path: root/cgan.py

from __future__ import print_function, division
import tensorflow.keras as keras
import tensorflow as tf
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
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
import matplotlib.pyplot as plt
from IPython.display import clear_output
from tqdm import tqdm

import numpy as np

class CGAN():
    def __init__(self, dense_layers = 3, dropout=0.4):
        # Input shape
        self.img_rows = 28
        self.img_cols = 28
        self.channels = 1
        self.img_shape = (self.img_rows, self.img_cols, self.channels)
        self.num_classes = 10
        self.latent_dim = 100
        self.dense_layers = dense_layers
        self.dropout = dropout

        optimizer = Adam(0.0002, 0.5)

        # Build and compile the discriminator
        self.discriminator = self.build_discriminator()
        self.discriminator.compile(loss=['binary_crossentropy'],
            optimizer=optimizer,
            metrics=['accuracy'])

        # Build the generator
        self.generator = self.build_generator()

        # The generator takes noise and the target label as input
        # and generates the corresponding digit of that label
        noise = Input(shape=(self.latent_dim,))
        label = Input(shape=(1,))
        img = self.generator([noise, label])

        # For the combined model we will only train the generator
        self.discriminator.trainable = False

        # The discriminator takes generated image as input and determines validity
        # and the label of that image
        valid = self.discriminator([img, label])

        # The combined model  (stacked generator and discriminator)
        # Trains generator to fool discriminator
        self.combined = Model([noise, label], valid)
        self.combined.compile(loss=['binary_crossentropy'],
            optimizer=optimizer)

    def build_generator(self):

        model = Sequential()

        for i in range(self.dense_layers):
            output_size = 2**(8+i)
            model.add(Dense(output_size, input_dim=self.latent_dim))
            model.add(LeakyReLU(alpha=0.2))
            model.add(BatchNormalization(momentum=0.8))

        model.add(Dense(np.prod(self.img_shape), activation='tanh'))
        model.add(Reshape(self.img_shape))

        #model.summary()

        noise = Input(shape=(self.latent_dim,))
        label = Input(shape=(1,), dtype='int32')
        label_embedding = Flatten()(Embedding(self.num_classes, self.latent_dim)(label))

        model_input = multiply([noise, label_embedding])
        img = model(model_input)

        return Model([noise, label], img)

    def build_discriminator(self):

        model = Sequential()

        model.add(Dense(512, input_dim=np.prod(self.img_shape)))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(self.dropout))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(self.dropout))
        model.add(Dense(1, activation='sigmoid'))
        
        #model.summary()

        img = Input(shape=self.img_shape)
        label = Input(shape=(1,), dtype='int32')

        label_embedding = Flatten()(Embedding(self.num_classes, np.prod(self.img_shape))(label))
        flat_img = Flatten()(img)

        model_input = multiply([flat_img, label_embedding])

        validity = model(model_input)

        return Model([img, label], validity)

    def train(self, epochs, batch_size=128, sample_interval=50, graph=False, smooth_real=1, smooth_fake=0, gdstep=1):

        # Load the dataset
        (X_train, y_train), (_, _) = mnist.load_data()

        # Configure input
        X_train = (X_train.astype(np.float32) - 127.5) / 127.5
        X_train = np.expand_dims(X_train, axis=3)
        y_train = y_train.reshape(-1, 1)

        # Adversarial ground truths
        valid = np.ones((batch_size, 1))
        fake = np.zeros((batch_size, 1))
        
        xaxis = np.arange(epochs)
        loss = np.zeros((2,epochs))
        for epoch in tqdm(range(epochs)):

            # ---------------------
            #  Train Discriminator
            # ---------------------

            # Select a random half batch of images
            idx = np.random.randint(0, X_train.shape[0], batch_size)
            imgs, labels = X_train[idx], y_train[idx]

            # Sample noise as generator input
            noise = np.random.normal(0, 1, (batch_size, 100))

            # Generate a half batch of new images
            gen_imgs = self.generator.predict([noise, labels])

            # Train the discriminator

            d_loss_real = self.discriminator.train_on_batch([imgs, labels], valid*smooth_real)
            d_loss_fake = self.discriminator.train_on_batch([gen_imgs, labels], valid*smooth_fake)
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

            # ---------------------
            #  Train Generator
            # ---------------------

            # Condition on labels
            sampled_labels = np.random.randint(0, 10, batch_size).reshape(-1, 1)
            # Train the generator
            if epoch % gdstep == 0:
                g_loss = self.combined.train_on_batch([noise, sampled_labels], valid)
            else:
                g_loss = 0

            # Plot the progress
            #print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
            loss[0][epoch] = d_loss[0]
            loss[1][epoch] = g_loss

            # If at save interval => save generated image samples
            if epoch % sample_interval == 0:
                self.sample_images(epoch)
            
        if graph:
          plt.plot(xaxis,loss[0])
          plt.plot(xaxis,loss[1])
          plt.legend(('Discriminator', 'Generator'), loc='best')
          plt.xlabel('Epoch')
          plt.ylabel('Binary Crossentropy Loss')

    def sample_images(self, epoch):
        r, c = 2, 5
        noise = np.random.normal(0, 1, (r * c, 100))
        sampled_labels = np.arange(0, 10).reshape(-1, 1)

        #using dummy_labels would just print zeros to help identify image quality
        #dummy_labels = np.zeros(32).reshape(-1, 1)
        
        gen_imgs = self.generator.predict([noise, sampled_labels])

        # Rescale images 0 - 1
        gen_imgs = 0.5 * gen_imgs + 0.5

        fig, axs = plt.subplots(r, c)
        cnt = 0
        for i in range(r):
            for j in range(c):
                axs[i,j].imshow(gen_imgs[cnt,:,:,0], cmap='gray')
                axs[i,j].set_title("Digit: %d" % sampled_labels[cnt])
                axs[i,j].axis('off')
                cnt += 1
        fig.savefig("images/%d.png" % epoch)
        plt.close()
        
    def generate_data(self, output_train = 55000):
      # with this output_train you specify how much training data you want. the other two variables produce validation
      # and testing data in proportions equal to the ones of MNIST dataset

      val_size = int(output_train/11)
      test_size = 2*val_size

      noise_train = np.random.normal(0, 1, (output_train, 100))
      noise_test = np.random.normal(0, 1, (test_size, 100))
      noise_val = np.random.normal(0, 1, (val_size, 100))

      labels_train = np.zeros(output_train).reshape(-1, 1)
      labels_test = np.zeros(test_size).reshape(-1, 1)
      labels_val = np.zeros(val_size).reshape(-1, 1)
      
      for i in range(10):
        labels_train[i*int(output_train/10):-1] = i
        labels_test[i*int(test_size/10):-1] = i
        labels_val[i*int(val_size/10):-1] = i

      train_data = self.generator.predict([noise_train, labels_train])
      test_data = self.generator.predict([noise_test, labels_test])
      val_data = self.generator.predict([noise_val, labels_val])

      labels_train = keras.utils.to_categorical(labels_train, 10)
      labels_test = keras.utils.to_categorical(labels_test, 10)
      labels_val = keras.utils.to_categorical(labels_val, 10)

      return train_data, test_data, val_data, labels_train, labels_test, labels_val


'''
if __name__ == '__main__':
    cgan = CGAN(dense_layers=1)
    cgan.train(epochs=7000, batch_size=32, sample_interval=200)
    train, test, tr_labels, te_labels = cgan.generate_data()
    print(train.shape, test.shape)
'''