text: ## USAGE: python 'bgan.py' --entity your-wandb-id --project your-project --latentdim 100 --epochs 30000
  
  from __future__ import print_function, division
  
  import argparse
  import numpy as np
  import matplotlib.pyplot as plt
  
  from tensorflow.keras.datasets import mnist
  from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout
  from tensorflow.keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
  from tensorflow.keras.layers import MaxPooling2D
  from tensorflow.keras.layers import LeakyReLU
  from tensorflow.keras.layers import UpSampling2D, Conv2D
  from tensorflow.keras.models import Sequential, Model
  from tensorflow.keras.optimizers import Adam
  import tensorflow.keras.backend as K
  
  
  import wandb
  from wandb.keras import WandbCallback
  
  parser = argparse.ArgumentParser()
  parser.add_argument('--entity', type=str, 
      help="provide wandb entity")
  parser.add_argument('--project', type=str, 
      help="provide wandb project name")
  parser.add_argument('--latentdim', type=int, default=100,
      help="specify the latent dimentions")
  parser.add_argument("--epochs", type=int, default=30000,
      help="number of epochs")
  parser.add_argument("--batch", type=int, default=32,
      help="batch size to be used")
  parser.add_argument("--gen_interval", type=int, default=10,
      help="log generated images after interval")
  args = parser.parse_args()
  
  
  class BGAN():
      """Reference: https://wiseodd.github.io/techblog/2017/03/07/boundary-seeking-gan/"""
      def __init__(self, latent_dim):
          self.img_rows = 28
          self.img_cols = 28
          self.channels = 1
          self.img_shape = (self.img_rows, self.img_cols, self.channels)
          self.latent_dim = latent_dim
  
          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 as input and generated imgs
          z = Input(shape=(self.latent_dim,))
          img = self.generator(z)
  
          # For the combined model we will only train the generator
          self.discriminator.trainable = False
  
          # The valid takes generated images as input and determines validity
          valid = self.discriminator(img)
  
          # The combined model  (stacked generator and discriminator)
          # Trains the generator to fool the discriminator
          self.combined = Model(z, valid)
          self.combined.compile(loss=self.boundary_loss, optimizer=optimizer)
  
      def build_generator(self):
  
          model = Sequential()
  
          model.add(Dense(256, input_dim=self.latent_dim))
          model.add(LeakyReLU(alpha=0.2))
          model.add(BatchNormalization(momentum=0.8))
          model.add(Dense(512))
          model.add(LeakyReLU(alpha=0.2))
          model.add(BatchNormalization(momentum=0.8))
          model.add(Dense(1024))
          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,))
          img = model(noise)
  
          return Model(noise, img)
  
      def build_discriminator(self):
  
          model = Sequential()
  
          model.add(Flatten(input_shape=self.img_shape))
          model.add(Dense(512))
          model.add(LeakyReLU(alpha=0.2))
          model.add(Dense(256))
          model.add(LeakyReLU(alpha=0.2))
          model.add(Dense(1, activation='sigmoid'))
          model.summary()
  
          img = Input(shape=self.img_shape)
          validity = model(img)
  
          return Model(img, validity)
  
      def boundary_loss(self, y_true, y_pred):
          """
          Boundary seeking loss.
          Reference: https://wiseodd.github.io/techblog/2017/03/07/boundary-seeking-gan/
          """
          return 0.5 * K.mean((K.log(y_pred) - K.log(1 - y_pred))**2)
  
      def train(self, epochs, batch_size=128, sample_interval=50):
  
          # Load the dataset
          (X_train, _), (_, _) = mnist.load_data()
  
          # Rescale -1 to 1
          X_train = X_train / 127.5 - 1.
          X_train = np.expand_dims(X_train, axis=3)
  
          # Adversarial ground truths
          valid = np.ones((batch_size, 1))
          fake = np.zeros((batch_size, 1))
  
          for epoch in range(epochs):
  
              # ---------------------
              #  Train Discriminator
              # ---------------------
  
              # Select a random batch of images
              idx = np.random.randint(0, X_train.shape[0], batch_size)
              imgs = X_train[idx]
  
              noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
  
              # Generate a batch of new images
              gen_imgs = self.generator.predict(noise)
  
              # Train the discriminator
              d_loss_real = self.discriminator.train_on_batch(imgs, valid)
              d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
              d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
  
  
              # ---------------------
              #  Train Generator
              # ---------------------
  
              g_loss = self.combined.train_on_batch(noise, valid)
  
              # Plot the progress
              print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
              wandb.log({'epoch': epoch, 'discriminator_loss': d_loss[0], 'accuracy': 100*d_loss[1], 'generator_loss': g_loss})
  
              # If at save interval => save generated image samples
              if epoch % sample_interval == 0:
                  self.sample_images(epoch)
  
      def sample_images(self, epoch):
          r, c = 5, 5
          noise = np.random.normal(0, 1, (r * c, self.latent_dim))
          gen_imgs = self.generator.predict(noise)
          # 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].axis('off')
                  cnt += 1
          fig.savefig("images/mnist_%d.png" % epoch)
          wandb.log({'bgan_generated_imgs': plt})
          plt.close()
  
  
  if __name__ == '__main__':
  
      wandb.init(entity=args.entity, project=args.project)
      config = wandb.config
      
      config.epochs = args.epochs
      config.batch_size = args.batch
      config.save_interval = args.gen_interval
      
      config.latent_dim = args.latentdim
  
      bgan = BGAN(config.latent_dim)
      bgan.train(epochs=config.epochs, batch_size=config.batch_size, sample_interval=config.save_interval)
  