib, numpy, tqdm, warnings, and tensorflow.
The Data
This GAN utilizes the MNIST and CelebA datasets. The MNIST data set is far simpler than the the celebA dataset. The MNIST is also black ans white instead of in RGB. Running the GAN on MNIST allows for testing of how well the model trains before moving onto the more complex data.
data_dir = '/input'
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don’t include a face, then resized down to 28x28.
The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels.
show_n_images = 25
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
plt.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')The CelebFaces Attributes Dataset (CelebA) is a dataset that contains over 202,599 celebrity images with annotations. For the purposes of generating new faces, the annotations are irrelevant.
show_n_images = 25
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
plt.imshow(helper.images_square_grid(mnist_images, 'RGB'))Constructing the Neural network
For building our GAN, we need the following parts:
- Model Inputs
- Discriminator
- Generator
- Model Loss
- Model Optimizer
- Training functions
Input
The function model_inputs() creates the model inputs. it returns a tuple of (tensor of real input images, tensor of z data, learning rate)
imagewidth: The input image width imageheight: The input image height z_dim: The dimension of Z
def model_inputs(image_width, image_height, image_channels, z_dim):
tensor_real_inputs = tf.placeholder(dtype = tf.float32, shape = (None, image_height, image_width, image_channels))
tensor_z_data = tf.placeholder(dtype = tf.float32, shape = (None, z_dim))
learning_rate = tf.placeholder(dtype = tf.float32, shape = ())
return tensor_real_inputs, tensor_z_data, learning_rateDiscriminator
The discriminator() function creates the discriminator network. It returns a tuple of the tensor output of the discriminator and the tensor logits of the discriminator.
images: Tensor of input image(s) reuse: Boolean if the weights should be reused
def discriminator(images, reuse = False):
with tf.variable_scope('discriminator', reuse = reuse):
x = tf.layers.conv2d(images, filters = 32,
kernel_size = 5,
strides = 2,
padding='same',
kernel_initializer = tf.contrib.layers.xavier_initializer())
x = tf.maximum(0.2 * x, x)
x = tf.layers.conv2d(images, filters = 64,
kernel_size = 5,
strides = 2,
padding='same',
kernel_initializer = tf.contrib.layers.xavier_initializer())
x = tf.layers.batch_normalization(x, training = True)
x = tf.maximum(0.2 * x, x)
x = tf.nn.dropout(x, keep_prob = 0.5)
x = tf.layers.conv2d(x, filters = 128,
kernel_size = 5,
strides = 2,
padding = 'same',
kernel_initializer = tf.contrib.layers.xavier_initializer())
x = tf.layers.batch_normalization(x, training = True)
x = tf.maximum(0.2 * x, x)
x = tf.nn.dropout(x, keep_prob = 0.5)
x = tf.layers.conv2d(x, filters = 256,
kernel_size = 5,
strides = 2,
padding = 'same',
kernel_initializer = tf.contrib.layers.xavier_initializer())
x = tf.layers.batch_normalization(x, training = True)
x = tf.maximum(0.2 * x, x)
flattened = tf.reshape(x, [-1, 2 * 2 * 256])
logits = tf.layers.dense(flattened, 1, activation = None)
output = tf.sigmoid(logits)
return output, logitsGenerator
The generator() function creates the generator network. It returns the tensor output of the generator
z: Input z outchanneldim: The number of channels in the output image is_train: Boolean if generator is being used for training
def generator(z, out_channel_dim, is_train=True):
with tf.variable_scope('generator', reuse = not is_train):
x = tf.layers.dense(z, units = 4 * 4 * 512)
x = tf.reshape(x, (-1, 4, 4, 512))
x = tf.layers.batch_normalization(x, training = is_train)
x = tf.maximum( 0.2 * x, x)
x = tf.layers.conv2d_transpose(x, filters = 128, kernel_size = 4, strides = 1, padding = 'valid')
x = tf.layers.batch_normalization(x, training = is_train)
x = tf.maximum(0.2 * x, x)
x = tf.layers.conv2d_transpose(x, filters = 64, kernel_size = 5, strides = 2, padding = 'same')
x = tf.layers.batch_normalization(x, training = is_train)
x = tf.maximum(0.2 * x, x)
x = tf.layers.conv2d_transpose(x, filters = 32, kernel_size = 5, strides = 2, padding = 'same')
x = tf.layers.batch_normalization(x, training = is_train)
x = tf.maximum(0.2 * x, x)
logits = tf.layers.conv2d_transpose(x, filters = out_channel_dim, kernel_size = 3, strides = 1,
padding = 'same')
out = tf.tanh(logits)
print(out.get_shape())
return outLoss
The function model_loss() gets the loss for the discriminator and generator. It returns a tuple of the discriminator loss and the generator loss.
