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ac_gan.py
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ac_gan.py
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from __future__ import print_function
from collections import defaultdict
try:
import cPickle as pickle
except ImportError:
import pickle
from PIL import Image
from six.moves import range
import tensorflow as tf
import keras.backend as K
from keras.datasets import mnist
from keras import layers
from keras.layers import Input, Dense, Reshape, Flatten, Embedding, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras.utils.generic_utils import Progbar
import numpy as np
import os
import argparse
training_size = 6000
np.random.seed(1337)
K.set_image_data_format('channels_first')
def build_generator(latent_size):
# we will map a pair of (z, L), where z is a latent vector and L is a
# label drawn from P_c, to image space (..., 1, 28, 28)
print('Generator')
cnn = Sequential()
cnn.add(Dense(256, input_dim=latent_size, activation='relu'))
cnn.add(Dense(32 * 3 * 4, activation='relu'))
cnn.add(Reshape((32, 3, 4)))
# upsample to (..., 14, 14)
cnn.add(UpSampling2D(size=(2, 6)))
cnn.add(Conv2D(256, 5, padding='same',
activation='relu',
kernel_initializer='glorot_normal'))
# take a channel axis reduction
cnn.add(Conv2D(1, 4, strides=2, padding='same',
activation='linear',
kernel_initializer='glorot_normal'))
# dense layer to reshape
cnn.summary()
# this is the z space commonly refered to in GAN papers
latent = Input(shape=(latent_size, ))
# this will be our label
patient_class = Input(shape=(1,), dtype='int32')
# 10 classes in MNIST
cls = Flatten()(Embedding(
2, latent_size,
embeddings_initializer='glorot_normal')(patient_class))
# hadamard product between z-space and a class conditional embedding
h = layers.multiply([latent, cls])
fake_patient = cnn(h)
return Model([latent, patient_class], fake_patient)
def build_discriminator():
# build a relatively standard conv net, with LeakyReLUs as suggested in
# the reference paper
print('Discriminator')
cnn = Sequential()
cnn.add(Conv2D(32, 3, padding='same', strides=2,
input_shape=(1, 3, 12)))
cnn.add(LeakyReLU())
cnn.add(Dropout(0.3))
cnn.add(Conv2D(64, 3, padding='same', strides=1))
cnn.add(LeakyReLU())
cnn.add(Dropout(0.3))
cnn.add(Flatten())
cnn.add(Dense(1024, activation='relu'))
cnn.add(Dropout(0.3))
cnn.add(Dense(1024, activation='relu'))
patient = Input(shape=(1, 3, 12))
features = cnn(patient)
cnn.summary()
# first output (name=generation) is whether or not the discriminator
# thinks the image that is being shown is fake, and the second output
# (name=auxiliary) is the class that the discriminator thinks the image
# belongs to.
fake = Dense(1, activation='sigmoid', name='generation')(features)
# aux could probably be 1 sigmoid too...
aux = Dense(2, activation='softmax', name='auxiliary')(features)
return Model(patient, [fake, aux])
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--epochs", type=int, default=200)
parser.add_argument("--lr", type=float, default=0.0002)
parser.add_argument("--batch_size", type=int, default=100)
args = parser.parse_args()
print(args)
epochs = args.epochs
batch_size = args.batch_size
latent_size = 100
# Adam parameters suggested in https://arxiv.org/abs/1511.06434
adam_lr = args.lr
adam_beta_1 = 0.5
directory = ('./output/acgan' + '_' + str(args.epochs) + '_' +
str(args.lr) + '_' + str(args.batch_size) + '/')
if not os.path.exists(directory):
os.mkdir(directory)
discriminator = build_discriminator()
discriminator.compile(
optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
loss=['binary_crossentropy', 'sparse_categorical_crossentropy']
)
# build the generator
generator = build_generator(latent_size)
generator.compile(optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
loss='binary_crossentropy')
latent = Input(shape=(latent_size, ))
image_class = Input(shape=(1,), dtype='int32')
# get a fake image
fake = generator([latent, image_class])
# we only want to be able to train generation for the combined model
discriminator.trainable = False
fake, aux = discriminator(fake)
combined = Model([latent, image_class], [fake, aux])
combined.compile(
optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
loss=['binary_crossentropy', 'sparse_categorical_crossentropy']
)
# get our mnist data, and force it to be of shape (..., 1, 28, 28) with
# range [-1, 1]
X_input = pickle.load(open('/data/SPRINT/X_processed.pkl', 'rb'))
y_input = pickle.load(open('/data/SPRINT/y_processed.pkl', 'rb'))
print(X_input.shape, y_input.shape)
X_train = X_input[:training_size]
X_test = X_input[training_size:]
X_train = np.expand_dims(X_train, axis=1)
X_test = np.expand_dims(X_test, axis=1)
y_train = y_input[:training_size]
y_test = y_input[training_size:]
num_train, num_test = X_train.shape[0], X_test.shape[0]
train_history = defaultdict(list)
test_history = defaultdict(list)
privacy_history = []
with tf.Session() as sess:
eps = tf.placeholder(tf.float32)
delta = tf.placeholder(tf.float32)
for epoch in range(epochs):
print('Epoch {} of {}'.format(epoch + 1, epochs))
num_batches = int(X_train.shape[0] / batch_size)
progress_bar = Progbar(target=num_batches)
epoch_gen_loss = []
epoch_disc_loss = []
for index in range(num_batches):
progress_bar.update(index)
# generate a new batch of noise
noise = np.random.uniform(-1, 1, (batch_size, latent_size))
# get a batch of real images
image_batch = X_train[index * batch_size:(index + 1) * batch_size]
label_batch = y_train[index * batch_size:(index + 1) * batch_size]
# sample some labels from p_c
sampled_labels = np.random.randint(0, 2, batch_size)
# generate a batch of fake images, using the generated labels as a
# conditioner. We reshape the sampled labels to be
# (batch_size, 1) so that we can feed them into the embedding
# layer as a length one sequence
generated_images = generator.predict(
[noise, sampled_labels.reshape((-1, 1))], verbose=0)
# print(image_batch.shape)
# print(generated_images.shape)
X = np.concatenate((image_batch, generated_images))
y = np.array([1] * batch_size + [0] * batch_size)
aux_y = np.concatenate((label_batch, sampled_labels), axis=0)
epoch_disc_loss.append(discriminator.train_on_batch(
X, [y, aux_y]))
# make new noise. we generate 2 * batch size here such that we have
# the generator optimize over an identical number of images as the
# discriminator
noise = np.random.uniform(-1, 1, (2 * batch_size, latent_size))
sampled_labels = np.random.randint(0, 2, 2 * batch_size)
# we want to train the generator to trick the discriminator
# For the generator, we want all the {fake, not-fake} labels to say
# not-fake
trick = np.ones(2 * batch_size)
epoch_gen_loss.append(combined.train_on_batch(
[noise, sampled_labels.reshape((-1, 1))],
[trick, sampled_labels]))
print('\nTesting for epoch {}:'.format(epoch + 1))
# evaluate the testing loss here
# generate a new batch of noise
noise = np.random.uniform(-1, 1, (num_test, latent_size))
# sample some labels from p_c and generate images from them
sampled_labels = np.random.randint(0, 2, num_test)
generated_images = generator.predict(
[noise, sampled_labels.reshape((-1, 1))], verbose=False)
print(sampled_labels[0])
print(generated_images[0].astype(int))
X = np.concatenate((X_test, generated_images))
y = np.array([1] * num_test + [0] * num_test)
aux_y = np.concatenate((y_test, sampled_labels), axis=0)
# see if the discriminator can figure itself out...
discriminator_test_loss = discriminator.evaluate(
X, [y, aux_y], verbose=False)
discriminator_train_loss = np.mean(np.array(epoch_disc_loss), axis=0)
# make new noise
noise = np.random.uniform(-1, 1, (2 * num_test, latent_size))
sampled_labels = np.random.randint(0, 2, 2 * num_test)
trick = np.ones(2 * num_test)
generator_test_loss = combined.evaluate(
[noise, sampled_labels.reshape((-1, 1))],
[trick, sampled_labels], verbose=False)
generator_train_loss = np.mean(np.array(epoch_gen_loss), axis=0)
# generate an epoch report on performance
train_history['generator'].append(generator_train_loss)
train_history['discriminator'].append(discriminator_train_loss)
test_history['generator'].append(generator_test_loss)
test_history['discriminator'].append(discriminator_test_loss)
print('{0:<22s} | {1:4s} | {2:15s} | {3:5s}'.format(
'component', *discriminator.metrics_names))
print('-' * 65)
ROW_FMT = '{0:<22s} | {1:<4.2f} | {2:<15.2f} | {3:<5.2f}'
print(ROW_FMT.format('generator (train)',
*train_history['generator'][-1]))
print(ROW_FMT.format('generator (test)',
*test_history['generator'][-1]))
print(ROW_FMT.format('discriminator (train)',
*train_history['discriminator'][-1]))
print(ROW_FMT.format('discriminator (test)',
*test_history['discriminator'][-1]))
generator.save(
directory +
'params_generator_epoch_{0:03d}.h5'.format(epoch))
if epoch > (epochs-10):
discriminator.save(
directory +
'params_discriminator_epoch_{0:03d}.h5'.format(epoch))
pickle.dump({'train': train_history, 'test': test_history,
'privacy': privacy_history},
open(directory + 'acgan-history.pkl', 'wb'))