用于图像去噪的卷积自编码器
作者： Santiago L. Valdarrama
创建日期 2021/03/01
最后修改日期 2021/03/01
描述：如何训练用于图像去噪的深度卷积自编码器。
简介
此示例演示如何实现用于图像去噪的深度卷积自编码器，将 MNIST 数据集中噪声数字图像映射到干净的数字图像。此实现基于 François Chollet 撰写的一篇名为 在 Keras 中构建自编码器 的原始博文。
设置
import numpy as np
import matplotlib.pyplot as plt
from keras import layers
from keras.datasets import mnist
from keras.models import Model
def preprocess(array):
"""Normalizes the supplied array and reshapes it."""
array = array.astype("float32") / 255.0
array = np.reshape(array, (len(array), 28, 28, 1))
return array
def noise(array):
"""Adds random noise to each image in the supplied array."""
noise_factor = 0.4
noisy_array = array + noise_factor * np.random.normal(
loc=0.0, scale=1.0, size=array.shape
)
return np.clip(noisy_array, 0.0, 1.0)
def display(array1, array2):
"""Displays ten random images from each array."""
n = 10
indices = np.random.randint(len(array1), size=n)
images1 = array1[indices, :]
images2 = array2[indices, :]
plt.figure(figsize=(20, 4))
for i, (image1, image2) in enumerate(zip(images1, images2)):
ax = plt.subplot(2, n, i + 1)
plt.imshow(image1.reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(image2.reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
准备数据
# Since we only need images from the dataset to encode and decode, we
# won't use the labels.
(train_data, _), (test_data, _) = mnist.load_data()
# Normalize and reshape the data
train_data = preprocess(train_data)
test_data = preprocess(test_data)
# Create a copy of the data with added noise
noisy_train_data = noise(train_data)
noisy_test_data = noise(test_data)
# Display the train data and a version of it with added noise
display(train_data, noisy_train_data)
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step
构建自编码器
我们将使用函数式 API 来构建我们的卷积自编码器。
input = layers.Input(shape=(28, 28, 1))
# Encoder
x = layers.Conv2D(32, (3, 3), activation="relu", padding="same")(input)
x = layers.MaxPooling2D((2, 2), padding="same")(x)
x = layers.Conv2D(32, (3, 3), activation="relu", padding="same")(x)
x = layers.MaxPooling2D((2, 2), padding="same")(x)
# Decoder
x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation="relu", padding="same")(x)
x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation="relu", padding="same")(x)
x = layers.Conv2D(1, (3, 3), activation="sigmoid", padding="same")(x)
# Autoencoder
autoencoder = Model(input, x)
autoencoder.compile(optimizer="adam", loss="binary_crossentropy")
autoencoder.summary()
Model: "functional_1"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ input_layer (InputLayer) │ (None, 28, 28, 1) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d (Conv2D) │ (None, 28, 28, 32) │ 320 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 14, 14, 32) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_1 (Conv2D) │ (None, 14, 14, 32) │ 9,248 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 7, 7, 32) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_transpose │ (None, 14, 14, 32) │ 9,248 │ │ (Conv2DTranspose) │ │ │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_transpose_1 │ (None, 28, 28, 32) │ 9,248 │ │ (Conv2DTranspose) │ │ │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_2 (Conv2D) │ (None, 28, 28, 1) │ 289 │ └─────────────────────────────────┴───────────────────────────┴────────────┘
Total params: 28,353 (110.75 KB)
Trainable params: 28,353 (110.75 KB)
Non-trainable params: 0 (0.00 B)
现在我们可以使用 train_data 作为我们的输入数据和目标来训练我们的自编码器。请注意，我们正在使用相同的格式设置验证数据。
autoencoder.fit(
x=train_data,
y=train_data,
epochs=50,
batch_size=128,
shuffle=True,
validation_data=(test_data, test_data),
)
Epoch 1/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 9ms/step - loss: 0.2537 - val_loss: 0.0723
Epoch 2/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0718 - val_loss: 0.0691
Epoch 3/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0695 - val_loss: 0.0677
Epoch 4/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0682 - val_loss: 0.0669
Epoch 5/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0673 - val_loss: 0.0664
Epoch 6/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0668 - val_loss: 0.0660
Epoch 7/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0664 - val_loss: 0.0657
Epoch 8/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0661 - val_loss: 0.0654
Epoch 9/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0657 - val_loss: 0.0651
Epoch 10/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0655 - val_loss: 0.0648
Epoch 11/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0653 - val_loss: 0.0646
Epoch 12/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0651 - val_loss: 0.0644
Epoch 13/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0649 - val_loss: 0.0643
Epoch 14/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0647 - val_loss: 0.0641
Epoch 15/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0646 - val_loss: 0.0640
Epoch 16/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0645 - val_loss: 0.0639
Epoch 17/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0642 - val_loss: 0.0638
Epoch 18/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0641 - val_loss: 0.0638
Epoch 19/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0640 - val_loss: 0.0636
Epoch 20/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0639 - val_loss: 0.0637
Epoch 21/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0639 - val_loss: 0.0634
Epoch 22/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0637 - val_loss: 0.0634
Epoch 23/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0636 - val_loss: 0.0633
