Auto-Encoders实战
TensorFlow2教程完整教程目录（更有python、go、pytorch、tensorflow、爬虫、人工智能教学等着你）：https://www.cnblogs.com/nickchen121/p/10840284.html
Outline
- Auto-Encoder
- Variational Auto-Encoders
Auto-Encoder
创建编解码器
import os
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import Sequential, layers
from PIL import Image
from matplotlib import pyplot as plt
tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')
def save_images(imgs, name):
new_im = Image.new('L', (280, 280))
index = 0
for i in range(0, 280, 28):
for j in range(0, 280, 28):
im = imgs[index]
im = Image.fromarray(im, mode='L')
new_im.paste(im, (i, j))
index += 1
new_im.save(name)
h_dim = 20 # 784降维20维
batchsz = 512
lr = 1e-3
(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()
x_train, x_test = x_train.astype(np.float32) / 255., x_test.astype(
np.float32) / 255.
# we do not need label
train_db = tf.data.Dataset.from_tensor_slices(x_train)
train_db = train_db.shuffle(batchsz * 5).batch(batchsz)
test_db = tf.data.Dataset.from_tensor_slices(x_test)
test_db = test_db.batch(batchsz)
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)
class AE(keras.Model):
def __init__(self):
super(AE, self).__init__()
# Encoders
self.encoder = Sequential([
layers.Dense(256, activation=tf.nn.relu),
layers.Dense(128, activation=tf.nn.relu),
layers.Dense(h_dim)
])
# Decoders
self.decoder = Sequential([
layers.Dense(128, activation=tf.nn.relu),
layers.Dense(256, activation=tf.nn.relu),
layers.Dense(784)
])
def call(self, inputs, training=None):
# [b,784] ==> [b,19]
h = self.encoder(inputs)
# [b,10] ==> [b,784]
x_hat = self.decoder(h)
return x_hat
model = AE()
model.build(input_shape=(None, 784)) # tensorflow尽量用元组
model.summary()
(60000, 28, 28) (60000,)
(10000, 28, 28) (10000,)
Model: "ae"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
sequential (Sequential) multiple 236436
_________________________________________________________________
sequential_1 (Sequential) multiple 237200
=================================================================
Total params: 473,636
Trainable params: 473,636
Non-trainable params: 0
_________________________________________________________________
训练
optimizer = tf.optimizers.Adam(lr=lr)
for epoch in range(10):
for step, x in enumerate(train_db):
# [b,28,28]==>[b,784]
x = tf.reshape(x, [-1, 784])
with tf.GradientTape() as tape:
x_rec_logits = model(x)
rec_loss = tf.losses.binary_crossentropy(x,
x_rec_logits,
from_logits=True)
rec_loss = tf.reduce_min(rec_loss)
grads = tape.gradient(rec_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
if step % 100 == 0:
print(epoch, step, float(rec_loss))
# evaluation
x = next(iter(test_db))
logits = model(tf.reshape(x, [-1, 784]))
x_hat = tf.sigmoid(logits)
# [b,784]==>[b,28,28]
x_hat = tf.reshape(x_hat, [-1, 28, 28])
# [b,28,28] ==> [2b,28,28]
x_concat = tf.concat([x, x_hat], axis=0)
# x_concat = x # 原始图片
x_concat = x_hat
x_concat = x_concat.numpy() * 255.
x_concat = x_concat.astype(np.uint8) # 保存为整型
if not os.path.exists('ae_images'):
os.mkdir('ae_images')
save_images(x_concat, 'ae_images/rec_epoch_%d.png' % epoch)
0 0 0.09717604517936707
0 100 0.12493347376585007
1 0 0.09747321903705597
1 100 0.12291513383388519
2 0 0.10048121958971024
2 100 0.12292417883872986
3 0 0.10093794018030167
3 100 0.12260882556438446
4 0 0.10006923228502274
4 100 0.12275046110153198
5 0 0.0993042066693306
5 100 0.12257824838161469
6 0 0.0967678651213646
6 100 0.12443818897008896
7 0 0.0965462476015091
7 100 0.12179268896579742
8 0 0.09197664260864258
8 100 0.12110235542058945
9 0 0.0913471132516861
9 100 0.12342415750026703
