Something is disastrously wrong with my neural network and what it's produced Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsDetecting anomalies with neural networkpredict sinus with keras feed forward neural networkMy Neural network in Tensorflow does a bad job in comparison to the same Neural network in KerasValue error in Merging two different models in kerasUnderstanding a Neural Network with Keras (preferably), TensorFlow or PyTorchHow to define a multi-dimensional neural network with kerasRunning Neural Network experiments in loopDealing with Error in Neural Network inputNeural Network Data Normalization SetupNeural network with different input shapes

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Something is disastrously wrong with my neural network and what it's produced



Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsDetecting anomalies with neural networkpredict sinus with keras feed forward neural networkMy Neural network in Tensorflow does a bad job in comparison to the same Neural network in KerasValue error in Merging two different models in kerasUnderstanding a Neural Network with Keras (preferably), TensorFlow or PyTorchHow to define a multi-dimensional neural network with kerasRunning Neural Network experiments in loopDealing with Error in Neural Network inputNeural Network Data Normalization SetupNeural network with different input shapes










0












$begingroup$


I just got a neural network to run and although it doesn't raise any exceptions, I'm left with a horrible mess after 80 to 100 epochs:



After 100 epochs: After 100 epochs



I am trying to generate a synthetic image of a cat from my own database of cat photos that I compiled using a crawler. I am using an adapted code originally intended for the MNSIT handwritten digits database (hence the shape of the grid).



The network doesn't appear to be training, generating or discriminating properly because the epochs aren't taking long at all and what is being produced is very poor.



To be clear, I've tried to adapt another author's code that I found online and I've added other snippets of code to try to get it to work. It's evident that my 'FrankenNet' has fallen to bits and my 'bolt it together and see what happens' approach has its limitations. In the future I plan to be more efficient and logical with how I learn Python because my experimental method has proved to be both time consuming and unpredictable.



Maybe I haven't loaded in the data correctly or perhaps there are a few other issues such as converting the data to a numpy array?



For the simple fact that I don't know exactly what is causing this (I have limited experience in programming), and because there are no exceptions raised when I run the program, I will offer the entire code below.



I'd love some advice because I really want to generate something and I've spent a long time trying to work it out through trial and error with no results. I'd especially appreciate some specific suggestions about what lines I need to change, add or remove to get this beast up to scratch.



Thank you for your time!



import os
import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib

matplotlib.use("TkAgg")
from matplotlib import pyplot as plt
from tqdm import tqdm
from keras.layers import Input
from keras.models import Model, Sequential
from keras.layers.core import Dense, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import Adam
from keras import initializers

os.environ["KERAS_BACKEND"] = "tensorflow"
np.random.seed(10)
random_dim = 100
from os import listdir
from PIL import Image as PImage


def loadImages(path):
# return array of images
imagesList = listdir(path)
loadedImages = []
for image in imagesList:
img = PImage.open(path + image)
loadedImages.append(img)
return loadedImages


DATASET_NAME = 'cats'
ROOT_DIR = '/Users/Darren/desktop'
DATASET_DIR = f'ROOT_DIR/DATASET_NAME'
input_files = [os.path.join(dp, f) for dp, dn, fn in
os.walk(os.path.expanduser(f'DATASET_DIR/processed')) for f in fn
if f != '.DS_Store']
imgs = np.ndarray(shape=(len(input_files), 100, 100, 3),
dtype=np.int)
for i, input_file in enumerate(input_files):
# print('processing file: '.format(input_file))
image = imread(input_file)
imgs[i] = image
# your images in an array
imgs = loadImages(path)

PATH = os.getcwd()

train_path = PATH + '/cats/train'
train_batch = os.listdir(train_path)
x_train = []

# if data are in form of images
img_path = train_path
test_path = PATH + '/cats/test'
test_batch = os.listdir(test_path)
x_test = []

