# 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.dragan.dragan_model as dragan
import awesome_gans.image_utils as iu
from awesome_gans.datasets import MNISTDataSet as DataSet



results = {'output': './gen_img/', 'model': './model/DRAGAN-model.ckpt'}

train_step = {
    'batch_size': 64,
    'global_step': 200001,
    'logging_interval': 2000,

# 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/dragan/dragan_train.py
import time
import numpy as np
import tensorflow as tf
import awesome_gans.dragan.dragan_model as dragan
import awesome_gans.image_utils as iu
from awesome_gans.datasets import MNISTDataSet as DataSet




results = {'output': './gen_img/', 'model': './model/DRAGAN-model.ckpt'}

train_step = {
    'batch_size': 64,
    'global_step': 200001,
    'logging_interval': 2000,

}