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人脸生成

12 May 2018   |  

layout: post title: 人脸生成 tags: deep-learning —

人脸生成

在这个项目中，我们将使用生成对抗网络来生成新的人脸图片。项目来源于udacity-deeplearning

获取数据

在这个项目中所使用的数据集是：

  • MNIST
  • CelebA

因为你celebA数据集比较复杂，所以在用GAN处理之前，我们先使用简单的数据集MNIST来验证，之后如果网络性能还不错，我们在用celebA数据集进行训练测试。

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Downloading mnist: 9.92MB [00:11, 870KB/s]                             
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:10<00:00, 5.59KFile/s]
Downloading celeba: 1.44GB [01:58, 12.2MB/s]                               


Extracting celeba...

查看数据

MNIST

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7f95676c1550>

png

CelebA

CelebFaces Attributes Dataset (CelebA) 数据集包含200,000张带有标记的图片，但是我们并不需要这些标记。

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f9571a9e518>

png

数据预处理

` MNIST 与 CelebA 数据集的值是在-0.5 到 0.5，图片是 28x28的。 MNIST的图片都是2维度的， CelebA`的图片都是3维的。

构建神经网络

GAN主要由一下几个部分构成：

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

检查tensorflow版本

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

输入

实现model_inputs 函数为网络构建输入张量:

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels ), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    lr = tf.placeholder(tf.float32, name='learning_rate')
    return input_real, input_z, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

判别器

实现函数 discriminator为网络创建判别器。

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28 x 28 x 3 to output layer is 14 x 14 x 64
        alpha = 0.1
        x1 = tf.layers.conv2d(images, 64, 3, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        # Input layer is 14 x 14 x 64 to output layer is 7 x 7 x 128
        x2 = tf.layers.conv2d(relu1, 128, 3, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        # Input layer is 7 x 7 x 128 to output layer is 7 x 7 x 256
        x3 = tf.layers.conv2d(relu2, 256, 3, strides=1, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        # Input layer is 7 x 7 x 256 to output layer is 7 x 7 x 512
        x4 = tf.layers.conv2d(relu3, 512, 3, strides=1, padding='same')
        bn4 = tf.layers.batch_normalization(x4, training=True)
        relu4 = tf.maximum(alpha * bn4, bn4)
        
        flat = tf.reshape(relu4, (-1, 7 * 7 * 512))
        logits = tf.layers.dense(flat,1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

生成器

实现函数 generator为网络构建输入。

def generator(z, out_channel_dim,is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.1
        # First fully connected layer
        x1 = tf.layers.dense(z, 7 * 7 * 512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 3 ,strides=1, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 3 ,strides=1, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        
        x4 = tf.layers.conv2d_transpose(x3, 64, 3 ,strides=2, padding='same')
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = tf.maximum(alpha * x4, x4)
        
        # Output layer, 28 x 28 x 3
        logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 3 ,strides=2, padding='same')
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

损失

实现函数 model_loss为网络计算损失。

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    g_out = generator(input_z, out_channel_dim, is_train=True)
    d_out_real, d_logits_real = discriminator(input_real)
    d_out_fake, d_logits_fake = discriminator(g_out, reuse=True)
    
    d_loss_real = tf.reduce_mean(
                    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_out_real) * 0.9))
    d_loss_fake = tf.reduce_mean(
                    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_out_fake)))
    
    g_loss = tf.reduce_mean(
                    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_out_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

优化

实现函数 model_opt 对网络进行优化。

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

训练神经网络

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

训练

实现函数 train 对网络进行训练，直接使用已经写好的函数就行:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_train_opt, g_train_opt= model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                
                batch_images = batch_images * 2 
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr:learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                if steps % 100 == 0:
                    show_generator_output(sess, 4, input_z, data_shape[3], data_image_mode)
                    
    print("Finish training")

MNIST

使用 MNIST 数据集来验证网络架构.