# finally converting list into numpy array
x_train = np.array(x_train)
x_test = np.array(x_test)

def get_optimizer():
return Adam(lr=0.0002, beta_1=0.5)


def get_generator(optimizer):
generator = Sequential()
generator.add(Dense(256, input_dim=random_dim,
kernel_initializer=initializers.RandomNormal(stddev=0.02)))
generator.add(LeakyReLU(0.2))
generator.add(Dense(512))
generator.add(LeakyReLU(0.2))
generator.add(Dense(1024))
generator.add(LeakyReLU(0.2))
generator.add(Dense(784, activation='tanh'))
generator.compile(loss='binary_crossentropy', optimizer=optimizer)
return generator


def get_discriminator(optimizer):
discriminator = Sequential()
discriminator.add(Dense(1024, input_dim=784,
kernel_initializer=initializers.RandomNormal(stddev=0.02)))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(512))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(256))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Dense(1, activation='sigmoid'))
discriminator.compile(loss='binary_crossentropy', optimizer=optimizer)
return discriminator


def get_gan_network(discriminator, random_dim, generator, optimizer):
discriminator.trainable = False
gan_input = Input(shape=(random_dim,))
x = generator(gan_input)
gan_output = discriminator(x)
gan = Model(inputs=gan_input, outputs=gan_output)
gan.compile(loss='binary_crossentropy', optimizer=optimizer)
return gan


def plot_generated_images(epoch, generator, examples=100, dim=(10, 10),
figsize=(10, 10)):
noise = np.random.normal(0, 1, size=[examples, random_dim])
generated_images = generator.predict(noise)
generated_images = generated_images.reshape(examples, 28, 28)
plt.figure(figsize=figsize)
for i in range(generated_images.shape[0]):
plt.subplot(dim[0], dim[1], i + 1)
plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r')
plt.axis('off')
plt.tight_layout()
plt.savefig('gan_generated_image_epoch_%d.png' % epoch)


def train(epochs=1, batch_size=128):
batch_count = x_train.shape[0] // batch_size
adam = get_optimizer()
generator = get_generator(adam)
discriminator = get_discriminator(adam)
gan = get_gan_network(discriminator, random_dim, generator, adam)
for e in range(1, epochs + 1):
print('-' * 15, 'Epoch %d' % e, '-' * 15)
for _ in tqdm(range(batch_count)):
noise = np.random.normal(0, 1, size=[batch_size, random_dim])
image_batch = x_train[np.random.randint(0, x_train.shape[0], size=batch_size)]
generated_images = generator.predict(noise)
X = np.concatenate([image_batch, generated_images])
y_dis = np.zeros(2 * batch_size)
y_dis[:batch_size] = 0.9
discriminator.trainable = True
discriminator.train_on_batch(X, y_dis)
noise = np.random.normal(0, 1, size=[batch_size, random_dim])
y_gen = np.ones(batch_size)
discriminator.trainable = False
gan.train_on_batch(noise, y_gen)
if e == 1 or e % 20 == 0:
plot_generated_images(e, generator)


if __name__ == '__main__':
train(400, 128)









share|improve this question









New contributor




Darren is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$
















    0












    $begingroup$


    I just got a neural network to run and although it doesn't raise any exceptions, I'm left with a horrible mess after 80 to 100 epochs:



    After 100 epochs: After 100 epochs



    I am trying to generate a synthetic image of a cat from my own database of cat photos that I compiled using a crawler. I am using an adapted code originally intended for the MNSIT handwritten digits database (hence the shape of the grid).



    The network doesn't appear to be training, generating or discriminating properly because the epochs aren't taking long at all and what is being produced is very poor.



    To be clear, I've tried to adapt another author's code that I found online and I've added other snippets of code to try to get it to work. It's evident that my 'FrankenNet' has fallen to bits and my 'bolt it together and see what happens' approach has its limitations. In the future I plan to be more efficient and logical with how I learn Python because my experimental method has proved to be both time consuming and unpredictable.



