Cifar10 数据集拟合错误的自动编码器
AutoEncoder on Cifar10 Dataset Fitting Error
我从 tf.keras.datasets.
加载了 Cifar10 数据集
(X_train, y_train), (X_test, y_test) = tk.datasets.cifar10.load_data() # tk = tensorflow.keras
火车测试形状是：train - (50000, 32, 32, 3) | test - (10000, 32, 32, 3)
然后我创建了具有以下层的编码器：
encoder = Sequential()
# L1
encoder.add(Conv2D(filters=32, kernel_size=(3,3), input_shape=(32,32,3), activation='relu'))
encoder.add(BatchNormalization())
encoder.add(Activation('relu'))
encoder.add(MaxPool2D(pool_size=(2,2)))
# L2
encoder.add(Conv2D(filters=16, kernel_size=(3,3), activation='relu'))
encoder.add(BatchNormalization())
encoder.add(Activation('relu'))
encoder.add(MaxPool2D(pool_size=(2,2)))
以及具有以下层的解码器：
decoder = Sequential()
#L3
decoder.add(Conv2D(filters=16, kernel_size=(3,3), activation='relu'))
decoder.add(BatchNormalization())
decoder.add(Activation('relu'))
decoder.add(UpSampling2D(size=(2,2)))
#L4
decoder.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu'))
decoder.add(BatchNormalization())
decoder.add(Activation('softmax'))
decoder.add(UpSampling2D(size=(2,2)))
我编译了它：
autoencoder = Sequential([encoder, decoder])
autoencoder.compile(loss='binary_crossentropy', optimizer='adam')
然后适合它：
autoencoder.fit(x=X_train, y=X_train, batch_size=1000, epochs=50)
我收到一个错误：
ValueError: A target array with shape (50000, 32, 32, 3) was passed for an output of shape (None, 12, 12, 32) while using as loss `binary_crossentropy`. This loss expects targets to have the same shape as the output.
此错误的可能原因是什么？
让我们了解问题
conv 层将裁剪与内核成比例的图像
因此，如果您调用以下行：
encoder.summary()
decoder.summary()
你会得到
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 30, 30, 32) 896
_________________________________________________________________
batch_normalization (BatchNo (None, 30, 30, 32) 128
_________________________________________________________________
activation (Activation) (None, 30, 30, 32) 0
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 15, 15, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 13, 13, 16) 4624
_________________________________________________________________
batch_normalization_1 (Batch (None, 13, 13, 16) 64
_________________________________________________________________
activation_1 (Activation) (None, 13, 13, 16) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 6, 6, 16) 0
=================================================================
Total params: 5,712
Trainable params: 5,616
Non-trainable params: 96
_________________________________________________________________
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2 (Conv2D) (None, 4, 4, 16) 2320
_________________________________________________________________
batch_normalization_2 (Batch (None, 4, 4, 16) 64
_________________________________________________________________
activation_2 (Activation) (None, 4, 4, 16) 0
_________________________________________________________________
up_sampling2d (UpSampling2D) (None, 8, 8, 16) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 6, 6, 32) 4640
_________________________________________________________________
batch_normalization_3 (Batch (None, 6, 6, 32) 128
_________________________________________________________________
activation_3 (Activation) (None, 6, 6, 32) 0
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 12, 12, 32) 0
=================================================================
但您希望尺寸是这样的（对吗？！）：
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 32, 32, 32) 896
_________________________________________________________________
batch_normalization (BatchNo (None, 32, 32, 32) 128
_________________________________________________________________
activation (Activation) (None, 32, 32, 32) 0
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 16, 16, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 16, 16, 16) 4624
_________________________________________________________________
batch_normalization_1 (Batch (None, 16, 16, 16) 64
_________________________________________________________________
activation_1 (Activation) (None, 16, 16, 16) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 8, 8, 16) 0
=================================================================