batch_size = 128
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

Epoch 1/2... Discriminator Loss: 2.5670... Generator Loss: 0.4125
Epoch 1/2... Discriminator Loss: 0.5662... Generator Loss: 5.8088
Epoch 1/2... Discriminator Loss: 2.6611... Generator Loss: 0.3710
Epoch 1/2... Discriminator Loss: 1.7154... Generator Loss: 0.5251
Epoch 1/2... Discriminator Loss: 1.4534... Generator Loss: 5.4623
Epoch 1/2... Discriminator Loss: 0.8610... Generator Loss: 1.7478
Epoch 1/2... Discriminator Loss: 0.8701... Generator Loss: 2.8445
Epoch 1/2... Discriminator Loss: 0.9579... Generator Loss: 3.6692
Epoch 1/2... Discriminator Loss: 0.7607... Generator Loss: 1.4849
Epoch 1/2... Discriminator Loss: 1.1003... Generator Loss: 0.9405

png

Epoch 1/2... Discriminator Loss: 0.8156... Generator Loss: 2.5537
Epoch 1/2... Discriminator Loss: 1.1982... Generator Loss: 0.7492
Epoch 1/2... Discriminator Loss: 0.7526... Generator Loss: 2.4862
Epoch 1/2... Discriminator Loss: 0.8648... Generator Loss: 1.1423
Epoch 1/2... Discriminator Loss: 1.6047... Generator Loss: 0.5022
Epoch 1/2... Discriminator Loss: 1.3362... Generator Loss: 0.7429
Epoch 1/2... Discriminator Loss: 0.7280... Generator Loss: 2.1738
Epoch 1/2... Discriminator Loss: 0.9456... Generator Loss: 1.0319
Epoch 1/2... Discriminator Loss: 0.6420... Generator Loss: 2.0461
Epoch 1/2... Discriminator Loss: 1.2976... Generator Loss: 0.6159

png

Epoch 1/2... Discriminator Loss: 1.1248... Generator Loss: 0.7653
Epoch 1/2... Discriminator Loss: 0.7744... Generator Loss: 1.6158
Epoch 1/2... Discriminator Loss: 0.6849... Generator Loss: 1.9795
Epoch 1/2... Discriminator Loss: 1.0444... Generator Loss: 0.9345
Epoch 1/2... Discriminator Loss: 2.5948... Generator Loss: 0.2506
Epoch 1/2... Discriminator Loss: 0.9160... Generator Loss: 2.5226
Epoch 1/2... Discriminator Loss: 0.8180... Generator Loss: 1.2042
Epoch 1/2... Discriminator Loss: 3.0264... Generator Loss: 0.1181
Epoch 1/2... Discriminator Loss: 1.5977... Generator Loss: 0.5586
Epoch 1/2... Discriminator Loss: 1.4799... Generator Loss: 0.5423

png

Epoch 1/2... Discriminator Loss: 1.7227... Generator Loss: 0.3871
Epoch 1/2... Discriminator Loss: 0.8242... Generator Loss: 1.3010
Epoch 1/2... Discriminator Loss: 0.8275... Generator Loss: 1.7792
Epoch 1/2... Discriminator Loss: 1.4575... Generator Loss: 0.5364
Epoch 1/2... Discriminator Loss: 1.6397... Generator Loss: 0.4820
Epoch 1/2... Discriminator Loss: 1.1027... Generator Loss: 2.5782
Epoch 1/2... Discriminator Loss: 1.1037... Generator Loss: 0.7890
Epoch 1/2... Discriminator Loss: 0.9587... Generator Loss: 1.2423
Epoch 1/2... Discriminator Loss: 1.5567... Generator Loss: 2.4239
Epoch 1/2... Discriminator Loss: 1.5680... Generator Loss: 0.6865