    Maybe I haven't loaded in the data correctly or perhaps there are a few other issues such as converting the data to a numpy array?



    For the simple fact that I don't know exactly what is causing this (I have limited experience in programming), and because there are no exceptions raised when I run the program, I will offer the entire code below.



    I'd love some advice because I really want to generate something and I've spent a long time trying to work it out through trial and error with no results. I'd especially appreciate some specific suggestions about what lines I need to change, add or remove to get this beast up to scratch.



    Thank you for your time!



    import os
    import numpy as np
    from sklearn.model_selection import train_test_split
    import matplotlib

    matplotlib.use("TkAgg")
    from matplotlib import pyplot as plt
    from tqdm import tqdm
    from keras.layers import Input
    from keras.models import Model, Sequential
    from keras.layers.core import Dense, Dropout
    from keras.layers.advanced_activations import LeakyReLU
    from keras.optimizers import Adam
    from keras import initializers

    os.environ["KERAS_BACKEND"] = "tensorflow"
    np.random.seed(10)
    random_dim = 100
    from os import listdir
    from PIL import Image as PImage


    def loadImages(path):
    # return array of images
    imagesList = listdir(path)
    loadedImages = []
    for image in imagesList:
    img = PImage.open(path + image)
    loadedImages.append(img)
    return loadedImages


    DATASET_NAME = 'cats'
    ROOT_DIR = '/Users/Darren/desktop'
    DATASET_DIR = f'ROOT_DIR/DATASET_NAME'
    input_files = [os.path.join(dp, f) for dp, dn, fn in
    os.walk(os.path.expanduser(f'DATASET_DIR/processed')) for f in fn
    if f != '.DS_Store']
    imgs = np.ndarray(shape=(len(input_files), 100, 100, 3),
    dtype=np.int)
    for i, input_file in enumerate(input_files):
    # print('processing file: '.format(input_file))
    image = imread(input_file)
    imgs[i] = image
    # your images in an array
    imgs = loadImages(path)

    PATH = os.getcwd()

    train_path = PATH + '/cats/train'
    train_batch = os.listdir(train_path)
    x_train = []

    # if data are in form of images
    img_path = train_path
    test_path = PATH + '/cats/test'
    test_batch = os.listdir(test_path)
    x_test = []

    # finally converting list into numpy array
    x_train = np.array(x_train)
    x_test = np.array(x_test)

    def get_optimizer():
    return Adam(lr=0.0002, beta_1=0.5)


    def get_generator(optimizer):
    generator = Sequential()
    generator.add(Dense(256, input_dim=random_dim,
    kernel_initializer=initializers.RandomNormal(stddev=0.02)))
    generator.add(LeakyReLU(0.2))
    generator.add(Dense(512))
    generator.add(LeakyReLU(0.2))
    generator.add(Dense(1024))
    generator.add(LeakyReLU(0.2))
    generator.add(Dense(784, activation='tanh'))
    generator.compile(loss='binary_crossentropy', optimizer=optimizer)
    return generator


    def get_discriminator(optimizer):
    discriminator = Sequential()
    discriminator.add(Dense(1024, input_dim=784,
    kernel_initializer=initializers.RandomNormal(stddev=0.02)))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))
    discriminator.add(Dense(512))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))
    discriminator.add(Dense(256))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))
    discriminator.add(Dense(1, activation='sigmoid'))
    discriminator.compile(loss='binary_crossentropy', optimizer=optimizer)
    return discriminator


    def get_gan_network(discriminator, random_dim, generator, optimizer):
    discriminator.trainable = False
    gan_input = Input(shape=(random_dim,))
    x = generator(gan_input)
    gan_output = discriminator(x)
    gan = Model(inputs=gan_input, outputs=gan_output)
    gan.compile(loss='binary_crossentropy', optimizer=optimizer)
    return gan


