Data-efficient GANs with Adaptive Discriminator Augmentation

Author: András Béres
Date created: 2021/10/28
Last modified: 2025/01/23
Description: Generating images from limited data using the Caltech Birds dataset.

View in Colab • GitHub source

Introduction

GANs

Generative Adversarial Networks (GANs) are a popular class of generative deep learning models, commonly used for image generation. They consist of a pair of dueling neural networks, called the discriminator and the generator. The discriminator's task is to distinguish real images from generated (fake) ones, while the generator network tries to fool the discriminator by generating more and more realistic images. If the generator is however too easy or too hard to fool, it might fail to provide useful learning signal for the generator, therefore training GANs is usually considered a difficult task.

Data augmentation for GANS

Data augmentation, a popular technique in deep learning, is the process of randomly applying semantics-preserving transformations to the input data to generate multiple realistic versions of it, thereby effectively multiplying the amount of training data available. The simplest example is left-right flipping an image, which preserves its contents while generating a second unique training sample. Data augmentation is commonly used in supervised learning to prevent overfitting and enhance generalization.

The authors of StyleGAN2-ADA show that discriminator overfitting can be an issue in GANs, especially when only low amounts of training data is available. They propose Adaptive Discriminator Augmentation to mitigate this issue.

Applying data augmentation to GANs however is not straightforward. Since the generator is updated using the discriminator's gradients, if the generated images are augmented, the augmentation pipeline has to be differentiable and also has to be GPU-compatible for computational efficiency. Luckily, the Keras image augmentation layers fulfill both these requirements, and are therefore very well suited for this task.

Invertible data augmentation

A possible difficulty when using data augmentation in generative models is the issue of "leaky augmentations" (section 2.2), namely when the model generates images that are already augmented. This would mean that it was not able to separate the augmentation from the underlying data distribution, which can be caused by using non-invertible data transformations. For example, if either 0, 90, 180 or 270 degree rotations are performed with equal probability, the original orientation of the images is impossible to infer, and this information is destroyed.

A simple trick to make data augmentations invertible is to only apply them with some probability. That way the original version of the images will be more common, and the data distribution can be inferred. By properly choosing this probability, one can effectively regularize the discriminator without making the augmentations leaky.

Setup

import os

os.environ["KERAS_BACKEND"] = "tensorflow"

import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_datasets as tfds

import keras
from keras import ops
from keras import layers

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1738798965.367584   17795 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1738798965.374084   17795 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

Hyperparameterers

# data
num_epochs = 10  # train for 400 epochs for good results
image_size = 64
# resolution of Kernel Inception Distance measurement, see related section
kid_image_size = 75
padding = 0.25
dataset_name = "caltech_birds2011"

# adaptive discriminator augmentation
max_translation = 0.125
max_rotation = 0.125
max_zoom = 0.25
target_accuracy = 0.85
integration_steps = 1000

# architecture
noise_size = 64
depth = 4
width = 128
leaky_relu_slope = 0.2
dropout_rate = 0.4

# optimization
batch_size = 128
learning_rate = 2e-4
beta_1 = 0.5  # not using the default value of 0.9 is important
ema = 0.99

Data pipeline

In this example, we will use the Caltech Birds (2011) dataset for generating images of birds, which is a diverse natural dataset containing less then 6000 images for training. When working with such low amounts of data, one has to take extra care to retain as high data quality as possible. In this example, we use the provided bounding boxes of the birds to cut them out with square crops while preserving their aspect ratios when possible.

def round_to_int(float_value):
    return ops.cast(ops.round(float_value), "int32")


def preprocess_image(data):
    # unnormalize bounding box coordinates
    height = ops.cast(ops.shape(data["image"])[0], "float32")
    width = ops.cast(ops.shape(data["image"])[1], "float32")
    bounding_box = data["bbox"] * ops.stack([height, width, height, width])

    # calculate center and length of longer side, add padding
    target_center_y = 0.5 * (bounding_box[0] + bounding_box[2])
    target_center_x = 0.5 * (bounding_box[1] + bounding_box[3])
    target_size = ops.maximum(
        (1.0 + padding) * (bounding_box[2] - bounding_box[0]),
        (1.0 + padding) * (bounding_box[3] - bounding_box[1]),
    )

    # modify crop size to fit into image
    target_height = ops.min(
        [target_size, 2.0 * target_center_y, 2.0 * (height - target_center_y)]
    )
    target_width = ops.min(
        [target_size, 2.0 * target_center_x, 2.0 * (width - target_center_x)]
    )

    # crop image, `ops.image.crop_images` only works with non-tensor croppings
    image = ops.slice(
        data["image"],
        start_indices=(
            round_to_int(target_center_y - 0.5 * target_height),
            round_to_int(target_center_x - 0.5 * target_width),
            0,
        ),
        shape=(round_to_int(target_height), round_to_int(target_width), 3),
    )

    # resize and clip
    image = ops.cast(image, "float32")
    image = ops.image.resize(image, [image_size, image_size])

    return ops.clip(image / 255.0, 0.0, 1.0)


def prepare_dataset(split):
    # the validation dataset is shuffled as well, because data order matters
    # for the KID calculation
    return (
        tfds.load(dataset_name, split=split, shuffle_files=True)
        .map(preprocess_image, num_parallel_calls=tf.data.AUTOTUNE)
        .cache()
        .shuffle(10 * batch_size)
        .batch(batch_size, drop_remainder=True)
        .prefetch(buffer_size=tf.data.AUTOTUNE)
    )


train_dataset = prepare_dataset("train")
val_dataset = prepare_dataset("test")

I0000 00:00:1738798971.054632   17795 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13840 MB memory:  -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5

After preprocessing the training images look like the following:

Kernel inception distance

Kernel Inception Distance (KID) was proposed as a replacement for the popular Frechet Inception Distance (FID) metric for measuring image generation quality. Both metrics measure the difference in the generated and training distributions in the representation space of an InceptionV3 network pretrained on ImageNet.

According to the paper, KID was proposed because FID has no unbiased estimator, its expected value is higher when it is measured on fewer images. KID is more suitable for small datasets because its expected value does not depend on the number of samples it is measured on. In my experience it is also computationally lighter, numerically more stable, and simpler to implement because it can be estimated in a per-batch manner.

In this example, the images are evaluated at the minimal possible resolution of the Inception network (75x75 instead of 299x299), and the metric is only measured on the validation set for computational efficiency.

class KID(keras.metrics.Metric):
    def __init__(self, name="kid", **kwargs):
        super().__init__(name=name, **kwargs)

        # KID is estimated per batch and is averaged across batches
        self.kid_tracker = keras.metrics.Mean()

        # a pretrained InceptionV3 is used without its classification layer
        # transform the pixel values to the 0-255 range, then use the same
        # preprocessing as during pretraining
        self.encoder = keras.Sequential(
            [
                layers.InputLayer(input_shape=(image_size, image_size, 3)),
                layers.Rescaling(255.0),
                layers.Resizing(height=kid_image_size, width=kid_image_size),
                layers.Lambda(keras.applications.inception_v3.preprocess_input),
                keras.applications.InceptionV3(
                    include_top=False,
                    input_shape=(kid_image_size, kid_image_size, 3),
                    weights="imagenet",
                ),
                layers.GlobalAveragePooling2D(),
            ],
            name="inception_encoder",
        )

    def polynomial_kernel(self, features_1, features_2):
        feature_dimensions = ops.cast(ops.shape(features_1)[1], "float32")
        return (
            features_1 @ ops.transpose(features_2) / feature_dimensions + 1.0
        ) ** 3.0

    def update_state(self, real_images, generated_images, sample_weight=None):
        real_features = self.encoder(real_images, training=False)
        generated_features = self.encoder(generated_images, training=False)

        # compute polynomial kernels using the two sets of features
        kernel_real = self.polynomial_kernel(real_features, real_features)
        kernel_generated = self.polynomial_kernel(
            generated_features, generated_features
        )
        kernel_cross = self.polynomial_kernel(real_features, generated_features)

        # estimate the squared maximum mean discrepancy using the average kernel values
        batch_size = ops.shape(real_features)[0]
        batch_size_f = ops.cast(batch_size, "float32")
        mean_kernel_real = ops.sum(kernel_real * (1.0 - ops.eye(batch_size))) / (
            batch_size_f * (batch_size_f - 1.0)
        )
        mean_kernel_generated = ops.sum(
            kernel_generated * (1.0 - ops.eye(batch_size))
        ) / (batch_size_f * (batch_size_f - 1.0))
        mean_kernel_cross = ops.mean(kernel_cross)
        kid = mean_kernel_real + mean_kernel_generated - 2.0 * mean_kernel_cross

        # update the average KID estimate
        self.kid_tracker.update_state(kid)

    def result(self):
        return self.kid_tracker.result()

    def reset_state(self):
        self.kid_tracker.reset_state()

Adaptive discriminator augmentation

The authors of StyleGAN2-ADA propose to change the augmentation probability adaptively during training. Though it is explained differently in the paper, they use integral control on the augmentation probability to keep the discriminator's accuracy on real images close to a target value. Note, that their controlled variable is actually the average sign of the discriminator logits (r_t in the paper), which corresponds to 2 * accuracy - 1.

This method requires two hyperparameters:

1. target_accuracy: the target value for the discriminator's accuracy on real images. I recommend selecting its value from the 80-90% range.
2. integration_steps: the number of update steps required for an accuracy error of 100% to transform into an augmentation probability increase of 100%. To give an intuition, this defines how slowly the augmentation probability is changed. I recommend setting this to a relatively high value (1000 in this case) so that the augmentation strength is only adjusted slowly.

The main motivation for this procedure is that the optimal value of the target accuracy is similar across different dataset sizes (see figure 4 and 5 in the paper), so it does not have to be re-tuned, because the process automatically applies stronger data augmentation when it is needed.

# "hard sigmoid", useful for binary accuracy calculation from logits
def step(values):
    # negative values -> 0.0, positive values -> 1.0
    return 0.5 * (1.0 + ops.sign(values))


# augments images with a probability that is dynamically updated during training
class AdaptiveAugmenter(keras.Model):
    def __init__(self):
        super().__init__()

        # stores the current probability of an image being augmented
        self.probability = keras.Variable(0.0)
        self.seed_generator = keras.random.SeedGenerator(42)

        # the corresponding augmentation names from the paper are shown above each layer
        # the authors show (see figure 4), that the blitting and geometric augmentations
        # are the most helpful in the low-data regime
        self.augmenter = keras.Sequential(
            [
                layers.InputLayer(input_shape=(image_size, image_size, 3)),
                # blitting/x-flip:
                layers.RandomFlip("horizontal"),
                # blitting/integer translation:
                layers.RandomTranslation(
                    height_factor=max_translation,
                    width_factor=max_translation,
                    interpolation="nearest",
                ),
                # geometric/rotation:
                layers.RandomRotation(factor=max_rotation),
                # geometric/isotropic and anisotropic scaling:
                layers.RandomZoom(
                    height_factor=(-max_zoom, 0.0), width_factor=(-max_zoom, 0.0)
                ),
            ],
            name="adaptive_augmenter",
        )

    def call(self, images, training):
        if training:
            augmented_images = self.augmenter(images, training=training)

            # during training either the original or the augmented images are selected
            # based on self.probability
            augmentation_values = keras.random.uniform(
                shape=(batch_size, 1, 1, 1), seed=self.seed_generator
            )
            augmentation_bools = ops.less(augmentation_values, self.probability)

            images = ops.where(augmentation_bools, augmented_images, images)
        return images

    def update(self, real_logits):
        current_accuracy = ops.mean(step(real_logits))

        # the augmentation probability is updated based on the discriminator's
        # accuracy on real images
        accuracy_error = current_accuracy - target_accuracy
        self.probability.assign(
            ops.clip(self.probability + accuracy_error / integration_steps, 0.0, 1.0)
        )

Network architecture

Here we specify the architecture of the two networks:

• generator: maps a random vector to an image, which should be as realistic as possible
• discriminator: maps an image to a scalar score, which should be high for real and low for generated images

GANs tend to be sensitive to the network architecture, I implemented a DCGAN architecture in this example, because it is relatively stable during training while being simple to implement. We use a constant number of filters throughout the network, use a sigmoid instead of tanh in the last layer of the generator, and use default initialization instead of random normal as further simplifications.

As a good practice, we disable the learnable scale parameter in the batch normalization layers, because on one hand the following relu + convolutional layers make it redundant (as noted in the documentation). But also because it should be disabled based on theory when using spectral normalization (section 4.1), which is not used here, but is common in GANs. We also disable the bias in the fully connected and convolutional layers, because the following batch normalization makes it redundant.