Epoch 24/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0637 - val_loss: 0.0632
Epoch 25/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0635 - val_loss: 0.0632
Epoch 26/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0635 - val_loss: 0.0631
Epoch 27/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0635 - val_loss: 0.0630
Epoch 28/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0635 - val_loss: 0.0629
Epoch 29/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0634 - val_loss: 0.0630
Epoch 30/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0633 - val_loss: 0.0629
Epoch 31/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0633 - val_loss: 0.0628
Epoch 32/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0632 - val_loss: 0.0628
Epoch 33/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0631 - val_loss: 0.0627
Epoch 34/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0631 - val_loss: 0.0627
Epoch 35/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0630 - val_loss: 0.0627
Epoch 36/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0631 - val_loss: 0.0626
Epoch 37/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0629 - val_loss: 0.0626
Epoch 38/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0630 - val_loss: 0.0627
Epoch 39/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0630 - val_loss: 0.0625
Epoch 40/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0629 - val_loss: 0.0625
Epoch 41/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0628 - val_loss: 0.0625
Epoch 42/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0629 - val_loss: 0.0625
Epoch 43/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0629 - val_loss: 0.0624
Epoch 44/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0628 - val_loss: 0.0624
Epoch 45/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0628 - val_loss: 0.0624
Epoch 46/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0627 - val_loss: 0.0625
Epoch 47/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0628 - val_loss: 0.0623
Epoch 48/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0627 - val_loss: 0.0623
Epoch 49/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0626 - val_loss: 0.0623
Epoch 50/50
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0626 - val_loss: 0.0622
<keras.src.callbacks.history.History at 0x7ff5889d9930>
让我们在我们的测试数据集上进行预测，并显示原始图像以及我们自编码器的预测结果。
请注意，预测结果非常接近原始图像，尽管并不完全相同。
predictions = autoencoder.predict(test_data)
display(test_data, predictions)
313/313 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step
现在我们知道我们的自编码器可以工作，让我们使用噪声数据作为我们的输入，干净数据作为我们的目标来重新训练它。我们希望我们的自编码器学习如何去除图像中的噪声。
autoencoder.fit(
x=noisy_train_data,
y=train_data,
epochs=100,
batch_size=128,
shuffle=True,
validation_data=(noisy_test_data, test_data),
)
Epoch 1/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.1110 - val_loss: 0.0922
Epoch 2/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0925 - val_loss: 0.0904
Epoch 3/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0910 - val_loss: 0.0895
Epoch 4/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0900 - val_loss: 0.0888
Epoch 5/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0892 - val_loss: 0.0882
Epoch 6/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0887 - val_loss: 0.0878
Epoch 7/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0884 - val_loss: 0.0874
Epoch 8/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0880 - val_loss: 0.0871
Epoch 9/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0876 - val_loss: 0.0869
Epoch 10/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0875 - val_loss: 0.0868
Epoch 11/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0872 - val_loss: 0.0864
Epoch 12/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0870 - val_loss: 0.0863
Epoch 13/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0869 - val_loss: 0.0860
Epoch 14/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0868 - val_loss: 0.0859
Epoch 15/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0865 - val_loss: 0.0857
Epoch 16/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0863 - val_loss: 0.0857
Epoch 17/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0863 - val_loss: 0.0858
Epoch 18/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0862 - val_loss: 0.0854
Epoch 19/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0859 - val_loss: 0.0856
Epoch 20/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0859 - val_loss: 0.0853
Epoch 21/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0858 - val_loss: 0.0851
Epoch 22/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0857 - val_loss: 0.0851
Epoch 23/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0856 - val_loss: 0.0850
Epoch 24/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0855 - val_loss: 0.0850
Epoch 25/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0853 - val_loss: 0.0849
Epoch 26/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0855 - val_loss: 0.0849
Epoch 27/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0853 - val_loss: 0.0849
Epoch 28/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0853 - val_loss: 0.0848
Epoch 29/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0853 - val_loss: 0.0850
Epoch 30/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0854 - val_loss: 0.0847
Epoch 31/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0851 - val_loss: 0.0846
Epoch 32/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0851 - val_loss: 0.0846
Epoch 33/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0849 - val_loss: 0.0846
