def extract_data():
"""
Extract data from MNIST & CelebA datasets
"""
helper.download_extract('mnist', DATA_DIR)
helper.download_extract('celeba', DATA_DIR)
# 由于 CelebA 数据集比较复杂，而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型，以让你更快的评估所建立模型的性能。
#
# 如果你在使用 [FloydHub](https://www.floydhub.com/), 请将 `data_dir` 设置为 "/input" 并使用 [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) "R5KrjnANiKVhLWAkpXhNBe".
# In[1]:
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
# ## 探索数据（Explore the Data）
# ### MNIST
# [MNIST](http://yann.lecun.com/exdb/mnist/) 是一个手写数字的图像数据集。你可以更改 `show_n_images` 探索此数据集。
# In[2]:
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
get_ipython().run_line_magic('matplotlib', 'inline')
import os
from glob import glob
# 如果你在使用 [FloydHub](https://www.floydhub.com/), 请将 `data_dir` 设置为 "/input" 并使用 [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) "R5KrjnANiKVhLWAkpXhNBe".
# In[88]:
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
# ## 探索数据（Explore the Data）
# ### MNIST
# [MNIST](http://yann.lecun.com/exdb/mnist/) 是一个手写数字的图像数据集。你可以更改 `show_n_images` 探索此数据集。
# In[90]:
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
get_ipython().magic(u'matplotlib inline')
import torchvision.utils as vutils
from torchvision.utils import save_image
import torch.nn as nn
import torch.nn.functional as F
import torch
#os.environ["CUDA_VISIBLE_DEVICES"] = "2"
# config = tf.compat.v1.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.6 # 每个GPU现存上届控制在60%以内
# config.gpu_options.visible_device_list = '2'
# session = tf.compat.v1.Session(config=config)#tf.compat.v1.keras.backend.set_session(session)
sys.argv
import helper
data_dir = './data'
helper.download_extract(data_dir)
# In[21]:
device = torch.device('cuda:2' if torch.cuda.is_available() else 'cpu')
print(device)
# In[22]:
z_dim= 100
class Generator(nn.Module):
def __init__(self):
import mlflow
import tensorflow as tf
print(tf.__version__)
import helper
helper.download_extract('celeba', 'data/')
# In[2]:
import os
from glob import glob
import numpy as np
from matplotlib import pyplot
show_n_images = 25
data_dir='data/'
celeba_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(celeba_images, 'RGB'))
# In[3]:
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
# In[4]:
celeba_dataset.shape
tests.test_model_inputs(model_inputs)
tests.test_discriminator(discriminator, tf)
tests.test_generator(generator, tf)
tests.test_model_loss(model_loss)
tests.test_model_opt(model_opt, tf)
# 학습 Parameters
show_n_images = 25
batch_size = 64
z_dim = 100
learning_rate = 0.00025
beta1 = 0.45
epochs = 100 # 시간/장비의 제약으로 인해 11번째 epoch 까지만 진행
# celeba.zip 데이터셋 다운로드
helper.download_extract('celeba', './celeba')
mnist_images = helper.get_batch(glob(os.path.join('./celeba', 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB')) # 샘플 데이터 1개 보여주기
# 다운로드 받은 celeba 이미지들을 학습시킬 수 있는 데이터셋으로 변환
celeba_dataset = helper.Dataset('celeba', glob(os.path.join('./celeba', 'img_align_celeba/*.jpg')))
# GPU 사용 여부 확인
if not tf.test.gpu_device_name():
warnings.warn('GPU가 없습니다. - GPU 사용 권장')
else:
print('사용중인 GPU: {}'.format(tf.test.gpu_device_name()))
# 실제 학습시키고 output을 생성하는 부분 (시간이 오래 걸립니다)
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches, celeba_dataset.shape, celeba_dataset.image_mode)
import helper as hp
import tensorflow as tf
import os
import numpy as np
import random
from PIL import Image
import tensorflow.keras.backend as K
from functools import partial
path = '../dataset/img_align_celeba/'
hp.download_extract('../dataset')
batch_size = 64
noise_size = 100
sl5 = 4
img_size = 64
rinit = tf.random_normal_initializer(stddev=0.02)
# Generator def
G = tf.keras.models.Sequential([
tf.keras.layers.Dense(units = 512*sl5*sl5, input_dim=noise_size, kernel_initializer=rinit),
tf.keras.layers.Reshape(target_shape=[sl5,sl5,512]),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.LeakyReLU(),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(filters=256, kernel_size=5, strides=1, padding='same', kernel_initializer=rinit),
tf.keras.layers.BatchNormalization(momentum=0.8),
tf.keras.layers.LeakyReLU(),
tf.keras.layers.UpSampling2D(),
