Auto-Encoders实战

目录

• Outline
• Auto-Encoder
• 创建编解码器
• 训练

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