# DCGAN generator
def get_generator():
    noise_input = keras.Input(shape=(noise_size,))
    x = layers.Dense(4 * 4 * width, use_bias=False)(noise_input)
    x = layers.BatchNormalization(scale=False)(x)
    x = layers.ReLU()(x)
    x = layers.Reshape(target_shape=(4, 4, width))(x)
    for _ in range(depth - 1):
        x = layers.Conv2DTranspose(
            width,
            kernel_size=4,
            strides=2,
            padding="same",
            use_bias=False,
        )(x)
        x = layers.BatchNormalization(scale=False)(x)
        x = layers.ReLU()(x)
    image_output = layers.Conv2DTranspose(
        3,
        kernel_size=4,
        strides=2,
        padding="same",
        activation="sigmoid",
    )(x)

    return keras.Model(noise_input, image_output, name="generator")


# DCGAN discriminator
def get_discriminator():
    image_input = keras.Input(shape=(image_size, image_size, 3))
    x = image_input
    for _ in range(depth):
        x = layers.Conv2D(
            width,
            kernel_size=4,
            strides=2,
            padding="same",
            use_bias=False,
        )(x)
        x = layers.BatchNormalization(scale=False)(x)
        x = layers.LeakyReLU(alpha=leaky_relu_slope)(x)
    x = layers.Flatten()(x)
    x = layers.Dropout(dropout_rate)(x)
    output_score = layers.Dense(1)(x)

    return keras.Model(image_input, output_score, name="discriminator")

GAN model

class GAN_ADA(keras.Model):
    def __init__(self):
        super().__init__()

        self.seed_generator = keras.random.SeedGenerator(seed=42)
        self.augmenter = AdaptiveAugmenter()
        self.generator = get_generator()
        self.ema_generator = keras.models.clone_model(self.generator)
        self.discriminator = get_discriminator()

        self.generator.summary()
        self.discriminator.summary()
        # we have created all layers at this point, so we can mark the model
        # as having been built
        self.built = True

    def compile(self, generator_optimizer, discriminator_optimizer, **kwargs):
        super().compile(**kwargs)

        # separate optimizers for the two networks
        self.generator_optimizer = generator_optimizer
        self.discriminator_optimizer = discriminator_optimizer

        self.generator_loss_tracker = keras.metrics.Mean(name="g_loss")
        self.discriminator_loss_tracker = keras.metrics.Mean(name="d_loss")
        self.real_accuracy = keras.metrics.BinaryAccuracy(name="real_acc")
        self.generated_accuracy = keras.metrics.BinaryAccuracy(name="gen_acc")
        self.augmentation_probability_tracker = keras.metrics.Mean(name="aug_p")
        self.kid = KID()

    @property
    def metrics(self):
        return [
            self.generator_loss_tracker,
            self.discriminator_loss_tracker,
            self.real_accuracy,
            self.generated_accuracy,
            self.augmentation_probability_tracker,
            self.kid,
        ]

    def generate(self, batch_size, training):
        latent_samples = keras.random.normal(
            shape=(batch_size, noise_size), seed=self.seed_generator
        )
        # use ema_generator during inference
        if training:
            generated_images = self.generator(latent_samples, training=training)
        else:
            generated_images = self.ema_generator(latent_samples, training=training)
        return generated_images

    def adversarial_loss(self, real_logits, generated_logits):
        # this is usually called the non-saturating GAN loss

        real_labels = ops.ones(shape=(batch_size, 1))
        generated_labels = ops.zeros(shape=(batch_size, 1))

        # the generator tries to produce images that the discriminator considers as real
        generator_loss = keras.losses.binary_crossentropy(
            real_labels, generated_logits, from_logits=True
        )
        # the discriminator tries to determine if images are real or generated
        discriminator_loss = keras.losses.binary_crossentropy(
            ops.concatenate([real_labels, generated_labels], axis=0),
            ops.concatenate([real_logits, generated_logits], axis=0),
            from_logits=True,
        )

        return ops.mean(generator_loss), ops.mean(discriminator_loss)

    def train_step(self, real_images):
        real_images = self.augmenter(real_images, training=True)

        # use persistent gradient tape because gradients will be calculated twice
        with tf.GradientTape(persistent=True) as tape:
            generated_images = self.generate(batch_size, training=True)
            # gradient is calculated through the image augmentation
            generated_images = self.augmenter(generated_images, training=True)

            # separate forward passes for the real and generated images, meaning
            # that batch normalization is applied separately
            real_logits = self.discriminator(real_images, training=True)
            generated_logits = self.discriminator(generated_images, training=True)

            generator_loss, discriminator_loss = self.adversarial_loss(
                real_logits, generated_logits
            )

        # calculate gradients and update weights
        generator_gradients = tape.gradient(
            generator_loss, self.generator.trainable_weights
        )
        discriminator_gradients = tape.gradient(
            discriminator_loss, self.discriminator.trainable_weights
        )
        self.generator_optimizer.apply_gradients(
            zip(generator_gradients, self.generator.trainable_weights)
        )
        self.discriminator_optimizer.apply_gradients(
            zip(discriminator_gradients, self.discriminator.trainable_weights)
        )

        # update the augmentation probability based on the discriminator's performance
        self.augmenter.update(real_logits)

        self.generator_loss_tracker.update_state(generator_loss)
        self.discriminator_loss_tracker.update_state(discriminator_loss)
        self.real_accuracy.update_state(1.0, step(real_logits))
        self.generated_accuracy.update_state(0.0, step(generated_logits))
        self.augmentation_probability_tracker.update_state(self.augmenter.probability)

        # track the exponential moving average of the generator's weights to decrease
        # variance in the generation quality
        for weight, ema_weight in zip(
            self.generator.weights, self.ema_generator.weights
        ):
            ema_weight.assign(ema * ema_weight + (1 - ema) * weight)

        # KID is not measured during the training phase for computational efficiency
        return {m.name: m.result() for m in self.metrics[:-1]}

    def test_step(self, real_images):
        generated_images = self.generate(batch_size, training=False)

        self.kid.update_state(real_images, generated_images)

        # only KID is measured during the evaluation phase for computational efficiency
        return {self.kid.name: self.kid.result()}

    def plot_images(self, epoch=None, logs=None, num_rows=3, num_cols=6, interval=5):
        # plot random generated images for visual evaluation of generation quality
        if epoch is None or (epoch + 1) % interval == 0:
            num_images = num_rows * num_cols
            generated_images = self.generate(num_images, training=False)

            plt.figure(figsize=(num_cols * 2.0, num_rows * 2.0))
            for row in range(num_rows):
                for col in range(num_cols):
                    index = row * num_cols + col
                    plt.subplot(num_rows, num_cols, index + 1)
                    plt.imshow(generated_images[index])
                    plt.axis("off")
            plt.tight_layout()
            plt.show()
            plt.close()

Training

One can should see from the metrics during training, that if the real accuracy (discriminator's accuracy on real images) is below the target accuracy, the augmentation probability is increased, and vice versa. In my experience, during a healthy GAN training, the discriminator accuracy should stay in the 80-95% range. Below that, the discriminator is too weak, above that it is too strong.

Note that we track the exponential moving average of the generator's weights, and use that for image generation and KID evaluation.

# create and compile the model
model = GAN_ADA()
model.compile(
    generator_optimizer=keras.optimizers.Adam(learning_rate, beta_1),
    discriminator_optimizer=keras.optimizers.Adam(learning_rate, beta_1),
)

# save the best model based on the validation KID metric
checkpoint_path = "gan_model.weights.h5"
checkpoint_callback = keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_path,
    save_weights_only=True,
    monitor="val_kid",
    mode="min",
    save_best_only=True,
)

# run training and plot generated images periodically
model.fit(
    train_dataset,
    epochs=num_epochs,
    validation_data=val_dataset,
    callbacks=[
        keras.callbacks.LambdaCallback(on_epoch_end=model.plot_images),
        checkpoint_callback,
    ],
)

/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(

/usr/local/lib/python3.11/dist-packages/keras/src/layers/activations/leaky_relu.py:41: UserWarning: Argument `alpha` is deprecated. Use `negative_slope` instead.
  warnings.warn(

Model: "generator"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ input_layer_1 (InputLayer)      │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 2048)           │       131,072 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization             │ (None, 2048)           │         6,144 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ re_lu (ReLU)                    │ (None, 2048)           │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ reshape (Reshape)               │ (None, 4, 4, 128)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_transpose                │ (None, 8, 8, 128)      │       262,144 │
│ (Conv2DTranspose)               │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_1           │ (None, 8, 8, 128)      │           384 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ re_lu_1 (ReLU)                  │ (None, 8, 8, 128)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_transpose_1              │ (None, 16, 16, 128)    │       262,144 │
│ (Conv2DTranspose)               │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_2           │ (None, 16, 16, 128)    │           384 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ re_lu_2 (ReLU)                  │ (None, 16, 16, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_transpose_2              │ (None, 32, 32, 128)    │       262,144 │
│ (Conv2DTranspose)               │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_3           │ (None, 32, 32, 128)    │           384 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ re_lu_3 (ReLU)                  │ (None, 32, 32, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_transpose_3              │ (None, 64, 64, 3)      │         6,147 │
│ (Conv2DTranspose)               │                        │               │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 930,947 (3.55 MB)

 Trainable params: 926,083 (3.53 MB)

 Non-trainable params: 4,864 (19.00 KB)

Model: "discriminator"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ input_layer_2 (InputLayer)      │ (None, 64, 64, 3)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d (Conv2D)                 │ (None, 32, 32, 128)    │         6,144 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_4           │ (None, 32, 32, 128)    │           384 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu (LeakyReLU)         │ (None, 32, 32, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_1 (Conv2D)               │ (None, 16, 16, 128)    │       262,144 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_5           │ (None, 16, 16, 128)    │           384 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu_1 (LeakyReLU)       │ (None, 16, 16, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_2 (Conv2D)               │ (None, 8, 8, 128)      │       262,144 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_6           │ (None, 8, 8, 128)      │           384 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu_2 (LeakyReLU)       │ (None, 8, 8, 128)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_3 (Conv2D)               │ (None, 4, 4, 128)      │       262,144 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_7           │ (None, 4, 4, 128)      │           384 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ leaky_re_lu_3 (LeakyReLU)       │ (None, 4, 4, 128)      │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten (Flatten)               │ (None, 2048)           │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 2048)           │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 1)              │         2,049 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 796,161 (3.04 MB)

 Trainable params: 795,137 (3.03 MB)

 Non-trainable params: 1,024 (4.00 KB)

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5

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Epoch 1/10

E0000 00:00:1738798983.901596   17795 meta_optimizer.cc:966] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inStatefulPartitionedCall/gradient_tape/adaptive_augmenter_3/SelectV2_1-1-TransposeNHWCToNCHW-LayoutOptimizer

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1738798987.822990   17861 cuda_solvers.cc:178] Creating GpuSolver handles for stream 0x9f45670

I0000 00:00:1738798988.976919   17862 cuda_dnn.cc:529] Loaded cuDNN version 90300

1/46 ━━━━━━━━━━━━━━━━━━━━ 13:22 18s/step - aug_p: 0.0000e+00 - d_loss: 0.8829 - g_loss: 0.5585 - gen_acc: 0.2812 - real_acc: 0.7031



2/46 ━━━━━━━━━━━━━━━━━━━━ 7s 173ms/step - aug_p: 0.0000e+00 - d_loss: 0.7985 - g_loss: 0.8154 - gen_acc: 0.4258 - real_acc: 0.6777



3/46 ━━━━━━━━━━━━━━━━━━━━ 8s 201ms/step - aug_p: 0.0000e+00 - d_loss: 0.7488 - g_loss: 0.9481 - gen_acc: 0.5052 - real_acc: 0.6680



4/46 ━━━━━━━━━━━━━━━━━━━━ 10s 249ms/step - aug_p: 0.0000e+00 - d_loss: 0.7106 - g_loss: 1.0407 - gen_acc: 0.5586 - real_acc: 0.6680



5/46 ━━━━━━━━━━━━━━━━━━━━ 11s 269ms/step - aug_p: 0.0000e+00 - d_loss: 0.6782 - g_loss: 1.1104 - gen_acc: 0.5991 - real_acc: 0.6744



6/46 ━━━━━━━━━━━━━━━━━━━━ 10s 273ms/step - aug_p: 0.0000e+00 - d_loss: 0.6506 - g_loss: 1.1692 - gen_acc: 0.6301 - real_acc: 0.6818



7/46 ━━━━━━━━━━━━━━━━━━━━ 10s 280ms/step - aug_p: 5.1020e-07 - d_loss: 0.6253 - g_loss: 1.2302 - gen_acc: 0.6558 - real_acc: 0.6902



8/46 ━━━━━━━━━━━━━━━━━━━━ 10s 289ms/step - aug_p: 1.4962e-06 - d_loss: 0.6030 - g_loss: 1.2838 - gen_acc: 0.6772 - real_acc: 0.6986



9/46 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - aug_p: 3.8570e-06 - d_loss: 0.5829 - g_loss: 1.3313 - gen_acc: 0.6952 - real_acc: 0.7074



10/46 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - aug_p: 7.9244e-06 - d_loss: 0.5644 - g_loss: 1.3805 - gen_acc: 0.7109 - real_acc: 0.7162



11/46 ━━━━━━━━━━━━━━━━━━━━ 11s 314ms/step - aug_p: 1.3531e-05 - d_loss: 0.5473 - g_loss: 1.4301 - gen_acc: 0.7249 - real_acc: 0.7245



12/46 ━━━━━━━━━━━━━━━━━━━━ 11s 333ms/step - aug_p: 2.0444e-05 - d_loss: 0.5318 - g_loss: 1.4743 - gen_acc: 0.7373 - real_acc: 0.7322



13/46 ━━━━━━━━━━━━━━━━━━━━ 11s 352ms/step - aug_p: 2.8561e-05 - d_loss: 0.5174 - g_loss: 1.5181 - gen_acc: 0.7485 - real_acc: 0.7394



14/46 ━━━━━━━━━━━━━━━━━━━━ 11s 365ms/step - aug_p: 3.7929e-05 - d_loss: 0.5040 - g_loss: 1.5586 - gen_acc: 0.7587 - real_acc: 0.7464



15/46 ━━━━━━━━━━━━━━━━━━━━ 11s 379ms/step - aug_p: 4.8560e-05 - d_loss: 0.4914 - g_loss: 1.5977 - gen_acc: 0.7680 - real_acc: 0.7530



16/46 ━━━━━━━━━━━━━━━━━━━━ 11s 387ms/step - aug_p: 6.0448e-05 - d_loss: 0.4795 - g_loss: 1.6355 - gen_acc: 0.7766 - real_acc: 0.7593



17/46 ━━━━━━━━━━━━━━━━━━━━ 11s 399ms/step - aug_p: 7.3577e-05 - d_loss: 0.4683 - g_loss: 1.6715 - gen_acc: 0.7844 - real_acc: 0.7654



18/46 ━━━━━━━━━━━━━━━━━━━━ 11s 414ms/step - aug_p: 8.7805e-05 - d_loss: 0.4578 - g_loss: 1.7063 - gen_acc: 0.7917 - real_acc: 0.7711



19/46 ━━━━━━━━━━━━━━━━━━━━ 11s 422ms/step - aug_p: 1.0308e-04 - d_loss: 0.4480 - g_loss: 1.7386 - gen_acc: 0.7984 - real_acc: 0.7765



20/46 ━━━━━━━━━━━━━━━━━━━━ 11s 433ms/step - aug_p: 1.1933e-04 - d_loss: 0.4386 - g_loss: 1.7736 - gen_acc: 0.8046 - real_acc: 0.7817



21/46 ━━━━━━━━━━━━━━━━━━━━ 11s 444ms/step - aug_p: 1.3652e-04 - d_loss: 0.4297 - g_loss: 1.8065 - gen_acc: 0.8104 - real_acc: 0.7866



22/46 ━━━━━━━━━━━━━━━━━━━━ 10s 440ms/step - aug_p: 1.5459e-04 - d_loss: 0.4213 - g_loss: 1.8383 - gen_acc: 0.8158 - real_acc: 0.7913



23/46 ━━━━━━━━━━━━━━━━━━━━ 9s 434ms/step - aug_p: 1.7347e-04 - d_loss: 0.4132 - g_loss: 1.8694 - gen_acc: 0.8209 - real_acc: 0.7958