Total params: 5,712
Trainable params: 5,616
Non-trainable params: 96
_________________________________________________________________
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2 (Conv2D) (None, 8, 8, 16) 2320
_________________________________________________________________
batch_normalization_2 (Batch (None, 8, 8, 16) 64
_________________________________________________________________
activation_2 (Activation) (None, 8, 8, 16) 0
_________________________________________________________________
up_sampling2d (UpSampling2D) (None, 16, 16, 16) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 16, 16, 32) 4640
_________________________________________________________________
batch_normalization_3 (Batch (None, 16, 16, 32) 128
_________________________________________________________________
activation_3 (Activation) (None, 16, 16, 32) 0
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 32, 32, 32) 0
=================================================================
使用Conv层中的padding="same"参数（不知道什么是padding的可以自己搜索）
那么还有另一个问题，你不能创建一个输出通道（32）不等于输入通道（3）的自动编码器，所以我的建议是另一层。
现在看看下面的解决方案，现在很容易。
现在，解决方案
import tensorflow as tf
import numpy as np
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()
encoder = tf.keras.Sequential()
# L1
encoder.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), input_shape=(32, 32, 3), activation='relu', padding='same'))
encoder.add(tf.keras.layers.BatchNormalization())
encoder.add(tf.keras.layers.Activation('relu'))
encoder.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))
# L2
encoder.add(tf.keras.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu', padding='same'))
encoder.add(tf.keras.layers.BatchNormalization())
encoder.add(tf.keras.layers.Activation('relu'))
encoder.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))
decoder = tf.keras.Sequential()
#L3
decoder.add(tf.keras.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu', padding='same'))
decoder.add(tf.keras.layers.BatchNormalization())
decoder.add(tf.keras.layers.Activation('relu'))
decoder.add(tf.keras.layers.UpSampling2D(size=(2,2)))
#L4
decoder.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding='same'))
decoder.add(tf.keras.layers.BatchNormalization())
decoder.add(tf.keras.layers.Activation('relu'))
decoder.add(tf.keras.layers.UpSampling2D(size=(2,2)))
#L5
decoder.add(tf.keras.layers.Conv2D(filters=3, kernel_size=(3,3), activation='relu', padding='same'))
decoder.add(tf.keras.layers.BatchNormalization())
decoder.add(tf.keras.layers.Activation('softmax'))
autoencoder = tf.keras.Sequential([encoder, decoder])
autoencoder.compile(loss='binary_crossentropy', optimizer='adam')
autoencoder.fit(x=X_train, y=X_train, batch_size=1000, epochs=50)
我从 tf.keras.datasets.
加载了 Cifar10 数据集(X_train, y_train), (X_test, y_test) = tk.datasets.cifar10.load_data() # tk = tensorflow.keras
火车测试形状是：train - (50000, 32, 32, 3) | test - (10000, 32, 32, 3)
然后我创建了具有以下层的编码器：
encoder = Sequential()
# L1
encoder.add(Conv2D(filters=32, kernel_size=(3,3), input_shape=(32,32,3), activation='relu'))
encoder.add(BatchNormalization())
encoder.add(Activation('relu'))
encoder.add(MaxPool2D(pool_size=(2,2)))
# L2
encoder.add(Conv2D(filters=16, kernel_size=(3,3), activation='relu'))
encoder.add(BatchNormalization())
encoder.add(Activation('relu'))
encoder.add(MaxPool2D(pool_size=(2,2)))
以及具有以下层的解码器：
decoder = Sequential()
#L3
decoder.add(Conv2D(filters=16, kernel_size=(3,3), activation='relu'))
decoder.add(BatchNormalization())
decoder.add(Activation('relu'))
decoder.add(UpSampling2D(size=(2,2)))
#L4
decoder.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu'))
decoder.add(BatchNormalization())
decoder.add(Activation('softmax'))
decoder.add(UpSampling2D(size=(2,2)))
我编译了它：
autoencoder = Sequential([encoder, decoder])
autoencoder.compile(loss='binary_crossentropy', optimizer='adam')
然后适合它：
autoencoder.fit(x=X_train, y=X_train, batch_size=1000, epochs=50)
我收到一个错误：
ValueError: A target array with shape (50000, 32, 32, 3) was passed for an output of shape (None, 12, 12, 32) while using as loss `binary_crossentropy`. This loss expects targets to have the same shape as the output.