inputreal: Images from the real dataset inputz: Z input outchanneldim: The number of channels in the output image
def model_loss(input_real, input_z, out_channel_dim):
generator_model = generator(input_z, out_channel_dim, is_train = True)
discriminator_model_real, discriminator_logits_real = discriminator(input_real, reuse = False)
discriminator_model_fake, discriminator_logits_fake = discriminator(generator_model, reuse = True)
discriminator_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits = discriminator_logits_real, labels = tf.ones_like(discriminator_model_real) * (1 - 0.1)))
discriminator_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits = discriminator_logits_fake, labels = tf.zeros_like(discriminator_model_fake)))
generator_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits = discriminator_logits_fake, labels = tf.ones_like(discriminator_model_fake)))
discriminator_loss = discriminator_loss_real + discriminator_loss_fake
return discriminator_loss, generator_lossOptimization
The function model_opt() gets the optimization operations. it returns a tuple of the discriminator training operation and the generator training operation)
dloss: Discriminator loss Tensor gloss: Generator loss Tensor learning_rate: Learning Rate Placeholder beta1: The exponential decay rate for the 1st moment in the optimizer
def model_opt(d_loss, g_loss, learning_rate, beta1):
training_vars = tf.trainable_variables()
discriminator_vars = [var for var in training_vars if var.name.startswith('discriminator')]
generator_vars = [var for var in training_vars if var.name.startswith('generator')]
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
discriminator_updates = [opt for opt in update_ops if opt.name.startswith('discriminator')]
generator_updates = [opt for opt in update_ops if opt.name.startswith('generator')]
with tf.control_dependencies(discriminator_updates):
discriminator_opt = tf.train.AdamOptimizer(
learning_rate = learning_rate, beta1 = beta1).minimize(d_loss, var_list = discriminator_vars)
with tf.control_dependencies(generator_updates):
generator_opt = tf.train.AdamOptimizer(
learning_rate = learning_rate, beta1 = beta1).minimize(g_loss, var_list = generator_vars)
return discriminator_opt, generator_optTraining
Showing Generator Output
The showgeneratoroutput() function shows an example output for the generator.
sess: TensorFlow session nimages: Number of Images to display inputz: Input Z Tensor outchanneldim: The number of channels in the output image image_mode: The mode to use for images (“RGB” or “L”)
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
plt.imshow(images_grid, cmap=cmap)
plt.show()Building and Training the GANs
The train() function trains the GAN.
epochcount: Number of epochs batchsize: Batch Size zdim: Z dimension learningrate: Learning Rate beta1: The exponential decay rate for the 1st moment in the optimizer getbatches: Function to get batches datashape: Shape of the data dataimagemode: The image mode to use for images (“RGB” or “L”)
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
n_samples, width, height, channels = data_shape
input_real, input_z, lr = model_inputs(width, height, channels, z_dim)
discriminator_loss, generator_loss = model_loss(input_real, input_z, channels)
discriminator_train_opt, generator_train_opt = model_opt(discriminator_loss, generator_loss, lr, beta1)
print(data_shape)
i = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
batch_images = batch_images * 2.0
batch_z = np.random.uniform(-1, 1, size = (batch_size, z_dim))
sess.run(discriminator_train_opt, feed_dict = {input_real: batch_images,
input_z: batch_z,
lr: learning_rate})
sess.run(generator_train_opt, feed_dict = {input_real: batch_images,
input_z: batch_z,
lr: learning_rate})
i += 1
if i % 10 == 0:
train_loss_discriminator = discriminator_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_generator = generator_loss.eval({input_z: batch_z})
print('Epoch %d/%d Discriminator loss %.4f Generator loss %.4f' % (epoch_i + 1,
epoch_count,
train_loss_discriminator,
train_loss_generator))
if i % 100 == 0:
show_generator_output(sess, 50, input_z, channels, data_image_mode)Testing our GAN on MNIST
batch_size = 64
z_dim = 128
learning_rate = 0.0005
beta1 = 0.1
epochs = 2
# After 2 epochs, the GANs are usually able to generate images that look like handwritten digits.
# When running, make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)Testing our GAN on CelebA
batch_size = 32
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5
epochs = 1
# ~20 minutes for typical GPU to run one epoch
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)As a result, we have a series of Celebrity images
Future Steps
- The current GAN makes realistic enough faces, but further hyperparameter optimization is needed.
- DRAW: A Recurrent Neural Network For Image Generation has demonstrated promising results on the MNIST generation task. Next step will involve applying this model to the CelebA dataset.
- Refactoring the code to make use of Google Brain’s TFGAN library will make future modifications easier, as well as provide more useful metrics of performance.
Cited as:
@article{mcateer2017facegan,
title = "Face-Generating GAN",
author = "McAteer, Matthew",
journal = "matthewmcateer.me",
year = "2017",
url = "https://matthewmcateer.me/blog/face-generating-gan/"
}If you notice mistakes and errors in this post, don’t hesitate to contact me at [contact at matthewmcateer dot me] and I will be very happy to correct them right away! Alternatily, you can follow me on Twitter and reach out to me there.
See you in the next post 😄