24/46 ━━━━━━━━━━━━━━━━━━━━ 9s 429ms/step - aug_p: 1.9312e-04 - d_loss: 0.4056 - g_loss: 1.8988 - gen_acc: 0.8257 - real_acc: 0.8000



25/46 ━━━━━━━━━━━━━━━━━━━━ 8s 426ms/step - aug_p: 2.1348e-04 - d_loss: 0.3983 - g_loss: 1.9278 - gen_acc: 0.8302 - real_acc: 0.8041



26/46 ━━━━━━━━━━━━━━━━━━━━ 8s 423ms/step - aug_p: 2.3451e-04 - d_loss: 0.3914 - g_loss: 1.9548 - gen_acc: 0.8345 - real_acc: 0.8079



27/46 ━━━━━━━━━━━━━━━━━━━━ 8s 427ms/step - aug_p: 2.5614e-04 - d_loss: 0.3848 - g_loss: 1.9828 - gen_acc: 0.8385 - real_acc: 0.8116



28/46 ━━━━━━━━━━━━━━━━━━━━ 7s 424ms/step - aug_p: 2.7834e-04 - d_loss: 0.3785 - g_loss: 2.0093 - gen_acc: 0.8423 - real_acc: 0.8151



29/46 ━━━━━━━━━━━━━━━━━━━━ 7s 420ms/step - aug_p: 3.0107e-04 - d_loss: 0.3724 - g_loss: 2.0347 - gen_acc: 0.8459 - real_acc: 0.8185



30/46 ━━━━━━━━━━━━━━━━━━━━ 6s 416ms/step - aug_p: 3.2432e-04 - d_loss: 0.3666 - g_loss: 2.0599 - gen_acc: 0.8493 - real_acc: 0.8218



31/46 ━━━━━━━━━━━━━━━━━━━━ 6s 413ms/step - aug_p: 3.4806e-04 - d_loss: 0.3610 - g_loss: 2.0840 - gen_acc: 0.8526 - real_acc: 0.8249



32/46 ━━━━━━━━━━━━━━━━━━━━ 5s 409ms/step - aug_p: 3.7225e-04 - d_loss: 0.3556 - g_loss: 2.1073 - gen_acc: 0.8556 - real_acc: 0.8279



33/46 ━━━━━━━━━━━━━━━━━━━━ 5s 406ms/step - aug_p: 3.9686e-04 - d_loss: 0.3505 - g_loss: 2.1300 - gen_acc: 0.8586 - real_acc: 0.8307



34/46 ━━━━━━━━━━━━━━━━━━━━ 4s 402ms/step - aug_p: 4.2187e-04 - d_loss: 0.3455 - g_loss: 2.1520 - gen_acc: 0.8614 - real_acc: 0.8335



35/46 ━━━━━━━━━━━━━━━━━━━━ 4s 400ms/step - aug_p: 4.4725e-04 - d_loss: 0.3407 - g_loss: 2.1736 - gen_acc: 0.8641 - real_acc: 0.8361



36/46 ━━━━━━━━━━━━━━━━━━━━ 3s 397ms/step - aug_p: 4.7297e-04 - d_loss: 0.3361 - g_loss: 2.1947 - gen_acc: 0.8667 - real_acc: 0.8387



37/46 ━━━━━━━━━━━━━━━━━━━━ 3s 393ms/step - aug_p: 4.9903e-04 - d_loss: 0.3316 - g_loss: 2.2152 - gen_acc: 0.8691 - real_acc: 0.8411



38/46 ━━━━━━━━━━━━━━━━━━━━ 3s 387ms/step - aug_p: 5.2539e-04 - d_loss: 0.3273 - g_loss: 2.2357 - gen_acc: 0.8715 - real_acc: 0.8435



39/46 ━━━━━━━━━━━━━━━━━━━━ 2s 382ms/step - aug_p: 5.5206e-04 - d_loss: 0.3231 - g_loss: 2.2554 - gen_acc: 0.8738 - real_acc: 0.8458



40/46 ━━━━━━━━━━━━━━━━━━━━ 2s 376ms/step - aug_p: 5.7902e-04 - d_loss: 0.3191 - g_loss: 2.2756 - gen_acc: 0.8759 - real_acc: 0.8480



41/46 ━━━━━━━━━━━━━━━━━━━━ 1s 371ms/step - aug_p: 6.0626e-04 - d_loss: 0.3151 - g_loss: 2.2954 - gen_acc: 0.8780 - real_acc: 0.8502



42/46 ━━━━━━━━━━━━━━━━━━━━ 1s 366ms/step - aug_p: 6.3377e-04 - d_loss: 0.3113 - g_loss: 2.3147 - gen_acc: 0.8800 - real_acc: 0.8522



43/46 ━━━━━━━━━━━━━━━━━━━━ 1s 362ms/step - aug_p: 6.6154e-04 - d_loss: 0.3076 - g_loss: 2.3339 - gen_acc: 0.8820 - real_acc: 0.8543



44/46 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step - aug_p: 6.8956e-04 - d_loss: 0.3041 - g_loss: 2.3524 - gen_acc: 0.8839 - real_acc: 0.8562



45/46 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step - aug_p: 7.1780e-04 - d_loss: 0.3006 - g_loss: 2.3703 - gen_acc: 0.8857 - real_acc: 0.8581



46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step - aug_p: 7.4625e-04 - d_loss: 0.2973 - g_loss: 2.3871 - gen_acc: 0.8874 - real_acc: 0.8599



46/46 ━━━━━━━━━━━━━━━━━━━━ 61s 958ms/step - aug_p: 7.7349e-04 - d_loss: 0.2942 - g_loss: 2.4032 - gen_acc: 0.8890 - real_acc: 0.8616 - val_kid: 9.1841

Epoch 2/10

1/46 ━━━━━━━━━━━━━━━━━━━━ 22:28 30s/step - aug_p: 0.0051 - d_loss: 0.1030 - g_loss: 8.8928 - gen_acc: 1.0000 - real_acc: 0.9375



2/46 ━━━━━━━━━━━━━━━━━━━━ 7s 176ms/step - aug_p: 0.0051 - d_loss: 0.1073 - g_loss: 8.4938 - gen_acc: 0.9980 - real_acc: 0.9297



3/46 ━━━━━━━━━━━━━━━━━━━━ 7s 176ms/step - aug_p: 0.0051 - d_loss: 0.1074 - g_loss: 7.7540 - gen_acc: 0.9952 - real_acc: 0.9340



4/46 ━━━━━━━━━━━━━━━━━━━━ 7s 176ms/step - aug_p: 0.0052 - d_loss: 0.1037 - g_loss: 7.2815 - gen_acc: 0.9945 - real_acc: 0.9388



5/46 ━━━━━━━━━━━━━━━━━━━━ 7s 175ms/step - aug_p: 0.0052 - d_loss: 0.0997 - g_loss: 6.9305 - gen_acc: 0.9943 - real_acc: 0.9432



6/46 ━━━━━━━━━━━━━━━━━━━━ 7s 175ms/step - aug_p: 0.0052 - d_loss: 0.0960 - g_loss: 6.6418 - gen_acc: 0.9942 - real_acc: 0.9473



7/46 ━━━━━━━━━━━━━━━━━━━━ 6s 175ms/step - aug_p: 0.0052 - d_loss: 0.0933 - g_loss: 6.4224 - gen_acc: 0.9939 - real_acc: 0.9503



8/46 ━━━━━━━━━━━━━━━━━━━━ 6s 175ms/step - aug_p: 0.0053 - d_loss: 0.0907 - g_loss: 6.2473 - gen_acc: 0.9937 - real_acc: 0.9530



9/46 ━━━━━━━━━━━━━━━━━━━━ 6s 175ms/step - aug_p: 0.0053 - d_loss: 0.0885 - g_loss: 6.0970 - gen_acc: 0.9936 - real_acc: 0.9552



10/46 ━━━━━━━━━━━━━━━━━━━━ 6s 175ms/step - aug_p: 0.0053 - d_loss: 0.0868 - g_loss: 5.9686 - gen_acc: 0.9936 - real_acc: 0.9571



11/46 ━━━━━━━━━━━━━━━━━━━━ 6s 175ms/step - aug_p: 0.0054 - d_loss: 0.0852 - g_loss: 5.8546 - gen_acc: 0.9936 - real_acc: 0.9588



12/46 ━━━━━━━━━━━━━━━━━━━━ 5s 176ms/step - aug_p: 0.0054 - d_loss: 0.0837 - g_loss: 5.7615 - gen_acc: 0.9937 - real_acc: 0.9602



13/46 ━━━━━━━━━━━━━━━━━━━━ 5s 175ms/step - aug_p: 0.0054 - d_loss: 0.0825 - g_loss: 5.6750 - gen_acc: 0.9937 - real_acc: 0.9614



14/46 ━━━━━━━━━━━━━━━━━━━━ 5s 176ms/step - aug_p: 0.0055 - d_loss: 0.0813 - g_loss: 5.5972 - gen_acc: 0.9937 - real_acc: 0.9626



15/46 ━━━━━━━━━━━━━━━━━━━━ 5s 176ms/step - aug_p: 0.0055 - d_loss: 0.0802 - g_loss: 5.5273 - gen_acc: 0.9938 - real_acc: 0.9636



16/46 ━━━━━━━━━━━━━━━━━━━━ 5s 176ms/step - aug_p: 0.0055 - d_loss: 0.0792 - g_loss: 5.4619 - gen_acc: 0.9939 - real_acc: 0.9645



17/46 ━━━━━━━━━━━━━━━━━━━━ 5s 176ms/step - aug_p: 0.0056 - d_loss: 0.0783 - g_loss: 5.4012 - gen_acc: 0.9940 - real_acc: 0.9654



18/46 ━━━━━━━━━━━━━━━━━━━━ 4s 176ms/step - aug_p: 0.0056 - d_loss: 0.0775 - g_loss: 5.3477 - gen_acc: 0.9941 - real_acc: 0.9661



19/46 ━━━━━━━━━━━━━━━━━━━━ 4s 176ms/step - aug_p: 0.0056 - d_loss: 0.0768 - g_loss: 5.2979 - gen_acc: 0.9941 - real_acc: 0.9667



20/46 ━━━━━━━━━━━━━━━━━━━━ 4s 176ms/step - aug_p: 0.0057 - d_loss: 0.0762 - g_loss: 5.2495 - gen_acc: 0.9941 - real_acc: 0.9673



21/46 ━━━━━━━━━━━━━━━━━━━━ 4s 176ms/step - aug_p: 0.0057 - d_loss: 0.0758 - g_loss: 5.2113 - gen_acc: 0.9940 - real_acc: 0.9677



22/46 ━━━━━━━━━━━━━━━━━━━━ 4s 176ms/step - aug_p: 0.0057 - d_loss: 0.0754 - g_loss: 5.1753 - gen_acc: 0.9940 - real_acc: 0.9681



23/46 ━━━━━━━━━━━━━━━━━━━━ 4s 176ms/step - aug_p: 0.0058 - d_loss: 0.0752 - g_loss: 5.1387 - gen_acc: 0.9940 - real_acc: 0.9684



24/46 ━━━━━━━━━━━━━━━━━━━━ 3s 176ms/step - aug_p: 0.0058 - d_loss: 0.0749 - g_loss: 5.1112 - gen_acc: 0.9939 - real_acc: 0.9688



25/46 ━━━━━━━━━━━━━━━━━━━━ 3s 176ms/step - aug_p: 0.0058 - d_loss: 0.0746 - g_loss: 5.0899 - gen_acc: 0.9939 - real_acc: 0.9691



26/46 ━━━━━━━━━━━━━━━━━━━━ 3s 177ms/step - aug_p: 0.0059 - d_loss: 0.0744 - g_loss: 5.0691 - gen_acc: 0.9939 - real_acc: 0.9693



27/46 ━━━━━━━━━━━━━━━━━━━━ 3s 177ms/step - aug_p: 0.0059 - d_loss: 0.0743 - g_loss: 5.0465 - gen_acc: 0.9937 - real_acc: 0.9696



28/46 ━━━━━━━━━━━━━━━━━━━━ 3s 177ms/step - aug_p: 0.0059 - d_loss: 0.0742 - g_loss: 5.0296 - gen_acc: 0.9935 - real_acc: 0.9698



29/46 ━━━━━━━━━━━━━━━━━━━━ 3s 177ms/step - aug_p: 0.0060 - d_loss: 0.0741 - g_loss: 5.0163 - gen_acc: 0.9934 - real_acc: 0.9701



30/46 ━━━━━━━━━━━━━━━━━━━━ 2s 177ms/step - aug_p: 0.0060 - d_loss: 0.0740 - g_loss: 5.0018 - gen_acc: 0.9932 - real_acc: 0.9703



31/46 ━━━━━━━━━━━━━━━━━━━━ 2s 177ms/step - aug_p: 0.0060 - d_loss: 0.0739 - g_loss: 4.9862 - gen_acc: 0.9931 - real_acc: 0.9705



32/46 ━━━━━━━━━━━━━━━━━━━━ 2s 177ms/step - aug_p: 0.0061 - d_loss: 0.0739 - g_loss: 4.9725 - gen_acc: 0.9929 - real_acc: 0.9707



33/46 ━━━━━━━━━━━━━━━━━━━━ 2s 177ms/step - aug_p: 0.0061 - d_loss: 0.0739 - g_loss: 4.9583 - gen_acc: 0.9928 - real_acc: 0.9709



34/46 ━━━━━━━━━━━━━━━━━━━━ 2s 178ms/step - aug_p: 0.0061 - d_loss: 0.0739 - g_loss: 4.9439 - gen_acc: 0.9927 - real_acc: 0.9711



35/46 ━━━━━━━━━━━━━━━━━━━━ 1s 178ms/step - aug_p: 0.0062 - d_loss: 0.0739 - g_loss: 4.9297 - gen_acc: 0.9926 - real_acc: 0.9712



36/46 ━━━━━━━━━━━━━━━━━━━━ 1s 178ms/step - aug_p: 0.0062 - d_loss: 0.0740 - g_loss: 4.9151 - gen_acc: 0.9925 - real_acc: 0.9714



37/46 ━━━━━━━━━━━━━━━━━━━━ 1s 178ms/step - aug_p: 0.0062 - d_loss: 0.0741 - g_loss: 4.9027 - gen_acc: 0.9924 - real_acc: 0.9714



38/46 ━━━━━━━━━━━━━━━━━━━━ 1s 178ms/step - aug_p: 0.0063 - d_loss: 0.0743 - g_loss: 4.8890 - gen_acc: 0.9921 - real_acc: 0.9715



