1. Network structure diagram
2. Distribution analysis
Import the corresponding modules and complete the data preparation work
import os
import tensorflow astf
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.')
h_dim = 20
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)
Write a function to save images
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)
Define AE network model
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, 10]
h = self.encoder(inputs)
# [b, 10] => [b, 784]
x_hat = self.decoder(h)
return x_hat
Initialize the model and select the optimizer
model = AE() model.build(input_shape=(None, 784)) model.summary() optimizer = tf.optimizers.Adam(lr=lr)
Start training and save the reconstructed images after each epoch to the folder ae_image
Pay attention to the size of the data in each step, which is achieved through reshape
for epoch in range(100):
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_mean(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_hat
x_concat = x_concat.numpy() * 255.
x_concat = x_concat.astype(np.uint8)
save_images(x_concat, 'ae_images/rec_epoch_%d.png'%epoch)
3. Complete code
import os
import tensorflow astf
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
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)
classAE(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, 10]
h = self.encoder(inputs)
# [b, 10] => [b, 784]
x_hat = self.decoder(h)
return x_hat
model = AE()
model.build(input_shape=(None, 784))
model.summary()
optimizer = tf.optimizers.Adam(lr=lr)
for epoch in range(100):
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_mean(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_hat
x_concat = x_concat.numpy() * 255.
x_concat = x_concat.astype(np.uint8)
save_images(x_concat, 'ae_images/rec_epoch_%d.png'%epoch)
4. Autoencoder variants
1. Denoising Auto-Encoder
Disturbance (noise) was added before encoding, but it was restored after encoding, which shows that the code really records the semantic features of the picture.
2. Dropout Auto-Encoder
(A dropout of 0 may cause overfitting)
3. Adversarial Auto-Encoder
Related code, pictures from:
Lesson 120 Auto-Encoders Principle_bilibili_bilibili
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