我目前正在使用一个在卷积网络上工作正常的生成器 . 但是,当我使用相同的生成器来安装Autoencoder时,会出现以下错误 .
**Exception: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: [[[[ 0.86666673 0.86666673 0.86666673 ..., 0.62352943 0.627451
0.63137257]
[ 0.86666673 0.86666673 0.86666673 ..., 0.63137257 0.627451
0.627451 ]
[ 0.86666673 0.86666673 0.86666673 ..., 0.63137257 0.627451
0.62352943]
...,**
我的代码如下
from keras.layers import Input, Dense, Convolution2D, MaxPooling2D,
from keras.models import Model,Sequential
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import h5py
img_width=140
img_height=140
train_data_dir=r'SitePhotos\train'
valid_data_dir=r'SitePhotos\validation'
input_img = Input(batch_shape=(32,3, img_width, img_width))
x = Convolution2D(16, 3, 3, activation='relu', border_mode='same')(input_img)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(x)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(x)
encoded = MaxPooling2D((2, 2), border_mode='same')(x)
# at this point the representation is (8, 4, 4) i.e. 128-dimensional
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(x)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(16, 3, 3, activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Convolution2D(1, 3, 3, activation='sigmoid', border_mode='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='mse')
valid_datagen = ImageDataGenerator(rescale=1./255)
train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode=None,
shuffle=True)
valid_generator = valid_datagen.flow_from_directory(
valid_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode=None,
shuffle=True)
autoencoder.fit_generator(train_generator,
nb_epoch=50,
validation_data=valid_generator,
samples_per_epoch=113,
nb_val_samples=32
)
我对生成器的唯一更改是将类模式设置为None . 将类模式保持为“二进制”也无济于事 . 由于拟合生成器需要一个元组,我尝试传递(train_generator,train_generator)和(valid_generator,valid_generator)作为fit_generator的参数 .
在这种情况下得到以下异常
检查模型输入时出错:数据应该是Numpy数组,或Numpy数组的列表/字典 . 实测:
但似乎没有任何效果 . 不确定我错过了什么 . 作为一个keras新手,任何帮助将不胜感激 .
谢谢SK
1 回答
看来,第一个问题是,训练数据确实也包含了预测的目标值 - 是吗?
无论如何这是我编码我的生成器的方式: