# Notebook from qztseng/stardist
Path: examples/2D/2_training_test3_gpu.ipynb

from __future__ import print_function, unicode_literals, absolute_import, division
import sys
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

from glob import glob
from tqdm import tqdm
from tifffile import imread
from csbdeep.utils import Path, normalize
from csbdeep.utils.plot_utils import *
from csbdeep.data.transform import Transform

from stardist import fill_label_holes, random_label_cmap, calculate_extents, gputools_available
from stardist.models import Config2D, StarDist2D, StarDistData2D
from stardist.models.sample_patches import sample_patches

import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)

np.random.seed(42)
lbl_cmap = random_label_cmap()

%reload_ext autoreload
%autoreload 2

print(tf.__version__)Using TensorFlow backend.
# import tensorflow as tf
# print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))_____no_output_____from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 6299433593832438252
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 2385849660503604949
physical_device_desc: "device: XLA_CPU device"
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 7710054810
locality {
  bus_id: 1
  links {
  }
}
incarnation: 10673698420361516924
physical_device_desc: "device: 0, name: Quadro RTX 4000, pci bus id: 0000:01:00.0, compute capability: 7.5"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 14379146810275313005
physical_device_desc: "device: XLA_GPU device"
]

## Load sample data_____no_output_____

%pwd_____no_output_____X = sorted(glob('/home/lis-paul/data/dsb2018/dsb2018_sub1/images/*.tif'))
Y = sorted(glob('/home/lis-paul/data/dsb2018/dsb2018_sub1/masks/*.tif'))
assert all(Path(x).name==Path(y).name for x,y in zip(X,Y))

X = list(map(imread,X))
Y = list(map(imread,Y))
n_channel = 1 if X[0].ndim == 2 else X[0].shape[-1]

axis_norm = (0,1)   # normalize channels independently
# axis_norm = (0,1,2) # normalize channels jointly
if n_channel > 1:
    print("Normalizing image channels %s." % ('jointly' if axis_norm is None or 2 in axis_norm else 'independently'))
    sys.stdout.flush()

X = [normalize(x,1,99.8,axis=axis_norm) for x in tqdm(X)]
Y = [fill_label_holes(y) for y in tqdm(Y)]100%|██████████| 292/292 [00:00<00:00, 753.69it/s]
100%|██████████| 292/292 [00:01<00:00, 203.76it/s]
assert len(X) > 1, "not enough training data"
rng = np.random.RandomState(42)
ind = rng.permutation(len(X))
n_val = max(1, int(round(0.15 * len(ind))))
ind_train, ind_val = ind[:-n_val], ind[-n_val:]

#manual assign train/val index for debugging
# ind_train = [1,2,3,4]
# ind_val = [0]

X_val, Y_val = [X[i] for i in ind_val]  , [Y[i] for i in ind_val]
X_trn, Y_trn = [X[i] for i in ind_train], [Y[i] for i in ind_train]
print('number of images: %3d' % len(X))
print('- training:       %3d' % len(X_trn))
print('- validation:     %3d' % len(X_val))number of images: 292
- training:       248
- validation:      44
i = 5
# img, lbl = X[i], Y[i]
img, lbl = X_val[i], Y_val[i]
assert img.ndim in (2,3)
img = img if (img.ndim==2 or img.shape[-1]==3) else img[...,0]
plt.figure(figsize=(16,10))
plt.subplot(121); plt.imshow(img,cmap='gray');   plt.axis('off'); plt.title('Raw image')
plt.subplot(122); plt.imshow(lbl,cmap=lbl_cmap); plt.axis('off'); plt.title('GT labels')
None;_____no_output_____img.shape_____no_output_____

## Try with other augmentation packages and use stardist generator pipe (keras sequence) _____no_output_____

# import cv2
from albumentations import *_____no_output_____AUGMENTATIONS_TRAIN = Compose([
    Flip(p=0.5),
    ShiftScaleRotate(scale_limit=(0.2, 0.8), rotate_limit=45, interpolation=0, border_mode=4, value=None, mask_value=None, always_apply=False, p=0.5),
#     OneOf([
# #         IAAAdditiveGaussianNoise(p=0.5),
#         Downscale(scale_min = 0.5, scale_max=0.5, p=0.5),
#         ElasticTransform(p=0.5),
#     ])
#     RandomScale(scale_limit=0.5, interpolation=1, always_apply=True, p=0.9),
    Downscale(scale_min = 0.5, scale_max=0.5, p=0.5),
    ElasticTransform(alpha=1000, sigma=100, p=0.5)
], p=0.9)_____no_output_____# data_train = StarDistData2D(X_trn, Y_trn, batch_size=8, n_rays=32, patch_size=(256,256), augmenter=AUGMENTATIONS_TRAIN, shape_completion=False)_____no_output_____# i = np.random.randint(0,len(data_train))
# XX, YY = data_train[i]

# X,dist_mask = XX
# prob,dist = YY

# plot_some(X, dist_mask, prob, dist, x_inch=30, y_inch=20)_____no_output_____

## Debug validation data part_____no_output_____

# validation_data=(X_val,Y_val)
# data_kwargs = {'n_rays': 32, 'patch_size': (256, 256), 'grid': (2, 2), 'shape_completion': False, 'b': 32, 'use_gpu': False, 'foreground_prob': 0.9}
# data_val = StarDistData2D(*validation_data, batch_size=1, **data_kwargs)

# data_val[0][0][0].shape_____no_output_____

## Train with augmentations_____no_output_____

# import gputools
# gputools_available()_____no_output_____# 32 is a good default choice (see 1_data.ipynb)
n_rays = 32

# Use OpenCL-based computations for data generator during training (requires 'gputools')
use_gpu = False and gputools_available()

# Predict on subsampled grid for increased efficiency and larger field of view
grid = (2,2)

conf = Config2D (
    n_rays       = n_rays,
    grid         = grid,
    use_gpu      = use_gpu,
    n_channel_in = n_channel,
    train_batch_size = 32,
    train_epochs = 400,
    train_steps_per_epoch = 200,
    train_one_cycle_lr_max = 0.003,
    y_range = [3.0,50.0],
    train_background_reg=0.0005,  #instead of 0.0001
#     train_learning_rate=0.008
#     net_input_shape = (X_trn[0].shape[0], X_trn[0].shape[1], n_channel)
)