png

Epoch 1/2... Discriminator Loss: 1.3410... Generator Loss: 2.2070
Epoch 1/2... Discriminator Loss: 1.7490... Generator Loss: 3.2341
Epoch 1/2... Discriminator Loss: 0.9146... Generator Loss: 1.9964
Epoch 1/2... Discriminator Loss: 2.8071... Generator Loss: 4.3768
Epoch 1/2... Discriminator Loss: 1.2631... Generator Loss: 0.7122
Epoch 1/2... Discriminator Loss: 1.0083... Generator Loss: 1.5106
Epoch 2/2... Discriminator Loss: 0.8099... Generator Loss: 1.7368
Epoch 2/2... Discriminator Loss: 0.9017... Generator Loss: 1.1692
Epoch 2/2... Discriminator Loss: 0.9267... Generator Loss: 2.0585
Epoch 2/2... Discriminator Loss: 4.4688... Generator Loss: 4.9588

png

Epoch 2/2... Discriminator Loss: 2.0713... Generator Loss: 0.2946
Epoch 2/2... Discriminator Loss: 1.5370... Generator Loss: 0.5216
Epoch 2/2... Discriminator Loss: 1.3747... Generator Loss: 0.6113
Epoch 2/2... Discriminator Loss: 1.5562... Generator Loss: 0.4826
Epoch 2/2... Discriminator Loss: 1.0379... Generator Loss: 1.0576
Epoch 2/2... Discriminator Loss: 1.7146... Generator Loss: 0.4087
Epoch 2/2... Discriminator Loss: 1.1321... Generator Loss: 2.0963
Epoch 2/2... Discriminator Loss: 1.6731... Generator Loss: 0.4216
Epoch 2/2... Discriminator Loss: 1.6752... Generator Loss: 0.4001
Epoch 2/2... Discriminator Loss: 1.1817... Generator Loss: 0.7585

png

Epoch 2/2... Discriminator Loss: 1.2264... Generator Loss: 3.2529
Epoch 2/2... Discriminator Loss: 0.9499... Generator Loss: 1.1761
Epoch 2/2... Discriminator Loss: 1.1673... Generator Loss: 0.7926
Epoch 2/2... Discriminator Loss: 0.9397... Generator Loss: 2.5452
Epoch 2/2... Discriminator Loss: 1.7447... Generator Loss: 0.4614
Epoch 2/2... Discriminator Loss: 0.9644... Generator Loss: 1.7437
Epoch 2/2... Discriminator Loss: 1.3251... Generator Loss: 2.7100
Epoch 2/2... Discriminator Loss: 2.1874... Generator Loss: 0.3228
Epoch 2/2... Discriminator Loss: 1.5370... Generator Loss: 0.4851
Epoch 2/2... Discriminator Loss: 0.9423... Generator Loss: 2.0052

png

Epoch 2/2... Discriminator Loss: 0.8966... Generator Loss: 1.3532
Epoch 2/2... Discriminator Loss: 0.9183... Generator Loss: 2.4473
Epoch 2/2... Discriminator Loss: 0.9276... Generator Loss: 1.0986
Epoch 2/2... Discriminator Loss: 1.0378... Generator Loss: 2.4902
Epoch 2/2... Discriminator Loss: 1.0954... Generator Loss: 0.8773
Epoch 2/2... Discriminator Loss: 1.4540... Generator Loss: 3.4679
Epoch 2/2... Discriminator Loss: 2.2756... Generator Loss: 0.2780
Epoch 2/2... Discriminator Loss: 0.9586... Generator Loss: 1.2197
Epoch 2/2... Discriminator Loss: 1.0734... Generator Loss: 0.8667
Epoch 2/2... Discriminator Loss: 0.8090... Generator Loss: 1.7418

