python - 验证卷积编码器的结构
问题描述
我正在构建一个卷积编码器来处理一些 128x128 图像 - 像这样。
我一直在通过使用 500 张图像的图像集对其进行测试来测试结构。生成的解码图像基本上是全黑的(不是我想要的!)
我希望在这里得到一些建议,因为我认为我犯了一些明显的错误。
一小部分图片可以在这里下载 -> https://www.dropbox.com/sh/0oj1p6sqal32cvx/AAAYQJSK2SPfynD8wYMSo9bPa?dl=0
当前代码
################################# SETUP #######################################
import glob
import pandas as pd
import numpy as np
import sys
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
np.set_printoptions(threshold=np.nan)
######################### DATA PREPARATION #####################################
# create a list of XML files within the raw data folder
image_list = glob.glob("Images/test_images/*.jpeg")
print(image_list)
l = []
for i in image_list:
img = np.array(cv2.imread(i, 0))
l.append(img)
T = np.array(l)
# split into training and testing sets
labels = image_list
data_train, data_test, labels_train, labels_test = train_test_split(T, labels, test_size=0.5, random_state=42)
# convert to 0-1 floats (reconversion by * 255)
data_train = data_train.astype('float32') / 255.
data_test = data_test.astype('float32') / 255.
print(data_train.shape)
# reshape from channels first to channels last
data_train = np.rollaxis(data_train, 0, 3)
data_test = np.rollaxis(data_test, 0, 3)
######################### ENCODER MODELING #####################################
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
input_img = Input(shape=(128, 128, 1)) # adapt this if using `channels_first` image data format
x = Conv2D(64, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
# create the model
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='mse')
# reference for reordering
data_train_dimensions = data_train.shape
data_test_dimensions = data_test.shape
# reshape the data sets
data_train = np.reshape(data_train, (data_train_dimensions[2], 128, 128, 1)) # adapt this if using `channels_first` image data format
data_test = np.reshape(data_test, (data_test_dimensions[2], 128, 128, 1))
from keras.callbacks import TensorBoard
autoencoder.fit(data_train, data_test,
epochs=10,
batch_size=128,
shuffle=True,
validation_data=(data_train, data_test),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
# create decoded images from model
decoded_imgs = autoencoder.predict(data_test)
# reorder columns
decoded_imgs = np.rollaxis(decoded_imgs, 3, 1)
# reshape from channels first to channels last
data_train = np.rollaxis(data_train, 0, 3)
data_test = np.rollaxis(data_test, 0, 3)
# convert to 0-1 floats (reconversion by * 255)
data_train = data_train.astype('float32') * 255.
data_test = data_test.astype('float32') * 255.
解决方案
我认为主要问题是你适合data_train, data_test
而不是data_train, labels_train
,也就是说,你应该在样本和相应的输出上拟合你的模型,但你只在输入上训练它,由于 50/50 分割,这些输入恰好是兼容的形状。
如果模型的目的是从压缩表示中再现图像,那么您可以训练fit(data_train, data_train, ..., validation_data=(data_test, data_test))
.
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