简单自动编码器设计中的尺寸值误差

Question

嗨，我正在使用Keras库在Python3.5中试用一个简单的自动编码器。我面临的问题是- ValueError:在检查输入时出错:期望input_40具有二维，但得到了形状为(32、256、256、3)的数组。我的数据集非常小(60幅RGB图像的维数为- 256*256，需要验证的图像类型相同)。我对Python有点陌生。请帮帮忙。

import matplotlib.pyplot as plt
from keras.layers import Input, Dense
from keras.models import Model

#Declaring the model
encoding_dim = 32
input_img = Input(shape=(65536,))
encoded = Dense(encoding_dim, activation='relu')(input_img)
decoded = Dense(65536, activation='sigmoid')(encoded)
autoencoder = Model(input_img, decoded)
encoder = Model(input_img, encoded)
encoded_input = Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, decoder_layer(encoded_input))
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')


#Constructing a data generator iterator
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set=
train_datagen.flow_from_directory('C:\\Users\\vlsi\\Desktop\\train',
batch_size = 32,
class_mode = 'binary')
test_set =     
test_datagen.flow_from_directory('C:\\Users\\vlsi\\Desktop\\validation',
batch_size = 32,
class_mode = 'binary')


#fitting data
autoencoder.fit_generator(training_set,
steps_per_epoch = 80,
epochs = 25,
validation_data = test_set,
validation_steps = 20)

import numpy as np from keras.preprocessing import image
test_image =            
image.load_img('C:\\Users\\vlsi\\Desktop\\validation\\validate\\apple1.jpg')
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)

#Displaying output
encoded_imgs = encoder.predict(test_image)
decoded_imgs = decoder.predict(encoded_imgs)
plt.imshow(decoded_imgs)

Answer 1

问题在于：

input_img = Input(shape=(65536,))

您告诉Keras网络的输入将有65K维，这意味着形状向量(样本，65536)，但是实际的输入有形状(样本，256,256，3)。任何简单的解决方案都是使用真正的输入形状，并对网络执行必要的整形：

input_img = Input(shape=((256, 256, 3))
flattened = Flatten()(input_img)
encoded = Dense(encoding_dim, activation='relu')(flattened)
decoded = Dense(256 * 256 * 3, activation='sigmoid')(encoded)
decoded = Reshape((256, 256, 3))(decoded)
autoencoder = Model(input_img, decoded)
encoder = Model(input_img, encoded)
encoded_input = Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, decoder_layer(encoded_input))

请注意，我首先添加了一个扁平层和一个重塑层，以使图像变平，然后将平坦的图像返回到形状(256、256、3)。