39/46 ━━━━━━━━━━━━━━━━━━━━ 1s 178ms/step - aug_p: 0.0063 - d_loss: 0.0748 - g_loss: 4.8802 - gen_acc: 0.9918 - real_acc: 0.9713



40/46 ━━━━━━━━━━━━━━━━━━━━ 1s 178ms/step - aug_p: 0.0063 - d_loss: 0.0752 - g_loss: 4.8742 - gen_acc: 0.9916 - real_acc: 0.9712



41/46 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - aug_p: 0.0064 - d_loss: 0.0756 - g_loss: 4.8685 - gen_acc: 0.9914 - real_acc: 0.9710



42/46 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - aug_p: 0.0064 - d_loss: 0.0759 - g_loss: 4.8620 - gen_acc: 0.9911 - real_acc: 0.9709



43/46 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - aug_p: 0.0064 - d_loss: 0.0762 - g_loss: 4.8555 - gen_acc: 0.9909 - real_acc: 0.9708



44/46 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - aug_p: 0.0064 - d_loss: 0.0765 - g_loss: 4.8492 - gen_acc: 0.9907 - real_acc: 0.9707



45/46 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - aug_p: 0.0065 - d_loss: 0.0768 - g_loss: 4.8424 - gen_acc: 0.9905 - real_acc: 0.9707



46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 178ms/step - aug_p: 0.0065 - d_loss: 0.0771 - g_loss: 4.8357 - gen_acc: 0.9902 - real_acc: 0.9706



46/46 ━━━━━━━━━━━━━━━━━━━━ 43s 280ms/step - aug_p: 0.0065 - d_loss: 0.0774 - g_loss: 4.8293 - gen_acc: 0.9900 - real_acc: 0.9705 - val_kid: 8.8293

Epoch 3/10

1/46 ━━━━━━━━━━━━━━━━━━━━ 5:54 8s/step - aug_p: 0.0105 - d_loss: 0.0941 - g_loss: 3.4148 - gen_acc: 0.9766 - real_acc: 0.9609



2/46 ━━━━━━━━━━━━━━━━━━━━ 8s 196ms/step - aug_p: 0.0105 - d_loss: 0.0925 - g_loss: 3.3668 - gen_acc: 0.9805 - real_acc: 0.9668



3/46 ━━━━━━━━━━━━━━━━━━━━ 8s 187ms/step - aug_p: 0.0106 - d_loss: 0.0918 - g_loss: 3.3820 - gen_acc: 0.9835 - real_acc: 0.9666



4/46 ━━━━━━━━━━━━━━━━━━━━ 7s 183ms/step - aug_p: 0.0106 - d_loss: 0.0932 - g_loss: 3.3732 - gen_acc: 0.9847 - real_acc: 0.9661



5/46 ━━━━━━━━━━━━━━━━━━━━ 7s 182ms/step - aug_p: 0.0106 - d_loss: 0.0941 - g_loss: 3.3531 - gen_acc: 0.9859 - real_acc: 0.9670



6/46 ━━━━━━━━━━━━━━━━━━━━ 7s 181ms/step - aug_p: 0.0107 - d_loss: 0.0942 - g_loss: 3.3519 - gen_acc: 0.9869 - real_acc: 0.9679



7/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0107 - d_loss: 0.0941 - g_loss: 3.3467 - gen_acc: 0.9877 - real_acc: 0.9690



8/46 ━━━━━━━━━━━━━━━━━━━━ 6s 179ms/step - aug_p: 0.0107 - d_loss: 0.0944 - g_loss: 3.3438 - gen_acc: 0.9882 - real_acc: 0.9693



9/46 ━━━━━━━━━━━━━━━━━━━━ 6s 179ms/step - aug_p: 0.0107 - d_loss: 0.0947 - g_loss: 3.3384 - gen_acc: 0.9886 - real_acc: 0.9696



10/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0108 - d_loss: 0.0948 - g_loss: 3.3468 - gen_acc: 0.9889 - real_acc: 0.9694



11/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0108 - d_loss: 0.0952 - g_loss: 3.3443 - gen_acc: 0.9888 - real_acc: 0.9695



12/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0108 - d_loss: 0.0955 - g_loss: 3.3676 - gen_acc: 0.9887 - real_acc: 0.9693



13/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0109 - d_loss: 0.0954 - g_loss: 3.3959 - gen_acc: 0.9888 - real_acc: 0.9693



14/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0109 - d_loss: 0.0957 - g_loss: 3.4125 - gen_acc: 0.9883 - real_acc: 0.9694



15/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0109 - d_loss: 0.0963 - g_loss: 3.4419 - gen_acc: 0.9880 - real_acc: 0.9688



16/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0110 - d_loss: 0.0969 - g_loss: 3.4641 - gen_acc: 0.9876 - real_acc: 0.9684



17/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0110 - d_loss: 0.0972 - g_loss: 3.4867 - gen_acc: 0.9873 - real_acc: 0.9681



18/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0110 - d_loss: 0.0975 - g_loss: 3.5046 - gen_acc: 0.9869 - real_acc: 0.9679



19/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0110 - d_loss: 0.0977 - g_loss: 3.5235 - gen_acc: 0.9866 - real_acc: 0.9678



20/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0111 - d_loss: 0.0978 - g_loss: 3.5387 - gen_acc: 0.9863 - real_acc: 0.9677



21/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0111 - d_loss: 0.0980 - g_loss: 3.5544 - gen_acc: 0.9861 - real_acc: 0.9676



22/46 ━━━━━━━━━━━━━━━━━━━━ 4s 179ms/step - aug_p: 0.0111 - d_loss: 0.0983 - g_loss: 3.5646 - gen_acc: 0.9857 - real_acc: 0.9675



23/46 ━━━━━━━━━━━━━━━━━━━━ 4s 179ms/step - aug_p: 0.0112 - d_loss: 0.0990 - g_loss: 3.5834 - gen_acc: 0.9853 - real_acc: 0.9670



24/46 ━━━━━━━━━━━━━━━━━━━━ 3s 179ms/step - aug_p: 0.0112 - d_loss: 0.0995 - g_loss: 3.6027 - gen_acc: 0.9850 - real_acc: 0.9665



25/46 ━━━━━━━━━━━━━━━━━━━━ 3s 179ms/step - aug_p: 0.0112 - d_loss: 0.1001 - g_loss: 3.6171 - gen_acc: 0.9845 - real_acc: 0.9662



26/46 ━━━━━━━━━━━━━━━━━━━━ 3s 179ms/step - aug_p: 0.0112 - d_loss: 0.1006 - g_loss: 3.6374 - gen_acc: 0.9840 - real_acc: 0.9659



27/46 ━━━━━━━━━━━━━━━━━━━━ 3s 179ms/step - aug_p: 0.0113 - d_loss: 0.1009 - g_loss: 3.6630 - gen_acc: 0.9836 - real_acc: 0.9656



28/46 ━━━━━━━━━━━━━━━━━━━━ 3s 179ms/step - aug_p: 0.0113 - d_loss: 0.1012 - g_loss: 3.6907 - gen_acc: 0.9833 - real_acc: 0.9654



29/46 ━━━━━━━━━━━━━━━━━━━━ 3s 179ms/step - aug_p: 0.0113 - d_loss: 0.1014 - g_loss: 3.7165 - gen_acc: 0.9830 - real_acc: 0.9652



30/46 ━━━━━━━━━━━━━━━━━━━━ 2s 179ms/step - aug_p: 0.0114 - d_loss: 0.1016 - g_loss: 3.7387 - gen_acc: 0.9827 - real_acc: 0.9651



31/46 ━━━━━━━━━━━━━━━━━━━━ 2s 179ms/step - aug_p: 0.0114 - d_loss: 0.1016 - g_loss: 3.7601 - gen_acc: 0.9824 - real_acc: 0.9650



32/46 ━━━━━━━━━━━━━━━━━━━━ 2s 179ms/step - aug_p: 0.0114 - d_loss: 0.1017 - g_loss: 3.7799 - gen_acc: 0.9822 - real_acc: 0.9649



33/46 ━━━━━━━━━━━━━━━━━━━━ 2s 179ms/step - aug_p: 0.0114 - d_loss: 0.1017 - g_loss: 3.7963 - gen_acc: 0.9820 - real_acc: 0.9649



34/46 ━━━━━━━━━━━━━━━━━━━━ 2s 179ms/step - aug_p: 0.0115 - d_loss: 0.1019 - g_loss: 3.8154 - gen_acc: 0.9818 - real_acc: 0.9647



35/46 ━━━━━━━━━━━━━━━━━━━━ 1s 179ms/step - aug_p: 0.0115 - d_loss: 0.1020 - g_loss: 3.8348 - gen_acc: 0.9816 - real_acc: 0.9645



36/46 ━━━━━━━━━━━━━━━━━━━━ 1s 179ms/step - aug_p: 0.0115 - d_loss: 0.1022 - g_loss: 3.8515 - gen_acc: 0.9813 - real_acc: 0.9644



37/46 ━━━━━━━━━━━━━━━━━━━━ 1s 179ms/step - aug_p: 0.0115 - d_loss: 0.1025 - g_loss: 3.8702 - gen_acc: 0.9810 - real_acc: 0.9642



38/46 ━━━━━━━━━━━━━━━━━━━━ 1s 179ms/step - aug_p: 0.0116 - d_loss: 0.1027 - g_loss: 3.8891 - gen_acc: 0.9807 - real_acc: 0.9640



39/46 ━━━━━━━━━━━━━━━━━━━━ 1s 179ms/step - aug_p: 0.0116 - d_loss: 0.1032 - g_loss: 3.9048 - gen_acc: 0.9803 - real_acc: 0.9638



40/46 ━━━━━━━━━━━━━━━━━━━━ 1s 179ms/step - aug_p: 0.0116 - d_loss: 0.1036 - g_loss: 3.9227 - gen_acc: 0.9799 - real_acc: 0.9636



41/46 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - aug_p: 0.0117 - d_loss: 0.1040 - g_loss: 3.9415 - gen_acc: 0.9796 - real_acc: 0.9633



42/46 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - aug_p: 0.0117 - d_loss: 0.1044 - g_loss: 3.9588 - gen_acc: 0.9792 - real_acc: 0.9631



43/46 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - aug_p: 0.0117 - d_loss: 0.1048 - g_loss: 3.9748 - gen_acc: 0.9789 - real_acc: 0.9629



44/46 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - aug_p: 0.0117 - d_loss: 0.1052 - g_loss: 3.9895 - gen_acc: 0.9785 - real_acc: 0.9627



45/46 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - aug_p: 0.0118 - d_loss: 0.1055 - g_loss: 4.0041 - gen_acc: 0.9782 - real_acc: 0.9626



46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - aug_p: 0.0118 - d_loss: 0.1058 - g_loss: 4.0177 - gen_acc: 0.9779 - real_acc: 0.9624



46/46 ━━━━━━━━━━━━━━━━━━━━ 22s 315ms/step - aug_p: 0.0118 - d_loss: 0.1061 - g_loss: 4.0306 - gen_acc: 0.9776 - real_acc: 0.9623 - val_kid: 8.4585

Epoch 4/10

1/46 ━━━━━━━━━━━━━━━━━━━━ 11s 263ms/step - aug_p: 0.0154 - d_loss: 0.1223 - g_loss: 2.5203 - gen_acc: 0.9688 - real_acc: 1.0000



2/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0154 - d_loss: 0.1018 - g_loss: 3.6445 - gen_acc: 0.9766 - real_acc: 0.9980



3/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0155 - d_loss: 0.0925 - g_loss: 4.2071 - gen_acc: 0.9809 - real_acc: 0.9926



4/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0155 - d_loss: 0.0875 - g_loss: 4.3535 - gen_acc: 0.9827 - real_acc: 0.9910



5/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0155 - d_loss: 0.0850 - g_loss: 4.3580 - gen_acc: 0.9843 - real_acc: 0.9900



6/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0156 - d_loss: 0.0830 - g_loss: 4.3789 - gen_acc: 0.9856 - real_acc: 0.9889



7/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0156 - d_loss: 0.0821 - g_loss: 4.3592 - gen_acc: 0.9864 - real_acc: 0.9879



8/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0156 - d_loss: 0.0814 - g_loss: 4.3377 - gen_acc: 0.9871 - real_acc: 0.9870



9/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0156 - d_loss: 0.0811 - g_loss: 4.3049 - gen_acc: 0.9876 - real_acc: 0.9864



10/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0157 - d_loss: 0.0807 - g_loss: 4.2813 - gen_acc: 0.9881 - real_acc: 0.9859



11/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0157 - d_loss: 0.0804 - g_loss: 4.2560 - gen_acc: 0.9884 - real_acc: 0.9855



12/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0157 - d_loss: 0.0804 - g_loss: 4.2284 - gen_acc: 0.9886 - real_acc: 0.9851



13/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0158 - d_loss: 0.0806 - g_loss: 4.2117 - gen_acc: 0.9888 - real_acc: 0.9847



14/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0158 - d_loss: 0.0807 - g_loss: 4.1925 - gen_acc: 0.9887 - real_acc: 0.9844



15/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0158 - d_loss: 0.0807 - g_loss: 4.1790 - gen_acc: 0.9888 - real_acc: 0.9841



16/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0159 - d_loss: 0.0807 - g_loss: 4.1631 - gen_acc: 0.9887 - real_acc: 0.9840



17/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0159 - d_loss: 0.0807 - g_loss: 4.1518 - gen_acc: 0.9887 - real_acc: 0.9838



18/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0159 - d_loss: 0.0807 - g_loss: 4.1398 - gen_acc: 0.9887 - real_acc: 0.9837



19/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0160 - d_loss: 0.0809 - g_loss: 4.1255 - gen_acc: 0.9887 - real_acc: 0.9837



20/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0160 - d_loss: 0.0810 - g_loss: 4.1171 - gen_acc: 0.9887 - real_acc: 0.9835



21/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0160 - d_loss: 0.0810 - g_loss: 4.1116 - gen_acc: 0.9886 - real_acc: 0.9835



22/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0161 - d_loss: 0.0811 - g_loss: 4.1037 - gen_acc: 0.9885 - real_acc: 0.9834



23/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0161 - d_loss: 0.0812 - g_loss: 4.1013 - gen_acc: 0.9885 - real_acc: 0.9833



24/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0161 - d_loss: 0.0813 - g_loss: 4.1000 - gen_acc: 0.9884 - real_acc: 0.9832



