具有可变大小图像的二维卷积神经网络

Question

我已经使用 Theano 后端通过 Keras 实现了一个卷积自动编码器。我正在改变我的方法来尝试处理不同尺寸的图像。只要我使用 numpy 的 stack 函数来构建数据集（相同大小的图像），我就是黄金。但是，对于不同大小的图像，我们不能使用 stack，而 fit 需要一个 numpy 数组。所以我改为 fit_generator 以避免大小检查。问题是最后一层期望 16 作为输入的最后一个维度，我不明白为什么它得到原始图像的维度。

看看下面的代码和错误输出。


import numpy as np
import keras
from keras.models import Sequential, Model
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D

AE_EPOCHS = 10
VERB = 1
batchsz = 16
outfun = 'sigmoid'

data = []
dimensions = [(10, 15), (12, 15), (7,15), (20,15), (25,15)]

for d in dimensions:
    dd = np.random.rand(*d)
    dd = dd.reshape((1,)+dd.shape)
    data.append(dd)

input_img = Input(shape=(1, None, 15))
filtersz = 3
pad_it = 'same'
size1 = 16
size2 = 8
x = Conv2D(size1, (filtersz, filtersz), activation='relu', padding=pad_it)(input_img)
x = MaxPooling2D((2, 2), padding=pad_it)(x)
x = Conv2D(size2, (filtersz, filtersz), activation='relu', padding=pad_it)(x)
x = MaxPooling2D((2, 2), padding=pad_it)(x)
x = Conv2D(size2, (filtersz, filtersz), activation='relu', padding=pad_it)(x)
encoded = MaxPooling2D((2, 2), padding=pad_it)(x)

x = Conv2D(size2, (filtersz, filtersz), activation='relu', padding=pad_it)(encoded)
x = UpSampling2D((2, 2), data_format="channels_first")(x)
x = Conv2D(size2, (filtersz, filtersz), activation='relu', padding=pad_it)(x)
x = UpSampling2D((2, 2), data_format="channels_first")(x)
x = Conv2D(size1, (filtersz, filtersz), activation='relu', padding=pad_it)(x)
x = UpSampling2D((2, 2), data_format="channels_first")(x)
decoded = Conv2D(1, (filtersz, filtersz), activation=outfun, padding=pad_it)(x)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss= 'binary_crossentropy')

x_train = data[1:]
x_test= data[0].reshape((1,)+ data[0].shape)

def mygen(xx, *args, **kwargs):
    for i in xx:
        yield (i,i)

thegen = mygen(x_train)
#If I use this generator somehow None is returned so it is not used
thegenval = mygen(np.array([x_test]))

hist = autoencoder.fit_generator(thegen,
                epochs=AE_EPOCHS,
                steps_per_epoch=4,
                verbose=VERB,
                validation_data=(x_test, x_test),
                validation_steps=1
                )

Traceback (most recent call last):

File "stacko.py", line 107, in validation_steps=1

File "/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py", line 88, in wrapper return func(*args, **kwargs)

File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1847, in fit_generator val_x, val_y, val_sample_weight)

File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1315, in _standardize_user_data exception_prefix='target')

File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 139, in _standardize_input_data str(array.shape))

ValueError: Error when checking target: expected conv2d_7 to have shape (None, 1, None, 16) but got array with shape (1, 1, 10, 15)

Answer 1

上面的代码有两个问题：首先，图像轴的大小必须是每层最小过滤器数量（在本例中为 8）的倍数；其次，fit_generator 的生成器必须 return 批次（4D numpy 数组）。

生成器是用 itertools.cycle 实现的，并将图形重塑为一个样本批次（如果使用多个具有相同尺寸的图像，则可以为每组维度设置可变大小的批次）。工作示例如下。


import numpy as np
from itertools import cycle

import keras
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D

AE_EPOCHS = 10
VERB = 1
outfun = 'sigmoid'

data = []
dimensions = [(16, 32), (24, 32), (8,32), (32,32)]
for d in dimensions:
    dd = np.random.rand(*d)
    dd = dd.reshape((1,)+dd.shape)
    data.append(dd)

input_img = Input(shape=(1, None, 32))
filtersz = 3
pad_it = 'same'
size1 = 16
size2 = 8
x = Conv2D(size1, (filtersz, filtersz), activation='relu', padding=pad_it)(input_img)
x = MaxPooling2D((2, 2), padding=pad_it)(x)
x = Conv2D(size2, (filtersz, filtersz), activation='relu', padding=pad_it)(x)
x = MaxPooling2D((2, 2), padding=pad_it)(x)
x = Conv2D(size2, (filtersz, filtersz), activation='relu', padding=pad_it)(x)
encoded = MaxPooling2D((2, 2), padding=pad_it)(x)

x = Conv2D(size2, (filtersz, filtersz), activation='relu', padding=pad_it)(encoded)
x = UpSampling2D((2, 2), data_format="channels_first")(x)
x = Conv2D(size2, (filtersz, filtersz), activation='relu', padding=pad_it)(x)
x = UpSampling2D((2, 2), data_format="channels_first")(x)
x = Conv2D(size1, (filtersz, filtersz), activation='relu', padding=pad_it)(x)
x = UpSampling2D((2, 2), data_format="channels_first")(x)
decoded = Conv2D(1, (filtersz, filtersz), activation=outfun, padding=pad_it)(x)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss= 'binary_crossentropy')


x_train = data[1:]
x_test= [data[0]]

def mygen(xx, *args, **kwargs):
    for i in cycle(xx):
        ii = i.reshape((1,)+i.shape)
        yield ii,ii

thegen = mygen(x_train)
thegenval = mygen(x_test)

hist = autoencoder.fit_generator(
                thegen,
                epochs=AE_EPOCHS,
                steps_per_epoch=3,
                verbose=VERB,
                validation_data=thegenval,
                validation_steps=1
                )

具有可变大小图像的二维卷积神经网络

2D Convolutional neural networks with variable size images

python-3.x

theano

convolution

keras-2