    def plot_generated_images(epoch, generator, examples=100, dim=(10, 10),
    figsize=(10, 10)):
    noise = np.random.normal(0, 1, size=[examples, random_dim])
    generated_images = generator.predict(noise)
    generated_images = generated_images.reshape(examples, 28, 28)
    plt.figure(figsize=figsize)
    for i in range(generated_images.shape[0]):
    plt.subplot(dim[0], dim[1], i + 1)
    plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r')
    plt.axis('off')
    plt.tight_layout()
    plt.savefig('gan_generated_image_epoch_%d.png' % epoch)


    def train(epochs=1, batch_size=128):
    batch_count = x_train.shape[0] // batch_size
    adam = get_optimizer()
    generator = get_generator(adam)
    discriminator = get_discriminator(adam)
    gan = get_gan_network(discriminator, random_dim, generator, adam)
    for e in range(1, epochs + 1):
    print('-' * 15, 'Epoch %d' % e, '-' * 15)
    for _ in tqdm(range(batch_count)):
    noise = np.random.normal(0, 1, size=[batch_size, random_dim])
    image_batch = x_train[np.random.randint(0, x_train.shape[0], size=batch_size)]
    generated_images = generator.predict(noise)
    X = np.concatenate([image_batch, generated_images])
    y_dis = np.zeros(2 * batch_size)
    y_dis[:batch_size] = 0.9
    discriminator.trainable = True
    discriminator.train_on_batch(X, y_dis)
    noise = np.random.normal(0, 1, size=[batch_size, random_dim])
    y_gen = np.ones(batch_size)
    discriminator.trainable = False
    gan.train_on_batch(noise, y_gen)
    if e == 1 or e % 20 == 0:
    plot_generated_images(e, generator)


    if __name__ == '__main__':
    train(400, 128)









    share|improve this question









    New contributor




    Darren is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      0












      0








      0





      $begingroup$


      I just got a neural network to run and although it doesn't raise any exceptions, I'm left with a horrible mess after 80 to 100 epochs:



      After 100 epochs: After 100 epochs



      I am trying to generate a synthetic image of a cat from my own database of cat photos that I compiled using a crawler. I am using an adapted code originally intended for the MNSIT handwritten digits database (hence the shape of the grid).



      The network doesn't appear to be training, generating or discriminating properly because the epochs aren't taking long at all and what is being produced is very poor.



      To be clear, I've tried to adapt another author's code that I found online and I've added other snippets of code to try to get it to work. It's evident that my 'FrankenNet' has fallen to bits and my 'bolt it together and see what happens' approach has its limitations. In the future I plan to be more efficient and logical with how I learn Python because my experimental method has proved to be both time consuming and unpredictable.



      Maybe I haven't loaded in the data correctly or perhaps there are a few other issues such as converting the data to a numpy array?



      For the simple fact that I don't know exactly what is causing this (I have limited experience in programming), and because there are no exceptions raised when I run the program, I will offer the entire code below.



      I'd love some advice because I really want to generate something and I've spent a long time trying to work it out through trial and error with no results. I'd especially appreciate some specific suggestions about what lines I need to change, add or remove to get this beast up to scratch.



      Thank you for your time!



      import os
      import numpy as np
      from sklearn.model_selection import train_test_split
      import matplotlib

      matplotlib.use("TkAgg")
      from matplotlib import pyplot as plt
      from tqdm import tqdm
      from keras.layers import Input
      from keras.models import Model, Sequential
      from keras.layers.core import Dense, Dropout
      from keras.layers.advanced_activations import LeakyReLU
      from keras.optimizers import Adam
      from keras import initializers

      os.environ["KERAS_BACKEND"] = "tensorflow"
      np.random.seed(10)
      random_dim = 100
      from os import listdir
      from PIL import Image as PImage


      def loadImages(path):
      # return array of images
      imagesList = listdir(path)
      loadedImages = []
      for image in imagesList:
      img = PImage.open(path + image)
      loadedImages.append(img)
      return loadedImages


