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cifar_ssgan_trainer.py
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# Implementation of Semi-Supervised Learning with Generative Adversarial Networks by Augustus Odena
# https://arxiv.org/pdf/1606.01583.pdf
# Also draws on UNSUPERVISED AND SEMI-SUPERVISED LEARNING WITH CATEGORICAL GENERATIVE ADVERSARIAL NETWORKS
# by Jost Tobias Springenberg
# https://arxiv.org/pdf/1511.06390.pdf
# Code (c) Sam Russell 2017
import base_trainer
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D
from keras.layers import Input
from keras.layers.core import Activation, Reshape
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam
from keras.activations import *
from keras.utils import to_categorical
from keras.datasets import cifar10
import keras
def selu(x):
"""Scaled Exponential Linear Unit. (Klambauer et al., 2017)
# Arguments
x: A tensor or variable to compute the activation function for.
# References
- [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
"""
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
return scale * K.elu(x, alpha)
class CifarSsganTrainer(base_trainer.BaseTrainer):
img_rows = 32
img_cols = 32
img_channels = 3
num_classes = 10
def build_models(self, input_shape):
self.discriminator = Sequential()
self.discriminator.add(Conv2D(64, (5, 5), strides=(2, 2), padding = 'same', input_shape=input_shape))
self.discriminator.add(LeakyReLU(0.2))
self.discriminator.add(Dropout(0.5))
self.discriminator.add(Conv2D(128, (5, 5), strides=(2, 2), padding = 'same'))
self.discriminator.add(LeakyReLU(0.2))
self.discriminator.add(Dropout(0.5))
self.discriminator.add(Conv2D(256, (5, 5), strides=(2, 2), padding = 'same'))
self.discriminator.add(LeakyReLU(0.2))
self.discriminator.add(Dropout(0.5))
# 8x8 for CIFAR
#self.discriminator.add(Conv2D(512, (5, 5), strides=(2, 2), padding = 'same', activation='relu'))
#self.discriminator.add(LeakyReLU(0.2))
#self.discriminator.add(Dropout(0.5))
self.discriminator.add(Flatten())
self.discriminator.add(Dense(1+self.num_classes,activation='softmax'))
self.discriminator.summary()
self.generator = Sequential()
self.generator.add(Dense(8*8*256, input_shape=(100,)))
#self.generator.add(BatchNormalization())
self.generator.add(Activation(selu))
if keras.backend.image_data_format() == 'channels_first':
self.generator.add(Reshape([256, 8, 8]))
else:
self.generator.add(Reshape([8, 8, 256]))
self.generator.add(Dropout(0.5))
self.generator.add(UpSampling2D(size=(2, 2)))
self.generator.add(Conv2D(128, (5, 5), padding='same'))
#self.generator.add(BatchNormalization())
self.generator.add(Activation(selu))
self.generator.add(Dropout(0.5))
self.generator.add(UpSampling2D(size=(2, 2)))
self.generator.add(Conv2D(64, (5, 5), padding='same'))
#self.generator.add(BatchNormalization())
self.generator.add(Activation(selu))
#self.generator.add(Dropout(0.5))
#self.generator.add(UpSampling2D(size=(2, 2)))
#self.generator.add(Conv2D(64, (5, 5), padding='same'))
#self.generator.add(BatchNormalization())
#self.generator.add(Activation('relu'))
# we're ignoring input shape - just assuming it's 4,4,3
self.generator.add(Conv2D(3, (5, 5), padding='same'))
self.generator.add(Activation('sigmoid'))
self.generator.summary()
#self.real_image_model = Sequential()
#self.real_image_model.add(self.discriminator)
#self.real_image_model.compile(loss='categorical_crossentropy',
# optimizer=Adam(lr=1e-4),
# metrics=['accuracy'])
self.generator.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=1e-6),
metrics=['accuracy'])
self.discriminator.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=1e-5),
metrics=['accuracy'])
self.real_image_model = self.discriminator
self.fake_image_model = Sequential()
self.fake_image_model.add(self.generator)
self.discriminator.trainable = False
self.fake_image_model.add(self.discriminator)
self.fake_image_model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=1e-6),
metrics=['accuracy'])
def load_data(self):
self.cifar_data = cifar10.load_data()
def load_training_data(self):
#training_dataframe = pandas.read_csv(self.commandline_args.train)
#values = training_dataframe.values[:,1:]
#labels = training_dataframe.values[:,0]
(X_train, y_train), (X_test, y_test) = self.cifar_data
shaped_labels = to_categorical(y_train, self.num_classes+1)
scaled_values = self.scale_values(X_train)
shaped_values = self.reshape_values(scaled_values)
return shaped_values, shaped_labels
def load_testing_data(self):
#testing_dataframe = pandas.read_csv(self.commandline_args.test)
#values = testing_dataframe.values
(X_train, y_train), (X_test, y_test) = self.cifar_data
shaped_labels = to_categorical(y_test, self.num_classes+1)
scaled_values = self.scale_values(X_test)
shaped_values = self.reshape_values(scaled_values)
return shaped_values, shaped_labels
if __name__ == "__main__":
CifarSsganTrainer().run()