conf_____no_output_____# if use_gpu:
#     from csbdeep.utils.tf import limit_gpu_memory
#     # adjust as necessary: limit GPU memory to be used by TensorFlow to leave some to OpenCL-based computations
#     limit_gpu_memory(0.8, allow_growth=True)_____no_output_____model = StarDist2D(conf, name='stardist', basedir='C_32_onecyclelr0.003_yrange3-50_bgreg0.0005_csbsub1')Using default values: prob_thresh=0.5, nms_thresh=0.4.
model.train(X_trn, Y_trn, validation_data=(X_val,Y_val), augmenter=AUGMENTATIONS_TRAIN, multi=True, ncpu=8)Epoch 1/400
150/200 [=====================>........] - ETA: 1:15 - loss: 2.3152 - prob_loss: 0.2397 - dist_loss: 10.3777 - prob_kld: 0.1642 - dist_relevant_mae: 10.3720 - dist_relevant_mse: 219.1246model.keras_model.save('8b_200s_400e.h5') _____no_output_____model.train(X_trn, Y_trn, validation_data=(X_val,Y_val), augmenter=AUGMENTATIONS_TRAIN)Epoch 1/200
100/100 [==============================] - 23s 232ms/step - loss: 0.3942 - prob_loss: 0.0955 - dist_loss: 1.4935 - prob_kld: 0.0110 - dist_relevant_mae: 1.4930 - dist_relevant_mse: 8.6116 - val_loss: 0.3463 - val_prob_loss: 0.0801 - val_dist_loss: 1.3311 - val_prob_kld: 0.0085 - val_dist_relevant_mae: 1.3306 - val_dist_relevant_mse: 7.1671
Epoch 2/200
100/100 [==============================] - 23s 230ms/step - loss: 0.3897 - prob_loss: 0.0817 - dist_loss: 1.5400 - prob_kld: 0.0114 - dist_relevant_mae: 1.5395 - dist_relevant_mse: 9.9819 - val_loss: 0.3468 - val_prob_loss: 0.0802 - val_dist_loss: 1.3328 - val_prob_kld: 0.0086 - val_dist_relevant_mae: 1.3323 - val_dist_relevant_mse: 6.4148
Epoch 3/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3966 - prob_loss: 0.0800 - dist_loss: 1.5830 - prob_kld: 0.0091 - dist_relevant_mae: 1.5825 - dist_relevant_mse: 10.1022 - val_loss: 0.3527 - val_prob_loss: 0.0795 - val_dist_loss: 1.3662 - val_prob_kld: 0.0079 - val_dist_relevant_mae: 1.3657 - val_dist_relevant_mse: 5.8245
Epoch 4/200
 35/100 [=========>....................] - ETA: 16s - loss: 0.4415 - prob_loss: 0.0949 - dist_loss: 1.7330 - prob_kld: 0.0129 - dist_relevant_mae: 1.7324 - dist_relevant_mse: 9.8555 sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 26s 255ms/step - loss: 0.3861 - prob_loss: 0.0860 - dist_loss: 1.5003 - prob_kld: 0.0110 - dist_relevant_mae: 1.4998 - dist_relevant_mse: 7.6727 - val_loss: 0.3578 - val_prob_loss: 0.0797 - val_dist_loss: 1.3903 - val_prob_kld: 0.0081 - val_dist_relevant_mae: 1.3898 - val_dist_relevant_mse: 5.6605
Epoch 5/200
100/100 [==============================] - 24s 236ms/step - loss: 0.4062 - prob_loss: 0.0989 - dist_loss: 1.5362 - prob_kld: 0.0162 - dist_relevant_mae: 1.5357 - dist_relevant_mse: 9.1871 - val_loss: 0.3785 - val_prob_loss: 0.0801 - val_dist_loss: 1.4919 - val_prob_kld: 0.0085 - val_dist_relevant_mae: 1.4914 - val_dist_relevant_mse: 6.3297
Epoch 6/200
100/100 [==============================] - 24s 241ms/step - loss: 0.3556 - prob_loss: 0.0737 - dist_loss: 1.4093 - prob_kld: 0.0080 - dist_relevant_mae: 1.4088 - dist_relevant_mse: 6.1947 - val_loss: 0.3488 - val_prob_loss: 0.0795 - val_dist_loss: 1.3464 - val_prob_kld: 0.0079 - val_dist_relevant_mae: 1.3460 - val_dist_relevant_mse: 5.5174
Epoch 7/200
 71/100 [====================>.........] - ETA: 6s - loss: 0.3625 - prob_loss: 0.0838 - dist_loss: 1.3937 - prob_kld: 0.0082 - dist_relevant_mae: 1.3932 - dist_relevant_mse: 7.1472sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 25s 250ms/step - loss: 0.3711 - prob_loss: 0.0912 - dist_loss: 1.3999 - prob_kld: 0.0087 - dist_relevant_mae: 1.3994 - dist_relevant_mse: 7.2540 - val_loss: 0.3345 - val_prob_loss: 0.0792 - val_dist_loss: 1.2765 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 1.2760 - val_dist_relevant_mse: 5.0287
Epoch 8/200
100/100 [==============================] - 24s 243ms/step - loss: 0.3970 - prob_loss: 0.0838 - dist_loss: 1.5660 - prob_kld: 0.0093 - dist_relevant_mae: 1.5655 - dist_relevant_mse: 8.3578 - val_loss: 0.3451 - val_prob_loss: 0.0797 - val_dist_loss: 1.3267 - val_prob_kld: 0.0081 - val_dist_relevant_mae: 1.3262 - val_dist_relevant_mse: 5.3035
Epoch 9/200
100/100 [==============================] - 24s 243ms/step - loss: 0.3506 - prob_loss: 0.0728 - dist_loss: 1.3889 - prob_kld: 0.0087 - dist_relevant_mae: 1.3884 - dist_relevant_mse: 6.6149 - val_loss: 0.3328 - val_prob_loss: 0.0793 - val_dist_loss: 1.2678 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 1.2673 - val_dist_relevant_mse: 5.3720
Epoch 10/200
100/100 [==============================] - 23s 234ms/step - loss: 0.3872 - prob_loss: 0.0875 - dist_loss: 1.4985 - prob_kld: 0.0103 - dist_relevant_mae: 1.4980 - dist_relevant_mse: 7.6887 - val_loss: 0.3426 - val_prob_loss: 0.0792 - val_dist_loss: 1.3170 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.3165 - val_dist_relevant_mse: 4.8317
Epoch 11/200
  7/100 [=>............................] - ETA: 20s - loss: 0.3670 - prob_loss: 0.0917 - dist_loss: 1.3760 - prob_kld: 0.0084 - dist_relevant_mae: 1.3754 - dist_relevant_mse: 5.9791sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 231ms/step - loss: 0.3995 - prob_loss: 0.0881 - dist_loss: 1.5571 - prob_kld: 0.0116 - dist_relevant_mae: 1.5565 - dist_relevant_mse: 9.1828 - val_loss: 0.3599 - val_prob_loss: 0.0797 - val_dist_loss: 1.4011 - val_prob_kld: 0.0081 - val_dist_relevant_mae: 1.4005 - val_dist_relevant_mse: 5.4823
Epoch 12/200
100/100 [==============================] - 23s 229ms/step - loss: 0.3426 - prob_loss: 0.0808 - dist_loss: 1.3093 - prob_kld: 0.0089 - dist_relevant_mae: 1.3087 - dist_relevant_mse: 5.4853 - val_loss: 0.3224 - val_prob_loss: 0.0796 - val_dist_loss: 1.2141 - val_prob_kld: 0.0080 - val_dist_relevant_mae: 1.2136 - val_dist_relevant_mse: 4.5997
Epoch 13/200
100/100 [==============================] - 23s 227ms/step - loss: 0.3936 - prob_loss: 0.0901 - dist_loss: 1.5174 - prob_kld: 0.0113 - dist_relevant_mae: 1.5169 - dist_relevant_mse: 7.6623 - val_loss: 0.3350 - val_prob_loss: 0.0795 - val_dist_loss: 1.2778 - val_prob_kld: 0.0079 - val_dist_relevant_mae: 1.2772 - val_dist_relevant_mse: 4.8453
Epoch 14/200
 43/100 [===========>..................] - ETA: 13s - loss: 0.3810 - prob_loss: 0.0837 - dist_loss: 1.4867 - prob_kld: 0.0082 - dist_relevant_mae: 1.4862 - dist_relevant_mse: 8.9001sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 243ms/step - loss: 0.4149 - prob_loss: 0.0849 - dist_loss: 1.6497 - prob_kld: 0.0089 - dist_relevant_mae: 1.6492 - dist_relevant_mse: 10.1303 - val_loss: 0.3604 - val_prob_loss: 0.0801 - val_dist_loss: 1.4016 - val_prob_kld: 0.0085 - val_dist_relevant_mae: 1.4011 - val_dist_relevant_mse: 5.8244
Epoch 15/200
100/100 [==============================] - 23s 226ms/step - loss: 0.3933 - prob_loss: 0.0883 - dist_loss: 1.5253 - prob_kld: 0.0103 - dist_relevant_mae: 1.5248 - dist_relevant_mse: 8.0487 - val_loss: 0.3975 - val_prob_loss: 0.0797 - val_dist_loss: 1.5890 - val_prob_kld: 0.0081 - val_dist_relevant_mae: 1.5885 - val_dist_relevant_mse: 6.9782
Epoch 16/200
100/100 [==============================] - 23s 227ms/step - loss: 0.3619 - prob_loss: 0.0800 - dist_loss: 1.4093 - prob_kld: 0.0091 - dist_relevant_mae: 1.4088 - dist_relevant_mse: 6.8399 - val_loss: 0.3260 - val_prob_loss: 0.0790 - val_dist_loss: 1.2351 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 1.2346 - val_dist_relevant_mse: 4.5340
Epoch 17/200
 79/100 [======================>.......] - ETA: 4s - loss: 0.3442 - prob_loss: 0.0830 - dist_loss: 1.3057 - prob_kld: 0.0093 - dist_relevant_mae: 1.3052 - dist_relevant_mse: 6.2988sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 228ms/step - loss: 0.3419 - prob_loss: 0.0793 - dist_loss: 1.3128 - prob_kld: 0.0090 - dist_relevant_mae: 1.3123 - dist_relevant_mse: 6.5457 - val_loss: 0.3435 - val_prob_loss: 0.0796 - val_dist_loss: 1.3193 - val_prob_kld: 0.0081 - val_dist_relevant_mae: 1.3188 - val_dist_relevant_mse: 6.5864
Epoch 18/200
100/100 [==============================] - 23s 226ms/step - loss: 0.4108 - prob_loss: 0.0825 - dist_loss: 1.6415 - prob_kld: 0.0099 - dist_relevant_mae: 1.6409 - dist_relevant_mse: 9.4440 - val_loss: 0.3487 - val_prob_loss: 0.0794 - val_dist_loss: 1.3464 - val_prob_kld: 0.0078 - val_dist_relevant_mae: 1.3459 - val_dist_relevant_mse: 5.7342
Epoch 19/200
100/100 [==============================] - 23s 228ms/step - loss: 0.3657 - prob_loss: 0.0855 - dist_loss: 1.4005 - prob_kld: 0.0089 - dist_relevant_mae: 1.4000 - dist_relevant_mse: 7.2616 - val_loss: 0.3240 - val_prob_loss: 0.0797 - val_dist_loss: 1.2216 - val_prob_kld: 0.0081 - val_dist_relevant_mae: 1.2211 - val_dist_relevant_mse: 4.7137
Epoch 20/200
100/100 [==============================] - 23s 230ms/step - loss: 0.3890 - prob_loss: 0.0904 - dist_loss: 1.4932 - prob_kld: 0.0098 - dist_relevant_mae: 1.4927 - dist_relevant_mse: 8.6720 - val_loss: 0.3320 - val_prob_loss: 0.0806 - val_dist_loss: 1.2568 - val_prob_kld: 0.0090 - val_dist_relevant_mae: 1.2563 - val_dist_relevant_mse: 4.9500
Epoch 21/200
 15/100 [===>..........................] - ETA: 17s - loss: 0.4113 - prob_loss: 0.0705 - dist_loss: 1.7042 - prob_kld: 0.0102 - dist_relevant_mae: 1.7037 - dist_relevant_mse: 13.6948sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 22s 223ms/step - loss: 0.3813 - prob_loss: 0.0816 - dist_loss: 1.4985 - prob_kld: 0.0085 - dist_relevant_mae: 1.4980 - dist_relevant_mse: 8.2457 - val_loss: 0.3326 - val_prob_loss: 0.0800 - val_dist_loss: 1.2627 - val_prob_kld: 0.0085 - val_dist_relevant_mae: 1.2622 - val_dist_relevant_mse: 5.2362
Epoch 22/200
100/100 [==============================] - 23s 227ms/step - loss: 0.3607 - prob_loss: 0.0837 - dist_loss: 1.3847 - prob_kld: 0.0086 - dist_relevant_mae: 1.3842 - dist_relevant_mse: 7.2229 - val_loss: 0.3682 - val_prob_loss: 0.0793 - val_dist_loss: 1.4447 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 1.4442 - val_dist_relevant_mse: 6.4834
Epoch 23/200
100/100 [==============================] - 23s 233ms/step - loss: 0.3483 - prob_loss: 0.0806 - dist_loss: 1.3387 - prob_kld: 0.0094 - dist_relevant_mae: 1.3383 - dist_relevant_mse: 6.8085 - val_loss: 0.3396 - val_prob_loss: 0.0803 - val_dist_loss: 1.2967 - val_prob_kld: 0.0087 - val_dist_relevant_mae: 1.2962 - val_dist_relevant_mse: 5.0136
Epoch 24/200
 51/100 [==============>...............] - ETA: 10s - loss: 0.3516 - prob_loss: 0.0889 - dist_loss: 1.3137 - prob_kld: 0.0097 - dist_relevant_mae: 1.3132 - dist_relevant_mse: 5.9773sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 229ms/step - loss: 0.3752 - prob_loss: 0.0920 - dist_loss: 1.4164 - prob_kld: 0.0104 - dist_relevant_mae: 1.4159 - dist_relevant_mse: 7.2877 - val_loss: 0.3158 - val_prob_loss: 0.0792 - val_dist_loss: 1.1830 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.1825 - val_dist_relevant_mse: 4.3999
Epoch 25/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3634 - prob_loss: 0.0884 - dist_loss: 1.3745 - prob_kld: 0.0106 - dist_relevant_mae: 1.3740 - dist_relevant_mse: 6.2015 - val_loss: 0.3165 - val_prob_loss: 0.0789 - val_dist_loss: 1.1879 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.1874 - val_dist_relevant_mse: 4.5330
Epoch 26/200
100/100 [==============================] - 23s 234ms/step - loss: 0.3470 - prob_loss: 0.0816 - dist_loss: 1.3271 - prob_kld: 0.0087 - dist_relevant_mae: 1.3266 - dist_relevant_mse: 6.7480 - val_loss: 0.3447 - val_prob_loss: 0.0791 - val_dist_loss: 1.3279 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.3274 - val_dist_relevant_mse: 6.2860
Epoch 27/200
 87/100 [=========================>....] - ETA: 2s - loss: 0.3307 - prob_loss: 0.0768 - dist_loss: 1.2697 - prob_kld: 0.0083 - dist_relevant_mae: 1.2692 - dist_relevant_mse: 4.9719sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 226ms/step - loss: 0.3343 - prob_loss: 0.0761 - dist_loss: 1.2912 - prob_kld: 0.0087 - dist_relevant_mae: 1.2908 - dist_relevant_mse: 5.2446 - val_loss: 0.3436 - val_prob_loss: 0.0788 - val_dist_loss: 1.3240 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.3235 - val_dist_relevant_mse: 5.1120
Epoch 28/200
100/100 [==============================] - 23s 228ms/step - loss: 0.3573 - prob_loss: 0.0853 - dist_loss: 1.3596 - prob_kld: 0.0097 - dist_relevant_mae: 1.3591 - dist_relevant_mse: 6.9277 - val_loss: 0.3348 - val_prob_loss: 0.0790 - val_dist_loss: 1.2787 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 1.2782 - val_dist_relevant_mse: 4.7763
Epoch 29/200
100/100 [==============================] - 23s 226ms/step - loss: 0.3843 - prob_loss: 0.0926 - dist_loss: 1.4587 - prob_kld: 0.0096 - dist_relevant_mae: 1.4582 - dist_relevant_mse: 6.7737 - val_loss: 0.3456 - val_prob_loss: 0.0791 - val_dist_loss: 1.3322 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.3317 - val_dist_relevant_mse: 5.0557
Epoch 30/200
100/100 [==============================] - 22s 225ms/step - loss: 0.3362 - prob_loss: 0.0735 - dist_loss: 1.3134 - prob_kld: 0.0072 - dist_relevant_mae: 1.3130 - dist_relevant_mse: 6.4207 - val_loss: 0.3507 - val_prob_loss: 0.0797 - val_dist_loss: 1.3549 - val_prob_kld: 0.0081 - val_dist_relevant_mae: 1.3544 - val_dist_relevant_mse: 5.9725
Epoch 31/200
 23/100 [=====>........................] - ETA: 15s - loss: 0.3701 - prob_loss: 0.0792 - dist_loss: 1.4546 - prob_kld: 0.0119 - dist_relevant_mae: 1.4541 - dist_relevant_mse: 5.4425sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 22s 224ms/step - loss: 0.3769 - prob_loss: 0.0822 - dist_loss: 1.4740 - prob_kld: 0.0099 - dist_relevant_mae: 1.4735 - dist_relevant_mse: 6.5593 - val_loss: 0.3579 - val_prob_loss: 0.0793 - val_dist_loss: 1.3927 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 1.3923 - val_dist_relevant_mse: 5.5335
Epoch 32/200
100/100 [==============================] - 22s 223ms/step - loss: 0.4077 - prob_loss: 0.0876 - dist_loss: 1.6009 - prob_kld: 0.0109 - dist_relevant_mae: 1.6004 - dist_relevant_mse: 9.4276 - val_loss: 0.3452 - val_prob_loss: 0.0798 - val_dist_loss: 1.3269 - val_prob_kld: 0.0082 - val_dist_relevant_mae: 1.3264 - val_dist_relevant_mse: 5.1996
Epoch 33/200
100/100 [==============================] - 22s 223ms/step - loss: 0.4368 - prob_loss: 0.0884 - dist_loss: 1.7418 - prob_kld: 0.0109 - dist_relevant_mae: 1.7413 - dist_relevant_mse: 11.6918 - val_loss: 0.3524 - val_prob_loss: 0.0791 - val_dist_loss: 1.3665 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.3661 - val_dist_relevant_mse: 5.3446
Epoch 34/200
 59/100 [================>.............] - ETA: 9s - loss: 0.3600 - prob_loss: 0.0820 - dist_loss: 1.3904 - prob_kld: 0.0080 - dist_relevant_mae: 1.3899 - dist_relevant_mse: 7.0110sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 239ms/step - loss: 0.3570 - prob_loss: 0.0811 - dist_loss: 1.3791 - prob_kld: 0.0079 - dist_relevant_mae: 1.3786 - dist_relevant_mse: 7.1246 - val_loss: 0.3351 - val_prob_loss: 0.0808 - val_dist_loss: 1.2717 - val_prob_kld: 0.0092 - val_dist_relevant_mae: 1.2712 - val_dist_relevant_mse: 5.0116
Epoch 35/200
100/100 [==============================] - 23s 227ms/step - loss: 0.3842 - prob_loss: 0.0875 - dist_loss: 1.4836 - prob_kld: 0.0093 - dist_relevant_mae: 1.4831 - dist_relevant_mse: 7.4458 - val_loss: 0.4125 - val_prob_loss: 0.0792 - val_dist_loss: 1.6666 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.6661 - val_dist_relevant_mse: 6.9203
Epoch 36/200
100/100 [==============================] - 22s 224ms/step - loss: 0.3710 - prob_loss: 0.0835 - dist_loss: 1.4374 - prob_kld: 0.0103 - dist_relevant_mae: 1.4370 - dist_relevant_mse: 8.3232 - val_loss: 0.3832 - val_prob_loss: 0.0837 - val_dist_loss: 1.4972 - val_prob_kld: 0.0121 - val_dist_relevant_mae: 1.4965 - val_dist_relevant_mse: 6.1328
Epoch 37/200
 95/100 [===========================>..] - ETA: 1s - loss: 0.4179 - prob_loss: 0.0887 - dist_loss: 1.6461 - prob_kld: 0.0106 - dist_relevant_mae: 1.6456 - dist_relevant_mse: 8.4889sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 225ms/step - loss: 0.4216 - prob_loss: 0.0895 - dist_loss: 1.6607 - prob_kld: 0.0105 - dist_relevant_mae: 1.6601 - dist_relevant_mse: 8.6122 - val_loss: 0.4220 - val_prob_loss: 0.0795 - val_dist_loss: 1.7127 - val_prob_kld: 0.0079 - val_dist_relevant_mae: 1.7122 - val_dist_relevant_mse: 7.5255
Epoch 38/200
100/100 [==============================] - 22s 222ms/step - loss: 0.3954 - prob_loss: 0.0899 - dist_loss: 1.5276 - prob_kld: 0.0102 - dist_relevant_mae: 1.5271 - dist_relevant_mse: 7.6021 - val_loss: 0.3259 - val_prob_loss: 0.0800 - val_dist_loss: 1.2295 - val_prob_kld: 0.0084 - val_dist_relevant_mae: 1.2290 - val_dist_relevant_mse: 4.4595
Epoch 39/200
100/100 [==============================] - 22s 222ms/step - loss: 0.3536 - prob_loss: 0.0836 - dist_loss: 1.3501 - prob_kld: 0.0095 - dist_relevant_mae: 1.3496 - dist_relevant_mse: 6.0527 - val_loss: 0.3213 - val_prob_loss: 0.0789 - val_dist_loss: 1.2115 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 1.2111 - val_dist_relevant_mse: 4.4198
Epoch 40/200
100/100 [==============================] - 22s 223ms/step - loss: 0.3697 - prob_loss: 0.0822 - dist_loss: 1.4373 - prob_kld: 0.0084 - dist_relevant_mae: 1.4368 - dist_relevant_mse: 8.4387 - val_loss: 0.3408 - val_prob_loss: 0.0789 - val_dist_loss: 1.3097 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.3092 - val_dist_relevant_mse: 5.1714
Epoch 41/200
 31/100 [========>.....................] - ETA: 14s - loss: 0.3409 - prob_loss: 0.0726 - dist_loss: 1.3413 - prob_kld: 0.0077 - dist_relevant_mae: 1.3408 - dist_relevant_mse: 6.2025sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 231ms/step - loss: 0.3597 - prob_loss: 0.0900 - dist_loss: 1.3485 - prob_kld: 0.0100 - dist_relevant_mae: 1.3480 - dist_relevant_mse: 6.7567 - val_loss: 0.3222 - val_prob_loss: 0.0792 - val_dist_loss: 1.2150 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.2144 - val_dist_relevant_mse: 4.5775
Epoch 42/200
100/100 [==============================] - 23s 229ms/step - loss: 0.4048 - prob_loss: 0.0835 - dist_loss: 1.6064 - prob_kld: 0.0096 - dist_relevant_mae: 1.6059 - dist_relevant_mse: 8.1669 - val_loss: 0.3543 - val_prob_loss: 0.0809 - val_dist_loss: 1.3669 - val_prob_kld: 0.0093 - val_dist_relevant_mae: 1.3665 - val_dist_relevant_mse: 5.7136
Epoch 43/200
100/100 [==============================] - 23s 225ms/step - loss: 0.3617 - prob_loss: 0.0805 - dist_loss: 1.4063 - prob_kld: 0.0090 - dist_relevant_mae: 1.4058 - dist_relevant_mse: 7.3833 - val_loss: 0.3278 - val_prob_loss: 0.0804 - val_dist_loss: 1.2369 - val_prob_kld: 0.0088 - val_dist_relevant_mae: 1.2364 - val_dist_relevant_mse: 5.2239
Epoch 44/200
 67/100 [===================>..........] - ETA: 7s - loss: 0.3898 - prob_loss: 0.0895 - dist_loss: 1.5017 - prob_kld: 0.0150 - dist_relevant_mae: 1.5011 - dist_relevant_mse: 8.8244sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 234ms/step - loss: 0.3817 - prob_loss: 0.0879 - dist_loss: 1.4690 - prob_kld: 0.0133 - dist_relevant_mae: 1.4685 - dist_relevant_mse: 8.1441 - val_loss: 0.3484 - val_prob_loss: 0.0799 - val_dist_loss: 1.3428 - val_prob_kld: 0.0083 - val_dist_relevant_mae: 1.3423 - val_dist_relevant_mse: 6.4458
Epoch 45/200
100/100 [==============================] - 23s 234ms/step - loss: 0.3503 - prob_loss: 0.0888 - dist_loss: 1.3074 - prob_kld: 0.0093 - dist_relevant_mae: 1.3068 - dist_relevant_mse: 5.8853 - val_loss: 0.3236 - val_prob_loss: 0.0792 - val_dist_loss: 1.2222 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.2217 - val_dist_relevant_mse: 5.4907
Epoch 46/200
100/100 [==============================] - 23s 233ms/step - loss: 0.3630 - prob_loss: 0.0889 - dist_loss: 1.3705 - prob_kld: 0.0091 - dist_relevant_mae: 1.3699 - dist_relevant_mse: 7.6990 - val_loss: 0.3443 - val_prob_loss: 0.0788 - val_dist_loss: 1.3272 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.3267 - val_dist_relevant_mse: 4.9967
Epoch 47/200
100/100 [==============================] - 23s 232ms/step - loss: 0.4141 - prob_loss: 0.0786 - dist_loss: 1.6776 - prob_kld: 0.0099 - dist_relevant_mae: 1.6771 - dist_relevant_mse: 10.3614 - val_loss: 0.3725 - val_prob_loss: 0.0799 - val_dist_loss: 1.4627 - val_prob_kld: 0.0083 - val_dist_relevant_mae: 1.4622 - val_dist_relevant_mse: 7.4616
Epoch 48/200
  3/100 [..............................] - ETA: 20s - loss: 0.2288 - prob_loss: 0.0380 - dist_loss: 0.9542 - prob_kld: 0.0039 - dist_relevant_mae: 0.9538 - dist_relevant_mse: 2.4623   sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 235ms/step - loss: 0.3321 - prob_loss: 0.0708 - dist_loss: 1.3065 - prob_kld: 0.0081 - dist_relevant_mae: 1.3060 - dist_relevant_mse: 6.3584 - val_loss: 0.3318 - val_prob_loss: 0.0788 - val_dist_loss: 1.2649 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.2644 - val_dist_relevant_mse: 4.8174
Epoch 49/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3816 - prob_loss: 0.0904 - dist_loss: 1.4560 - prob_kld: 0.0086 - dist_relevant_mae: 1.4554 - dist_relevant_mse: 6.3808 - val_loss: 0.3250 - val_prob_loss: 0.0791 - val_dist_loss: 1.2292 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.2287 - val_dist_relevant_mse: 4.6698
Epoch 50/200
100/100 [==============================] - 23s 231ms/step - loss: 0.3988 - prob_loss: 0.0880 - dist_loss: 1.5539 - prob_kld: 0.0104 - dist_relevant_mae: 1.5534 - dist_relevant_mse: 9.6723 - val_loss: 0.3583 - val_prob_loss: 0.0816 - val_dist_loss: 1.3835 - val_prob_kld: 0.0100 - val_dist_relevant_mae: 1.3830 - val_dist_relevant_mse: 7.2521
Epoch 51/200
 39/100 [==========>...................] - ETA: 13s - loss: 0.3947 - prob_loss: 0.0867 - dist_loss: 1.5401 - prob_kld: 0.0124 - dist_relevant_mae: 1.5396 - dist_relevant_mse: 9.3946sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 235ms/step - loss: 0.3503 - prob_loss: 0.0846 - dist_loss: 1.3285 - prob_kld: 0.0100 - dist_relevant_mae: 1.3280 - dist_relevant_mse: 6.4020 - val_loss: 0.3271 - val_prob_loss: 0.0789 - val_dist_loss: 1.2415 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.2410 - val_dist_relevant_mse: 6.3732
Epoch 52/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3687 - prob_loss: 0.0851 - dist_loss: 1.4180 - prob_kld: 0.0104 - dist_relevant_mae: 1.4175 - dist_relevant_mse: 6.7580 - val_loss: 0.3676 - val_prob_loss: 0.0807 - val_dist_loss: 1.4342 - val_prob_kld: 0.0092 - val_dist_relevant_mae: 1.4338 - val_dist_relevant_mse: 10.5702
Epoch 53/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3787 - prob_loss: 0.0870 - dist_loss: 1.4587 - prob_kld: 0.0095 - dist_relevant_mae: 1.4582 - dist_relevant_mse: 8.1111 - val_loss: 0.3443 - val_prob_loss: 0.0790 - val_dist_loss: 1.3263 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.3258 - val_dist_relevant_mse: 6.0113
Epoch 54/200
 75/100 [=====================>........] - ETA: 5s - loss: 0.3187 - prob_loss: 0.0789 - dist_loss: 1.1990 - prob_kld: 0.0072 - dist_relevant_mae: 1.1985 - dist_relevant_mse: 4.9804sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 232ms/step - loss: 0.3431 - prob_loss: 0.0817 - dist_loss: 1.3067 - prob_kld: 0.0078 - dist_relevant_mae: 1.3062 - dist_relevant_mse: 6.0746 - val_loss: 0.3297 - val_prob_loss: 0.0789 - val_dist_loss: 1.2540 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.2535 - val_dist_relevant_mse: 5.1424
Epoch 55/200
100/100 [==============================] - 23s 230ms/step - loss: 0.3490 - prob_loss: 0.0728 - dist_loss: 1.3809 - prob_kld: 0.0080 - dist_relevant_mae: 1.3804 - dist_relevant_mse: 6.6031 - val_loss: 0.3547 - val_prob_loss: 0.0792 - val_dist_loss: 1.3775 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.3770 - val_dist_relevant_mse: 5.6447
Epoch 56/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3833 - prob_loss: 0.0884 - dist_loss: 1.4748 - prob_kld: 0.0100 - dist_relevant_mae: 1.4743 - dist_relevant_mse: 7.5434 - val_loss: 0.3738 - val_prob_loss: 0.0810 - val_dist_loss: 1.4641 - val_prob_kld: 0.0094 - val_dist_relevant_mae: 1.4636 - val_dist_relevant_mse: 6.4286
Epoch 57/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3930 - prob_loss: 0.0920 - dist_loss: 1.5050 - prob_kld: 0.0102 - dist_relevant_mae: 1.5046 - dist_relevant_mse: 9.2867 - val_loss: 0.3315 - val_prob_loss: 0.0792 - val_dist_loss: 1.2616 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.2611 - val_dist_relevant_mse: 5.5144
Epoch 58/200
 11/100 [==>...........................] - ETA: 19s - loss: 0.2970 - prob_loss: 0.0644 - dist_loss: 1.1629 - prob_kld: 0.0081 - dist_relevant_mae: 1.1624 - dist_relevant_mse: 4.2964sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 240ms/step - loss: 0.3477 - prob_loss: 0.0790 - dist_loss: 1.3434 - prob_kld: 0.0091 - dist_relevant_mae: 1.3429 - dist_relevant_mse: 5.9953 - val_loss: 0.3459 - val_prob_loss: 0.0789 - val_dist_loss: 1.3350 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.3345 - val_dist_relevant_mse: 5.3262
Epoch 59/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3872 - prob_loss: 0.0930 - dist_loss: 1.4709 - prob_kld: 0.0095 - dist_relevant_mae: 1.4704 - dist_relevant_mse: 8.3346 - val_loss: 0.3211 - val_prob_loss: 0.0792 - val_dist_loss: 1.2094 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.2089 - val_dist_relevant_mse: 4.6876
Epoch 60/200
100/100 [==============================] - 24s 239ms/step - loss: 0.4097 - prob_loss: 0.0871 - dist_loss: 1.6129 - prob_kld: 0.0130 - dist_relevant_mae: 1.6124 - dist_relevant_mse: 13.6113 - val_loss: 0.3375 - val_prob_loss: 0.0788 - val_dist_loss: 1.2934 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.2929 - val_dist_relevant_mse: 5.1520
Epoch 61/200
 47/100 [=============>................] - ETA: 11s - loss: 0.3272 - prob_loss: 0.0721 - dist_loss: 1.2754 - prob_kld: 0.0085 - dist_relevant_mae: 1.2749 - dist_relevant_mse: 5.9880sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 238ms/step - loss: 0.3210 - prob_loss: 0.0739 - dist_loss: 1.2357 - prob_kld: 0.0080 - dist_relevant_mae: 1.2352 - dist_relevant_mse: 5.1058 - val_loss: 0.3164 - val_prob_loss: 0.0790 - val_dist_loss: 1.1873 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 1.1869 - val_dist_relevant_mse: 4.3518
Epoch 62/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3815 - prob_loss: 0.0874 - dist_loss: 1.4704 - prob_kld: 0.0112 - dist_relevant_mae: 1.4699 - dist_relevant_mse: 7.7142 - val_loss: 0.3234 - val_prob_loss: 0.0792 - val_dist_loss: 1.2214 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.2209 - val_dist_relevant_mse: 4.8514
Epoch 63/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3362 - prob_loss: 0.0746 - dist_loss: 1.3083 - prob_kld: 0.0090 - dist_relevant_mae: 1.3078 - dist_relevant_mse: 5.8156 - val_loss: 0.3447 - val_prob_loss: 0.0789 - val_dist_loss: 1.3292 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.3287 - val_dist_relevant_mse: 5.1571
Epoch 64/200
 83/100 [=======================>......] - ETA: 3s - loss: 0.4185 - prob_loss: 0.0943 - dist_loss: 1.6209 - prob_kld: 0.0092 - dist_relevant_mae: 1.6204 - dist_relevant_mse: 9.5795sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 239ms/step - loss: 0.4004 - prob_loss: 0.0896 - dist_loss: 1.5544 - prob_kld: 0.0086 - dist_relevant_mae: 1.5539 - dist_relevant_mse: 8.8937 - val_loss: 0.3226 - val_prob_loss: 0.0792 - val_dist_loss: 1.2172 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.2167 - val_dist_relevant_mse: 4.6760