      DATASET_NAME = 'cats'
      ROOT_DIR = '/Users/Darren/desktop'
      DATASET_DIR = f'ROOT_DIR/DATASET_NAME'
      input_files = [os.path.join(dp, f) for dp, dn, fn in
      os.walk(os.path.expanduser(f'DATASET_DIR/processed')) for f in fn
      if f != '.DS_Store']
      imgs = np.ndarray(shape=(len(input_files), 100, 100, 3),
      dtype=np.int)
      for i, input_file in enumerate(input_files):
      # print('processing file: '.format(input_file))
      image = imread(input_file)
      imgs[i] = image
      # your images in an array
      imgs = loadImages(path)

      PATH = os.getcwd()

      train_path = PATH + '/cats/train'
      train_batch = os.listdir(train_path)
      x_train = []

      # if data are in form of images
      img_path = train_path
      test_path = PATH + '/cats/test'
      test_batch = os.listdir(test_path)
      x_test = []

      # finally converting list into numpy array
      x_train = np.array(x_train)
      x_test = np.array(x_test)

      def get_optimizer():
      return Adam(lr=0.0002, beta_1=0.5)


      def get_generator(optimizer):
      generator = Sequential()
      generator.add(Dense(256, input_dim=random_dim,
      kernel_initializer=initializers.RandomNormal(stddev=0.02)))
      generator.add(LeakyReLU(0.2))
      generator.add(Dense(512))
      generator.add(LeakyReLU(0.2))
      generator.add(Dense(1024))
      generator.add(LeakyReLU(0.2))
      generator.add(Dense(784, activation='tanh'))
      generator.compile(loss='binary_crossentropy', optimizer=optimizer)
      return generator


      def get_discriminator(optimizer):
      discriminator = Sequential()
      discriminator.add(Dense(1024, input_dim=784,
      kernel_initializer=initializers.RandomNormal(stddev=0.02)))
      discriminator.add(LeakyReLU(0.2))
      discriminator.add(Dropout(0.3))
      discriminator.add(Dense(512))
      discriminator.add(LeakyReLU(0.2))
      discriminator.add(Dropout(0.3))
      discriminator.add(Dense(256))
      discriminator.add(LeakyReLU(0.2))
      discriminator.add(Dropout(0.3))
      discriminator.add(Dense(1, activation='sigmoid'))
      discriminator.compile(loss='binary_crossentropy', optimizer=optimizer)
      return discriminator


      def get_gan_network(discriminator, random_dim, generator, optimizer):
      discriminator.trainable = False
      gan_input = Input(shape=(random_dim,))
      x = generator(gan_input)
      gan_output = discriminator(x)
      gan = Model(inputs=gan_input, outputs=gan_output)
      gan.compile(loss='binary_crossentropy', optimizer=optimizer)
      return gan


      def plot_generated_images(epoch, generator, examples=100, dim=(10, 10),
      figsize=(10, 10)):
      noise = np.random.normal(0, 1, size=[examples, random_dim])
      generated_images = generator.predict(noise)
      generated_images = generated_images.reshape(examples, 28, 28)
      plt.figure(figsize=figsize)
      for i in range(generated_images.shape[0]):
      plt.subplot(dim[0], dim[1], i + 1)
      plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r')
      plt.axis('off')
      plt.tight_layout()
      plt.savefig('gan_generated_image_epoch_%d.png' % epoch)


