diff --git a/Python/GenerativeAdveserialNetwork.py b/Python/GenerativeAdveserialNetwork.py
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+!pip install git+https://www.github.com/keras-team/keras-contrib.git
+
+# example of preparing the horses and zebra dataset
+from os import listdir
+from numpy import asarray
+from numpy import vstack
+from keras.preprocessing.image import img_to_array
+from keras.preprocessing.image import load_img
+from random import random
+from numpy import load
+from numpy import zeros
+from numpy import ones
+from numpy import asarray
+from numpy.random import randint
+from tensorflow.keras.optimizers import Adam
+from keras.initializers import RandomNormal
+from keras.models import Model
+from keras.models import Input
+from keras.layers import Conv2D
+from keras.layers import Conv2DTranspose
+from keras.layers import LeakyReLU
+from keras.layers import Activation
+from keras.layers import Concatenate
+from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
+from matplotlib import pyplot
+
+from numpy import savez_compressed
+# load all images in a directory into memory
+def load_images(path, size=(256,256)):
+    data_list = list()
+# enumerate filenames in directory, assume all are images
+    for filename in listdir(path):
+# load and resize the image
+       pixels = load_img(path + filename, target_size=size)
+# convert to numpy array
+       pixels = img_to_array(pixels)
+# store
+       data_list.append(pixels)
+    return asarray(data_list)
+
+
+# dataset path
+path = "/content/drive/MyDrive/Internship/Infosys Internship/Datasets/"
+# load dataset A
+dataA1 = load_images(path + 'trainA/')
+dataA2 = load_images(path + 'testA/')
+dataA = vstack((dataA1, dataA2))
+print('Loaded dataA: ', dataA.shape)
+# load dataset B
+dataB1 = load_images(path + 'trainB/')
+dataB2 = load_images(path + 'testB/')
+dataB = vstack((dataB1, dataB2))
+print('Loaded dataB: ', dataB.shape)
+# save as compressed numpy array
+filename = 'horse2zebra_256.npz'
+savez_compressed(filename, dataA, dataB)
+print('Saved dataset: ', filename)
+
+# load and plot the prepared dataset
+from numpy import load
+from matplotlib import pyplot
+# load the face dataset
+data = load('horse2zebra_256.npz')
+dataA, dataB = data['arr_0'], data['arr_1']
+print('Loaded: ', dataA.shape, dataB.shape)
+# plot source images
+n_samples = 3
+for i in range(n_samples):
+   pyplot.subplot(2, n_samples, 1 + i)
+   pyplot.axis('off')
+   pyplot.imshow(dataA[i].astype('uint8'))
+
+# plot target image
+for i in range(n_samples):
+  pyplot.subplot(2, n_samples, 1 + n_samples + i)
+  pyplot.axis('off')
+  pyplot.imshow(dataB[i].astype('uint8'))
+pyplot.show()
+
+# define the discriminator model
+def define_discriminator(image_shape):
+   # weight initialization
+   init = RandomNormal(stddev=0.02)
+   # source image input
+   in_image = Input(shape=image_shape)
+# C64
+   d = Conv2D(64, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(in_image)
+   d = LeakyReLU(alpha=0.2)(d)
+# C128
+   d = Conv2D(128, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)
+   d = InstanceNormalization(axis=-1)(d)
+   d = LeakyReLU(alpha=0.2)(d)
+# C256
+   d = Conv2D(256, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)
+   d = InstanceNormalization(axis=-1)(d)
+   d = LeakyReLU(alpha=0.2)(d)
+# C512
+   d = Conv2D(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)
+   d = InstanceNormalization(axis=-1)(d)
+   d = LeakyReLU(alpha=0.2)(d)
+# second last output layer
+   d = Conv2D(512, (4,4), padding='same', kernel_initializer=init)(d)
+   d = InstanceNormalization(axis=-1)(d)
+   d = LeakyReLU(alpha=0.2)(d)
+# patch output
+   patch_out = Conv2D(1, (4,4), padding='same', kernel_initializer=init)(d)
+# define model
+   model = Model(in_image, patch_out)
+# compile model
+   model.compile(loss='mse', optimizer=Adam(learning_rate=0.0002, beta_1=0.5), loss_weights=[0.5])
+   return model
+   
+# generator a resnet block
+def resnet_block(n_filters, input_layer):
+# weight initialization
+  init = RandomNormal(stddev=0.02)
+# first layer convolutional layer
+  g = Conv2D(n_filters, (3,3), padding='same', kernel_initializer=init)(input_layer)
+  g = InstanceNormalization(axis=-1)(g)
+  g = Activation('relu')(g)
+# second convolutional layer
