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pixera_gen
File size: 6,750 Bytes 
0103298
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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import os 
import cv2
import keras
import warnings
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, LeakyReLU, Reshape, Flatten, Input
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Conv2DTranspose

from tensorflow.compat.v1.keras.layers import BatchNormalization

images = []
def load_images(size=(64,64)):
  pixed_faces =  os.listdir("kaggle/working/results/pixed_faces")
  images_Path = "kaggle/working/results/pixed_faces"
  for i in pixed_faces:
    try:
      image = cv2.imread(f"{images_Path}/{i}")
      image = cv2.resize(image,size)
      images.append(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    except:
      pass

load_images()


#--------vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
#Author: https://www.kaggle.com/nassimyagoub
#--------^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
def __init__(self):
    self.img_shape = (64, 64, 3)
        
    self.noise_size = 100

    optimizer = Adam(0.0002,0.5)

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

    self.generator = self.build_generator()
    self.generator.compile(loss='binary_crossentropy', optimizer=optimizer)
        
    self.combined = Sequential()
    self.combined.add(self.generator)
    self.combined.add(self.discriminator)
        
    self.discriminator.trainable = False
        
    self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)
        
    self.combined.summary()
        
def build_generator(self):
    epsilon = 0.00001
    noise_shape = (self.noise_size,)
        
    model = Sequential()
        
    model.add(Dense(4*4*512, activation='linear', input_shape=noise_shape))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Reshape((4, 4, 512)))
        
    model.add(Conv2DTranspose(512, kernel_size=[4,4], strides=[2,2], padding="same",
                                  kernel_initializer= keras.initializers.TruncatedNormal(stddev=0.02)))
    model.add(BatchNormalization(momentum=0.9, epsilon=epsilon))
    model.add(LeakyReLU(alpha=0.2))
        
    model.add(Conv2DTranspose(256, kernel_size=[4,4], strides=[2,2], padding="same",
                                  kernel_initializer= keras.initializers.TruncatedNormal(stddev=0.02)))
    model.add(BatchNormalization(momentum=0.9, epsilon=epsilon))
    model.add(LeakyReLU(alpha=0.2))
        
    model.add(Conv2DTranspose(128, kernel_size=[4,4], strides=[2,2], padding="same",
                                  kernel_initializer= keras.initializers.TruncatedNormal(stddev=0.02)))
    model.add(BatchNormalization(momentum=0.9, epsilon=epsilon))
    model.add(LeakyReLU(alpha=0.2))
        
    model.add(Conv2DTranspose(64, kernel_size=[4,4], strides=[2,2], padding="same",
                                  kernel_initializer= keras.initializers.TruncatedNormal(stddev=0.02)))
    model.add(BatchNormalization(momentum=0.9, epsilon=epsilon))
    model.add(LeakyReLU(alpha=0.2))
        
    model.add(Conv2DTranspose(3, kernel_size=[4,4], strides=[1,1], padding="same",
                                  kernel_initializer= keras.initializers.TruncatedNormal(stddev=0.02)))

    model.add(Activation("tanh"))
        
    model.summary()

    noise = Input(shape=noise_shape)
    img = model(noise)

    return Model(noise, img)

def build_discriminator(self):

    model = Sequential()

    model.add(Conv2D(128, (3,3), padding='same', input_shape=self.img_shape))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization())
    model.add(Conv2D(128, (3,3), padding='same'))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(3,3)))
    model.add(Dropout(0.2))

    model.add(Conv2D(128, (3,3), padding='same'))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization())
    model.add(Conv2D(128, (3,3), padding='same'))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(3,3)))
    model.add(Dropout(0.3))

    model.add(Flatten())
    model.add(Dense(128))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dense(128))
    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 train(self, epochs, batch_size=128, metrics_update=50, save_images=100, save_model=2000):

    X_train = np.array(images)
    X_train = (X_train.astype(np.float32) - 127.5) / 127.5

    half_batch = int(batch_size / 2)
        
    mean_d_loss=[0,0]
    mean_g_loss=0

    for epoch in range(epochs):
        idx = np.random.randint(0, X_train.shape[0], half_batch)
        imgs = X_train[idx]

        noise = np.random.normal(0, 1, (half_batch, self.noise_size))
        gen_imgs = self.generator.predict(noise)


            

        d_loss = 0.5 * np.add(self.discriminator.train_on_batch(imgs, np.ones((half_batch, 1))),
                                  self.discriminator.train_on_batch(gen_imgs, np.zeros((half_batch, 1))))


        noise = np.random.normal(0, 1, (batch_size, self.noise_size))

        valid_y = np.array([1] * batch_size)
        g_loss = self.combined.train_on_batch(noise, valid_y)
            
        mean_d_loss[0] += d_loss[0]
        mean_d_loss[1] += d_loss[1]
        mean_g_loss += g_loss
            

        if epoch % metrics_update == 0:
            print ("%d [Discriminator loss: %f, acc.: %.2f%%] [Generator loss: %f]" % (epoch, mean_d_loss[0]/metrics_update, 100*mean_d_loss[1]/metrics_update, mean_g_loss/metrics_update))
            mean_d_loss=[0,0]
            mean_g_loss=0
            
        if epoch % save_images == 0:
            self.save_images(epoch)
            

        if epoch % save_model == 0:
            self.generator.save("kaggle/working/results/generators/generator_%d" % epoch)
            self.discriminator.save("kaggle/working/results/discriminators/discriminator_%d" % epoch)


def save_images(self, epoch):
    noise = np.random.normal(0, 1, (25, self.noise_size))
    gen_imgs = self.generator.predict(noise)
        

    gen_imgs = 0.5 * gen_imgs + 0.5

    fig, axs = plt.subplots(5,5, figsize = (8,8))

    for i in range(5):
        for j in range(5):
            axs[i,j].imshow(gen_imgs[5*i+j])
            axs[i,j].axis('off')

    plt.show()
        
    fig.savefig("kaggle/working/results/pandaS_%d.png" % epoch)
    plt.close()