Epoch 00064: ReduceLROnPlateau reducing learning rate to 0.0001500000071246177.
Epoch 65/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3870 - prob_loss: 0.0947 - dist_loss: 1.4615 - prob_kld: 0.0098 - dist_relevant_mae: 1.4610 - dist_relevant_mse: 8.1127 - val_loss: 0.3357 - val_prob_loss: 0.0788 - val_dist_loss: 1.2844 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.2839 - val_dist_relevant_mse: 5.1598
Epoch 66/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3469 - prob_loss: 0.0874 - dist_loss: 1.2976 - prob_kld: 0.0102 - dist_relevant_mae: 1.2971 - dist_relevant_mse: 6.1547 - val_loss: 0.3106 - val_prob_loss: 0.0788 - val_dist_loss: 1.1593 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.1589 - val_dist_relevant_mse: 4.3476
Epoch 67/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3024 - prob_loss: 0.0719 - dist_loss: 1.1526 - prob_kld: 0.0068 - dist_relevant_mae: 1.1522 - dist_relevant_mse: 4.7374 - val_loss: 0.3095 - val_prob_loss: 0.0786 - val_dist_loss: 1.1541 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1536 - val_dist_relevant_mse: 4.2285
Epoch 68/200
 19/100 [====>.........................] - ETA: 18s - loss: 0.3172 - prob_loss: 0.0778 - dist_loss: 1.1971 - prob_kld: 0.0085 - dist_relevant_mae: 1.1966 - dist_relevant_mse: 5.8797sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 237ms/step - loss: 0.3180 - prob_loss: 0.0724 - dist_loss: 1.2278 - prob_kld: 0.0074 - dist_relevant_mae: 1.2273 - dist_relevant_mse: 6.3654 - val_loss: 0.3074 - val_prob_loss: 0.0787 - val_dist_loss: 1.1436 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1431 - val_dist_relevant_mse: 4.0565
Epoch 69/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3518 - prob_loss: 0.0838 - dist_loss: 1.3403 - prob_kld: 0.0095 - dist_relevant_mae: 1.3399 - dist_relevant_mse: 6.6622 - val_loss: 0.3216 - val_prob_loss: 0.0787 - val_dist_loss: 1.2145 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.2141 - val_dist_relevant_mse: 4.6051
Epoch 70/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3488 - prob_loss: 0.0870 - dist_loss: 1.3088 - prob_kld: 0.0084 - dist_relevant_mae: 1.3083 - dist_relevant_mse: 6.2538 - val_loss: 0.3068 - val_prob_loss: 0.0786 - val_dist_loss: 1.1408 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1404 - val_dist_relevant_mse: 3.9615
Epoch 71/200
 55/100 [===============>..............] - ETA: 9s - loss: 0.3511 - prob_loss: 0.0964 - dist_loss: 1.2732 - prob_kld: 0.0098 - dist_relevant_mae: 1.2727 - dist_relevant_mse: 5.9449 sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 237ms/step - loss: 0.3606 - prob_loss: 0.0969 - dist_loss: 1.3185 - prob_kld: 0.0100 - dist_relevant_mae: 1.3181 - dist_relevant_mse: 5.8877 - val_loss: 0.3126 - val_prob_loss: 0.0788 - val_dist_loss: 1.1692 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.1688 - val_dist_relevant_mse: 4.1013
Epoch 72/200
100/100 [==============================] - 23s 234ms/step - loss: 0.3633 - prob_loss: 0.0933 - dist_loss: 1.3502 - prob_kld: 0.0098 - dist_relevant_mae: 1.3498 - dist_relevant_mse: 7.4422 - val_loss: 0.3099 - val_prob_loss: 0.0785 - val_dist_loss: 1.1569 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1565 - val_dist_relevant_mse: 4.0375
Epoch 73/200
100/100 [==============================] - 23s 232ms/step - loss: 0.3218 - prob_loss: 0.0778 - dist_loss: 1.2203 - prob_kld: 0.0080 - dist_relevant_mae: 1.2198 - dist_relevant_mse: 6.2636 - val_loss: 0.3328 - val_prob_loss: 0.0789 - val_dist_loss: 1.2698 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.2694 - val_dist_relevant_mse: 4.5372
Epoch 74/200
 91/100 [==========================>...] - ETA: 1s - loss: 0.3396 - prob_loss: 0.0725 - dist_loss: 1.3352 - prob_kld: 0.0069 - dist_relevant_mae: 1.3348 - dist_relevant_mse: 9.0004sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 229ms/step - loss: 0.3367 - prob_loss: 0.0739 - dist_loss: 1.3139 - prob_kld: 0.0070 - dist_relevant_mae: 1.3135 - dist_relevant_mse: 8.5773 - val_loss: 0.3088 - val_prob_loss: 0.0786 - val_dist_loss: 1.1508 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1504 - val_dist_relevant_mse: 3.8831
Epoch 75/200
100/100 [==============================] - 23s 233ms/step - loss: 0.3287 - prob_loss: 0.0740 - dist_loss: 1.2733 - prob_kld: 0.0071 - dist_relevant_mae: 1.2729 - dist_relevant_mse: 6.1160 - val_loss: 0.3024 - val_prob_loss: 0.0787 - val_dist_loss: 1.1185 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1181 - val_dist_relevant_mse: 3.8418
Epoch 76/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3439 - prob_loss: 0.0913 - dist_loss: 1.2629 - prob_kld: 0.0088 - dist_relevant_mae: 1.2625 - dist_relevant_mse: 6.1409 - val_loss: 0.3023 - val_prob_loss: 0.0786 - val_dist_loss: 1.1186 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1182 - val_dist_relevant_mse: 3.8664
Epoch 77/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3713 - prob_loss: 0.0834 - dist_loss: 1.4394 - prob_kld: 0.0102 - dist_relevant_mae: 1.4390 - dist_relevant_mse: 7.1643 - val_loss: 0.3239 - val_prob_loss: 0.0793 - val_dist_loss: 1.2228 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 1.2224 - val_dist_relevant_mse: 4.6261
Epoch 78/200
 27/100 [=======>......................] - ETA: 16s - loss: 0.3322 - prob_loss: 0.0869 - dist_loss: 1.2262 - prob_kld: 0.0090 - dist_relevant_mae: 1.2258 - dist_relevant_mse: 6.5615sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 25s 252ms/step - loss: 0.3577 - prob_loss: 0.0856 - dist_loss: 1.3603 - prob_kld: 0.0101 - dist_relevant_mae: 1.3598 - dist_relevant_mse: 6.8137 - val_loss: 0.3117 - val_prob_loss: 0.0792 - val_dist_loss: 1.1624 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.1620 - val_dist_relevant_mse: 4.3922
Epoch 79/200
100/100 [==============================] - 25s 247ms/step - loss: 0.3509 - prob_loss: 0.0892 - dist_loss: 1.3084 - prob_kld: 0.0090 - dist_relevant_mae: 1.3080 - dist_relevant_mse: 7.3416 - val_loss: 0.3087 - val_prob_loss: 0.0787 - val_dist_loss: 1.1501 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1497 - val_dist_relevant_mse: 4.0809
Epoch 80/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3414 - prob_loss: 0.0782 - dist_loss: 1.3161 - prob_kld: 0.0090 - dist_relevant_mae: 1.3157 - dist_relevant_mse: 5.8685 - val_loss: 0.3054 - val_prob_loss: 0.0792 - val_dist_loss: 1.1309 - val_prob_kld: 0.0077 - val_dist_relevant_mae: 1.1305 - val_dist_relevant_mse: 3.9987
Epoch 81/200
 63/100 [=================>............] - ETA: 8s - loss: 0.3297 - prob_loss: 0.0845 - dist_loss: 1.2260 - prob_kld: 0.0081 - dist_relevant_mae: 1.2256 - dist_relevant_mse: 5.5736sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 238ms/step - loss: 0.3272 - prob_loss: 0.0818 - dist_loss: 1.2270 - prob_kld: 0.0076 - dist_relevant_mae: 1.2265 - dist_relevant_mse: 5.8192 - val_loss: 0.3035 - val_prob_loss: 0.0787 - val_dist_loss: 1.1241 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1237 - val_dist_relevant_mse: 3.9220
Epoch 82/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3540 - prob_loss: 0.0799 - dist_loss: 1.3701 - prob_kld: 0.0079 - dist_relevant_mae: 1.3697 - dist_relevant_mse: 7.5630 - val_loss: 0.3060 - val_prob_loss: 0.0787 - val_dist_loss: 1.1368 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1363 - val_dist_relevant_mse: 3.8644
Epoch 83/200
100/100 [==============================] - 23s 234ms/step - loss: 0.3614 - prob_loss: 0.0890 - dist_loss: 1.3619 - prob_kld: 0.0101 - dist_relevant_mae: 1.3614 - dist_relevant_mse: 6.2482 - val_loss: 0.3263 - val_prob_loss: 0.0794 - val_dist_loss: 1.2344 - val_prob_kld: 0.0078 - val_dist_relevant_mae: 1.2339 - val_dist_relevant_mse: 6.5819
Epoch 84/200
 99/100 [============================>.] - ETA: 0s - loss: 0.3625 - prob_loss: 0.0847 - dist_loss: 1.3891 - prob_kld: 0.0100 - dist_relevant_mae: 1.3886 - dist_relevant_mse: 7.6947sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 233ms/step - loss: 0.3601 - prob_loss: 0.0840 - dist_loss: 1.3807 - prob_kld: 0.0099 - dist_relevant_mae: 1.3803 - dist_relevant_mse: 7.6233 - val_loss: 0.3368 - val_prob_loss: 0.0790 - val_dist_loss: 1.2892 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 1.2888 - val_dist_relevant_mse: 6.8048
Epoch 85/200
100/100 [==============================] - 23s 234ms/step - loss: 0.3301 - prob_loss: 0.0832 - dist_loss: 1.2345 - prob_kld: 0.0085 - dist_relevant_mae: 1.2341 - dist_relevant_mse: 5.4923 - val_loss: 0.3216 - val_prob_loss: 0.0788 - val_dist_loss: 1.2139 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.2135 - val_dist_relevant_mse: 5.6027
Epoch 86/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3533 - prob_loss: 0.0893 - dist_loss: 1.3197 - prob_kld: 0.0087 - dist_relevant_mae: 1.3192 - dist_relevant_mse: 7.2882 - val_loss: 0.3046 - val_prob_loss: 0.0788 - val_dist_loss: 1.1292 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.1288 - val_dist_relevant_mse: 4.0749
Epoch 87/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3276 - prob_loss: 0.0759 - dist_loss: 1.2589 - prob_kld: 0.0079 - dist_relevant_mae: 1.2585 - dist_relevant_mse: 6.2085 - val_loss: 0.3200 - val_prob_loss: 0.0787 - val_dist_loss: 1.2063 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.2059 - val_dist_relevant_mse: 4.4515
Epoch 88/200
 35/100 [=========>....................] - ETA: 14s - loss: 0.3878 - prob_loss: 0.0955 - dist_loss: 1.4616 - prob_kld: 0.0105 - dist_relevant_mae: 1.4611 - dist_relevant_mse: 6.7649sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 239ms/step - loss: 0.3403 - prob_loss: 0.0860 - dist_loss: 1.2715 - prob_kld: 0.0086 - dist_relevant_mae: 1.2711 - dist_relevant_mse: 6.0436 - val_loss: 0.3008 - val_prob_loss: 0.0787 - val_dist_loss: 1.1108 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1104 - val_dist_relevant_mse: 3.9079
Epoch 89/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3641 - prob_loss: 0.0907 - dist_loss: 1.3671 - prob_kld: 0.0099 - dist_relevant_mae: 1.3666 - dist_relevant_mse: 6.0362 - val_loss: 0.3124 - val_prob_loss: 0.0792 - val_dist_loss: 1.1660 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.1656 - val_dist_relevant_mse: 4.4447
Epoch 90/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3096 - prob_loss: 0.0775 - dist_loss: 1.1608 - prob_kld: 0.0072 - dist_relevant_mae: 1.1603 - dist_relevant_mse: 5.2194 - val_loss: 0.2995 - val_prob_loss: 0.0786 - val_dist_loss: 1.1044 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1039 - val_dist_relevant_mse: 3.7438
Epoch 91/200
 71/100 [====================>.........] - ETA: 6s - loss: 0.3319 - prob_loss: 0.0807 - dist_loss: 1.2564 - prob_kld: 0.0088 - dist_relevant_mae: 1.2559 - dist_relevant_mse: 5.6272sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 238ms/step - loss: 0.3300 - prob_loss: 0.0806 - dist_loss: 1.2469 - prob_kld: 0.0083 - dist_relevant_mae: 1.2465 - dist_relevant_mse: 5.7405 - val_loss: 0.3037 - val_prob_loss: 0.0784 - val_dist_loss: 1.1262 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1258 - val_dist_relevant_mse: 3.9054
Epoch 92/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3366 - prob_loss: 0.0844 - dist_loss: 1.2610 - prob_kld: 0.0091 - dist_relevant_mae: 1.2606 - dist_relevant_mse: 5.1284 - val_loss: 0.3173 - val_prob_loss: 0.0786 - val_dist_loss: 1.1937 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1933 - val_dist_relevant_mse: 4.3871
Epoch 93/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3274 - prob_loss: 0.0733 - dist_loss: 1.2707 - prob_kld: 0.0075 - dist_relevant_mae: 1.2702 - dist_relevant_mse: 5.6511 - val_loss: 0.3053 - val_prob_loss: 0.0789 - val_dist_loss: 1.1320 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.1316 - val_dist_relevant_mse: 3.8820
Epoch 94/200
100/100 [==============================] - 23s 234ms/step - loss: 0.3689 - prob_loss: 0.0948 - dist_loss: 1.3702 - prob_kld: 0.0097 - dist_relevant_mae: 1.3698 - dist_relevant_mse: 6.9162 - val_loss: 0.3150 - val_prob_loss: 0.0786 - val_dist_loss: 1.1819 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1815 - val_dist_relevant_mse: 3.9927
Epoch 95/200
  7/100 [=>............................] - ETA: 19s - loss: 0.3104 - prob_loss: 0.0642 - dist_loss: 1.2308 - prob_kld: 0.0048 - dist_relevant_mae: 1.2304 - dist_relevant_mse: 4.1836sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 231ms/step - loss: 0.3105 - prob_loss: 0.0751 - dist_loss: 1.1770 - prob_kld: 0.0072 - dist_relevant_mae: 1.1766 - dist_relevant_mse: 5.0490 - val_loss: 0.3224 - val_prob_loss: 0.0796 - val_dist_loss: 1.2143 - val_prob_kld: 0.0080 - val_dist_relevant_mae: 1.2139 - val_dist_relevant_mse: 4.3560
Epoch 96/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3695 - prob_loss: 0.0900 - dist_loss: 1.3975 - prob_kld: 0.0096 - dist_relevant_mae: 1.3971 - dist_relevant_mse: 6.9685 - val_loss: 0.3023 - val_prob_loss: 0.0786 - val_dist_loss: 1.1189 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1184 - val_dist_relevant_mse: 3.8186
Epoch 97/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3456 - prob_loss: 0.0860 - dist_loss: 1.2980 - prob_kld: 0.0089 - dist_relevant_mae: 1.2975 - dist_relevant_mse: 5.3212 - val_loss: 0.3042 - val_prob_loss: 0.0786 - val_dist_loss: 1.1279 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1274 - val_dist_relevant_mse: 3.8490
Epoch 98/200
 43/100 [===========>..................] - ETA: 12s - loss: 0.3313 - prob_loss: 0.0809 - dist_loss: 1.2518 - prob_kld: 0.0086 - dist_relevant_mae: 1.2514 - dist_relevant_mse: 7.3718sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 235ms/step - loss: 0.3484 - prob_loss: 0.0863 - dist_loss: 1.3107 - prob_kld: 0.0091 - dist_relevant_mae: 1.3102 - dist_relevant_mse: 6.9295 - val_loss: 0.3053 - val_prob_loss: 0.0787 - val_dist_loss: 1.1330 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1325 - val_dist_relevant_mse: 3.8327