      def train(epochs=1, batch_size=128):
      batch_count = x_train.shape[0] // batch_size
      adam = get_optimizer()
      generator = get_generator(adam)
      discriminator = get_discriminator(adam)
      gan = get_gan_network(discriminator, random_dim, generator, adam)
      for e in range(1, epochs + 1):
      print('-' * 15, 'Epoch %d' % e, '-' * 15)
      for _ in tqdm(range(batch_count)):
      noise = np.random.normal(0, 1, size=[batch_size, random_dim])
      image_batch = x_train[np.random.randint(0, x_train.shape[0], size=batch_size)]
      generated_images = generator.predict(noise)
      X = np.concatenate([image_batch, generated_images])
      y_dis = np.zeros(2 * batch_size)
      y_dis[:batch_size] = 0.9
      discriminator.trainable = True
      discriminator.train_on_batch(X, y_dis)
      noise = np.random.normal(0, 1, size=[batch_size, random_dim])
      y_gen = np.ones(batch_size)
      discriminator.trainable = False
      gan.train_on_batch(noise, y_gen)
      if e == 1 or e % 20 == 0:
      plot_generated_images(e, generator)


      if __name__ == '__main__':
      train(400, 128)









      share|improve this question









      New contributor




      Darren is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I just got a neural network to run and although it doesn't raise any exceptions, I'm left with a horrible mess after 80 to 100 epochs:



      After 100 epochs: After 100 epochs



      I am trying to generate a synthetic image of a cat from my own database of cat photos that I compiled using a crawler. I am using an adapted code originally intended for the MNSIT handwritten digits database (hence the shape of the grid).



      The network doesn't appear to be training, generating or discriminating properly because the epochs aren't taking long at all and what is being produced is very poor.



      To be clear, I've tried to adapt another author's code that I found online and I've added other snippets of code to try to get it to work. It's evident that my 'FrankenNet' has fallen to bits and my 'bolt it together and see what happens' approach has its limitations. In the future I plan to be more efficient and logical with how I learn Python because my experimental method has proved to be both time consuming and unpredictable.



      Maybe I haven't loaded in the data correctly or perhaps there are a few other issues such as converting the data to a numpy array?



      For the simple fact that I don't know exactly what is causing this (I have limited experience in programming), and because there are no exceptions raised when I run the program, I will offer the entire code below.



      I'd love some advice because I really want to generate something and I've spent a long time trying to work it out through trial and error with no results. I'd especially appreciate some specific suggestions about what lines I need to change, add or remove to get this beast up to scratch.



      Thank you for your time!



      import os
      import numpy as np
      from sklearn.model_selection import train_test_split
      import matplotlib

      matplotlib.use("TkAgg")
      from matplotlib import pyplot as plt
      from tqdm import tqdm
      from keras.layers import Input
      from keras.models import Model, Sequential
      from keras.layers.core import Dense, Dropout
      from keras.layers.advanced_activations import LeakyReLU
      from keras.optimizers import Adam
      from keras import initializers

      os.environ["KERAS_BACKEND"] = "tensorflow"
      np.random.seed(10)
      random_dim = 100
      from os import listdir
      from PIL import Image as PImage


      def loadImages(path):
      # return array of images
      imagesList = listdir(path)
      loadedImages = []
      for image in imagesList:
      img = PImage.open(path + image)
      loadedImages.append(img)
      return loadedImages


      DATASET_NAME = 'cats'
      ROOT_DIR = '/Users/Darren/desktop'
      DATASET_DIR = f'ROOT_DIR/DATASET_NAME'
      input_files = [os.path.join(dp, f) for dp, dn, fn in
      os.walk(os.path.expanduser(f'DATASET_DIR/processed')) for f in fn
      if f != '.DS_Store']
      imgs = np.ndarray(shape=(len(input_files), 100, 100, 3),
      dtype=np.int)
      for i, input_file in enumerate(input_files):
      # print('processing file: '.format(input_file))
      image = imread(input_file)
      imgs[i] = image
      # your images in an array
      imgs = loadImages(path)

      PATH = os.getcwd()

      train_path = PATH + '/cats/train'
      train_batch = os.listdir(train_path)
      x_train = []

      # if data are in form of images
      img_path = train_path
      test_path = PATH + '/cats/test'
      test_batch = os.listdir(test_path)
      x_test = []

