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  • Auto-Encoders实战

    Outline

    • Auto-Encoder

    • Variational Auto-Encoders

    Auto-Encoder

    51-AutoEncoders实战-autoencoder.jpg

    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
    
    
    
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  • 原文地址:https://www.cnblogs.com/abdm-989/p/14123449.html
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