Epoch 99/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3118 - prob_loss: 0.0756 - dist_loss: 1.1810 - prob_kld: 0.0074 - dist_relevant_mae: 1.1806 - dist_relevant_mse: 4.4970 - val_loss: 0.3477 - val_prob_loss: 0.0785 - val_dist_loss: 1.3459 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.3455 - val_dist_relevant_mse: 4.8685
Epoch 100/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3543 - prob_loss: 0.0900 - dist_loss: 1.3217 - prob_kld: 0.0095 - dist_relevant_mae: 1.3212 - dist_relevant_mse: 6.1200 - val_loss: 0.3291 - val_prob_loss: 0.0795 - val_dist_loss: 1.2479 - val_prob_kld: 0.0079 - val_dist_relevant_mae: 1.2474 - val_dist_relevant_mse: 4.5116
Epoch 101/200
 79/100 [======================>.......] - ETA: 4s - loss: 0.3423 - prob_loss: 0.0756 - dist_loss: 1.3335 - prob_kld: 0.0072 - dist_relevant_mae: 1.3331 - dist_relevant_mse: 7.0419sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 237ms/step - loss: 0.3437 - prob_loss: 0.0796 - dist_loss: 1.3202 - prob_kld: 0.0074 - dist_relevant_mae: 1.3197 - dist_relevant_mse: 6.9486 - val_loss: 0.2977 - val_prob_loss: 0.0784 - val_dist_loss: 1.0967 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.0963 - val_dist_relevant_mse: 3.5647
Epoch 102/200
100/100 [==============================] - 24s 235ms/step - loss: 0.3237 - prob_loss: 0.0766 - dist_loss: 1.2357 - prob_kld: 0.0078 - dist_relevant_mae: 1.2353 - dist_relevant_mse: 5.7773 - val_loss: 0.3147 - val_prob_loss: 0.0789 - val_dist_loss: 1.1787 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.1782 - val_dist_relevant_mse: 3.8678
Epoch 103/200
100/100 [==============================] - 24s 235ms/step - loss: 0.3583 - prob_loss: 0.0905 - dist_loss: 1.3393 - prob_kld: 0.0095 - dist_relevant_mae: 1.3388 - dist_relevant_mse: 6.7443 - val_loss: 0.3003 - val_prob_loss: 0.0785 - val_dist_loss: 1.1092 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1088 - val_dist_relevant_mse: 3.6757
Epoch 104/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3340 - prob_loss: 0.0812 - dist_loss: 1.2638 - prob_kld: 0.0084 - dist_relevant_mae: 1.2633 - dist_relevant_mse: 5.9563 - val_loss: 0.3088 - val_prob_loss: 0.0789 - val_dist_loss: 1.1498 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.1494 - val_dist_relevant_mse: 4.2341
Epoch 105/200
 15/100 [===>..........................] - ETA: 18s - loss: 0.3359 - prob_loss: 0.0752 - dist_loss: 1.3035 - prob_kld: 0.0061 - dist_relevant_mae: 1.3030 - dist_relevant_mse: 11.2899sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 238ms/step - loss: 0.3755 - prob_loss: 0.0902 - dist_loss: 1.4268 - prob_kld: 0.0089 - dist_relevant_mae: 1.4263 - dist_relevant_mse: 8.5974 - val_loss: 0.3044 - val_prob_loss: 0.0787 - val_dist_loss: 1.1286 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1281 - val_dist_relevant_mse: 4.2389
Epoch 106/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3113 - prob_loss: 0.0833 - dist_loss: 1.1399 - prob_kld: 0.0091 - dist_relevant_mae: 1.1395 - dist_relevant_mse: 4.5543 - val_loss: 0.3028 - val_prob_loss: 0.0785 - val_dist_loss: 1.1214 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1210 - val_dist_relevant_mse: 3.8919
Epoch 107/200
100/100 [==============================] - 23s 231ms/step - loss: 0.3064 - prob_loss: 0.0773 - dist_loss: 1.1451 - prob_kld: 0.0074 - dist_relevant_mae: 1.1447 - dist_relevant_mse: 4.5361 - val_loss: 0.3048 - val_prob_loss: 0.0786 - val_dist_loss: 1.1311 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1307 - val_dist_relevant_mse: 3.9011
Epoch 108/200
 51/100 [==============>...............] - ETA: 10s - loss: 0.3758 - prob_loss: 0.0913 - dist_loss: 1.4226 - prob_kld: 0.0108 - dist_relevant_mae: 1.4221 - dist_relevant_mse: 5.9724sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 232ms/step - loss: 0.3714 - prob_loss: 0.0902 - dist_loss: 1.4059 - prob_kld: 0.0101 - dist_relevant_mae: 1.4055 - dist_relevant_mse: 5.9677 - val_loss: 0.3048 - val_prob_loss: 0.0789 - val_dist_loss: 1.1293 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.1289 - val_dist_relevant_mse: 3.9884
Epoch 109/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3178 - prob_loss: 0.0814 - dist_loss: 1.1817 - prob_kld: 0.0078 - dist_relevant_mae: 1.1813 - dist_relevant_mse: 5.1030 - val_loss: 0.3045 - val_prob_loss: 0.0785 - val_dist_loss: 1.1300 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1296 - val_dist_relevant_mse: 3.8761
Epoch 110/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3193 - prob_loss: 0.0743 - dist_loss: 1.2252 - prob_kld: 0.0079 - dist_relevant_mae: 1.2248 - dist_relevant_mse: 5.2448 - val_loss: 0.3098 - val_prob_loss: 0.0787 - val_dist_loss: 1.1557 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1553 - val_dist_relevant_mse: 4.0092
Epoch 111/200
 87/100 [=========================>....] - ETA: 2s - loss: 0.3563 - prob_loss: 0.0862 - dist_loss: 1.3503 - prob_kld: 0.0087 - dist_relevant_mae: 1.3499 - dist_relevant_mse: 6.7257sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 238ms/step - loss: 0.3522 - prob_loss: 0.0856 - dist_loss: 1.3330 - prob_kld: 0.0090 - dist_relevant_mae: 1.3326 - dist_relevant_mse: 6.3890 - val_loss: 0.3033 - val_prob_loss: 0.0786 - val_dist_loss: 1.1236 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1232 - val_dist_relevant_mse: 4.0015
Epoch 112/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3317 - prob_loss: 0.0807 - dist_loss: 1.2551 - prob_kld: 0.0087 - dist_relevant_mae: 1.2546 - dist_relevant_mse: 5.1086 - val_loss: 0.3128 - val_prob_loss: 0.0787 - val_dist_loss: 1.1708 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1703 - val_dist_relevant_mse: 4.6024
Epoch 113/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3397 - prob_loss: 0.0835 - dist_loss: 1.2814 - prob_kld: 0.0079 - dist_relevant_mae: 1.2810 - dist_relevant_mse: 5.8307 - val_loss: 0.3092 - val_prob_loss: 0.0786 - val_dist_loss: 1.1531 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1527 - val_dist_relevant_mse: 4.0437
Epoch 114/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3534 - prob_loss: 0.0902 - dist_loss: 1.3163 - prob_kld: 0.0096 - dist_relevant_mae: 1.3159 - dist_relevant_mse: 5.9148 - val_loss: 0.3136 - val_prob_loss: 0.0790 - val_dist_loss: 1.1732 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 1.1727 - val_dist_relevant_mse: 4.1927
Epoch 115/200
 23/100 [=====>........................] - ETA: 16s - loss: 0.3229 - prob_loss: 0.0831 - dist_loss: 1.1990 - prob_kld: 0.0084 - dist_relevant_mae: 1.1986 - dist_relevant_mse: 4.4089sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 236ms/step - loss: 0.3625 - prob_loss: 0.0846 - dist_loss: 1.3898 - prob_kld: 0.0090 - dist_relevant_mae: 1.3894 - dist_relevant_mse: 7.0380 - val_loss: 0.3291 - val_prob_loss: 0.0794 - val_dist_loss: 1.2483 - val_prob_kld: 0.0078 - val_dist_relevant_mae: 1.2478 - val_dist_relevant_mse: 4.9627
Epoch 116/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3232 - prob_loss: 0.0756 - dist_loss: 1.2379 - prob_kld: 0.0087 - dist_relevant_mae: 1.2374 - dist_relevant_mse: 5.3005 - val_loss: 0.3282 - val_prob_loss: 0.0803 - val_dist_loss: 1.2393 - val_prob_kld: 0.0087 - val_dist_relevant_mae: 1.2388 - val_dist_relevant_mse: 4.7749
Epoch 117/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3753 - prob_loss: 0.0969 - dist_loss: 1.3917 - prob_kld: 0.0099 - dist_relevant_mae: 1.3912 - dist_relevant_mse: 6.8303 - val_loss: 0.3087 - val_prob_loss: 0.0785 - val_dist_loss: 1.1507 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1503 - val_dist_relevant_mse: 4.4215
Epoch 118/200
 59/100 [================>.............] - ETA: 8s - loss: 0.3136 - prob_loss: 0.0749 - dist_loss: 1.1934 - prob_kld: 0.0070 - dist_relevant_mae: 1.1929 - dist_relevant_mse: 5.4491sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 236ms/step - loss: 0.3213 - prob_loss: 0.0779 - dist_loss: 1.2173 - prob_kld: 0.0075 - dist_relevant_mae: 1.2168 - dist_relevant_mse: 5.6250 - val_loss: 0.3025 - val_prob_loss: 0.0787 - val_dist_loss: 1.1191 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1187 - val_dist_relevant_mse: 3.8340
Epoch 119/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3378 - prob_loss: 0.0820 - dist_loss: 1.2794 - prob_kld: 0.0087 - dist_relevant_mae: 1.2789 - dist_relevant_mse: 6.3739 - val_loss: 0.3001 - val_prob_loss: 0.0784 - val_dist_loss: 1.1081 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1077 - val_dist_relevant_mse: 3.7331
Epoch 120/200
100/100 [==============================] - 24s 235ms/step - loss: 0.3312 - prob_loss: 0.0840 - dist_loss: 1.2359 - prob_kld: 0.0085 - dist_relevant_mae: 1.2355 - dist_relevant_mse: 5.0418 - val_loss: 0.2989 - val_prob_loss: 0.0784 - val_dist_loss: 1.1025 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.1021 - val_dist_relevant_mse: 3.7609
Epoch 121/200
 95/100 [===========================>..] - ETA: 1s - loss: 0.3321 - prob_loss: 0.0859 - dist_loss: 1.2314 - prob_kld: 0.0085 - dist_relevant_mae: 1.2309 - dist_relevant_mse: 5.6813sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 237ms/step - loss: 0.3429 - prob_loss: 0.0885 - dist_loss: 1.2722 - prob_kld: 0.0088 - dist_relevant_mae: 1.2718 - dist_relevant_mse: 6.0269 - val_loss: 0.3023 - val_prob_loss: 0.0785 - val_dist_loss: 1.1188 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1183 - val_dist_relevant_mse: 3.8257
Epoch 122/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3655 - prob_loss: 0.0912 - dist_loss: 1.3718 - prob_kld: 0.0094 - dist_relevant_mae: 1.3713 - dist_relevant_mse: 8.0823 - val_loss: 0.3197 - val_prob_loss: 0.0786 - val_dist_loss: 1.2055 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.2050 - val_dist_relevant_mse: 4.1030
Epoch 123/200
100/100 [==============================] - 24s 235ms/step - loss: 0.3236 - prob_loss: 0.0730 - dist_loss: 1.2530 - prob_kld: 0.0075 - dist_relevant_mae: 1.2526 - dist_relevant_mse: 5.1812 - val_loss: 0.3040 - val_prob_loss: 0.0785 - val_dist_loss: 1.1277 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1273 - val_dist_relevant_mse: 4.1394
Epoch 124/200
100/100 [==============================] - 23s 233ms/step - loss: 0.3450 - prob_loss: 0.0809 - dist_loss: 1.3206 - prob_kld: 0.0083 - dist_relevant_mae: 1.3201 - dist_relevant_mse: 6.7568 - val_loss: 0.3347 - val_prob_loss: 0.0791 - val_dist_loss: 1.2782 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.2777 - val_dist_relevant_mse: 4.9306
Epoch 125/200
 31/100 [========>.....................] - ETA: 15s - loss: 0.3473 - prob_loss: 0.0826 - dist_loss: 1.3234 - prob_kld: 0.0075 - dist_relevant_mae: 1.3230 - dist_relevant_mse: 9.4623sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 236ms/step - loss: 0.3463 - prob_loss: 0.0864 - dist_loss: 1.2996 - prob_kld: 0.0084 - dist_relevant_mae: 1.2991 - dist_relevant_mse: 7.6245 - val_loss: 0.3059 - val_prob_loss: 0.0785 - val_dist_loss: 1.1371 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1366 - val_dist_relevant_mse: 3.9830
Epoch 126/200
100/100 [==============================] - 23s 228ms/step - loss: 0.3153 - prob_loss: 0.0819 - dist_loss: 1.1670 - prob_kld: 0.0084 - dist_relevant_mae: 1.1665 - dist_relevant_mse: 4.3074 - val_loss: 0.3265 - val_prob_loss: 0.0804 - val_dist_loss: 1.2304 - val_prob_kld: 0.0089 - val_dist_relevant_mae: 1.2299 - val_dist_relevant_mse: 4.1675
Epoch 127/200
100/100 [==============================] - 22s 222ms/step - loss: 0.3450 - prob_loss: 0.0889 - dist_loss: 1.2806 - prob_kld: 0.0091 - dist_relevant_mae: 1.2801 - dist_relevant_mse: 6.8396 - val_loss: 0.3025 - val_prob_loss: 0.0791 - val_dist_loss: 1.1171 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.1166 - val_dist_relevant_mse: 3.8152
Epoch 128/200
 67/100 [===================>..........] - ETA: 6s - loss: 0.3139 - prob_loss: 0.0702 - dist_loss: 1.2183 - prob_kld: 0.0083 - dist_relevant_mae: 1.2179 - dist_relevant_mse: 5.2609sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 229ms/step - loss: 0.3420 - prob_loss: 0.0804 - dist_loss: 1.3079 - prob_kld: 0.0094 - dist_relevant_mae: 1.3075 - dist_relevant_mse: 5.6058 - val_loss: 0.3039 - val_prob_loss: 0.0798 - val_dist_loss: 1.1205 - val_prob_kld: 0.0082 - val_dist_relevant_mae: 1.1201 - val_dist_relevant_mse: 3.8785
Epoch 129/200
100/100 [==============================] - 23s 230ms/step - loss: 0.3249 - prob_loss: 0.0812 - dist_loss: 1.2183 - prob_kld: 0.0087 - dist_relevant_mae: 1.2179 - dist_relevant_mse: 5.4713 - val_loss: 0.3021 - val_prob_loss: 0.0786 - val_dist_loss: 1.1175 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1171 - val_dist_relevant_mse: 3.8329
Epoch 130/200
100/100 [==============================] - 23s 229ms/step - loss: 0.3073 - prob_loss: 0.0737 - dist_loss: 1.1680 - prob_kld: 0.0064 - dist_relevant_mae: 1.1676 - dist_relevant_mse: 5.0051 - val_loss: 0.3002 - val_prob_loss: 0.0785 - val_dist_loss: 1.1084 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.1079 - val_dist_relevant_mse: 3.7286
Epoch 131/200
100/100 [==============================] - 23s 228ms/step - loss: 0.3496 - prob_loss: 0.0861 - dist_loss: 1.3173 - prob_kld: 0.0084 - dist_relevant_mae: 1.3169 - dist_relevant_mse: 6.7424 - val_loss: 0.3060 - val_prob_loss: 0.0785 - val_dist_loss: 1.1374 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1370 - val_dist_relevant_mse: 3.8485
Epoch 132/200
  3/100 [..............................] - ETA: 20s - loss: 0.4469 - prob_loss: 0.1310 - dist_loss: 1.5797 - prob_kld: 0.0196 - dist_relevant_mae: 1.5791 - dist_relevant_mse: 6.2818sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 233ms/step - loss: 0.3395 - prob_loss: 0.0840 - dist_loss: 1.2774 - prob_kld: 0.0084 - dist_relevant_mae: 1.2769 - dist_relevant_mse: 5.7251 - val_loss: 0.3168 - val_prob_loss: 0.0789 - val_dist_loss: 1.1900 - val_prob_kld: 0.0073 - val_dist_relevant_mae: 1.1895 - val_dist_relevant_mse: 4.2212