      # finally converting list into numpy array
      x_train = np.array(x_train)
      x_test = np.array(x_test)

      def get_optimizer():
      return Adam(lr=0.0002, beta_1=0.5)


      def get_generator(optimizer):
      generator = Sequential()
      generator.add(Dense(256, input_dim=random_dim,
      kernel_initializer=initializers.RandomNormal(stddev=0.02)))
      generator.add(LeakyReLU(0.2))
      generator.add(Dense(512))
      generator.add(LeakyReLU(0.2))
      generator.add(Dense(1024))
      generator.add(LeakyReLU(0.2))
      generator.add(Dense(784, activation='tanh'))
      generator.compile(loss='binary_crossentropy', optimizer=optimizer)
      return generator


      def get_discriminator(optimizer):
      discriminator = Sequential()
      discriminator.add(Dense(1024, input_dim=784,
      kernel_initializer=initializers.RandomNormal(stddev=0.02)))
      discriminator.add(LeakyReLU(0.2))
      discriminator.add(Dropout(0.3))
      discriminator.add(Dense(512))
      discriminator.add(LeakyReLU(0.2))
      discriminator.add(Dropout(0.3))
      discriminator.add(Dense(256))
      discriminator.add(LeakyReLU(0.2))
      discriminator.add(Dropout(0.3))
      discriminator.add(Dense(1, activation='sigmoid'))
      discriminator.compile(loss='binary_crossentropy', optimizer=optimizer)
      return discriminator


      def get_gan_network(discriminator, random_dim, generator, optimizer):
      discriminator.trainable = False
      gan_input = Input(shape=(random_dim,))
      x = generator(gan_input)
      gan_output = discriminator(x)
      gan = Model(inputs=gan_input, outputs=gan_output)
      gan.compile(loss='binary_crossentropy', optimizer=optimizer)
      return gan


      def plot_generated_images(epoch, generator, examples=100, dim=(10, 10),
      figsize=(10, 10)):
      noise = np.random.normal(0, 1, size=[examples, random_dim])
      generated_images = generator.predict(noise)
      generated_images = generated_images.reshape(examples, 28, 28)
      plt.figure(figsize=figsize)
      for i in range(generated_images.shape[0]):
      plt.subplot(dim[0], dim[1], i + 1)
      plt.imshow(generated_images[i], interpolation='nearest', cmap='gray_r')
      plt.axis('off')
      plt.tight_layout()
      plt.savefig('gan_generated_image_epoch_%d.png' % epoch)


      def train(epochs=1, batch_size=128):
      batch_count = x_train.shape[0] // batch_size
      adam = get_optimizer()
      generator = get_generator(adam)
      discriminator = get_discriminator(adam)
      gan = get_gan_network(discriminator, random_dim, generator, adam)
      for e in range(1, epochs + 1):
      print('-' * 15, 'Epoch %d' % e, '-' * 15)
      for _ in tqdm(range(batch_count)):
      noise = np.random.normal(0, 1, size=[batch_size, random_dim])
      image_batch = x_train[np.random.randint(0, x_train.shape[0], size=batch_size)]
      generated_images = generator.predict(noise)
      X = np.concatenate([image_batch, generated_images])
      y_dis = np.zeros(2 * batch_size)
      y_dis[:batch_size] = 0.9
      discriminator.trainable = True
      discriminator.train_on_batch(X, y_dis)
      noise = np.random.normal(0, 1, size=[batch_size, random_dim])
      y_gen = np.ones(batch_size)
      discriminator.trainable = False
      gan.train_on_batch(noise, y_gen)
      if e == 1 or e % 20 == 0:
      plot_generated_images(e, generator)


      if __name__ == '__main__':
      train(400, 128)






      python keras tensorflow gan






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      edited 5 mins ago









      Pedro Henrique Monforte

      502115




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      asked 18 mins ago









      DarrenDarren

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