25/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0162 - d_loss: 0.0814 - g_loss: 4.0967 - gen_acc: 0.9883 - real_acc: 0.9832



26/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0162 - d_loss: 0.0815 - g_loss: 4.0951 - gen_acc: 0.9882 - real_acc: 0.9831



27/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0162 - d_loss: 0.0815 - g_loss: 4.0930 - gen_acc: 0.9882 - real_acc: 0.9830



28/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0163 - d_loss: 0.0817 - g_loss: 4.0887 - gen_acc: 0.9880 - real_acc: 0.9830



29/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0163 - d_loss: 0.0818 - g_loss: 4.0890 - gen_acc: 0.9879 - real_acc: 0.9829



30/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0163 - d_loss: 0.0819 - g_loss: 4.0918 - gen_acc: 0.9878 - real_acc: 0.9828



31/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0164 - d_loss: 0.0821 - g_loss: 4.0923 - gen_acc: 0.9877 - real_acc: 0.9826



32/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0164 - d_loss: 0.0823 - g_loss: 4.0957 - gen_acc: 0.9876 - real_acc: 0.9826



33/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0164 - d_loss: 0.0824 - g_loss: 4.1014 - gen_acc: 0.9874 - real_acc: 0.9825



34/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0165 - d_loss: 0.0824 - g_loss: 4.1072 - gen_acc: 0.9873 - real_acc: 0.9824



35/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0165 - d_loss: 0.0825 - g_loss: 4.1116 - gen_acc: 0.9872 - real_acc: 0.9823



36/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0165 - d_loss: 0.0826 - g_loss: 4.1168 - gen_acc: 0.9871 - real_acc: 0.9822



37/46 ━━━━━━━━━━━━━━━━━━━━ 1s 181ms/step - aug_p: 0.0166 - d_loss: 0.0827 - g_loss: 4.1217 - gen_acc: 0.9870 - real_acc: 0.9822



38/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0166 - d_loss: 0.0827 - g_loss: 4.1262 - gen_acc: 0.9869 - real_acc: 0.9821



39/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0166 - d_loss: 0.0828 - g_loss: 4.1298 - gen_acc: 0.9868 - real_acc: 0.9820



40/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0167 - d_loss: 0.0828 - g_loss: 4.1333 - gen_acc: 0.9868 - real_acc: 0.9820



41/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0167 - d_loss: 0.0828 - g_loss: 4.1361 - gen_acc: 0.9867 - real_acc: 0.9819



42/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0167 - d_loss: 0.0828 - g_loss: 4.1389 - gen_acc: 0.9866 - real_acc: 0.9819



43/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0168 - d_loss: 0.0828 - g_loss: 4.1408 - gen_acc: 0.9866 - real_acc: 0.9819



44/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0168 - d_loss: 0.0828 - g_loss: 4.1438 - gen_acc: 0.9865 - real_acc: 0.9818



45/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0168 - d_loss: 0.0828 - g_loss: 4.1466 - gen_acc: 0.9865 - real_acc: 0.9818



46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0168 - d_loss: 0.0829 - g_loss: 4.1480 - gen_acc: 0.9864 - real_acc: 0.9818



46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 316ms/step - aug_p: 0.0169 - d_loss: 0.0829 - g_loss: 4.1493 - gen_acc: 0.9863 - real_acc: 0.9817 - val_kid: 6.6764

Epoch 5/10

1/46 ━━━━━━━━━━━━━━━━━━━━ 10s 237ms/step - aug_p: 0.0212 - d_loss: 0.3046 - g_loss: 11.2403 - gen_acc: 1.0000 - real_acc: 0.7734



2/46 ━━━━━━━━━━━━━━━━━━━━ 8s 197ms/step - aug_p: 0.0212 - d_loss: 0.2549 - g_loss: 10.8464 - gen_acc: 1.0000 - real_acc: 0.8086



3/46 ━━━━━━━━━━━━━━━━━━━━ 8s 190ms/step - aug_p: 0.0212 - d_loss: 0.2217 - g_loss: 10.0394 - gen_acc: 0.9983 - real_acc: 0.8359



4/46 ━━━━━━━━━━━━━━━━━━━━ 7s 187ms/step - aug_p: 0.0212 - d_loss: 0.2183 - g_loss: 9.2019 - gen_acc: 0.9753 - real_acc: 0.8560



5/46 ━━━━━━━━━━━━━━━━━━━━ 7s 185ms/step - aug_p: 0.0212 - d_loss: 0.2125 - g_loss: 8.8056 - gen_acc: 0.9652 - real_acc: 0.8676



6/46 ━━━━━━━━━━━━━━━━━━━━ 7s 185ms/step - aug_p: 0.0213 - d_loss: 0.2060 - g_loss: 8.5755 - gen_acc: 0.9606 - real_acc: 0.8755



7/46 ━━━━━━━━━━━━━━━━━━━━ 7s 184ms/step - aug_p: 0.0213 - d_loss: 0.1995 - g_loss: 8.3695 - gen_acc: 0.9579 - real_acc: 0.8823



8/46 ━━━━━━━━━━━━━━━━━━━━ 7s 184ms/step - aug_p: 0.0213 - d_loss: 0.1938 - g_loss: 8.1574 - gen_acc: 0.9561 - real_acc: 0.8884



9/46 ━━━━━━━━━━━━━━━━━━━━ 6s 184ms/step - aug_p: 0.0214 - d_loss: 0.1881 - g_loss: 7.9590 - gen_acc: 0.9552 - real_acc: 0.8939



10/46 ━━━━━━━━━━━━━━━━━━━━ 6s 184ms/step - aug_p: 0.0214 - d_loss: 0.1827 - g_loss: 7.7719 - gen_acc: 0.9550 - real_acc: 0.8989



11/46 ━━━━━━━━━━━━━━━━━━━━ 6s 183ms/step - aug_p: 0.0214 - d_loss: 0.1785 - g_loss: 7.5867 - gen_acc: 0.9546 - real_acc: 0.9034



12/46 ━━━━━━━━━━━━━━━━━━━━ 6s 183ms/step - aug_p: 0.0214 - d_loss: 0.1748 - g_loss: 7.4330 - gen_acc: 0.9546 - real_acc: 0.9072



13/46 ━━━━━━━━━━━━━━━━━━━━ 6s 183ms/step - aug_p: 0.0215 - d_loss: 0.1717 - g_loss: 7.2895 - gen_acc: 0.9548 - real_acc: 0.9103



14/46 ━━━━━━━━━━━━━━━━━━━━ 5s 183ms/step - aug_p: 0.0215 - d_loss: 0.1693 - g_loss: 7.1489 - gen_acc: 0.9544 - real_acc: 0.9132



15/46 ━━━━━━━━━━━━━━━━━━━━ 5s 182ms/step - aug_p: 0.0215 - d_loss: 0.1674 - g_loss: 7.0344 - gen_acc: 0.9543 - real_acc: 0.9153



16/46 ━━━━━━━━━━━━━━━━━━━━ 5s 182ms/step - aug_p: 0.0215 - d_loss: 0.1654 - g_loss: 6.9321 - gen_acc: 0.9544 - real_acc: 0.9173



17/46 ━━━━━━━━━━━━━━━━━━━━ 5s 182ms/step - aug_p: 0.0216 - d_loss: 0.1637 - g_loss: 6.8304 - gen_acc: 0.9541 - real_acc: 0.9191



18/46 ━━━━━━━━━━━━━━━━━━━━ 5s 183ms/step - aug_p: 0.0216 - d_loss: 0.1620 - g_loss: 6.7449 - gen_acc: 0.9540 - real_acc: 0.9209



19/46 ━━━━━━━━━━━━━━━━━━━━ 4s 183ms/step - aug_p: 0.0216 - d_loss: 0.1603 - g_loss: 6.6702 - gen_acc: 0.9540 - real_acc: 0.9225



20/46 ━━━━━━━━━━━━━━━━━━━━ 4s 182ms/step - aug_p: 0.0217 - d_loss: 0.1587 - g_loss: 6.5977 - gen_acc: 0.9541 - real_acc: 0.9240



21/46 ━━━━━━━━━━━━━━━━━━━━ 4s 182ms/step - aug_p: 0.0217 - d_loss: 0.1572 - g_loss: 6.5271 - gen_acc: 0.9542 - real_acc: 0.9255



22/46 ━━━━━━━━━━━━━━━━━━━━ 4s 182ms/step - aug_p: 0.0217 - d_loss: 0.1556 - g_loss: 6.4626 - gen_acc: 0.9544 - real_acc: 0.9269



23/46 ━━━━━━━━━━━━━━━━━━━━ 4s 182ms/step - aug_p: 0.0217 - d_loss: 0.1540 - g_loss: 6.4028 - gen_acc: 0.9546 - real_acc: 0.9282



24/46 ━━━━━━━━━━━━━━━━━━━━ 4s 182ms/step - aug_p: 0.0218 - d_loss: 0.1525 - g_loss: 6.3440 - gen_acc: 0.9548 - real_acc: 0.9295



25/46 ━━━━━━━━━━━━━━━━━━━━ 3s 183ms/step - aug_p: 0.0218 - d_loss: 0.1510 - g_loss: 6.2898 - gen_acc: 0.9551 - real_acc: 0.9307



26/46 ━━━━━━━━━━━━━━━━━━━━ 3s 183ms/step - aug_p: 0.0218 - d_loss: 0.1495 - g_loss: 6.2380 - gen_acc: 0.9554 - real_acc: 0.9318



27/46 ━━━━━━━━━━━━━━━━━━━━ 3s 183ms/step - aug_p: 0.0219 - d_loss: 0.1481 - g_loss: 6.1880 - gen_acc: 0.9558 - real_acc: 0.9330



28/46 ━━━━━━━━━━━━━━━━━━━━ 3s 182ms/step - aug_p: 0.0219 - d_loss: 0.1468 - g_loss: 6.1413 - gen_acc: 0.9561 - real_acc: 0.9340



29/46 ━━━━━━━━━━━━━━━━━━━━ 3s 183ms/step - aug_p: 0.0219 - d_loss: 0.1454 - g_loss: 6.0966 - gen_acc: 0.9565 - real_acc: 0.9350



30/46 ━━━━━━━━━━━━━━━━━━━━ 2s 182ms/step - aug_p: 0.0220 - d_loss: 0.1441 - g_loss: 6.0534 - gen_acc: 0.9569 - real_acc: 0.9360



31/46 ━━━━━━━━━━━━━━━━━━━━ 2s 183ms/step - aug_p: 0.0220 - d_loss: 0.1428 - g_loss: 6.0122 - gen_acc: 0.9573 - real_acc: 0.9370



32/46 ━━━━━━━━━━━━━━━━━━━━ 2s 182ms/step - aug_p: 0.0220 - d_loss: 0.1415 - g_loss: 5.9738 - gen_acc: 0.9577 - real_acc: 0.9379



33/46 ━━━━━━━━━━━━━━━━━━━━ 2s 182ms/step - aug_p: 0.0220 - d_loss: 0.1403 - g_loss: 5.9369 - gen_acc: 0.9581 - real_acc: 0.9388



34/46 ━━━━━━━━━━━━━━━━━━━━ 2s 182ms/step - aug_p: 0.0221 - d_loss: 0.1390 - g_loss: 5.9020 - gen_acc: 0.9585 - real_acc: 0.9396



35/46 ━━━━━━━━━━━━━━━━━━━━ 2s 182ms/step - aug_p: 0.0221 - d_loss: 0.1378 - g_loss: 5.8680 - gen_acc: 0.9589 - real_acc: 0.9404



36/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0221 - d_loss: 0.1366 - g_loss: 5.8355 - gen_acc: 0.9592 - real_acc: 0.9412



37/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0222 - d_loss: 0.1355 - g_loss: 5.8042 - gen_acc: 0.9596 - real_acc: 0.9420



38/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0222 - d_loss: 0.1344 - g_loss: 5.7737 - gen_acc: 0.9600 - real_acc: 0.9427



39/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0222 - d_loss: 0.1333 - g_loss: 5.7447 - gen_acc: 0.9604 - real_acc: 0.9434



40/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0223 - d_loss: 0.1323 - g_loss: 5.7161 - gen_acc: 0.9608 - real_acc: 0.9441



41/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0223 - d_loss: 0.1313 - g_loss: 5.6892 - gen_acc: 0.9611 - real_acc: 0.9447



42/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0223 - d_loss: 0.1304 - g_loss: 5.6621 - gen_acc: 0.9615 - real_acc: 0.9453



43/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0223 - d_loss: 0.1296 - g_loss: 5.6390 - gen_acc: 0.9618 - real_acc: 0.9458



44/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0224 - d_loss: 0.1288 - g_loss: 5.6185 - gen_acc: 0.9621 - real_acc: 0.9463



45/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0224 - d_loss: 0.1280 - g_loss: 5.5982 - gen_acc: 0.9623 - real_acc: 0.9468



46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0224 - d_loss: 0.1273 - g_loss: 5.5795 - gen_acc: 0.9626 - real_acc: 0.9473



46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 317ms/step - aug_p: 0.0225 - d_loss: 0.1265 - g_loss: 5.5616 - gen_acc: 0.9629 - real_acc: 0.9478 - val_kid: 4.7496

Epoch 6/10

1/46 ━━━━━━━━━━━━━━━━━━━━ 10s 236ms/step - aug_p: 0.0268 - d_loss: 0.0745 - g_loss: 5.1780 - gen_acc: 0.9922 - real_acc: 0.9688



2/46 ━━━━━━━━━━━━━━━━━━━━ 8s 184ms/step - aug_p: 0.0269 - d_loss: 0.0774 - g_loss: 4.5412 - gen_acc: 0.9883 - real_acc: 0.9766



3/46 ━━━━━━━━━━━━━━━━━━━━ 7s 183ms/step - aug_p: 0.0269 - d_loss: 0.0743 - g_loss: 4.5406 - gen_acc: 0.9887 - real_acc: 0.9783



4/46 ━━━━━━━━━━━━━━━━━━━━ 7s 184ms/step - aug_p: 0.0269 - d_loss: 0.0724 - g_loss: 4.5764 - gen_acc: 0.9896 - real_acc: 0.9779



5/46 ━━━━━━━━━━━━━━━━━━━━ 7s 183ms/step - aug_p: 0.0270 - d_loss: 0.0732 - g_loss: 4.5209 - gen_acc: 0.9882 - real_acc: 0.9785



6/46 ━━━━━━━━━━━━━━━━━━━━ 7s 182ms/step - aug_p: 0.0270 - d_loss: 0.0738 - g_loss: 4.5449 - gen_acc: 0.9878 - real_acc: 0.9782