png

Epoch 2/2... Discriminator Loss: 1.2716... Generator Loss: 0.7460
Epoch 2/2... Discriminator Loss: 0.8679... Generator Loss: 2.0178
Epoch 2/2... Discriminator Loss: 1.0569... Generator Loss: 0.9753
Epoch 2/2... Discriminator Loss: 1.3461... Generator Loss: 0.6070
Epoch 2/2... Discriminator Loss: 1.0119... Generator Loss: 2.3068
Epoch 2/2... Discriminator Loss: 0.8770... Generator Loss: 1.3781
Epoch 2/2... Discriminator Loss: 0.9784... Generator Loss: 1.2783
Epoch 2/2... Discriminator Loss: 1.0566... Generator Loss: 0.9749
Epoch 2/2... Discriminator Loss: 1.1071... Generator Loss: 0.8777
Epoch 2/2... Discriminator Loss: 1.1916... Generator Loss: 0.8338

png

Epoch 2/2... Discriminator Loss: 0.8422... Generator Loss: 1.3306
Epoch 2/2... Discriminator Loss: 1.2982... Generator Loss: 0.6625
Epoch 2/2... Discriminator Loss: 1.6153... Generator Loss: 0.6769
Finish training

CelebA

使用数据集CelebA来验证网络架构.

batch_size = 128
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Epoch 1/1... Discriminator Loss: 0.7530... Generator Loss: 8.6871
Epoch 1/1... Discriminator Loss: 0.9298... Generator Loss: 15.7398
Epoch 1/1... Discriminator Loss: 2.8263... Generator Loss: 0.2851
Epoch 1/1... Discriminator Loss: 1.6981... Generator Loss: 0.6993
Epoch 1/1... Discriminator Loss: 1.5391... Generator Loss: 1.0450
Epoch 1/1... Discriminator Loss: 1.4355... Generator Loss: 0.7837
Epoch 1/1... Discriminator Loss: 1.3560... Generator Loss: 0.9238
Epoch 1/1... Discriminator Loss: 1.9604... Generator Loss: 4.4131
Epoch 1/1... Discriminator Loss: 2.1643... Generator Loss: 0.3025
Epoch 1/1... Discriminator Loss: 2.4849... Generator Loss: 0.2103

png

Epoch 1/1... Discriminator Loss: 0.9599... Generator Loss: 1.2785
Epoch 1/1... Discriminator Loss: 0.8042... Generator Loss: 1.8656
Epoch 1/1... Discriminator Loss: 2.9833... Generator Loss: 0.1235
Epoch 1/1... Discriminator Loss: 1.0662... Generator Loss: 1.1685
Epoch 1/1... Discriminator Loss: 1.0549... Generator Loss: 1.1269
Epoch 1/1... Discriminator Loss: 1.0664... Generator Loss: 1.4223
Epoch 1/1... Discriminator Loss: 1.4356... Generator Loss: 0.6057
Epoch 1/1... Discriminator Loss: 1.3182... Generator Loss: 0.7285
Epoch 1/1... Discriminator Loss: 2.1460... Generator Loss: 0.3177
Epoch 1/1... Discriminator Loss: 0.8770... Generator Loss: 1.4075

png

Epoch 1/1... Discriminator Loss: 1.2831... Generator Loss: 0.7655
Epoch 1/1... Discriminator Loss: 1.5447... Generator Loss: 2.6052
Epoch 1/1... Discriminator Loss: 2.0801... Generator Loss: 3.9197
Epoch 1/1... Discriminator Loss: 1.4028... Generator Loss: 2.4999
Epoch 1/1... Discriminator Loss: 1.2157... Generator Loss: 2.1566
Epoch 1/1... Discriminator Loss: 0.7763... Generator Loss: 1.6683
Epoch 1/1... Discriminator Loss: 1.2153... Generator Loss: 1.7246
Epoch 1/1... Discriminator Loss: 0.9812... Generator Loss: 1.2962
Epoch 1/1... Discriminator Loss: 1.0366... Generator Loss: 0.9386
Epoch 1/1... Discriminator Loss: 1.0379... Generator Loss: 1.5070

