设计简单的自动编码器时出现尺寸误差 [英] Value error with dimensions in designing a simple autoencoder

问题描述

我正在尝试使用Keras库在Python 3.5中测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具有2维，但数组的形状为(32，256，256，3).我的数据集非常小(60张RGB图像，尺寸为256 * 256，并且要验证一种相同类型的图像).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

问题在这里:

input_img = Input(shape=(65536,))

您告诉Keras，网络的输入将具有65K尺寸，这意味着形状的矢量(样本，65536)，但是您的实际输入具有形状(样本，256，256，3).任何简单的解决方案都是使用实际的输入形状并使网络执行必要的重塑:

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图层以使图像变平，然后将经过变平的图像恢复为形状(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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