设计一个简单的自动编码器时的尺寸值误差 [英] Value error with dimensions in designing a simple autoencoder

问题描述

我正在使用 Keras 库在 Python 3.5 中尝试一个简单的自动编码器.我面临的问题是 - ValueError:检查输入时出错:预期 input_40 有 2 个维度，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，尺寸为 256*256 和一种要验证的相同类型的图像).我对 Python 有点陌生.请帮忙.

Hi I am trying out a simple autoencoder in Python 3.5 using Keras library. The issue I face is - ValueError: Error when checking input: expected input_40 to have 2 dimensions, but got array with shape (32, 256, 256, 3). My dataset is very small (60 RGB images with dimension - 256*256 and one same type of image to validate). I am a bit new to Python. Please help.

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)

推荐答案

问题出在这里:

input_img = Input(shape=(65536,))

您告诉 Keras 网络的输入将具有 65K 维，这意味着形状向量 (samples, 65536)，但您的实际输入具有形状 (samples, 256, 256, 3).任何简单的解决方案都是使用真实的输入形状并让网络执行必要的整形:

You told Keras the input to the network will have 65K dimensions, meaning a vector of shape (samples, 65536), but your actual inputs have shape(samples, 256, 256, 3). Any easy solution would be to use the real input shape and for the network to perform the necessary reshaping:

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 Flatten 和 Reshape 图层来展平图像，然后将展平后的图像恢复为形状 (256, 256, 3).

Note that I added a Flatten and a Reshape layer first to flatten the image, and then to take the flattened image back to the shape (256, 256, 3).

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