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Epoch 1/1... Discriminator Loss: 1.0926... Generator Loss: 0.9736
Epoch 1/1... Discriminator Loss: 1.0136... Generator Loss: 1.6398
Epoch 1/1... Discriminator Loss: 1.1990... Generator Loss: 1.2142
Epoch 1/1... Discriminator Loss: 1.1516... Generator Loss: 0.9133
Epoch 1/1... Discriminator Loss: 1.4690... Generator Loss: 0.5589
Epoch 1/1... Discriminator Loss: 1.2786... Generator Loss: 0.7575
Epoch 1/1... Discriminator Loss: 1.1647... Generator Loss: 1.2803
Epoch 1/1... Discriminator Loss: 1.6237... Generator Loss: 0.4568
Epoch 1/1... Discriminator Loss: 1.1671... Generator Loss: 2.3657
Epoch 1/1... Discriminator Loss: 1.3119... Generator Loss: 0.6340

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Epoch 1/1... Discriminator Loss: 1.0171... Generator Loss: 1.0055
Epoch 1/1... Discriminator Loss: 1.0119... Generator Loss: 1.4181
Epoch 1/1... Discriminator Loss: 0.9128... Generator Loss: 1.1611
Epoch 1/1... Discriminator Loss: 1.1578... Generator Loss: 0.8007
Epoch 1/1... Discriminator Loss: 1.2020... Generator Loss: 1.6199
Epoch 1/1... Discriminator Loss: 1.3808... Generator Loss: 0.5759
Epoch 1/1... Discriminator Loss: 1.0826... Generator Loss: 1.0683
Epoch 1/1... Discriminator Loss: 1.2612... Generator Loss: 0.7148
Epoch 1/1... Discriminator Loss: 1.0948... Generator Loss: 1.7166
Epoch 1/1... Discriminator Loss: 1.2473... Generator Loss: 1.0475

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Epoch 1/1... Discriminator Loss: 2.3647... Generator Loss: 3.9816
Epoch 1/1... Discriminator Loss: 1.7586... Generator Loss: 0.4085
Epoch 1/1... Discriminator Loss: 1.3968... Generator Loss: 0.6017
Epoch 1/1... Discriminator Loss: 1.2627... Generator Loss: 0.8135
Epoch 1/1... Discriminator Loss: 1.1537... Generator Loss: 1.4317
Epoch 1/1... Discriminator Loss: 1.2106... Generator Loss: 0.9707
Epoch 1/1... Discriminator Loss: 1.2024... Generator Loss: 1.0595
Epoch 1/1... Discriminator Loss: 1.1008... Generator Loss: 1.7186
Epoch 1/1... Discriminator Loss: 1.1918... Generator Loss: 0.9864
Epoch 1/1... Discriminator Loss: 1.6864... Generator Loss: 2.5932

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Epoch 1/1... Discriminator Loss: 1.9789... Generator Loss: 2.8634
Epoch 1/1... Discriminator Loss: 1.0457... Generator Loss: 1.1890
Epoch 1/1... Discriminator Loss: 1.2952... Generator Loss: 0.5719
Epoch 1/1... Discriminator Loss: 0.8850... Generator Loss: 1.5313
Epoch 1/1... Discriminator Loss: 1.6824... Generator Loss: 0.5078
Epoch 1/1... Discriminator Loss: 1.1641... Generator Loss: 0.7723
Epoch 1/1... Discriminator Loss: 1.1622... Generator Loss: 1.4156
Epoch 1/1... Discriminator Loss: 1.4422... Generator Loss: 0.5242
Epoch 1/1... Discriminator Loss: 1.4936... Generator Loss: 0.5004
Epoch 1/1... Discriminator Loss: 1.0462... Generator Loss: 1.5340

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Epoch 1/1... Discriminator Loss: 1.3382... Generator Loss: 0.6465
Epoch 1/1... Discriminator Loss: 1.6028... Generator Loss: 0.6008
Epoch 1/1... Discriminator Loss: 1.1647... Generator Loss: 1.0593
Epoch 1/1... Discriminator Loss: 1.0436... Generator Loss: 0.9482
Epoch 1/1... Discriminator Loss: 1.6042... Generator Loss: 0.4477
Epoch 1/1... Discriminator Loss: 1.9857... Generator Loss: 2.4600
Epoch 1/1... Discriminator Loss: 1.0933... Generator Loss: 1.0793
Epoch 1/1... Discriminator Loss: 1.3125... Generator Loss: 0.6747
Epoch 1/1... Discriminator Loss: 1.2430... Generator Loss: 0.8488
Epoch 1/1... Discriminator Loss: 1.6676... Generator Loss: 2.6519

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Epoch 1/1... Discriminator Loss: 1.1548... Generator Loss: 1.0204
Epoch 1/1... Discriminator Loss: 0.9753... Generator Loss: 1.3047
Epoch 1/1... Discriminator Loss: 1.2424... Generator Loss: 1.6186
Epoch 1/1... Discriminator Loss: 1.1086... Generator Loss: 0.7826
Epoch 1/1... Discriminator Loss: 1.1165... Generator Loss: 1.5525
Epoch 1/1... Discriminator Loss: 1.2074... Generator Loss: 1.2530
Epoch 1/1... Discriminator Loss: 1.1495... Generator Loss: 0.9164
Epoch 1/1... Discriminator Loss: 1.1734... Generator Loss: 0.8843
Epoch 1/1... Discriminator Loss: 1.2493... Generator Loss: 1.7003
Epoch 1/1... Discriminator Loss: 1.3382... Generator Loss: 2.3015

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Epoch 1/1... Discriminator Loss: 1.3273... Generator Loss: 0.6338
Epoch 1/1... Discriminator Loss: 1.0015... Generator Loss: 0.9939
Epoch 1/1... Discriminator Loss: 0.8871... Generator Loss: 1.0526
Epoch 1/1... Discriminator Loss: 1.8449... Generator Loss: 0.3400
Epoch 1/1... Discriminator Loss: 1.1675... Generator Loss: 1.3872
Epoch 1/1... Discriminator Loss: 1.4931... Generator Loss: 0.7872
Epoch 1/1... Discriminator Loss: 0.9699... Generator Loss: 0.9360
Epoch 1/1... Discriminator Loss: 1.0660... Generator Loss: 1.2564
Epoch 1/1... Discriminator Loss: 1.4150... Generator Loss: 0.5328
Epoch 1/1... Discriminator Loss: 1.1689... Generator Loss: 1.0345

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Epoch 1/1... Discriminator Loss: 1.4803... Generator Loss: 1.9765
Epoch 1/1... Discriminator Loss: 1.1737... Generator Loss: 1.4223
Epoch 1/1... Discriminator Loss: 1.0635... Generator Loss: 0.9262
Epoch 1/1... Discriminator Loss: 1.5213... Generator Loss: 2.0839
Epoch 1/1... Discriminator Loss: 1.1173... Generator Loss: 1.3409
Epoch 1/1... Discriminator Loss: 0.9329... Generator Loss: 1.2308
Epoch 1/1... Discriminator Loss: 1.4536... Generator Loss: 0.6013
Epoch 1/1... Discriminator Loss: 1.4308... Generator Loss: 0.5963
Epoch 1/1... Discriminator Loss: 1.0420... Generator Loss: 1.1049
Epoch 1/1... Discriminator Loss: 1.5334... Generator Loss: 0.5061

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Epoch 1/1... Discriminator Loss: 2.4393... Generator Loss: 3.5009
Epoch 1/1... Discriminator Loss: 1.3208... Generator Loss: 0.7757
Epoch 1/1... Discriminator Loss: 1.3835... Generator Loss: 0.6689
Epoch 1/1... Discriminator Loss: 1.1176... Generator Loss: 0.9044
Epoch 1/1... Discriminator Loss: 1.1363... Generator Loss: 1.6484
Epoch 1/1... Discriminator Loss: 1.0346... Generator Loss: 1.6511
Epoch 1/1... Discriminator Loss: 1.0575... Generator Loss: 1.4003
Epoch 1/1... Discriminator Loss: 1.2227... Generator Loss: 1.5416
Epoch 1/1... Discriminator Loss: 1.3300... Generator Loss: 0.7293
Epoch 1/1... Discriminator Loss: 1.7233... Generator Loss: 2.1256