7/46 ━━━━━━━━━━━━━━━━━━━━ 7s 182ms/step - aug_p: 0.0270 - d_loss: 0.0747 - g_loss: 4.5880 - gen_acc: 0.9878 - real_acc: 0.9769



8/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0271 - d_loss: 0.0766 - g_loss: 4.5791 - gen_acc: 0.9857 - real_acc: 0.9763



9/46 ━━━━━━━━━━━━━━━━━━━━ 6s 183ms/step - aug_p: 0.0271 - d_loss: 0.0777 - g_loss: 4.6269 - gen_acc: 0.9844 - real_acc: 0.9757



10/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0271 - d_loss: 0.0786 - g_loss: 4.7075 - gen_acc: 0.9836 - real_acc: 0.9749



11/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0271 - d_loss: 0.0792 - g_loss: 4.7786 - gen_acc: 0.9826 - real_acc: 0.9745



12/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0272 - d_loss: 0.0793 - g_loss: 4.8440 - gen_acc: 0.9820 - real_acc: 0.9744



13/46 ━━━━━━━━━━━━━━━━━━━━ 6s 183ms/step - aug_p: 0.0272 - d_loss: 0.0792 - g_loss: 4.9001 - gen_acc: 0.9816 - real_acc: 0.9744



14/46 ━━━━━━━━━━━━━━━━━━━━ 5s 182ms/step - aug_p: 0.0272 - d_loss: 0.0789 - g_loss: 4.9354 - gen_acc: 0.9814 - real_acc: 0.9745



15/46 ━━━━━━━━━━━━━━━━━━━━ 5s 182ms/step - aug_p: 0.0273 - d_loss: 0.0785 - g_loss: 4.9643 - gen_acc: 0.9813 - real_acc: 0.9747



16/46 ━━━━━━━━━━━━━━━━━━━━ 5s 182ms/step - aug_p: 0.0273 - d_loss: 0.0781 - g_loss: 4.9864 - gen_acc: 0.9814 - real_acc: 0.9749



17/46 ━━━━━━━━━━━━━━━━━━━━ 5s 182ms/step - aug_p: 0.0273 - d_loss: 0.0778 - g_loss: 4.9973 - gen_acc: 0.9814 - real_acc: 0.9751



18/46 ━━━━━━━━━━━━━━━━━━━━ 5s 182ms/step - aug_p: 0.0274 - d_loss: 0.0774 - g_loss: 5.0125 - gen_acc: 0.9815 - real_acc: 0.9753



19/46 ━━━━━━━━━━━━━━━━━━━━ 4s 182ms/step - aug_p: 0.0274 - d_loss: 0.0770 - g_loss: 5.0280 - gen_acc: 0.9816 - real_acc: 0.9755



20/46 ━━━━━━━━━━━━━━━━━━━━ 4s 182ms/step - aug_p: 0.0274 - d_loss: 0.0765 - g_loss: 5.0398 - gen_acc: 0.9818 - real_acc: 0.9757



21/46 ━━━━━━━━━━━━━━━━━━━━ 4s 182ms/step - aug_p: 0.0275 - d_loss: 0.0760 - g_loss: 5.0455 - gen_acc: 0.9819 - real_acc: 0.9759



22/46 ━━━━━━━━━━━━━━━━━━━━ 4s 182ms/step - aug_p: 0.0275 - d_loss: 0.0756 - g_loss: 5.0535 - gen_acc: 0.9820 - real_acc: 0.9760



23/46 ━━━━━━━━━━━━━━━━━━━━ 4s 181ms/step - aug_p: 0.0275 - d_loss: 0.0752 - g_loss: 5.0590 - gen_acc: 0.9822 - real_acc: 0.9762



24/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0276 - d_loss: 0.0749 - g_loss: 5.0595 - gen_acc: 0.9823 - real_acc: 0.9763



25/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0276 - d_loss: 0.0746 - g_loss: 5.0650 - gen_acc: 0.9825 - real_acc: 0.9764



26/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0276 - d_loss: 0.0743 - g_loss: 5.0742 - gen_acc: 0.9826 - real_acc: 0.9765



27/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0277 - d_loss: 0.0740 - g_loss: 5.0823 - gen_acc: 0.9828 - real_acc: 0.9766



28/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0277 - d_loss: 0.0737 - g_loss: 5.0871 - gen_acc: 0.9829 - real_acc: 0.9767



29/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0277 - d_loss: 0.0734 - g_loss: 5.0913 - gen_acc: 0.9831 - real_acc: 0.9768



30/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0278 - d_loss: 0.0731 - g_loss: 5.0957 - gen_acc: 0.9832 - real_acc: 0.9769



31/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0278 - d_loss: 0.0727 - g_loss: 5.0986 - gen_acc: 0.9834 - real_acc: 0.9770



32/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0278 - d_loss: 0.0725 - g_loss: 5.0992 - gen_acc: 0.9835 - real_acc: 0.9771



33/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0278 - d_loss: 0.0722 - g_loss: 5.1012 - gen_acc: 0.9836 - real_acc: 0.9772



34/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0279 - d_loss: 0.0719 - g_loss: 5.1022 - gen_acc: 0.9838 - real_acc: 0.9773



35/46 ━━━━━━━━━━━━━━━━━━━━ 1s 181ms/step - aug_p: 0.0279 - d_loss: 0.0718 - g_loss: 5.1007 - gen_acc: 0.9838 - real_acc: 0.9773



36/46 ━━━━━━━━━━━━━━━━━━━━ 1s 181ms/step - aug_p: 0.0279 - d_loss: 0.0717 - g_loss: 5.1026 - gen_acc: 0.9839 - real_acc: 0.9773



37/46 ━━━━━━━━━━━━━━━━━━━━ 1s 181ms/step - aug_p: 0.0280 - d_loss: 0.0716 - g_loss: 5.1070 - gen_acc: 0.9840 - real_acc: 0.9772



38/46 ━━━━━━━━━━━━━━━━━━━━ 1s 181ms/step - aug_p: 0.0280 - d_loss: 0.0715 - g_loss: 5.1124 - gen_acc: 0.9840 - real_acc: 0.9772



39/46 ━━━━━━━━━━━━━━━━━━━━ 1s 181ms/step - aug_p: 0.0280 - d_loss: 0.0714 - g_loss: 5.1178 - gen_acc: 0.9841 - real_acc: 0.9773



40/46 ━━━━━━━━━━━━━━━━━━━━ 1s 181ms/step - aug_p: 0.0281 - d_loss: 0.0712 - g_loss: 5.1221 - gen_acc: 0.9842 - real_acc: 0.9773



41/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0281 - d_loss: 0.0710 - g_loss: 5.1258 - gen_acc: 0.9843 - real_acc: 0.9773



42/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0281 - d_loss: 0.0708 - g_loss: 5.1290 - gen_acc: 0.9843 - real_acc: 0.9773



43/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0282 - d_loss: 0.0707 - g_loss: 5.1315 - gen_acc: 0.9844 - real_acc: 0.9774



44/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0282 - d_loss: 0.0705 - g_loss: 5.1332 - gen_acc: 0.9845 - real_acc: 0.9774



45/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0282 - d_loss: 0.0703 - g_loss: 5.1347 - gen_acc: 0.9845 - real_acc: 0.9775



46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - aug_p: 0.0283 - d_loss: 0.0701 - g_loss: 5.1357 - gen_acc: 0.9846 - real_acc: 0.9775



46/46 ━━━━━━━━━━━━━━━━━━━━ 12s 267ms/step - aug_p: 0.0283 - d_loss: 0.0699 - g_loss: 5.1367 - gen_acc: 0.9846 - real_acc: 0.9776 - val_kid: 6.2893

Epoch 7/10

1/46 ━━━━━━━━━━━━━━━━━━━━ 7s 174ms/step - aug_p: 0.0328 - d_loss: 0.0456 - g_loss: 3.5202 - gen_acc: 1.0000 - real_acc: 1.0000



2/46 ━━━━━━━━━━━━━━━━━━━━ 7s 179ms/step - aug_p: 0.0329 - d_loss: 0.0466 - g_loss: 3.7961 - gen_acc: 0.9980 - real_acc: 0.9980



3/46 ━━━━━━━━━━━━━━━━━━━━ 7s 179ms/step - aug_p: 0.0329 - d_loss: 0.0471 - g_loss: 3.9462 - gen_acc: 0.9970 - real_acc: 0.9961



4/46 ━━━━━━━━━━━━━━━━━━━━ 7s 179ms/step - aug_p: 0.0329 - d_loss: 0.0469 - g_loss: 4.0184 - gen_acc: 0.9967 - real_acc: 0.9946



5/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0330 - d_loss: 0.0463 - g_loss: 4.0670 - gen_acc: 0.9968 - real_acc: 0.9941



6/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0330 - d_loss: 0.0458 - g_loss: 4.1012 - gen_acc: 0.9969 - real_acc: 0.9938



7/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0330 - d_loss: 0.0451 - g_loss: 4.1240 - gen_acc: 0.9970 - real_acc: 0.9937



8/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0331 - d_loss: 0.0444 - g_loss: 4.1347 - gen_acc: 0.9971 - real_acc: 0.9938



9/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0331 - d_loss: 0.0438 - g_loss: 4.1433 - gen_acc: 0.9971 - real_acc: 0.9937



10/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0331 - d_loss: 0.0434 - g_loss: 4.1560 - gen_acc: 0.9970 - real_acc: 0.9936



11/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0332 - d_loss: 0.0431 - g_loss: 4.1654 - gen_acc: 0.9969 - real_acc: 0.9936



12/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0332 - d_loss: 0.0429 - g_loss: 4.1695 - gen_acc: 0.9969 - real_acc: 0.9935



13/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0333 - d_loss: 0.0428 - g_loss: 4.1758 - gen_acc: 0.9969 - real_acc: 0.9934



14/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0333 - d_loss: 0.0427 - g_loss: 4.1789 - gen_acc: 0.9969 - real_acc: 0.9932



15/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0333 - d_loss: 0.0426 - g_loss: 4.1799 - gen_acc: 0.9970 - real_acc: 0.9929



16/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0334 - d_loss: 0.0425 - g_loss: 4.1823 - gen_acc: 0.9970 - real_acc: 0.9927



17/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0334 - d_loss: 0.0425 - g_loss: 4.1836 - gen_acc: 0.9970 - real_acc: 0.9926



18/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0334 - d_loss: 0.0425 - g_loss: 4.1854 - gen_acc: 0.9971 - real_acc: 0.9923



19/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0335 - d_loss: 0.0426 - g_loss: 4.1843 - gen_acc: 0.9971 - real_acc: 0.9921



20/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0335 - d_loss: 0.0427 - g_loss: 4.1873 - gen_acc: 0.9971 - real_acc: 0.9920



21/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0335 - d_loss: 0.0427 - g_loss: 4.1927 - gen_acc: 0.9972 - real_acc: 0.9918



22/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0336 - d_loss: 0.0428 - g_loss: 4.1952 - gen_acc: 0.9972 - real_acc: 0.9916



23/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0336 - d_loss: 0.0428 - g_loss: 4.2017 - gen_acc: 0.9972 - real_acc: 0.9915



24/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0336 - d_loss: 0.0428 - g_loss: 4.2106 - gen_acc: 0.9972 - real_acc: 0.9914



25/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0337 - d_loss: 0.0428 - g_loss: 4.2181 - gen_acc: 0.9972 - real_acc: 0.9913



26/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0337 - d_loss: 0.0428 - g_loss: 4.2229 - gen_acc: 0.9972 - real_acc: 0.9912



27/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0337 - d_loss: 0.0429 - g_loss: 4.2318 - gen_acc: 0.9972 - real_acc: 0.9911



28/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0338 - d_loss: 0.0429 - g_loss: 4.2416 - gen_acc: 0.9972 - real_acc: 0.9910



29/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0338 - d_loss: 0.0430 - g_loss: 4.2491 - gen_acc: 0.9971 - real_acc: 0.9909



30/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0338 - d_loss: 0.0430 - g_loss: 4.2604 - gen_acc: 0.9971 - real_acc: 0.9908



31/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0339 - d_loss: 0.0431 - g_loss: 4.2736 - gen_acc: 0.9971 - real_acc: 0.9907



32/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0339 - d_loss: 0.0432 - g_loss: 4.2834 - gen_acc: 0.9970 - real_acc: 0.9906



33/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0339 - d_loss: 0.0439 - g_loss: 4.3010 - gen_acc: 0.9968 - real_acc: 0.9901



34/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0340 - d_loss: 0.0444 - g_loss: 4.3187 - gen_acc: 0.9967 - real_acc: 0.9897



35/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0340 - d_loss: 0.0455 - g_loss: 4.3319 - gen_acc: 0.9961 - real_acc: 0.9892



36/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0340 - d_loss: 0.0464 - g_loss: 4.3508 - gen_acc: 0.9956 - real_acc: 0.9889



37/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0341 - d_loss: 0.0474 - g_loss: 4.3765 - gen_acc: 0.9951 - real_acc: 0.9884



38/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0341 - d_loss: 0.0483 - g_loss: 4.4070 - gen_acc: 0.9947 - real_acc: 0.9880



39/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0341 - d_loss: 0.0492 - g_loss: 4.4400 - gen_acc: 0.9943 - real_acc: 0.9875



40/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0342 - d_loss: 0.0499 - g_loss: 4.4739 - gen_acc: 0.9939 - real_acc: 0.9872



41/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0342 - d_loss: 0.0506 - g_loss: 4.5070 - gen_acc: 0.9935 - real_acc: 0.9868



42/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0342 - d_loss: 0.0513 - g_loss: 4.5375 - gen_acc: 0.9932 - real_acc: 0.9865



43/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0343 - d_loss: 0.0519 - g_loss: 4.5646 - gen_acc: 0.9929 - real_acc: 0.9862



44/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0343 - d_loss: 0.0525 - g_loss: 4.5904 - gen_acc: 0.9925 - real_acc: 0.9859



45/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0343 - d_loss: 0.0530 - g_loss: 4.6149 - gen_acc: 0.9923 - real_acc: 0.9857



46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0344 - d_loss: 0.0536 - g_loss: 4.6368 - gen_acc: 0.9920 - real_acc: 0.9854



46/46 ━━━━━━━━━━━━━━━━━━━━ 13s 294ms/step - aug_p: 0.0344 - d_loss: 0.0542 - g_loss: 4.6579 - gen_acc: 0.9917 - real_acc: 0.9852 - val_kid: 6.7378

