# 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.bgan.bgan_model as bgan
import awesome_gans.image_utils as iu
from awesome_gans.datasets import MNISTDataSet as DataSet
            print("[+] global step : %d" % saved_global_step, " successfully loaded")
        else:
            print('[-] No checkpoint file found')

        d_loss = 0.0
        d_overpowered = False
        for global_step in range(saved_global_step, train_step['global_step']):
            batch_x, _ = mnist.train.next_batch(model.batch_size)
            batch_x = batch_x.reshape(-1, model.n_input)
            batch_z = np.random.uniform(-1.0, 1.0, [model.batch_size, model.z_dim]).astype(np.float32)

            # Update D network
            if not d_overpowered:
                _, d_loss = s.run(
                    [model.d_op, model.d_loss],
                    feed_dict={
                        model.x: batch_x,
                        model.z: batch_z,
                    },
                )

            # Update G network
            _, g_loss = s.run(
                [model.g_op, model.g_loss],
                feed_dict={
                    model.x: batch_x,
                    model.z: batch_z,
                },
            )


# 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/bgan/bgan_train.py
import time
import numpy as np
import tensorflow as tf
import awesome_gans.bgan.bgan_model as bgan
import awesome_gans.image_utils as iu
from awesome_gans.datasets import MNISTDataSet as DataSet

            print("[+] global step : %d" % saved_global_step, " successfully loaded")
        else:
            print('[-] No checkpoint file found')

        d_loss = 0.0
        d_overpowered = False
        for global_step in range(saved_global_step, train_step['global_step']):
            batch_x, _ = mnist.train.next_batch(model.batch_size)
            batch_x = batch_x.reshape(-1, model.n_input)
            batch_z = np.random.uniform(-1.0, 1.0, [model.batch_size, model.z_dim]).astype(np.float32)

            # Update D network
            if not d_overpowered:
                _, d_loss = s.run(
                    [model.d_op, model.d_loss],
                    feed_dict={
                        model.x: batch_x,
                        model.z: batch_z,
                    },
                )

            # Update G network
            _, g_loss = s.run(
                [model.g_op, model.g_loss],
                feed_dict={
                    model.x: batch_x,
                    model.z: batch_z,
                },
            )


            d_overpowered = d_loss < g_loss / 2.0