Epoch 133/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3634 - prob_loss: 0.0896 - dist_loss: 1.3687 - prob_kld: 0.0098 - dist_relevant_mae: 1.3682 - dist_relevant_mse: 7.0243 - val_loss: 0.3001 - val_prob_loss: 0.0788 - val_dist_loss: 1.1065 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.1060 - val_dist_relevant_mse: 3.8080
Epoch 134/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3344 - prob_loss: 0.0847 - dist_loss: 1.2481 - prob_kld: 0.0098 - dist_relevant_mae: 1.2477 - dist_relevant_mse: 7.0769 - val_loss: 0.3113 - val_prob_loss: 0.0862 - val_dist_loss: 1.1255 - val_prob_kld: 0.0146 - val_dist_relevant_mae: 1.1251 - val_dist_relevant_mse: 3.9574
Epoch 135/200
 39/100 [==========>...................] - ETA: 13s - loss: 0.3279 - prob_loss: 0.0738 - dist_loss: 1.2702 - prob_kld: 0.0093 - dist_relevant_mae: 1.2698 - dist_relevant_mse: 6.1801sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 240ms/step - loss: 0.3359 - prob_loss: 0.0826 - dist_loss: 1.2664 - prob_kld: 0.0086 - dist_relevant_mae: 1.2659 - dist_relevant_mse: 5.9611 - val_loss: 0.2981 - val_prob_loss: 0.0784 - val_dist_loss: 1.0985 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.0980 - val_dist_relevant_mse: 3.6201
Epoch 136/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3211 - prob_loss: 0.0812 - dist_loss: 1.1991 - prob_kld: 0.0078 - dist_relevant_mae: 1.1987 - dist_relevant_mse: 5.7467 - val_loss: 0.3042 - val_prob_loss: 0.0785 - val_dist_loss: 1.1285 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1281 - val_dist_relevant_mse: 3.8253
Epoch 137/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3543 - prob_loss: 0.0849 - dist_loss: 1.3472 - prob_kld: 0.0089 - dist_relevant_mae: 1.3468 - dist_relevant_mse: 7.5184 - val_loss: 0.3008 - val_prob_loss: 0.0787 - val_dist_loss: 1.1101 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.1096 - val_dist_relevant_mse: 3.6799
Epoch 138/200
 75/100 [=====================>........] - ETA: 5s - loss: 0.3460 - prob_loss: 0.0832 - dist_loss: 1.3140 - prob_kld: 0.0086 - dist_relevant_mae: 1.3136 - dist_relevant_mse: 7.1251sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 238ms/step - loss: 0.3305 - prob_loss: 0.0794 - dist_loss: 1.2559 - prob_kld: 0.0082 - dist_relevant_mae: 1.2555 - dist_relevant_mse: 6.3210 - val_loss: 0.2970 - val_prob_loss: 0.0786 - val_dist_loss: 1.0919 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.0915 - val_dist_relevant_mse: 3.6307
Epoch 139/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3869 - prob_loss: 0.0919 - dist_loss: 1.4748 - prob_kld: 0.0089 - dist_relevant_mae: 1.4743 - dist_relevant_mse: 10.2513 - val_loss: 0.3094 - val_prob_loss: 0.0805 - val_dist_loss: 1.1447 - val_prob_kld: 0.0089 - val_dist_relevant_mae: 1.1443 - val_dist_relevant_mse: 3.8485
Epoch 140/200
100/100 [==============================] - 24s 236ms/step - loss: 0.3414 - prob_loss: 0.0790 - dist_loss: 1.3118 - prob_kld: 0.0096 - dist_relevant_mae: 1.3114 - dist_relevant_mse: 5.4164 - val_loss: 0.3015 - val_prob_loss: 0.0784 - val_dist_loss: 1.1154 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.1150 - val_dist_relevant_mse: 3.8771
Epoch 141/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3260 - prob_loss: 0.0838 - dist_loss: 1.2110 - prob_kld: 0.0090 - dist_relevant_mae: 1.2106 - dist_relevant_mse: 5.9531 - val_loss: 0.3039 - val_prob_loss: 0.0831 - val_dist_loss: 1.1042 - val_prob_kld: 0.0115 - val_dist_relevant_mae: 1.1037 - val_dist_relevant_mse: 3.6736
Epoch 142/200
 11/100 [==>...........................] - ETA: 19s - loss: 0.2791 - prob_loss: 0.0707 - dist_loss: 1.0419 - prob_kld: 0.0074 - dist_relevant_mae: 1.0415 - dist_relevant_mse: 4.5898sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 241ms/step - loss: 0.3303 - prob_loss: 0.0787 - dist_loss: 1.2581 - prob_kld: 0.0084 - dist_relevant_mae: 1.2577 - dist_relevant_mse: 6.3531 - val_loss: 0.3011 - val_prob_loss: 0.0787 - val_dist_loss: 1.1118 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1114 - val_dist_relevant_mse: 3.7463
Epoch 143/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3373 - prob_loss: 0.0851 - dist_loss: 1.2613 - prob_kld: 0.0087 - dist_relevant_mae: 1.2608 - dist_relevant_mse: 5.5689 - val_loss: 0.2976 - val_prob_loss: 0.0792 - val_dist_loss: 1.0920 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.0916 - val_dist_relevant_mse: 3.5368
Epoch 144/200
100/100 [==============================] - 24s 242ms/step - loss: 0.3241 - prob_loss: 0.0808 - dist_loss: 1.2163 - prob_kld: 0.0079 - dist_relevant_mae: 1.2158 - dist_relevant_mse: 6.5165 - val_loss: 0.2975 - val_prob_loss: 0.0792 - val_dist_loss: 1.0916 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.0912 - val_dist_relevant_mse: 3.6838
Epoch 145/200
 47/100 [=============>................] - ETA: 11s - loss: 0.3778 - prob_loss: 0.0912 - dist_loss: 1.4330 - prob_kld: 0.0092 - dist_relevant_mae: 1.4325 - dist_relevant_mse: 9.4922sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 239ms/step - loss: 0.3490 - prob_loss: 0.0880 - dist_loss: 1.3053 - prob_kld: 0.0100 - dist_relevant_mae: 1.3049 - dist_relevant_mse: 7.0300 - val_loss: 0.3062 - val_prob_loss: 0.0788 - val_dist_loss: 1.1371 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.1366 - val_dist_relevant_mse: 3.6856
Epoch 146/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3360 - prob_loss: 0.0842 - dist_loss: 1.2589 - prob_kld: 0.0084 - dist_relevant_mae: 1.2584 - dist_relevant_mse: 5.4922 - val_loss: 0.2935 - val_prob_loss: 0.0784 - val_dist_loss: 1.0757 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.0753 - val_dist_relevant_mse: 3.4517
Epoch 147/200
100/100 [==============================] - 26s 259ms/step - loss: 0.3319 - prob_loss: 0.0839 - dist_loss: 1.2402 - prob_kld: 0.0081 - dist_relevant_mae: 1.2398 - dist_relevant_mse: 5.9346 - val_loss: 0.3040 - val_prob_loss: 0.0784 - val_dist_loss: 1.1283 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.1279 - val_dist_relevant_mse: 3.6858
Epoch 148/200
 83/100 [=======================>......] - ETA: 3s - loss: 0.3493 - prob_loss: 0.0851 - dist_loss: 1.3208 - prob_kld: 0.0090 - dist_relevant_mae: 1.3204 - dist_relevant_mse: 6.3076sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 240ms/step - loss: 0.3381 - prob_loss: 0.0846 - dist_loss: 1.2676 - prob_kld: 0.0085 - dist_relevant_mae: 1.2671 - dist_relevant_mse: 5.7766 - val_loss: 0.3088 - val_prob_loss: 0.0786 - val_dist_loss: 1.1508 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1504 - val_dist_relevant_mse: 4.1929
Epoch 149/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3368 - prob_loss: 0.0845 - dist_loss: 1.2614 - prob_kld: 0.0082 - dist_relevant_mae: 1.2610 - dist_relevant_mse: 5.6610 - val_loss: 0.3121 - val_prob_loss: 0.0792 - val_dist_loss: 1.1645 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.1641 - val_dist_relevant_mse: 4.1338
Epoch 150/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3389 - prob_loss: 0.0821 - dist_loss: 1.2841 - prob_kld: 0.0087 - dist_relevant_mae: 1.2837 - dist_relevant_mse: 5.5974 - val_loss: 0.3167 - val_prob_loss: 0.0784 - val_dist_loss: 1.1915 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.1911 - val_dist_relevant_mse: 3.9046
Epoch 151/200
100/100 [==============================] - 23s 233ms/step - loss: 0.3225 - prob_loss: 0.0822 - dist_loss: 1.2015 - prob_kld: 0.0078 - dist_relevant_mae: 1.2011 - dist_relevant_mse: 5.4198 - val_loss: 0.2974 - val_prob_loss: 0.0785 - val_dist_loss: 1.0945 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.0941 - val_dist_relevant_mse: 3.5142
Epoch 152/200
 19/100 [====>.........................] - ETA: 17s - loss: 0.3451 - prob_loss: 0.0871 - dist_loss: 1.2903 - prob_kld: 0.0091 - dist_relevant_mae: 1.2899 - dist_relevant_mse: 9.1840sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 231ms/step - loss: 0.3366 - prob_loss: 0.0826 - dist_loss: 1.2703 - prob_kld: 0.0081 - dist_relevant_mae: 1.2699 - dist_relevant_mse: 8.3598 - val_loss: 0.2955 - val_prob_loss: 0.0784 - val_dist_loss: 1.0855 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.0851 - val_dist_relevant_mse: 3.6916
Epoch 153/200
100/100 [==============================] - 23s 234ms/step - loss: 0.3539 - prob_loss: 0.0882 - dist_loss: 1.3285 - prob_kld: 0.0094 - dist_relevant_mae: 1.3280 - dist_relevant_mse: 5.7977 - val_loss: 0.3050 - val_prob_loss: 0.0798 - val_dist_loss: 1.1259 - val_prob_kld: 0.0082 - val_dist_relevant_mae: 1.1255 - val_dist_relevant_mse: 3.6409
Epoch 154/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3148 - prob_loss: 0.0771 - dist_loss: 1.1883 - prob_kld: 0.0085 - dist_relevant_mae: 1.1879 - dist_relevant_mse: 4.6063 - val_loss: 0.3054 - val_prob_loss: 0.0787 - val_dist_loss: 1.1333 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.1329 - val_dist_relevant_mse: 3.7858
Epoch 155/200
 55/100 [===============>..............] - ETA: 9s - loss: 0.3805 - prob_loss: 0.0968 - dist_loss: 1.4181 - prob_kld: 0.0113 - dist_relevant_mae: 1.4176 - dist_relevant_mse: 8.6011 sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 239ms/step - loss: 0.3707 - prob_loss: 0.0930 - dist_loss: 1.3887 - prob_kld: 0.0103 - dist_relevant_mae: 1.3882 - dist_relevant_mse: 8.1410 - val_loss: 0.2933 - val_prob_loss: 0.0783 - val_dist_loss: 1.0751 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.0746 - val_dist_relevant_mse: 3.4736
Epoch 156/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3417 - prob_loss: 0.0851 - dist_loss: 1.2828 - prob_kld: 0.0093 - dist_relevant_mae: 1.2824 - dist_relevant_mse: 5.5287 - val_loss: 0.3315 - val_prob_loss: 0.0790 - val_dist_loss: 1.2625 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 1.2621 - val_dist_relevant_mse: 4.0372
Epoch 157/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3366 - prob_loss: 0.0834 - dist_loss: 1.2660 - prob_kld: 0.0078 - dist_relevant_mae: 1.2656 - dist_relevant_mse: 6.1726 - val_loss: 0.2955 - val_prob_loss: 0.0785 - val_dist_loss: 1.0851 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.0846 - val_dist_relevant_mse: 3.5567
Epoch 158/200
 91/100 [==========================>...] - ETA: 1s - loss: 0.3191 - prob_loss: 0.0752 - dist_loss: 1.2193 - prob_kld: 0.0069 - dist_relevant_mae: 1.2189 - dist_relevant_mse: 5.3918sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 237ms/step - loss: 0.3255 - prob_loss: 0.0789 - dist_loss: 1.2329 - prob_kld: 0.0071 - dist_relevant_mae: 1.2325 - dist_relevant_mse: 5.6691 - val_loss: 0.2912 - val_prob_loss: 0.0783 - val_dist_loss: 1.0648 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 1.0643 - val_dist_relevant_mse: 3.3928
Epoch 159/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3270 - prob_loss: 0.0882 - dist_loss: 1.1940 - prob_kld: 0.0087 - dist_relevant_mae: 1.1935 - dist_relevant_mse: 4.9285 - val_loss: 0.2906 - val_prob_loss: 0.0785 - val_dist_loss: 1.0606 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.0601 - val_dist_relevant_mse: 3.4063
Epoch 160/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3673 - prob_loss: 0.0863 - dist_loss: 1.4048 - prob_kld: 0.0087 - dist_relevant_mae: 1.4043 - dist_relevant_mse: 8.2466 - val_loss: 0.2931 - val_prob_loss: 0.0784 - val_dist_loss: 1.0737 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.0733 - val_dist_relevant_mse: 3.4157
Epoch 161/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3039 - prob_loss: 0.0729 - dist_loss: 1.1547 - prob_kld: 0.0074 - dist_relevant_mae: 1.1542 - dist_relevant_mse: 4.3195 - val_loss: 0.3193 - val_prob_loss: 0.0786 - val_dist_loss: 1.2034 - val_prob_kld: 0.0070 - val_dist_relevant_mae: 1.2030 - val_dist_relevant_mse: 5.2382
Epoch 162/200
 27/100 [=======>......................] - ETA: 15s - loss: 0.3051 - prob_loss: 0.0826 - dist_loss: 1.1127 - prob_kld: 0.0088 - dist_relevant_mae: 1.1123 - dist_relevant_mse: 3.9311sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 239ms/step - loss: 0.3261 - prob_loss: 0.0811 - dist_loss: 1.2250 - prob_kld: 0.0075 - dist_relevant_mae: 1.2246 - dist_relevant_mse: 5.0528 - val_loss: 0.2969 - val_prob_loss: 0.0783 - val_dist_loss: 1.0933 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 1.0928 - val_dist_relevant_mse: 3.6546
Epoch 163/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3467 - prob_loss: 0.0841 - dist_loss: 1.3130 - prob_kld: 0.0087 - dist_relevant_mae: 1.3126 - dist_relevant_mse: 7.2610 - val_loss: 0.3091 - val_prob_loss: 0.0785 - val_dist_loss: 1.1528 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1524 - val_dist_relevant_mse: 5.1460
Epoch 164/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3351 - prob_loss: 0.0795 - dist_loss: 1.2778 - prob_kld: 0.0089 - dist_relevant_mae: 1.2774 - dist_relevant_mse: 6.3383 - val_loss: 0.3046 - val_prob_loss: 0.0784 - val_dist_loss: 1.1308 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.1304 - val_dist_relevant_mse: 4.3829
Epoch 165/200
 63/100 [=================>............] - ETA: 8s - loss: 0.3483 - prob_loss: 0.0917 - dist_loss: 1.2830 - prob_kld: 0.0079 - dist_relevant_mae: 1.2825 - dist_relevant_mse: 5.7463sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 239ms/step - loss: 0.3462 - prob_loss: 0.0893 - dist_loss: 1.2847 - prob_kld: 0.0082 - dist_relevant_mae: 1.2843 - dist_relevant_mse: 5.5465 - val_loss: 0.3180 - val_prob_loss: 0.0785 - val_dist_loss: 1.1978 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1974 - val_dist_relevant_mse: 4.9259
Epoch 166/200
100/100 [==============================] - 24s 242ms/step - loss: 0.3496 - prob_loss: 0.0868 - dist_loss: 1.3143 - prob_kld: 0.0080 - dist_relevant_mae: 1.3139 - dist_relevant_mse: 6.9546 - val_loss: 0.3024 - val_prob_loss: 0.0784 - val_dist_loss: 1.1196 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.1192 - val_dist_relevant_mse: 3.9552