此错误的可能原因是什么？
让我们了解问题
conv 层将裁剪与内核成比例的图像 因此，如果您调用以下行：
encoder.summary()
decoder.summary()
你会得到
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 30, 30, 32) 896
_________________________________________________________________
batch_normalization (BatchNo (None, 30, 30, 32) 128
_________________________________________________________________
activation (Activation) (None, 30, 30, 32) 0
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 15, 15, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 13, 13, 16) 4624
_________________________________________________________________
batch_normalization_1 (Batch (None, 13, 13, 16) 64
_________________________________________________________________
activation_1 (Activation) (None, 13, 13, 16) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 6, 6, 16) 0
=================================================================
Total params: 5,712
Trainable params: 5,616
Non-trainable params: 96
_________________________________________________________________
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2 (Conv2D) (None, 4, 4, 16) 2320
_________________________________________________________________
batch_normalization_2 (Batch (None, 4, 4, 16) 64
_________________________________________________________________
activation_2 (Activation) (None, 4, 4, 16) 0
_________________________________________________________________
up_sampling2d (UpSampling2D) (None, 8, 8, 16) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 6, 6, 32) 4640
_________________________________________________________________
batch_normalization_3 (Batch (None, 6, 6, 32) 128
_________________________________________________________________
activation_3 (Activation) (None, 6, 6, 32) 0
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 12, 12, 32) 0
=================================================================
但您希望尺寸是这样的（对吗？！）：
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 32, 32, 32) 896
_________________________________________________________________
batch_normalization (BatchNo (None, 32, 32, 32) 128
_________________________________________________________________
activation (Activation) (None, 32, 32, 32) 0
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 16, 16, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 16, 16, 16) 4624
_________________________________________________________________
batch_normalization_1 (Batch (None, 16, 16, 16) 64
_________________________________________________________________
activation_1 (Activation) (None, 16, 16, 16) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 8, 8, 16) 0
=================================================================
Total params: 5,712
Trainable params: 5,616
Non-trainable params: 96
_________________________________________________________________
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_2 (Conv2D) (None, 8, 8, 16) 2320
_________________________________________________________________
batch_normalization_2 (Batch (None, 8, 8, 16) 64
_________________________________________________________________
activation_2 (Activation) (None, 8, 8, 16) 0
_________________________________________________________________
up_sampling2d (UpSampling2D) (None, 16, 16, 16) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 16, 16, 32) 4640
_________________________________________________________________
batch_normalization_3 (Batch (None, 16, 16, 32) 128
_________________________________________________________________
activation_3 (Activation) (None, 16, 16, 32) 0
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 32, 32, 32) 0
=================================================================
使用Conv层中的padding="same"参数（不知道什么是padding的可以自己搜索）
那么还有另一个问题，你不能创建一个输出通道（32）不等于输入通道（3）的自动编码器，所以我的建议是另一层。
现在看看下面的解决方案，现在很容易。
现在，解决方案
import tensorflow as tf
import numpy as np
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()
encoder = tf.keras.Sequential()
# L1
encoder.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), input_shape=(32, 32, 3), activation='relu', padding='same'))
encoder.add(tf.keras.layers.BatchNormalization())
encoder.add(tf.keras.layers.Activation('relu'))
encoder.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))
# L2
encoder.add(tf.keras.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu', padding='same'))
encoder.add(tf.keras.layers.BatchNormalization())
encoder.add(tf.keras.layers.Activation('relu'))
encoder.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))
decoder = tf.keras.Sequential()
#L3
decoder.add(tf.keras.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu', padding='same'))
decoder.add(tf.keras.layers.BatchNormalization())
decoder.add(tf.keras.layers.Activation('relu'))
decoder.add(tf.keras.layers.UpSampling2D(size=(2,2)))
#L4
decoder.add(tf.keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding='same'))
decoder.add(tf.keras.layers.BatchNormalization())
decoder.add(tf.keras.layers.Activation('relu'))
decoder.add(tf.keras.layers.UpSampling2D(size=(2,2)))
#L5
decoder.add(tf.keras.layers.Conv2D(filters=3, kernel_size=(3,3), activation='relu', padding='same'))
decoder.add(tf.keras.layers.BatchNormalization())
decoder.add(tf.keras.layers.Activation('softmax'))
autoencoder = tf.keras.Sequential([encoder, decoder])
autoencoder.compile(loss='binary_crossentropy', optimizer='adam')
autoencoder.fit(x=X_train, y=X_train, batch_size=1000, epochs=50)