Epoch 8/10

1/46 ━━━━━━━━━━━━━━━━━━━━ 7s 167ms/step - aug_p: 0.0384 - d_loss: 0.1191 - g_loss: 4.3279 - gen_acc: 1.0000 - real_acc: 0.9219



2/46 ━━━━━━━━━━━━━━━━━━━━ 7s 179ms/step - aug_p: 0.0384 - d_loss: 0.1470 - g_loss: 3.7525 - gen_acc: 0.9590 - real_acc: 0.9219



3/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0384 - d_loss: 0.1768 - g_loss: 4.0819 - gen_acc: 0.9544 - real_acc: 0.8950



4/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0384 - d_loss: 0.1801 - g_loss: 4.1693 - gen_acc: 0.9551 - real_acc: 0.8910



5/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0384 - d_loss: 0.1829 - g_loss: 4.1280 - gen_acc: 0.9491 - real_acc: 0.8934



6/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0384 - d_loss: 0.1828 - g_loss: 4.2346 - gen_acc: 0.9471 - real_acc: 0.8949



7/46 ━━━━━━━━━━━━━━━━━━━━ 7s 180ms/step - aug_p: 0.0385 - d_loss: 0.1806 - g_loss: 4.3823 - gen_acc: 0.9470 - real_acc: 0.8968



8/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0385 - d_loss: 0.1765 - g_loss: 4.5079 - gen_acc: 0.9478 - real_acc: 0.8997



9/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0385 - d_loss: 0.1723 - g_loss: 4.5814 - gen_acc: 0.9486 - real_acc: 0.9028



10/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0385 - d_loss: 0.1679 - g_loss: 4.6213 - gen_acc: 0.9496 - real_acc: 0.9061



11/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0385 - d_loss: 0.1637 - g_loss: 4.6466 - gen_acc: 0.9507 - real_acc: 0.9092



12/46 ━━━━━━━━━━━━━━━━━━━━ 6s 180ms/step - aug_p: 0.0386 - d_loss: 0.1595 - g_loss: 4.6599 - gen_acc: 0.9520 - real_acc: 0.9122



13/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0386 - d_loss: 0.1561 - g_loss: 4.6625 - gen_acc: 0.9531 - real_acc: 0.9148



14/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0386 - d_loss: 0.1535 - g_loss: 4.6513 - gen_acc: 0.9537 - real_acc: 0.9172



15/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0386 - d_loss: 0.1530 - g_loss: 4.6600 - gen_acc: 0.9544 - real_acc: 0.9175



16/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0387 - d_loss: 0.1612 - g_loss: 4.6490 - gen_acc: 0.9512 - real_acc: 0.9180



17/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0387 - d_loss: 0.1727 - g_loss: 4.6715 - gen_acc: 0.9488 - real_acc: 0.9157



18/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0387 - d_loss: 0.1825 - g_loss: 4.7072 - gen_acc: 0.9469 - real_acc: 0.9135



19/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0387 - d_loss: 0.1904 - g_loss: 4.7428 - gen_acc: 0.9454 - real_acc: 0.9118



20/46 ━━━━━━━━━━━━━━━━━━━━ 4s 179ms/step - aug_p: 0.0387 - d_loss: 0.1970 - g_loss: 4.7693 - gen_acc: 0.9440 - real_acc: 0.9106



21/46 ━━━━━━━━━━━━━━━━━━━━ 4s 179ms/step - aug_p: 0.0387 - d_loss: 0.2029 - g_loss: 4.7854 - gen_acc: 0.9424 - real_acc: 0.9098



22/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0387 - d_loss: 0.2079 - g_loss: 4.7960 - gen_acc: 0.9409 - real_acc: 0.9092



23/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0388 - d_loss: 0.2119 - g_loss: 4.8033 - gen_acc: 0.9397 - real_acc: 0.9090



24/46 ━━━━━━━━━━━━━━━━━━━━ 3s 179ms/step - aug_p: 0.0388 - d_loss: 0.2153 - g_loss: 4.8076 - gen_acc: 0.9387 - real_acc: 0.9088



25/46 ━━━━━━━━━━━━━━━━━━━━ 3s 179ms/step - aug_p: 0.0388 - d_loss: 0.2182 - g_loss: 4.8077 - gen_acc: 0.9378 - real_acc: 0.9087



26/46 ━━━━━━━━━━━━━━━━━━━━ 3s 179ms/step - aug_p: 0.0388 - d_loss: 0.2207 - g_loss: 4.8051 - gen_acc: 0.9371 - real_acc: 0.9087



27/46 ━━━━━━━━━━━━━━━━━━━━ 3s 179ms/step - aug_p: 0.0388 - d_loss: 0.2229 - g_loss: 4.8007 - gen_acc: 0.9365 - real_acc: 0.9086



28/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0388 - d_loss: 0.2249 - g_loss: 4.7934 - gen_acc: 0.9360 - real_acc: 0.9086



29/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0388 - d_loss: 0.2265 - g_loss: 4.7860 - gen_acc: 0.9355 - real_acc: 0.9086



30/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0389 - d_loss: 0.2278 - g_loss: 4.7775 - gen_acc: 0.9352 - real_acc: 0.9087



31/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0389 - d_loss: 0.2290 - g_loss: 4.7677 - gen_acc: 0.9349 - real_acc: 0.9087



32/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0389 - d_loss: 0.2299 - g_loss: 4.7575 - gen_acc: 0.9347 - real_acc: 0.9089



33/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0389 - d_loss: 0.2305 - g_loss: 4.7470 - gen_acc: 0.9346 - real_acc: 0.9091



34/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0389 - d_loss: 0.2310 - g_loss: 4.7363 - gen_acc: 0.9345 - real_acc: 0.9093



35/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0389 - d_loss: 0.2314 - g_loss: 4.7249 - gen_acc: 0.9344 - real_acc: 0.9095



36/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0389 - d_loss: 0.2317 - g_loss: 4.7149 - gen_acc: 0.9344 - real_acc: 0.9098



37/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0390 - d_loss: 0.2319 - g_loss: 4.7045 - gen_acc: 0.9345 - real_acc: 0.9101



38/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0390 - d_loss: 0.2319 - g_loss: 4.6937 - gen_acc: 0.9345 - real_acc: 0.9104



39/46 ━━━━━━━━━━━━━━━━━━━━ 1s 179ms/step - aug_p: 0.0390 - d_loss: 0.2319 - g_loss: 4.6838 - gen_acc: 0.9346 - real_acc: 0.9107



40/46 ━━━━━━━━━━━━━━━━━━━━ 1s 179ms/step - aug_p: 0.0390 - d_loss: 0.2318 - g_loss: 4.6734 - gen_acc: 0.9347 - real_acc: 0.9110



41/46 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - aug_p: 0.0390 - d_loss: 0.2316 - g_loss: 4.6636 - gen_acc: 0.9349 - real_acc: 0.9114



42/46 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - aug_p: 0.0390 - d_loss: 0.2313 - g_loss: 4.6532 - gen_acc: 0.9350 - real_acc: 0.9117



43/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0391 - d_loss: 0.2310 - g_loss: 4.6442 - gen_acc: 0.9352 - real_acc: 0.9120



44/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0391 - d_loss: 0.2306 - g_loss: 4.6361 - gen_acc: 0.9354 - real_acc: 0.9124



45/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0391 - d_loss: 0.2302 - g_loss: 4.6279 - gen_acc: 0.9356 - real_acc: 0.9127



46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0391 - d_loss: 0.2297 - g_loss: 4.6201 - gen_acc: 0.9358 - real_acc: 0.9131



46/46 ━━━━━━━━━━━━━━━━━━━━ 12s 266ms/step - aug_p: 0.0391 - d_loss: 0.2292 - g_loss: 4.6126 - gen_acc: 0.9361 - real_acc: 0.9134 - val_kid: 5.7109

Epoch 9/10

1/46 ━━━━━━━━━━━━━━━━━━━━ 8s 180ms/step - aug_p: 0.0422 - d_loss: 0.0668 - g_loss: 3.8939 - gen_acc: 0.9922 - real_acc: 0.9922



2/46 ━━━━━━━━━━━━━━━━━━━━ 8s 187ms/step - aug_p: 0.0422 - d_loss: 0.0676 - g_loss: 3.8295 - gen_acc: 0.9863 - real_acc: 0.9941



3/46 ━━━━━━━━━━━━━━━━━━━━ 7s 185ms/step - aug_p: 0.0422 - d_loss: 0.0659 - g_loss: 3.8676 - gen_acc: 0.9865 - real_acc: 0.9944



4/46 ━━━━━━━━━━━━━━━━━━━━ 7s 183ms/step - aug_p: 0.0423 - d_loss: 0.0703 - g_loss: 3.8084 - gen_acc: 0.9831 - real_acc: 0.9928



5/46 ━━━━━━━━━━━━━━━━━━━━ 7s 183ms/step - aug_p: 0.0423 - d_loss: 0.0755 - g_loss: 3.9384 - gen_acc: 0.9821 - real_acc: 0.9880



6/46 ━━━━━━━━━━━━━━━━━━━━ 7s 183ms/step - aug_p: 0.0423 - d_loss: 0.0781 - g_loss: 4.0291 - gen_acc: 0.9818 - real_acc: 0.9846



7/46 ━━━━━━━━━━━━━━━━━━━━ 7s 182ms/step - aug_p: 0.0424 - d_loss: 0.0831 - g_loss: 4.0366 - gen_acc: 0.9779 - real_acc: 0.9828



8/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0424 - d_loss: 0.0888 - g_loss: 4.1585 - gen_acc: 0.9756 - real_acc: 0.9782



9/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0424 - d_loss: 0.0921 - g_loss: 4.3258 - gen_acc: 0.9744 - real_acc: 0.9750



10/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0424 - d_loss: 0.0937 - g_loss: 4.4967 - gen_acc: 0.9737 - real_acc: 0.9729



11/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0425 - d_loss: 0.0944 - g_loss: 4.6444 - gen_acc: 0.9732 - real_acc: 0.9715



12/46 ━━━━━━━━━━━━━━━━━━━━ 6s 181ms/step - aug_p: 0.0425 - d_loss: 0.0945 - g_loss: 4.7625 - gen_acc: 0.9730 - real_acc: 0.9706



13/46 ━━━━━━━━━━━━━━━━━━━━ 5s 181ms/step - aug_p: 0.0425 - d_loss: 0.0943 - g_loss: 4.8487 - gen_acc: 0.9728 - real_acc: 0.9701



14/46 ━━━━━━━━━━━━━━━━━━━━ 5s 181ms/step - aug_p: 0.0425 - d_loss: 0.0940 - g_loss: 4.9110 - gen_acc: 0.9726 - real_acc: 0.9698



15/46 ━━━━━━━━━━━━━━━━━━━━ 5s 181ms/step - aug_p: 0.0426 - d_loss: 0.0935 - g_loss: 4.9645 - gen_acc: 0.9725 - real_acc: 0.9696



16/46 ━━━━━━━━━━━━━━━━━━━━ 5s 181ms/step - aug_p: 0.0426 - d_loss: 0.0931 - g_loss: 5.0047 - gen_acc: 0.9726 - real_acc: 0.9694



17/46 ━━━━━━━━━━━━━━━━━━━━ 5s 181ms/step - aug_p: 0.0426 - d_loss: 0.0930 - g_loss: 5.0287 - gen_acc: 0.9723 - real_acc: 0.9693



18/46 ━━━━━━━━━━━━━━━━━━━━ 5s 181ms/step - aug_p: 0.0426 - d_loss: 0.0941 - g_loss: 5.0578 - gen_acc: 0.9721 - real_acc: 0.9679



19/46 ━━━━━━━━━━━━━━━━━━━━ 4s 181ms/step - aug_p: 0.0427 - d_loss: 0.0976 - g_loss: 5.0709 - gen_acc: 0.9702 - real_acc: 0.9669



20/46 ━━━━━━━━━━━━━━━━━━━━ 4s 181ms/step - aug_p: 0.0427 - d_loss: 0.1023 - g_loss: 5.0961 - gen_acc: 0.9687 - real_acc: 0.9645



21/46 ━━━━━━━━━━━━━━━━━━━━ 4s 181ms/step - aug_p: 0.0427 - d_loss: 0.1064 - g_loss: 5.1232 - gen_acc: 0.9674 - real_acc: 0.9623



22/46 ━━━━━━━━━━━━━━━━━━━━ 4s 181ms/step - aug_p: 0.0427 - d_loss: 0.1101 - g_loss: 5.1442 - gen_acc: 0.9662 - real_acc: 0.9603



23/46 ━━━━━━━━━━━━━━━━━━━━ 4s 181ms/step - aug_p: 0.0428 - d_loss: 0.1136 - g_loss: 5.1570 - gen_acc: 0.9649 - real_acc: 0.9587



24/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0428 - d_loss: 0.1166 - g_loss: 5.1674 - gen_acc: 0.9638 - real_acc: 0.9573



25/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0428 - d_loss: 0.1192 - g_loss: 5.1751 - gen_acc: 0.9628 - real_acc: 0.9561



26/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0428 - d_loss: 0.1216 - g_loss: 5.1786 - gen_acc: 0.9620 - real_acc: 0.9550



27/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0428 - d_loss: 0.1238 - g_loss: 5.1785 - gen_acc: 0.9612 - real_acc: 0.9539



28/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0429 - d_loss: 0.1258 - g_loss: 5.1765 - gen_acc: 0.9605 - real_acc: 0.9530



29/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0429 - d_loss: 0.1276 - g_loss: 5.1726 - gen_acc: 0.9599 - real_acc: 0.9521



30/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0429 - d_loss: 0.1294 - g_loss: 5.1667 - gen_acc: 0.9595 - real_acc: 0.9513



31/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0429 - d_loss: 0.1309 - g_loss: 5.1594 - gen_acc: 0.9590 - real_acc: 0.9506



32/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0429 - d_loss: 0.1323 - g_loss: 5.1512 - gen_acc: 0.9587 - real_acc: 0.9500



33/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0429 - d_loss: 0.1335 - g_loss: 5.1414 - gen_acc: 0.9584 - real_acc: 0.9494



34/46 ━━━━━━━━━━━━━━━━━━━━ 2s 180ms/step - aug_p: 0.0430 - d_loss: 0.1346 - g_loss: 5.1320 - gen_acc: 0.9582 - real_acc: 0.9489



35/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0430 - d_loss: 0.1356 - g_loss: 5.1216 - gen_acc: 0.9580 - real_acc: 0.9484



