Tensorflow image_dataset_from_directory用于输入数据集和输出数据集 [英] Tensorflow image_dataset_from_directory for input dataset and output dataset
问题描述
我正在尝试学习图像自动编码,但是我无法使用输入和输出图像来训练模型
I am trying to learn image auto encoding but I can't use input and output images to train the model
例如:
输入图像文件夹:".../Pictures/Input"
输出图像文件夹:".../Pictures/Output"
ex:
input images folder: ".../Pictures/Input"
output images folder: ".../Pictures/Output"
#get input images from data_dir_input
ds_input = tf.keras.preprocessing.image_dataset_from_directory(
data_dir_input,
seed=123,
image_size=(img_height, img_width),
label_mode=None,
batch_size=batch_size)
#get output images from data_dir_output
ds_output = tf.keras.preprocessing.image_dataset_from_directory(
data_dir_output,
seed=123,
image_size=(img_height, img_width),
label_mode=None,
batch_size=batch_size)
# --------- model init etc --------------
# ...
model.fit(x=ds_input, y=ds_output, batch_size=32, epochs=50)
但是我这样说出错:
`y` argument is not supported when using dataset as input
训练模型时如何使用自己的输入图像和输出图像?
How can i use my own input images and output images when training model?
推荐答案
您可以使用tf.data.Dataset
以获得更大的灵活性.据我了解,image_dataset_from_directory
除了整数以外不支持任何自定义标签.
You could use tf.data.Dataset
for some more flexibility. From what I read, image_dataset_from_directory
doesn't support any custom label other than an integer.
尝试一下:
import os
import tensorflow as tf
os.chdir(r'c:/users/user/Pictures')
from glob2 import glob
x_files = glob('inputs/*.jpg')
y_files = glob('targets/*.jpg')
files_ds = tf.data.Dataset.from_tensor_slices((x_files, y_files))
def process_img(file_path):
img = tf.io.read_file(file_path)
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
img = tf.image.resize(img, size=(28, 28))
return img
files_ds = files_ds.map(lambda x, y: (process_img(x), process_img(y))).batch(1)
original, target = next(iter(files_ds))
<tf.Tensor: shape=(1, 28, 28, 3), dtype=float32, numpy=
array([[[[0.357423 , 0.3325731 , 0.20412168],
[0.36274514, 0.21940777, 0.17623049],
[0.34821934, 0.13921566, 0.06858743],
...,
[0.25486213, 0.27446997, 0.2520612 ],
[0.04925931, 0.26666668, 0.07619007],
[0.48167226, 0.5287311 , 0.520888 ]]]
然后,您无需将y
传递给fit()
调用.您将可以这样使用它:
Then you won't need to pass an y
to the fit()
call. You will be able to use it as such:
model.fit(ds, epochs=5)
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