Epoch 167/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3319 - prob_loss: 0.0832 - dist_loss: 1.2433 - prob_kld: 0.0095 - dist_relevant_mae: 1.2429 - dist_relevant_mse: 5.9231 - val_loss: 0.3079 - val_prob_loss: 0.0784 - val_dist_loss: 1.1477 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.1472 - val_dist_relevant_mse: 4.3262
Epoch 168/200
 99/100 [============================>.] - ETA: 0s - loss: 0.3342 - prob_loss: 0.0807 - dist_loss: 1.2672 - prob_kld: 0.0074 - dist_relevant_mae: 1.2667 - dist_relevant_mse: 5.4887sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 240ms/step - loss: 0.3333 - prob_loss: 0.0804 - dist_loss: 1.2647 - prob_kld: 0.0073 - dist_relevant_mae: 1.2642 - dist_relevant_mse: 5.4549 - val_loss: 0.3030 - val_prob_loss: 0.0783 - val_dist_loss: 1.1237 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 1.1232 - val_dist_relevant_mse: 3.7373
Epoch 169/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3281 - prob_loss: 0.0846 - dist_loss: 1.2173 - prob_kld: 0.0087 - dist_relevant_mae: 1.2168 - dist_relevant_mse: 5.5136 - val_loss: 0.2950 - val_prob_loss: 0.0784 - val_dist_loss: 1.0832 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.0827 - val_dist_relevant_mse: 3.5717
Epoch 170/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3457 - prob_loss: 0.0841 - dist_loss: 1.3078 - prob_kld: 0.0092 - dist_relevant_mae: 1.3073 - dist_relevant_mse: 6.2614 - val_loss: 0.3058 - val_prob_loss: 0.0791 - val_dist_loss: 1.1338 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.1333 - val_dist_relevant_mse: 3.9806
Epoch 171/200
100/100 [==============================] - 23s 231ms/step - loss: 0.3543 - prob_loss: 0.0859 - dist_loss: 1.3420 - prob_kld: 0.0090 - dist_relevant_mae: 1.3416 - dist_relevant_mse: 6.5762 - val_loss: 0.3016 - val_prob_loss: 0.0788 - val_dist_loss: 1.1141 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.1137 - val_dist_relevant_mse: 3.8632
Epoch 172/200
 35/100 [=========>....................] - ETA: 13s - loss: 0.2887 - prob_loss: 0.0685 - dist_loss: 1.1010 - prob_kld: 0.0060 - dist_relevant_mae: 1.1007 - dist_relevant_mse: 3.8521sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 235ms/step - loss: 0.3223 - prob_loss: 0.0819 - dist_loss: 1.2022 - prob_kld: 0.0079 - dist_relevant_mae: 1.2018 - dist_relevant_mse: 4.8382 - val_loss: 0.2942 - val_prob_loss: 0.0783 - val_dist_loss: 1.0791 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.0787 - val_dist_relevant_mse: 3.6099
Epoch 173/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3321 - prob_loss: 0.0808 - dist_loss: 1.2565 - prob_kld: 0.0075 - dist_relevant_mae: 1.2561 - dist_relevant_mse: 5.6708 - val_loss: 0.3133 - val_prob_loss: 0.0785 - val_dist_loss: 1.1739 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1735 - val_dist_relevant_mse: 4.6293
Epoch 174/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3415 - prob_loss: 0.0795 - dist_loss: 1.3101 - prob_kld: 0.0084 - dist_relevant_mae: 1.3097 - dist_relevant_mse: 8.3230 - val_loss: 0.2933 - val_prob_loss: 0.0783 - val_dist_loss: 1.0751 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 1.0747 - val_dist_relevant_mse: 3.5992
Epoch 175/200
 71/100 [====================>.........] - ETA: 6s - loss: 0.3447 - prob_loss: 0.0839 - dist_loss: 1.3037 - prob_kld: 0.0094 - dist_relevant_mae: 1.3033 - dist_relevant_mse: 6.6139sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 240ms/step - loss: 0.3645 - prob_loss: 0.0918 - dist_loss: 1.3636 - prob_kld: 0.0099 - dist_relevant_mae: 1.3632 - dist_relevant_mse: 6.9107 - val_loss: 0.2988 - val_prob_loss: 0.0790 - val_dist_loss: 1.0990 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 1.0985 - val_dist_relevant_mse: 3.6967
Epoch 176/200
100/100 [==============================] - 24s 239ms/step - loss: 0.2908 - prob_loss: 0.0739 - dist_loss: 1.0843 - prob_kld: 0.0064 - dist_relevant_mae: 1.0839 - dist_relevant_mse: 3.7540 - val_loss: 0.2960 - val_prob_loss: 0.0783 - val_dist_loss: 1.0888 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 1.0884 - val_dist_relevant_mse: 3.6753
Epoch 177/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3223 - prob_loss: 0.0821 - dist_loss: 1.2007 - prob_kld: 0.0083 - dist_relevant_mae: 1.2003 - dist_relevant_mse: 4.8122 - val_loss: 0.3009 - val_prob_loss: 0.0794 - val_dist_loss: 1.1074 - val_prob_kld: 0.0078 - val_dist_relevant_mae: 1.1070 - val_dist_relevant_mse: 3.7516
Epoch 178/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3539 - prob_loss: 0.0851 - dist_loss: 1.3437 - prob_kld: 0.0099 - dist_relevant_mae: 1.3433 - dist_relevant_mse: 6.9858 - val_loss: 0.3153 - val_prob_loss: 0.0785 - val_dist_loss: 1.1841 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1837 - val_dist_relevant_mse: 4.2601
Epoch 179/200
  7/100 [=>............................] - ETA: 20s - loss: 0.3746 - prob_loss: 0.1049 - dist_loss: 1.3486 - prob_kld: 0.0090 - dist_relevant_mae: 1.3481 - dist_relevant_mse: 5.4130sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 239ms/step - loss: 0.3372 - prob_loss: 0.0911 - dist_loss: 1.2308 - prob_kld: 0.0098 - dist_relevant_mae: 1.2303 - dist_relevant_mse: 5.0979 - val_loss: 0.2966 - val_prob_loss: 0.0784 - val_dist_loss: 1.0910 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.0905 - val_dist_relevant_mse: 3.5501
Epoch 180/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3389 - prob_loss: 0.0848 - dist_loss: 1.2703 - prob_kld: 0.0086 - dist_relevant_mae: 1.2698 - dist_relevant_mse: 7.7586 - val_loss: 0.3955 - val_prob_loss: 0.0788 - val_dist_loss: 1.5838 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.5832 - val_dist_relevant_mse: 6.0584
Epoch 181/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3832 - prob_loss: 0.0872 - dist_loss: 1.4796 - prob_kld: 0.0134 - dist_relevant_mae: 1.4792 - dist_relevant_mse: 12.5194 - val_loss: 0.3199 - val_prob_loss: 0.0847 - val_dist_loss: 1.1758 - val_prob_kld: 0.0131 - val_dist_relevant_mae: 1.1754 - val_dist_relevant_mse: 4.2116
Epoch 182/200
 43/100 [===========>..................] - ETA: 12s - loss: 0.3278 - prob_loss: 0.0737 - dist_loss: 1.2705 - prob_kld: 0.0073 - dist_relevant_mae: 1.2701 - dist_relevant_mse: 5.4669sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 238ms/step - loss: 0.3538 - prob_loss: 0.0831 - dist_loss: 1.3532 - prob_kld: 0.0088 - dist_relevant_mae: 1.3528 - dist_relevant_mse: 6.2642 - val_loss: 0.3025 - val_prob_loss: 0.0791 - val_dist_loss: 1.1170 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.1166 - val_dist_relevant_mse: 3.9314
Epoch 183/200
100/100 [==============================] - 24s 239ms/step - loss: 0.3493 - prob_loss: 0.0872 - dist_loss: 1.3103 - prob_kld: 0.0087 - dist_relevant_mae: 1.3098 - dist_relevant_mse: 8.1249 - val_loss: 0.3014 - val_prob_loss: 0.0820 - val_dist_loss: 1.0972 - val_prob_kld: 0.0104 - val_dist_relevant_mae: 1.0968 - val_dist_relevant_mse: 3.6584
Epoch 184/200
100/100 [==============================] - 24s 238ms/step - loss: 0.3151 - prob_loss: 0.0763 - dist_loss: 1.1938 - prob_kld: 0.0085 - dist_relevant_mae: 1.1934 - dist_relevant_mse: 5.6865 - val_loss: 0.2955 - val_prob_loss: 0.0787 - val_dist_loss: 1.0841 - val_prob_kld: 0.0071 - val_dist_relevant_mae: 1.0837 - val_dist_relevant_mse: 3.8210
Epoch 185/200
 79/100 [======================>.......] - ETA: 4s - loss: 0.3507 - prob_loss: 0.0823 - dist_loss: 1.3420 - prob_kld: 0.0084 - dist_relevant_mae: 1.3415 - dist_relevant_mse: 6.6019sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 243ms/step - loss: 0.3432 - prob_loss: 0.0794 - dist_loss: 1.3189 - prob_kld: 0.0080 - dist_relevant_mae: 1.3184 - dist_relevant_mse: 6.3868 - val_loss: 0.2951 - val_prob_loss: 0.0783 - val_dist_loss: 1.0842 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 1.0837 - val_dist_relevant_mse: 3.4428
Epoch 186/200
100/100 [==============================] - 24s 243ms/step - loss: 0.3518 - prob_loss: 0.0858 - dist_loss: 1.3303 - prob_kld: 0.0088 - dist_relevant_mae: 1.3298 - dist_relevant_mse: 6.6064 - val_loss: 0.2936 - val_prob_loss: 0.0782 - val_dist_loss: 1.0769 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 1.0765 - val_dist_relevant_mse: 3.5634
Epoch 187/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3134 - prob_loss: 0.0812 - dist_loss: 1.1613 - prob_kld: 0.0089 - dist_relevant_mae: 1.1609 - dist_relevant_mse: 5.0249 - val_loss: 0.3615 - val_prob_loss: 0.0791 - val_dist_loss: 1.4119 - val_prob_kld: 0.0075 - val_dist_relevant_mae: 1.4115 - val_dist_relevant_mse: 5.8637
Epoch 188/200
100/100 [==============================] - 24s 242ms/step - loss: 0.3562 - prob_loss: 0.0894 - dist_loss: 1.3342 - prob_kld: 0.0093 - dist_relevant_mae: 1.3337 - dist_relevant_mse: 5.8500 - val_loss: 0.3051 - val_prob_loss: 0.0785 - val_dist_loss: 1.1331 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.1326 - val_dist_relevant_mse: 3.7017
Epoch 189/200
 15/100 [===>..........................] - ETA: 18s - loss: 0.2370 - prob_loss: 0.0573 - dist_loss: 0.8985 - prob_kld: 0.0050 - dist_relevant_mae: 0.8981 - dist_relevant_mse: 2.8734sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 243ms/step - loss: 0.3391 - prob_loss: 0.0812 - dist_loss: 1.2897 - prob_kld: 0.0072 - dist_relevant_mae: 1.2893 - dist_relevant_mse: 6.6111 - val_loss: 0.3213 - val_prob_loss: 0.0785 - val_dist_loss: 1.2140 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.2136 - val_dist_relevant_mse: 4.5199
Epoch 190/200
100/100 [==============================] - 24s 241ms/step - loss: 0.3813 - prob_loss: 0.0844 - dist_loss: 1.4843 - prob_kld: 0.0125 - dist_relevant_mae: 1.4839 - dist_relevant_mse: 10.5596 - val_loss: 0.3222 - val_prob_loss: 0.0788 - val_dist_loss: 1.2170 - val_prob_kld: 0.0072 - val_dist_relevant_mae: 1.2166 - val_dist_relevant_mse: 4.3810
Epoch 191/200
100/100 [==============================] - 24s 240ms/step - loss: 0.3232 - prob_loss: 0.0817 - dist_loss: 1.2076 - prob_kld: 0.0088 - dist_relevant_mae: 1.2071 - dist_relevant_mse: 5.4445 - val_loss: 0.2938 - val_prob_loss: 0.0784 - val_dist_loss: 1.0769 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.0764 - val_dist_relevant_mse: 3.4536
Epoch 192/200
 51/100 [==============>...............] - ETA: 10s - loss: 0.3399 - prob_loss: 0.0861 - dist_loss: 1.2690 - prob_kld: 0.0095 - dist_relevant_mae: 1.2685 - dist_relevant_mse: 5.0270sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 231ms/step - loss: 0.3260 - prob_loss: 0.0794 - dist_loss: 1.2333 - prob_kld: 0.0081 - dist_relevant_mae: 1.2329 - dist_relevant_mse: 5.1816 - val_loss: 0.3010 - val_prob_loss: 0.0783 - val_dist_loss: 1.1133 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.1129 - val_dist_relevant_mse: 4.1430
Epoch 193/200
100/100 [==============================] - 25s 252ms/step - loss: 0.3430 - prob_loss: 0.0861 - dist_loss: 1.2844 - prob_kld: 0.0094 - dist_relevant_mae: 1.2839 - dist_relevant_mse: 5.7223 - val_loss: 0.3016 - val_prob_loss: 0.0810 - val_dist_loss: 1.1030 - val_prob_kld: 0.0094 - val_dist_relevant_mae: 1.1026 - val_dist_relevant_mse: 3.5861
Epoch 194/200
100/100 [==============================] - 23s 231ms/step - loss: 0.3106 - prob_loss: 0.0793 - dist_loss: 1.1567 - prob_kld: 0.0085 - dist_relevant_mae: 1.1562 - dist_relevant_mse: 4.9864 - val_loss: 0.2913 - val_prob_loss: 0.0790 - val_dist_loss: 1.0618 - val_prob_kld: 0.0074 - val_dist_relevant_mae: 1.0614 - val_dist_relevant_mse: 3.3795
Epoch 195/200
 87/100 [=========================>....] - ETA: 2s - loss: 0.3449 - prob_loss: 0.0904 - dist_loss: 1.2726 - prob_kld: 0.0086 - dist_relevant_mae: 1.2721 - dist_relevant_mse: 5.7851sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 24s 235ms/step - loss: 0.3436 - prob_loss: 0.0880 - dist_loss: 1.2783 - prob_kld: 0.0097 - dist_relevant_mae: 1.2778 - dist_relevant_mse: 5.6197 - val_loss: 0.3023 - val_prob_loss: 0.0792 - val_dist_loss: 1.1159 - val_prob_kld: 0.0076 - val_dist_relevant_mae: 1.1155 - val_dist_relevant_mse: 4.0045
Epoch 196/200
100/100 [==============================] - 23s 235ms/step - loss: 0.3473 - prob_loss: 0.0932 - dist_loss: 1.2705 - prob_kld: 0.0098 - dist_relevant_mae: 1.2701 - dist_relevant_mse: 6.1965 - val_loss: 0.2954 - val_prob_loss: 0.0785 - val_dist_loss: 1.0846 - val_prob_kld: 0.0069 - val_dist_relevant_mae: 1.0841 - val_dist_relevant_mse: 3.7265
Epoch 197/200
100/100 [==============================] - 23s 232ms/step - loss: 0.3155 - prob_loss: 0.0806 - dist_loss: 1.1749 - prob_kld: 0.0088 - dist_relevant_mae: 1.1745 - dist_relevant_mse: 5.6811 - val_loss: 0.2996 - val_prob_loss: 0.0784 - val_dist_loss: 1.1057 - val_prob_kld: 0.0068 - val_dist_relevant_mae: 1.1053 - val_dist_relevant_mse: 3.6198
Epoch 198/200
100/100 [==============================] - 23s 232ms/step - loss: 0.3339 - prob_loss: 0.0831 - dist_loss: 1.2535 - prob_kld: 0.0080 - dist_relevant_mae: 1.2531 - dist_relevant_mse: 7.2648 - val_loss: 0.2950 - val_prob_loss: 0.0783 - val_dist_loss: 1.0833 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 1.0829 - val_dist_relevant_mse: 3.4450
Epoch 199/200
 23/100 [=====>........................] - ETA: 16s - loss: 0.2810 - prob_loss: 0.0742 - dist_loss: 1.0343 - prob_kld: 0.0067 - dist_relevant_mae: 1.0339 - dist_relevant_mse: 4.9348   sequence getitem on_epoch_end: shuffle the self.perm index list (by the StarDistDataBase class )
100/100 [==============================] - 23s 233ms/step - loss: 0.3281 - prob_loss: 0.0849 - dist_loss: 1.2161 - prob_kld: 0.0084 - dist_relevant_mae: 1.2157 - dist_relevant_mse: 5.1134 - val_loss: 0.2950 - val_prob_loss: 0.0783 - val_dist_loss: 1.0832 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 1.0828 - val_dist_relevant_mse: 3.5203