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Epoch 1/1... Discriminator Loss: 1.1216... Generator Loss: 1.2158
Epoch 1/1... Discriminator Loss: 1.2921... Generator Loss: 1.4218
Epoch 1/1... Discriminator Loss: 1.2042... Generator Loss: 1.2719
Epoch 1/1... Discriminator Loss: 1.1837... Generator Loss: 0.8664
Epoch 1/1... Discriminator Loss: 2.2012... Generator Loss: 0.2531
Epoch 1/1... Discriminator Loss: 1.0234... Generator Loss: 0.9543
Epoch 1/1... Discriminator Loss: 1.1222... Generator Loss: 1.3613
Epoch 1/1... Discriminator Loss: 1.1693... Generator Loss: 1.2244
Epoch 1/1... Discriminator Loss: 1.1226... Generator Loss: 0.9946
Epoch 1/1... Discriminator Loss: 1.1872... Generator Loss: 0.9885

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Epoch 1/1... Discriminator Loss: 1.8105... Generator Loss: 0.4197
Epoch 1/1... Discriminator Loss: 1.1215... Generator Loss: 1.0932
Epoch 1/1... Discriminator Loss: 0.9746... Generator Loss: 1.2067
Epoch 1/1... Discriminator Loss: 1.1488... Generator Loss: 0.7006
Epoch 1/1... Discriminator Loss: 1.2598... Generator Loss: 0.6282
Epoch 1/1... Discriminator Loss: 1.1882... Generator Loss: 0.7306
Epoch 1/1... Discriminator Loss: 1.5521... Generator Loss: 2.4704
Epoch 1/1... Discriminator Loss: 1.4303... Generator Loss: 0.6865
Epoch 1/1... Discriminator Loss: 1.1430... Generator Loss: 1.3274
Epoch 1/1... Discriminator Loss: 1.0744... Generator Loss: 1.7194

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Epoch 1/1... Discriminator Loss: 1.1720... Generator Loss: 0.9640
Epoch 1/1... Discriminator Loss: 1.1455... Generator Loss: 1.1421
Epoch 1/1... Discriminator Loss: 1.1719... Generator Loss: 0.8071
Epoch 1/1... Discriminator Loss: 1.1591... Generator Loss: 1.6390
Epoch 1/1... Discriminator Loss: 2.7530... Generator Loss: 3.1758
Epoch 1/1... Discriminator Loss: 1.1756... Generator Loss: 1.0707
Epoch 1/1... Discriminator Loss: 1.1652... Generator Loss: 0.7919
Epoch 1/1... Discriminator Loss: 1.0631... Generator Loss: 1.0180
Epoch 1/1... Discriminator Loss: 1.3033... Generator Loss: 0.8269
Epoch 1/1... Discriminator Loss: 0.9151... Generator Loss: 1.2596

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Epoch 1/1... Discriminator Loss: 1.0702... Generator Loss: 1.2079
Epoch 1/1... Discriminator Loss: 1.1871... Generator Loss: 0.6943
Epoch 1/1... Discriminator Loss: 1.1647... Generator Loss: 0.9901
Epoch 1/1... Discriminator Loss: 1.1179... Generator Loss: 1.0453
Epoch 1/1... Discriminator Loss: 1.1129... Generator Loss: 1.2422
Epoch 1/1... Discriminator Loss: 0.9526... Generator Loss: 1.2542
Epoch 1/1... Discriminator Loss: 1.4053... Generator Loss: 0.5359
Epoch 1/1... Discriminator Loss: 1.5096... Generator Loss: 0.5478

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