keras 自动编码器“检查目标时出错" [英] keras autoencoder "Error when checking target"
问题描述
我正在尝试改编 keras 网站上的 2d 卷积自动编码器示例:https://blog.keras.io/building-autoencoders-in-keras.html
i'm trying to adapt the 2d convolutional autoencoder example from the keras website: https://blog.keras.io/building-autoencoders-in-keras.html
对于我自己使用一维输入的情况:
to my own case where i use 1d inputs:
from keras.layers import Input, Dense, Conv1D, MaxPooling1D, UpSampling1D
from keras.models import Model
from keras import backend as K
import scipy as scipy
import numpy as np
mat = scipy.io.loadmat('edata.mat')
emat = mat['edata']
input_img = Input(shape=(64,1)) # adapt this if using `channels_first` image data format
x = Conv1D(32, (9), activation='relu', padding='same')(input_img)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(8, (9), activation='relu', padding='same')(x)
encoded = MaxPooling1D(4, padding='same')(x)
x = Conv1D(8, (9), activation='relu', padding='same')(encoded)
x = UpSampling1D((4))(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = UpSampling1D((4))(x)
x = Conv1D(32, (9), activation='relu')(x)
x = UpSampling1D((4))(x)
decoded = Conv1D(1, (9), activation='sigmoid', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
x_train = emat[:,0:80000]
x_train = np.reshape(x_train, (x_train.shape[1], 64, 1))
x_test = emat[:,80000:120000]
x_test = np.reshape(x_test, (x_test.shape[1], 64, 1))
from keras.callbacks import TensorBoard
autoencoder.fit(x_train, x_train,
epochs=50,
batch_size=128,
shuffle=True,
validation_data=(x_test, x_test),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
但是，当我尝试运行 autoencoder.fit() 时收到此错误:
however, i receive this error when i try to run the autoencoder.fit():
ValueError:检查目标时出错:预期 conv1d_165 具有shape (None, 32, 1) 但得到了形状为 (80000, 64, 1) 的数组
ValueError: Error when checking target: expected conv1d_165 to have shape (None, 32, 1) but got array with shape (80000, 64, 1)
我知道我在设置图层时可能做错了什么，我只是将 maxpool 和 conv2d 大小更改为一维形式...我对 keras 或自动编码器的经验很少，任何人都知道我在做什么做错了吗?
i know i'm probably doing something wrong when i set up my layers, i just changed the maxpool and conv2d sizes to a 1d form...i have very little experience with keras or autoencoders, anyone see what i'm doing wrong?
谢谢
我在新的控制台上运行它时的错误:
the error when i run it on a fresh console:
ValueError:检查目标时出错:预期 conv1d_7 具有shape (None, 32, 1) 但得到了形状为 (80000, 64, 1) 的数组
ValueError: Error when checking target: expected conv1d_7 to have shape (None, 32, 1) but got array with shape (80000, 64, 1)
这是autoencoder.summary()
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 64, 1) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 64, 32) 320
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 16, 32) 0
_________________________________________________________________
conv1d_2 (Conv1D) (None, 16, 16) 4624
_________________________________________________________________
max_pooling1d_2 (MaxPooling1 (None, 4, 16) 0
_________________________________________________________________
conv1d_3 (Conv1D) (None, 4, 8) 1160
_________________________________________________________________
max_pooling1d_3 (MaxPooling1 (None, 1, 8) 0
_________________________________________________________________
conv1d_4 (Conv1D) (None, 1, 8) 584
_________________________________________________________________
up_sampling1d_1 (UpSampling1 (None, 4, 8) 0
_________________________________________________________________
conv1d_5 (Conv1D) (None, 4, 16) 1168
_________________________________________________________________
up_sampling1d_2 (UpSampling1 (None, 16, 16) 0
_________________________________________________________________
conv1d_6 (Conv1D) (None, 8, 32) 4640
_________________________________________________________________
up_sampling1d_3 (UpSampling1 (None, 32, 32) 0
_________________________________________________________________
conv1d_7 (Conv1D) (None, 32, 1) 289
=================================================================
Total params: 12,785
Trainable params: 12,785
Non-trainable params: 0
_________________________________________________________________
推荐答案
由于自动编码器的输出应该重构输入，所以最低要求是它们的维度应该匹配，对吧?
Since the autoencoder output should reconstruct the input, a minimum requirement is that their dimensions should match, right?
