# Path: awesome_gans/datasets.py
# class MNISTDataSet:
#     def __init__(self, use_split=False, split_rate=0.15, random_state=42, ds_path=None):
#         self.use_split = use_split
#         self.split_rate = split_rate
#         self.random_state = random_state
# 
#         self.ds_path = ds_path
# 
#         try:
#             assert self.ds_path
#         except AssertionError:
#             raise AssertionError("[-] MNIST DataSet Path is required!")
# 
#         from tensorflow.examples.tutorials.mnist import input_data
# 
#         self.data = input_data.read_data_sets(self.ds_path, one_hot=True)  # download MNIST
# 
#         # training data
#         self.train_data = self.data.train
# 
#         self.train_images = self.train_data.images
#         self.train_labels = self.train_data.labels
#         self.valid_images = None
#         self.valid_labels = None
# 
#         # test data
#         self.test_data = self.data.test
# 
#         self.test_images = self.test_data.images
#         self.test_labels = self.test_data.labels
# 
#         # split training data set into train, valid
#         if self.use_split:
#             self.train_images, self.valid_images, self.train_labels, self.valid_labels = train_test_split(
#                 self.train_images, self.train_labels, test_size=self.split_rate, random_state=self.random_state
#             )


import time
import numpy as np
import tensorflow as tf
import awesome_gans.image_utils as iu
import awesome_gans.sgan.sgan_model as sgan
from awesome_gans.datasets import MNISTDataSet as DataSet
                        model.z_0: batch_z_0,
                    },
                )

                d_overpowered = d_0_loss < g_0_loss / 2

                # Print loss
                print(
                    "[+] Step %08d => " % step, " D loss : {:.8f}".format(d_0_loss), " G loss : {:.8f}".format(g_0_loss)
                )

                # Training G model with sample image and noise
                sample_z_0 = np.random.uniform(-1.0, 1.0, [model.sample_num, model.z_dim]).astype(np.float32)
                sample_z_1 = np.random.uniform(-1.0, 1.0, [model.sample_num, model.z_dim]).astype(np.float32)
                _, samples = s.run(
                    [model.g_1, model.g_0],
                    feed_dict={
                        model.y: sample_y,
                        model.z_1: sample_z_1,
                        model.z_0: sample_z_0,
                    },
                )

                samples = np.reshape(samples, [model.batch_size] + model.image_shape)

                # Summary saver
                model.writer.add_summary(summary, step)

                # Export image generated by model G
                sample_image_height = model.sample_size

# Path: awesome_gans/datasets.py
# class MNISTDataSet:
#     def __init__(self, use_split=False, split_rate=0.15, random_state=42, ds_path=None):
#         self.use_split = use_split
#         self.split_rate = split_rate
#         self.random_state = random_state
# 
#         self.ds_path = ds_path
# 
#         try:
#             assert self.ds_path
#         except AssertionError:
#             raise AssertionError("[-] MNIST DataSet Path is required!")
# 
#         from tensorflow.examples.tutorials.mnist import input_data
# 
#         self.data = input_data.read_data_sets(self.ds_path, one_hot=True)  # download MNIST
# 
#         # training data
#         self.train_data = self.data.train
# 
#         self.train_images = self.train_data.images
#         self.train_labels = self.train_data.labels
#         self.valid_images = None
#         self.valid_labels = None
# 
#         # test data
#         self.test_data = self.data.test
# 
#         self.test_images = self.test_data.images
#         self.test_labels = self.test_data.labels
# 
#         # split training data set into train, valid
#         if self.use_split:
#             self.train_images, self.valid_images, self.train_labels, self.valid_labels = train_test_split(
#                 self.train_images, self.train_labels, test_size=self.split_rate, random_state=self.random_state
#             )


# Path: awesome_gans/sgan/sgan_train.py
import time
import numpy as np
import tensorflow as tf
import awesome_gans.image_utils as iu
import awesome_gans.sgan.sgan_model as sgan
from awesome_gans.datasets import MNISTDataSet as DataSet

                        model.z_0: batch_z_0,
                    },
                )

                d_overpowered = d_0_loss < g_0_loss / 2

                # Print loss
                print(
                    "[+] Step %08d => " % step, " D loss : {:.8f}".format(d_0_loss), " G loss : {:.8f}".format(g_0_loss)
                )

                # Training G model with sample image and noise
                sample_z_0 = np.random.uniform(-1.0, 1.0, [model.sample_num, model.z_dim]).astype(np.float32)
                sample_z_1 = np.random.uniform(-1.0, 1.0, [model.sample_num, model.z_dim]).astype(np.float32)
                _, samples = s.run(
                    [model.g_1, model.g_0],
                    feed_dict={
                        model.y: sample_y,
                        model.z_1: sample_z_1,
                        model.z_0: sample_z_0,
                    },
                )

                samples = np.reshape(samples, [model.batch_size] + model.image_shape)

                # Summary saver
                model.writer.add_summary(summary, step)

                # Export image generated by model G
                sample_image_height = model.sample_size

                sample_image_width = model.sample_size