Epoch 00199: ReduceLROnPlateau reducing learning rate to 7.500000356230885e-05.
Epoch 200/200
100/100 [==============================] - 24s 237ms/step - loss: 0.3516 - prob_loss: 0.0925 - dist_loss: 1.2955 - prob_kld: 0.0090 - dist_relevant_mae: 1.2951 - dist_relevant_mse: 6.5537 - val_loss: 0.2903 - val_prob_loss: 0.0783 - val_dist_loss: 1.0596 - val_prob_kld: 0.0067 - val_dist_relevant_mae: 1.0592 - val_dist_relevant_mse: 3.4731

Loading network weights from 'weights_best.h5'.
model.keras_model.save('1b_100s_400e.h5') _____no_output_____model.keras_model.load_weights('1b_100s_400e.h5')_____no_output_____model.keras_model.summary()Model: "model_2"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
input (InputLayer)              (None, None, None, 1 0
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, None, None, 3 320         input[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, None, None, 3 9248        conv2d_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, None, None, 3 0           conv2d_4[0][0]
__________________________________________________________________________________________________
down_level_0_no_0 (Conv2D)      (None, None, None, 3 9248        max_pooling2d_2[0][0]
__________________________________________________________________________________________________
down_level_0_no_1 (Conv2D)      (None, None, None, 3 9248        down_level_0_no_0[0][0]
__________________________________________________________________________________________________
max_0 (MaxPooling2D)            (None, None, None, 3 0           down_level_0_no_1[0][0]
__________________________________________________________________________________________________
down_level_1_no_0 (Conv2D)      (None, None, None, 6 18496       max_0[0][0]
__________________________________________________________________________________________________
down_level_1_no_1 (Conv2D)      (None, None, None, 6 36928       down_level_1_no_0[0][0]
__________________________________________________________________________________________________
max_1 (MaxPooling2D)            (None, None, None, 6 0           down_level_1_no_1[0][0]
__________________________________________________________________________________________________
down_level_2_no_0 (Conv2D)      (None, None, None, 1 73856       max_1[0][0]
__________________________________________________________________________________________________
down_level_2_no_1 (Conv2D)      (None, None, None, 1 147584      down_level_2_no_0[0][0]
__________________________________________________________________________________________________
max_2 (MaxPooling2D)            (None, None, None, 1 0           down_level_2_no_1[0][0]
__________________________________________________________________________________________________
middle_0 (Conv2D)               (None, None, None, 2 295168      max_2[0][0]
__________________________________________________________________________________________________
middle_2 (Conv2D)               (None, None, None, 1 295040      middle_0[0][0]
__________________________________________________________________________________________________
up_sampling2d_4 (UpSampling2D)  (None, None, None, 1 0           middle_2[0][0]
__________________________________________________________________________________________________
concatenate_4 (Concatenate)     (None, None, None, 2 0           up_sampling2d_4[0][0]
                                                                 down_level_2_no_1[0][0]
__________________________________________________________________________________________________
up_level_2_no_0 (Conv2D)        (None, None, None, 1 295040      concatenate_4[0][0]
__________________________________________________________________________________________________
up_level_2_no_2 (Conv2D)        (None, None, None, 6 73792       up_level_2_no_0[0][0]
__________________________________________________________________________________________________
up_sampling2d_5 (UpSampling2D)  (None, None, None, 6 0           up_level_2_no_2[0][0]
__________________________________________________________________________________________________
concatenate_5 (Concatenate)     (None, None, None, 1 0           up_sampling2d_5[0][0]
                                                                 down_level_1_no_1[0][0]
__________________________________________________________________________________________________
up_level_1_no_0 (Conv2D)        (None, None, None, 6 73792       concatenate_5[0][0]
__________________________________________________________________________________________________
up_level_1_no_2 (Conv2D)        (None, None, None, 3 18464       up_level_1_no_0[0][0]
__________________________________________________________________________________________________
up_sampling2d_6 (UpSampling2D)  (None, None, None, 3 0           up_level_1_no_2[0][0]
__________________________________________________________________________________________________
concatenate_6 (Concatenate)     (None, None, None, 6 0           up_sampling2d_6[0][0]
                                                                 down_level_0_no_1[0][0]
__________________________________________________________________________________________________
up_level_0_no_0 (Conv2D)        (None, None, None, 3 18464       concatenate_6[0][0]
__________________________________________________________________________________________________
up_level_0_no_2 (Conv2D)        (None, None, None, 3 9248        up_level_0_no_0[0][0]
__________________________________________________________________________________________________
features (Conv2D)               (None, None, None, 1 36992       up_level_0_no_2[0][0]
__________________________________________________________________________________________________
prob (Conv2D)                   (None, None, None, 1 129         features[0][0]
__________________________________________________________________________________________________
dist (Conv2D)                   (None, None, None, 3 4128        features[0][0]
==================================================================================================
Total params: 1,425,185
Trainable params: 1,425,185
Non-trainable params: 0
__________________________________________________________________________________________________