查看您的 autoencoder.summary()，很容易确认情况并非如此:您的输入的形状为 (64,1)，而最后一个卷积层 conv1d_7 的输出是 (32,1)(我们忽略第一维中的 None，因为它们指的是批量大小).
Looking at your autoencoder.summary(), it is easy to confirm that this is not the case: your input is of shape (64,1), while the output of your last convolutional layer conv1d_7 is (32,1) (we ignore the None in the first dimension, since they refer to the batch size).
让我们看看 Keras 博客中的 示例你链接到(它是一个 2D 自动编码器，但想法是一样的):
Let's have a look at the example in the Keras blog you link to (it is a 2D autoencoder, but the idea is the same):
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
input_img = Input(shape=(28, 28, 1)) # adapt this if using `channels_first` image data format
x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
# at this point the representation is (4, 4, 8) i.e. 128-dimensional
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
这是本例中 autoencoder.summary() 的结果:
Here is the result of autoencoder.summary() in this case:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 28, 28, 1) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 28, 28, 16) 160
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 16) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 14, 14, 8) 1160
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 7, 7, 8) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 7, 7, 8) 584
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 4, 4, 8) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 4, 4, 8) 584
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 8, 8, 8) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 8, 8, 8) 584
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, 16, 16, 8) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 14, 14, 16) 1168
_________________________________________________________________
up_sampling2d_3 (UpSampling2 (None, 28, 28, 16) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 28, 28, 1) 145
=================================================================
Total params: 4,385
Trainable params: 4,385
Non-trainable params: 0
很容易确认，这里输入和输出(最后一个卷积层conv2d_7)的维度确实都是(28, 28, 1).
It is easy to confirm that here the dimensions of the input and the output (last convolutional layer conv2d_7) are indeed both (28, 28, 1).
因此，summary() 方法是您构建自动编码器时的朋友；您应该对参数进行试验，直到您确定产生与输入具有相同维度的输出.我通过将最后一个 UpSampling1D 层的 size 参数从 4 更改为 8 来设法使用您的自动编码器做到这一点:
So, the summary() method is your friend when building autoencoders; you should experiment with the parameters until you are sure that you produce an output of the same dimensionality as your input. I managed to do so with your autoencoder simply by changing the size argument of the last UpSampling1D layer from 4 to 8:
input_img = Input(shape=(64,1))
x = Conv1D(32, (9), activation='relu', padding='same')(input_img)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(8, (9), activation='relu', padding='same')(x)
encoded = MaxPooling1D(4, padding='same')(x)
x = Conv1D(8, (9), activation='relu', padding='same')(encoded)
x = UpSampling1D((4))(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = UpSampling1D((4))(x)
x = Conv1D(32, (9), activation='relu')(x)
x = UpSampling1D((8))(x) ## <-- change here (was 4)
decoded = Conv1D(1, (9), activation='sigmoid', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
在这种情况下，autoencoder.summary() 变为:
In which case, the autoencoder.summary() becomes:
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 64, 1) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 64, 32) 320
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 16, 32) 0
_________________________________________________________________
conv1d_2 (Conv1D) (None, 16, 16) 4624
_________________________________________________________________
max_pooling1d_2 (MaxPooling1 (None, 4, 16) 0
_________________________________________________________________
conv1d_3 (Conv1D) (None, 4, 8) 1160
_________________________________________________________________
max_pooling1d_3 (MaxPooling1 (None, 1, 8) 0
_________________________________________________________________
conv1d_4 (Conv1D) (None, 1, 8) 584
_________________________________________________________________
up_sampling1d_1 (UpSampling1 (None, 4, 8) 0
_________________________________________________________________
conv1d_5 (Conv1D) (None, 4, 16) 1168
_________________________________________________________________
up_sampling1d_2 (UpSampling1 (None, 16, 16) 0
_________________________________________________________________
conv1d_6 (Conv1D) (None, 8, 32) 4640
_________________________________________________________________
up_sampling1d_3 (UpSampling1 (None, 64, 32) 0
_________________________________________________________________
conv1d_7 (Conv1D) (None, 64, 1) 289
=================================================================
Total params: 12,785
Trainable params: 12,785
Non-trainable params: 0
输入和输出的维度匹配，应该是......
with the dimensionality of your input and output matched, as it should be...
这篇关于keras 自动编码器“检查目标时出错"的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
