无监督学习之AutoEncoder

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
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, 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, 'ckpt/rec_epoch_%d.png'%epoch)