Tensorflow之GAN实战——Anime数据集

AutoEncoder实战

# main.py
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)					# 设置tf随机种子
np.random.seed(22) 						# 设置np随机种子
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'	# 设置tf日志，只显示error和warning
assert tf.__version__.startswith('2.')	# 保证tf2.x版本

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.

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)

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


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