+  g = Conv2D(n_filters, (3,3), padding='same', kernel_initializer=init)(g)
+  g = InstanceNormalization(axis=-1)(g)
+# concatenate merge channel-wise with input layer
+  g = Concatenate()([g, input_layer])
+  return g
+
+# define the standalone generator model
+def define_generator(image_shape, n_resnet=9):
+# weight initialization
+  init = RandomNormal(stddev=0.02)
+# image input
+  in_image = Input(shape=image_shape)
+# c7s1-64
+  g = Conv2D(64, (7,7), padding='same', kernel_initializer=init)(in_image)
+  g = InstanceNormalization(axis=-1)(g)
+  g = Activation('relu')(g)
+# d128
+  g = Conv2D(128, (3,3), strides=(2,2), padding='same', kernel_initializer=init)(g)
+  g = InstanceNormalization(axis=-1)(g)
+  g = Activation('relu')(g)
+# d256
+  g = Conv2D(256, (3,3), strides=(2,2), padding='same', kernel_initializer=init)(g)
+  g = InstanceNormalization(axis=-1)(g)
+  g = Activation('relu')(g)
+# R256
+  for _ in range(n_resnet):
+    g = resnet_block(256, g)
+# u128
+  g = Conv2DTranspose(128, (3,3), strides=(2,2), padding='same', kernel_initializer=init)(g)
+  g = InstanceNormalization(axis=-1)(g)
+  g = Activation('relu')(g)
+# u64
+  g = Conv2DTranspose(64, (3,3), strides=(2,2), padding='same', kernel_initializer=init)(g)
+  g = InstanceNormalization(axis=-1)(g)
+  g = Activation('relu')(g)
+# c7s1-3
+  g = Conv2D(3, (7,7), padding='same', kernel_initializer=init)(g)
+  g = InstanceNormalization(axis=-1)(g)
+  out_image = Activation('tanh')(g)
+# define model
+  model = Model(in_image, out_image)
+  return model
+
+# define a composite model for updating generators by adversarial and cycle loss
+def define_composite_model(g_model_1, d_model, g_model_2, image_shape):
+# ensure the model we✬re updating is trainable
+  g_model_1.trainable = True
+# mark discriminator as not trainable
+  d_model.trainable = False
+# mark other generator model as not trainable
+  g_model_2.trainable = False
+# discriminator element
+  input_gen = Input(shape=image_shape)
+  gen1_out = g_model_1(input_gen)
+  output_d = d_model(gen1_out)
+# identity element
+  input_id = Input(shape=image_shape)
+  output_id = g_model_1(input_id)
+# forward cycle
+  output_f = g_model_2(gen1_out)
+# backward cycle
+  gen2_out = g_model_2(input_id)
+  output_b = g_model_1(gen2_out)
+# define model graph
+  model = Model([input_gen, input_id], [output_d, output_id, output_f, output_b])
+# define optimization algorithm configuration
+  opt = Adam(learning_rate=0.0002, beta_1=0.5)
+# compile model with weighting of least squares loss and L1 loss
+  model.compile(loss=['mse', 'mae', 'mae', 'mae'], loss_weights=[1, 5, 10, 10],
+  optimizer=opt)
+  return model
+
+# load and prepare training images
+def load_real_samples(filename):
+# load the dataset
+  data = load(filename)
+# unpack arrays
+  X1, X2 = data['arr_0'], data['arr_1']
+# scale from [0,255] to [-1,1]
+  X1 = (X1 - 127.5) / 127.5
+  X2 = (X2 - 127.5) / 127.5
+  return [X1, X2]
+  
+# select a batch of random samples, returns images and target
+def generate_real_samples(dataset, n_samples, patch_shape):
+# choose random instances
+   ix = randint(0, dataset.shape[0], n_samples)
+   # retrieve selected images
+   X = dataset[ix]
+# generate ✬real✬ class labels (1)
+   y = ones((n_samples, patch_shape, patch_shape, 1))
+   return X, y
+
+# generate a batch of images, returns images and targets
+def generate_fake_samples(g_model, dataset, patch_shape):
+# generate fake instance
+   X = g_model.predict(dataset)
+# create ✬fake✬ class labels (0)
+   y = zeros((len(X), patch_shape, patch_shape, 1))
+   return X, y
+   
+# save the generator models to file
+def save_models(step, g_model_AtoB, g_model_BtoA):
+# save the first generator model
+  filename1 = 'g_model_AtoB_%06d.h5' % (step+1)
+  g_model_AtoB.save(filename1)
+# save the second generator model
+  filename2 = 'g_model_BtoA_%06d.h5' % (step+1)
+  g_model_BtoA.save(filename2)
+  print('>Saved: %s and %s' % (filename1, filename2))
+
+# generate samples and save as a plot and save the model
+def summarize_performance(step, g_model, trainX, name, n_samples=5):
+# select a sample of input images
+  X_in, _ = generate_real_samples(trainX, n_samples, 0)
+# generate translated images
+  X_out, _ = generate_fake_samples(g_model, X_in, 0)