36/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0430 - d_loss: 0.1365 - g_loss: 5.1109 - gen_acc: 0.9579 - real_acc: 0.9479



37/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0430 - d_loss: 0.1373 - g_loss: 5.0996 - gen_acc: 0.9578 - real_acc: 0.9475



38/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0430 - d_loss: 0.1379 - g_loss: 5.0882 - gen_acc: 0.9577 - real_acc: 0.9472



39/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0431 - d_loss: 0.1385 - g_loss: 5.0769 - gen_acc: 0.9577 - real_acc: 0.9468



40/46 ━━━━━━━━━━━━━━━━━━━━ 1s 180ms/step - aug_p: 0.0431 - d_loss: 0.1391 - g_loss: 5.0648 - gen_acc: 0.9577 - real_acc: 0.9466



41/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0431 - d_loss: 0.1395 - g_loss: 5.0535 - gen_acc: 0.9577 - real_acc: 0.9463



42/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0431 - d_loss: 0.1400 - g_loss: 5.0419 - gen_acc: 0.9576 - real_acc: 0.9461



43/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0431 - d_loss: 0.1403 - g_loss: 5.0307 - gen_acc: 0.9577 - real_acc: 0.9459



44/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0431 - d_loss: 0.1406 - g_loss: 5.0198 - gen_acc: 0.9577 - real_acc: 0.9458



45/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0432 - d_loss: 0.1408 - g_loss: 5.0087 - gen_acc: 0.9577 - real_acc: 0.9456



46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 180ms/step - aug_p: 0.0432 - d_loss: 0.1410 - g_loss: 4.9981 - gen_acc: 0.9578 - real_acc: 0.9455



46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - aug_p: 0.0432 - d_loss: 0.1411 - g_loss: 4.9879 - gen_acc: 0.9579 - real_acc: 0.9455 - val_kid: 3.6018

Epoch 10/10

1/46 ━━━━━━━━━━━━━━━━━━━━ 5:15 7s/step - aug_p: 0.0464 - d_loss: 0.0324 - g_loss: 4.1750 - gen_acc: 1.0000 - real_acc: 0.9922



2/46 ━━━━━━━━━━━━━━━━━━━━ 8s 195ms/step - aug_p: 0.0464 - d_loss: 0.0337 - g_loss: 4.0349 - gen_acc: 0.9980 - real_acc: 0.9941



3/46 ━━━━━━━━━━━━━━━━━━━━ 8s 186ms/step - aug_p: 0.0464 - d_loss: 0.0367 - g_loss: 4.0199 - gen_acc: 0.9978 - real_acc: 0.9918



4/46 ━━━━━━━━━━━━━━━━━━━━ 7s 184ms/step - aug_p: 0.0465 - d_loss: 0.0374 - g_loss: 4.0297 - gen_acc: 0.9979 - real_acc: 0.9909



5/46 ━━━━━━━━━━━━━━━━━━━━ 7s 183ms/step - aug_p: 0.0465 - d_loss: 0.0380 - g_loss: 4.0271 - gen_acc: 0.9980 - real_acc: 0.9902



6/46 ━━━━━━━━━━━━━━━━━━━━ 7s 183ms/step - aug_p: 0.0465 - d_loss: 0.0383 - g_loss: 4.0130 - gen_acc: 0.9981 - real_acc: 0.9901



7/46 ━━━━━━━━━━━━━━━━━━━━ 7s 183ms/step - aug_p: 0.0466 - d_loss: 0.0385 - g_loss: 4.0148 - gen_acc: 0.9982 - real_acc: 0.9901



8/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0466 - d_loss: 0.0389 - g_loss: 4.0141 - gen_acc: 0.9983 - real_acc: 0.9902



9/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0467 - d_loss: 0.0393 - g_loss: 4.0076 - gen_acc: 0.9984 - real_acc: 0.9903



10/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0467 - d_loss: 0.0397 - g_loss: 4.0031 - gen_acc: 0.9985 - real_acc: 0.9903



11/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0467 - d_loss: 0.0402 - g_loss: 3.9981 - gen_acc: 0.9985 - real_acc: 0.9902



12/46 ━━━━━━━━━━━━━━━━━━━━ 6s 182ms/step - aug_p: 0.0468 - d_loss: 0.0406 - g_loss: 3.9968 - gen_acc: 0.9985 - real_acc: 0.9902



13/46 ━━━━━━━━━━━━━━━━━━━━ 5s 181ms/step - aug_p: 0.0468 - d_loss: 0.0411 - g_loss: 3.9967 - gen_acc: 0.9985 - real_acc: 0.9899



14/46 ━━━━━━━━━━━━━━━━━━━━ 5s 181ms/step - aug_p: 0.0468 - d_loss: 0.0418 - g_loss: 3.9930 - gen_acc: 0.9984 - real_acc: 0.9897



15/46 ━━━━━━━━━━━━━━━━━━━━ 5s 181ms/step - aug_p: 0.0469 - d_loss: 0.0428 - g_loss: 3.9956 - gen_acc: 0.9982 - real_acc: 0.9893



16/46 ━━━━━━━━━━━━━━━━━━━━ 5s 181ms/step - aug_p: 0.0469 - d_loss: 0.0436 - g_loss: 3.9957 - gen_acc: 0.9980 - real_acc: 0.9890



17/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0469 - d_loss: 0.0443 - g_loss: 3.9983 - gen_acc: 0.9978 - real_acc: 0.9887



18/46 ━━━━━━━━━━━━━━━━━━━━ 5s 180ms/step - aug_p: 0.0470 - d_loss: 0.0450 - g_loss: 3.9978 - gen_acc: 0.9977 - real_acc: 0.9885



19/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0470 - d_loss: 0.0457 - g_loss: 3.9987 - gen_acc: 0.9976 - real_acc: 0.9883



20/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0470 - d_loss: 0.0464 - g_loss: 3.9966 - gen_acc: 0.9974 - real_acc: 0.9880



21/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0471 - d_loss: 0.0472 - g_loss: 3.9956 - gen_acc: 0.9973 - real_acc: 0.9877



22/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0471 - d_loss: 0.0482 - g_loss: 3.9910 - gen_acc: 0.9969 - real_acc: 0.9874



23/46 ━━━━━━━━━━━━━━━━━━━━ 4s 180ms/step - aug_p: 0.0471 - d_loss: 0.0501 - g_loss: 3.9936 - gen_acc: 0.9965 - real_acc: 0.9862



24/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0472 - d_loss: 0.0532 - g_loss: 3.9900 - gen_acc: 0.9949 - real_acc: 0.9853



25/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0472 - d_loss: 0.0576 - g_loss: 3.9964 - gen_acc: 0.9935 - real_acc: 0.9832



26/46 ━━━━━━━━━━━━━━━━━━━━ 3s 180ms/step - aug_p: 0.0472 - d_loss: 0.0624 - g_loss: 3.9986 - gen_acc: 0.9917 - real_acc: 0.9813



27/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0472 - d_loss: 0.0667 - g_loss: 4.0030 - gen_acc: 0.9901 - real_acc: 0.9795



28/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0473 - d_loss: 0.0707 - g_loss: 4.0083 - gen_acc: 0.9887 - real_acc: 0.9778



29/46 ━━━━━━━━━━━━━━━━━━━━ 3s 181ms/step - aug_p: 0.0473 - d_loss: 0.0744 - g_loss: 4.0128 - gen_acc: 0.9873 - real_acc: 0.9762



30/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0473 - d_loss: 0.0776 - g_loss: 4.0161 - gen_acc: 0.9862 - real_acc: 0.9748



31/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0473 - d_loss: 0.0806 - g_loss: 4.0186 - gen_acc: 0.9851 - real_acc: 0.9735



32/46 ━━━━━━━━━━━━━━━━━━━━ 2s 181ms/step - aug_p: 0.0474 - d_loss: 0.0832 - g_loss: 4.0199 - gen_acc: 0.9841 - real_acc: 0.9724



33/46 ━━━━━━━━━━━━━━━━━━━━ 2s 182ms/step - aug_p: 0.0474 - d_loss: 0.0856 - g_loss: 4.0204 - gen_acc: 0.9832 - real_acc: 0.9714



34/46 ━━━━━━━━━━━━━━━━━━━━ 2s 182ms/step - aug_p: 0.0474 - d_loss: 0.0878 - g_loss: 4.0206 - gen_acc: 0.9825 - real_acc: 0.9705



35/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0474 - d_loss: 0.0898 - g_loss: 4.0206 - gen_acc: 0.9818 - real_acc: 0.9697



36/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0475 - d_loss: 0.0916 - g_loss: 4.0200 - gen_acc: 0.9811 - real_acc: 0.9690



37/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0475 - d_loss: 0.0933 - g_loss: 4.0193 - gen_acc: 0.9805 - real_acc: 0.9683



38/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0475 - d_loss: 0.0948 - g_loss: 4.0185 - gen_acc: 0.9800 - real_acc: 0.9677



39/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0475 - d_loss: 0.0961 - g_loss: 4.0171 - gen_acc: 0.9796 - real_acc: 0.9672



40/46 ━━━━━━━━━━━━━━━━━━━━ 1s 182ms/step - aug_p: 0.0475 - d_loss: 0.0974 - g_loss: 4.0158 - gen_acc: 0.9791 - real_acc: 0.9667



41/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0476 - d_loss: 0.0985 - g_loss: 4.0146 - gen_acc: 0.9787 - real_acc: 0.9662



42/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0476 - d_loss: 0.0995 - g_loss: 4.0133 - gen_acc: 0.9784 - real_acc: 0.9658



43/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0476 - d_loss: 0.1005 - g_loss: 4.0119 - gen_acc: 0.9781 - real_acc: 0.9655



44/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0476 - d_loss: 0.1013 - g_loss: 4.0102 - gen_acc: 0.9778 - real_acc: 0.9652



45/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0476 - d_loss: 0.1021 - g_loss: 4.0083 - gen_acc: 0.9775 - real_acc: 0.9649



46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 182ms/step - aug_p: 0.0477 - d_loss: 0.1028 - g_loss: 4.0070 - gen_acc: 0.9773 - real_acc: 0.9647



46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 304ms/step - aug_p: 0.0477 - d_loss: 0.1035 - g_loss: 4.0058 - gen_acc: 0.9771 - real_acc: 0.9644 - val_kid: 3.0212

<keras.src.callbacks.history.History at 0x794f705d3390>

Inference

# load the best model and generate images
model.load_weights(checkpoint_path)
model.plot_images()

Results

By running the training for 400 epochs (which takes 2-3 hours in a Colab notebook), one can get high quality image generations using this code example.

The evolution of a random batch of images over a 400 epoch training (ema=0.999 for animation smoothness):

Latent-space interpolation between a batch of selected images:

I also recommend trying out training on other datasets, such as CelebA for example. In my experience good results can be achieved without changing any hyperparameters (though discriminator augmentation might not be necessary).

GAN tips and tricks

My goal with this example was to find a good tradeoff between ease of implementation and generation quality for GANs. During preparation, I have run numerous ablations using this repository.

In this section I list the lessons learned and my recommendations in my subjective order of importance.

I recommend checking out the DCGAN paper, this NeurIPS talk, and this large scale GAN study for others' takes on this subject.

Architectural tips

• resolution: Training GANs at higher resolutions tends to get more difficult, I recommend experimenting at 32x32 or 64x64 resolutions initially.
• initialization: If you see strong colorful patterns early on in the training, the initialization might be the issue. Set the kernel_initializer parameters of layers to random normal, and decrease the standard deviation (recommended value: 0.02, following DCGAN) until the issue disappears.
• upsampling: There are two main methods for upsampling in the generator. Transposed convolution is faster, but can lead to checkerboard artifacts, which can be reduced by using a kernel size that is divisible with the stride (recommended kernel size is 4 for a stride of 2). Upsampling + standard convolution can have slightly lower quality, but checkerboard artifacts are not an issue. I recommend using nearest-neighbor interpolation over bilinear for it.
• batch normalization in discriminator: Sometimes has a high impact, I recommend trying out both ways.
• spectral normalization: A popular technique for training GANs, can help with stability. I recommend disabling batch normalization's learnable scale parameters along with it.
• residual connections: While residual discriminators behave similarly, residual generators are more difficult to train in my experience. They are however necessary for training large and deep architectures. I recommend starting with non-residual architectures.
• dropout: Using dropout before the last layer of the discriminator improves generation quality in my experience. Recommended dropout rate is below 0.5.
• leaky ReLU: Use leaky ReLU activations in the discriminator to make its gradients less sparse. Recommended slope/alpha is 0.2 following DCGAN.

Algorithmic tips

• loss functions: Numerous losses have been proposed over the years for training GANs, promising improved performance and stability. I have implemented 5 of them in this repository, and my experience is in line with this GAN study: no loss seems to consistently outperform the default non-saturating GAN loss. I recommend using that as a default.
• Adam's beta_1 parameter: The beta_1 parameter in Adam can be interpreted as the momentum of mean gradient estimation. Using 0.5 or even 0.0 instead of the default 0.9 value was proposed in DCGAN and is important. This example would not work using its default value.
• separate batch normalization for generated and real images: The forward pass of the discriminator should be separate for the generated and real images. Doing otherwise can lead to artifacts (45 degree stripes in my case) and decreased performance.
• exponential moving average of generator's weights: This helps to reduce the variance of the KID measurement, and helps in averaging out the rapid color palette changes during training.
• different learning rate for generator and discriminator: If one has the resources, it can help to tune the learning rates of the two networks separately. A similar idea is to update either network's (usually the discriminator's) weights multiple times for each of the other network's updates. I recommend using the same learning rate of 2e-4 (Adam), following DCGAN for both networks, and only updating both of them once as a default.
• label noise: One-sided label smoothing (using less than 1.0 for real labels), or adding noise to the labels can regularize the discriminator not to get overconfident, however in my case they did not improve performance.
• adaptive data augmentation: Since it adds another dynamic component to the training process, disable it as a default, and only enable it when the other components already work well.

Related works

Other GAN-related Keras code examples:

• DCGAN + CelebA
• WGAN + FashionMNIST
• WGAN + Molecules
• ConditionalGAN + MNIST
• CycleGAN + Horse2Zebra
• StyleGAN

Modern GAN architecture-lines:

• SAGAN, BigGAN
• ProgressiveGAN, StyleGAN, StyleGAN2, StyleGAN2-ADA, AliasFreeGAN

Concurrent papers on discriminator data augmentation: 1, 2, 3

Recent literature overview on GANs: talk