Epoch 34/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0851 - val_loss: 0.0847
Epoch 35/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0849 - val_loss: 0.0846
Epoch 36/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0849 - val_loss: 0.0844
Epoch 37/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0849 - val_loss: 0.0845
Epoch 38/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0848 - val_loss: 0.0844
Epoch 39/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0849 - val_loss: 0.0843
Epoch 40/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0849 - val_loss: 0.0844
Epoch 41/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0848 - val_loss: 0.0844
Epoch 42/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0848 - val_loss: 0.0844
Epoch 43/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0849 - val_loss: 0.0846
Epoch 44/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0846 - val_loss: 0.0843
Epoch 45/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0847 - val_loss: 0.0845
Epoch 46/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0846 - val_loss: 0.0843
Epoch 47/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0845 - val_loss: 0.0842
Epoch 48/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0846 - val_loss: 0.0842
Epoch 49/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0847 - val_loss: 0.0846
Epoch 50/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0847 - val_loss: 0.0843
Epoch 51/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0846 - val_loss: 0.0842
Epoch 52/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0846 - val_loss: 0.0844
Epoch 53/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0844 - val_loss: 0.0842
Epoch 54/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0845 - val_loss: 0.0842
Epoch 55/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0845 - val_loss: 0.0841
Epoch 56/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0844
Epoch 57/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0845 - val_loss: 0.0841
Epoch 58/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0843
Epoch 59/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0842
Epoch 60/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0844 - val_loss: 0.0847
Epoch 61/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0846 - val_loss: 0.0840
Epoch 62/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0840
Epoch 63/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0841
Epoch 64/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0844 - val_loss: 0.0841
Epoch 65/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0841
Epoch 66/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0841
Epoch 67/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0840
Epoch 68/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0841
Epoch 69/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0840
Epoch 70/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0841
Epoch 71/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0844 - val_loss: 0.0841
Epoch 72/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0842 - val_loss: 0.0840
Epoch 73/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0841
Epoch 74/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0844 - val_loss: 0.0840
Epoch 75/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0842 - val_loss: 0.0840
Epoch 76/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0842
Epoch 77/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0842 - val_loss: 0.0841
Epoch 78/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0841
Epoch 79/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0840
Epoch 80/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0843 - val_loss: 0.0839
Epoch 81/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0842 - val_loss: 0.0842
Epoch 82/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0842 - val_loss: 0.0839
Epoch 83/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0840
Epoch 84/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0839
Epoch 85/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0839
Epoch 86/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0840 - val_loss: 0.0838
Epoch 87/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0839
Epoch 88/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0842 - val_loss: 0.0838
Epoch 89/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0842 - val_loss: 0.0838
Epoch 90/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0840
Epoch 91/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0839
Epoch 92/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0842 - val_loss: 0.0838
Epoch 93/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0838
Epoch 94/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0838
Epoch 95/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0840 - val_loss: 0.0837
Epoch 96/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0838
Epoch 97/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0838
Epoch 98/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0837
Epoch 99/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0841 - val_loss: 0.0838
Epoch 100/100
469/469 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - loss: 0.0839 - val_loss: 0.0839
<keras.src.callbacks.history.History at 0x7ff5889da230>
现在让我们对噪声数据进行预测，并显示我们自编码器的结果。
请注意，自编码器在去除输入图像中的噪声方面做得非常出色。
predictions = autoencoder.predict(noisy_test_data)
display(noisy_test_data, predictions)
313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 523us/step