+# scale all pixels from [-1,1] to [0,1]
+  X_in = (X_in + 1) / 2.0
+  X_out = (X_out + 1) / 2.0
+# plot real images
+  for i in range(n_samples):
+    pyplot.subplot(2, n_samples, 1 + i)
+    pyplot.axis('off')
+    pyplot.imshow(X_in[i])
+# plot translated image
+  for i in range(n_samples):
+    pyplot.subplot(2, n_samples, 1 + n_samples + i)
+    pyplot.axis('off')
+    pyplot.imshow(X_out[i])
+# save plot to file
+  filename1 = '%s_generated_plot_%06d.png' % (name, (step+1))
+  pyplot.savefig(filename1)
+  pyplot.close()
+  
+# update image pool for fake images
+def update_image_pool(pool, images, max_size=50):
+   selected = list()
+   for image in images:
+      if len(pool) < max_size:
+# stock the pool
+        pool.append(image)
+        selected.append(image)
+      elif random() < 0.5:
+# use image, but don✬t add it to the pool
+        selected.append(image)
+      else:
+# replace an existing image and use replaced image
+        ix = randint(0, len(pool))
+        selected.append(pool[ix])
+        pool[ix] = image
+   return asarray(selected)
+
+# train cyclegan models
+def train(d_model_A, d_model_B, g_model_AtoB, g_model_BtoA, c_model_AtoB, c_model_BtoA,dataset):
+# define properties of the training run
+  n_epochs, n_batch, = 1, 1
+# determine the output square shape of the discriminator
+  n_patch = d_model_A.output_shape[1]
+# unpack dataset
+  trainA, trainB = dataset
+# prepare image pool for fakes
+  poolA, poolB = list(), list()
+# calculate the number of batches per training epoch
+  bat_per_epo = int(len(trainA) / n_batch)
+# calculate the number of training iterations
+  n_steps = bat_per_epo * n_epochs
+# manually enumerate epochs
+  for i in range(n_steps):
+# select a batch of real samples
+    X_realA, y_realA = generate_real_samples(trainA, n_batch, n_patch)
+    X_realB, y_realB = generate_real_samples(trainB, n_batch, n_patch)
+# generate a batch of fake samples
+    X_fakeA, y_fakeA = generate_fake_samples(g_model_BtoA, X_realB, n_patch)
+    X_fakeB, y_fakeB = generate_fake_samples(g_model_AtoB, X_realA, n_patch)
+# update fakes from pool
+    X_fakeA = update_image_pool(poolA, X_fakeA)
+    X_fakeB = update_image_pool(poolB, X_fakeB)
+# update generator B->A via adversarial and cycle loss
+    g_loss2, _, _, _, _ = c_model_BtoA.train_on_batch([X_realB, X_realA], [y_realA,X_realA, X_realB, X_realA])
+# update discriminator for A -> [real/fake]
+    dA_loss1 = d_model_A.train_on_batch(X_realA, y_realA)
+    dA_loss2 = d_model_A.train_on_batch(X_fakeA, y_fakeA)
+# update generator A->B via adversarial and cycle loss
+    g_loss1, _, _, _, _ = c_model_AtoB.train_on_batch([X_realA, X_realB], [y_realB,X_realB, X_realA, X_realB])
+# update discriminator for B -> [real/fake]
+    dB_loss1 = d_model_B.train_on_batch(X_realB, y_realB)
+    dB_loss2 = d_model_B.train_on_batch(X_fakeB, y_fakeB)
+# summarize performance
+    print('>%d, dA[%.3f,%.3f] dB[%.3f,%.3f] g[%.3f,%.3f]' % (i+1, dA_loss1,dA_loss2,dB_loss1,dB_loss2, g_loss1,g_loss2))
+# evaluate the model performance every so often
+    if (i+1) % (bat_per_epo * 1) == 0:
+# plot A->B translation
+       summarize_performance(i, g_model_AtoB, trainA, 'AtoB')
+# plot B->A translation
+       summarize_performance(i, g_model_BtoA, trainB, 'BtoA')
+    if (i+1) % (bat_per_epo * 1) == 0:
+# save the models
+       save_models(i, g_model_AtoB, g_model_BtoA)
+
+
+
+# load image data
+dataset = load_real_samples('horse2zebra_256.npz')
+print('Loaded', dataset[0].shape, dataset[1].shape)
+# define input shape based on the loaded dataset
+image_shape = dataset[0].shape[1:]
+# generator: A -> B
+g_model_AtoB = define_generator(image_shape)
+# generator: B -> A
+g_model_BtoA = define_generator(image_shape)
+# discriminator: A -> [real/fake]
+d_model_A = define_discriminator(image_shape)
+# discriminator: B -> [real/fake]
+d_model_B = define_discriminator(image_shape)
+# composite: A -> B -> [real/fake, A]
+c_model_AtoB = define_composite_model(g_model_AtoB, d_model_B, g_model_BtoA, image_shape)
+# composite: B -> A -> [real/fake, B]
+c_model_BtoA = define_composite_model(g_model_BtoA, d_model_A, g_model_AtoB, image_shape)
+# train models
+train(d_model_A, d_model_B, g_model_AtoB, g_model_BtoA, c_model_AtoB, c_model_BtoA, dataset)
+       
+