Comments (2)
Hi @sumiya-kuroda ,
I will take a look and get back to you asap. Would you be able to share your module parameters here with me? Specifically data
and model
dictionaries.
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Thanks @hosseybposh ! Here are the data parameters. I did not change the model parameters. I have been trying to run this on TensorFlow 2.13 if this is also relevant.
data = {'path' : 'DeepCGH_Datasets/Disks',# path to store the data
'shape' : (1024, 1024, 6),# shape of the holograms. The last dimension determines the number of depth planes
'object_type' : 'Disk',# shape of the object in simulated images, can be disk, square, or line
'object_size' : 10,# has no effect if object type is 'Line'
'object_count' : [27, 48],# number of random objects to be created
'intensity' : 1,# the (range of) intensity of each object. If a range is specified, for each object the intensity is randomly determined
'normalize' : True,# if the data is 3D, it'll normalize the intensities from plane to plane (see manuscript fot more info)
'centralized' : False,# avoids putting objects near the edges of the hologram (useful for practical optogenetics applications)
'N' : 50, # number of sample holograms to generate
'train_ratio' : 400/5000,# the ratio of N that will be used for training
'compression' : 'GZIP',# tfrecords compression format
'name' : 'target',# name of the dictionary that contains the targets (leave as "target" if you're not changing the structure of network input)
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