### play and experiment with the original code_____no_output_____

i=0

idx = slice(i*data_train.batch_size,(i+1)*data_train.batch_size);idx

idx = list(data_train.perm[idx])
idx_____no_output_____arrays = [sample_patches((data_train.Y[k],) + data_train.channels_as_tuple(data_train.X[k]),
             patch_size=data_train.patch_size, n_samples=1,
             valid_inds=data_train.get_valid_inds(k)) for k in idx]

len(arrays)_____no_output_____

For each idx, sample_patches will extract patches from image and mask , the resulting "arrays" is actually a list of arrays(lenth of 2: mask, image).
As we pass 4 indexes , the length of this list is 4_____no_output_____

len(arrays[0])_____no_output_____

Here the Image(X) and Mask(Y) array was again extracted from the arrays list. Not in array type but as tuple..._____no_output_____

X, Y = list(zip(*[(x[0][data_train.b],y[0]) for y,x in arrays]))
X.shape  --> X is not an array_____no_output_____# plt.imshow(np.squeeze(X))_____no_output_____

In order to test the augmentation (using the albumatation package), images need to be passed as arrays, and then grouped in dict with key image and mask._____no_output_____

Xa = np.asanyarray(X)
Ya = np.asanyarray(Y)
Xa.shape, Ya.shape_____no_output_____data_train.augmenter = AUGMENTATIONS_TRAIN
xx, yy = zip(*[data_train.augmenter(**{"image":_x,"mask": _y}).values() for _x, _y in zip(Xa,Ya)])
type(xx)_____no_output_____plot_some(xx,yy)_____no_output_____

The original code when augmenter was set to None_____no_output_____

data_train.augmenter = lambda *args: args
Xb, Yb = tuple(zip(*tuple(data_train.augmenter(_x, _y) for _x, _y in zip(X,Y))))
type(Xb)_____no_output_____Xb_____no_output_____

### Try a more understandable style (at least by myself...)_____no_output_____

i=0
idx = slice(i*data_train.batch_size,(i+1)*data_train.batch_size);idx # here the idx progresses in step of batch_size as the i progresses
idx = list(data_train.perm[idx])
_____no_output_____idx_____no_output_____# the output from the sample_patches is also a list..... so basically we turn a list of list into a n-dim array
arrays = np.hstack([sample_patches((data_train.Y[k],) + data_train.channels_as_tuple(data_train.X[k]),
             patch_size=data_train.patch_size, n_samples=1,
             valid_inds=data_train.get_valid_inds(k)) for k in idx])_____no_output_____type(arrays), arrays.shape_____no_output_____X = arrays[1,...,data_train.bb,data_train.bb]  #the elipsis at second dimension is reserved for channels>1, but not really tested
Y = arrays[0,...,data_train.bb,data_train.bb].astype('uint16')
X.shape, Y.shape_____no_output_____data_train.augmenter = AUGMENTATIONS_TRAIN
XX, YY = zip(*[data_train.augmenter(**{"image":_x,"mask": _y}).values() for _x, _y in zip(X,Y)]) ## output XX, YY are tuples so we can plug them back into the pipe
from stardist.utils import (edt_prob, _normalize_grid)

prob = np.stack([edt_prob(lbl[data_train.b]) for lbl in YY])
plot_some(XX, YY, prob, x_inch=25, y_inch=18)_____no_output_____from stardist.geometry import star_dist
if data_train.shape_completion:
    Y_cleared = [clear_border(lbl) for lbl in YY]
    dist      = np.stack([star_dist(lbl,data_train.n_rays,mode=data_train.sd_mode)[data_train.b+(slice(None),)] for lbl in Y_cleared])
    dist_mask = np.stack([edt_prob(lbl[data_train.b]) for lbl in Y_cleared])
else:
    dist      = np.stack([star_dist(lbl,data_train.n_rays,mode=data_train.sd_mode) for lbl in YY])
    dist_mask = prob

XX = np.stack(XX)
if XX.ndim == 3: # input image has no channel axis
    XX = np.expand_dims(XX,-1)
    prob = np.expand_dims(prob,-1)
    dist_mask = np.expand_dims(dist_mask,-1)

# subsample wth given grid
dist_mask = dist_mask[data_train.ss_grid]
prob      = prob[data_train.ss_grid]
dist      = dist[data_train.ss_grid]

# return [X,dist_mask], [prob,dist]_____no_output_____dist_mask.shape_____no_output_____plot_some(dist_mask, prob, dist)_____no_output_____dist.shape  #dist is the star radius of the 32 (n_rays) at each pixel value. _____no_output_____data_train = StarDistData2D(X_trn, Y_trn, batch_size=4, n_rays=32, patch_size=(256,256), augmenter=None, shape_completion=False)_____no_output_____# %debug
# data_train[0]> /home/qzt/miniconda3/envs/stardist/lib/python3.7/site-packages/stardist/models/model2d.py(60)<genexpr>()
     58         else:
     59             X, Y = list(zip(*[(np.stack([_x[0] for _x in x],axis=-1)[self.b], y[0]) for y,*x in arrays]))
---> 60
     61         X, Y = tuple(zip(*tuple(self.augmenter(_x, _y) for _x, _y in zip(X,Y))))
     62

## test code to understand tuple and list comprehension_____no_output_____

i = np.arange(10)
j = ['a','b','c','d','e','f','g','h','i','j']_____no_output_____dict = {"num":i, "alphbet":j}_____no_output_____def noop(**kwargs):
    return kwargs

noop(**dict)_____no_output_____

dict.values() is used intead of items() so that the output is directly arrays of values instead of arrays of dict (which need to retrieved again..)_____no_output_____

out = [{"num":_i, "alphbet":_j}.values() for _i, _j in zip(i,j)]
out_____no_output_____out1 = [{"num":_i, "alphbet":_j} for _i, _j in zip(i,j)]
out1_____no_output_____out2 = ({"num":_i, "alphbet":_j}.items() for _i, _j in zip(i,j))

out2_____no_output_____out3 = tuple({"num":_i, "alphbet":_j}.items() for _i, _j in zip(i,j))
out3_____no_output_____

use of [] list comprehension or tuple()  to enclose the for loop has no difference as we pass the output by * unpacking to the zip so that each element (columns in contrast to row) can be retrieved by the a,b at LHS. If the zip(\*XXX) trick is not used, a , b will be each row of the list not column_____no_output_____

a, b = zip(*out)
a,b_____no_output_____a, b = zip(*out3)
a,b_____no_output_____c = list(zip(*out3))
type(c), len(c)_____no_output_____c[0]_____no_output_____for _x, _y in zip(Xa,Ya):
    res = data_train.augmenter(**{"image":_x,"mask": _y})
X = res['image']
Y = res['mask']_____no_output_____X_____no_output_____xx, yy = data_train.__getitem__(0)

plot_some(xx)_____no_output_____plt.imshow(np.squeeze(xx[1]))_____no_output_____

Normalize images and fill small label holes._____no_output_____Split into train and validation datasets._____no_output_____Training data consists of pairs of input image and label instances._____no_output_____# Configuration

A `StarDist2D` model is specified via a `Config2D` object._____no_output_____

print(Config2D.__doc__)_____no_output_____# 32 is a good default choice (see 1_data.ipynb)
n_rays = 32

# Use OpenCL-based computations for data generator during training (requires 'gputools')
use_gpu = False and gputools_available()

# Predict on subsampled grid for increased efficiency and larger field of view
grid = (2,2)

conf = Config2D (
    n_rays       = n_rays,
    grid         = grid,
    use_gpu      = use_gpu,
    n_channel_in = n_channel,
    train_batch_size = 1,  #no gpu
    train_epochs = 400,
    train_steps_per_epoch = 100
)
print(conf)
vars(conf)_____no_output_____# if use_gpu:
#     from csbdeep.utils.tf import limit_gpu_memory
#     # adjust as necessary: limit GPU memory to be used by TensorFlow to leave some to OpenCL-based computations
#     limit_gpu_memory(0.8)_____no_output_____

**Note:** The trained `StarDist2D` model will *not* predict completed shapes for partially visible objects at the image boundary if `train_shape_completion=False` (which is the default option)._____no_output_____

%pwd_____no_output_____model = StarDist2D(conf, name='stardist', basedir='models_test2')_____no_output_____

Check if the neural network has a large enough field of view to see up to the boundary of most objects._____no_output_____

#skipped temporarily
# median_size = calculate_extents(list(Y), np.median)
# fov = np.array(model._axes_tile_overlap('YX'))
# if any(median_size > fov):
#     print("WARNING: median object size larger than field of view of the neural network.")_____no_output_____

# Training_____no_output_____You can define a function/callable that applies augmentation to each batch of the data generator._____no_output_____

augmenter = None

# def augmenter(x, y):
#     """Augmentation of a single input/label image pair.
#     x is an input image
#     y is the corresponding ground-truth label image
#     """
#     # modify a copy of x and/or y...
#     return x, y_____no_output_____

We recommend to monitor the progress during training with [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard). You can start it in the shell from the current working directory like this:

    $ tensorboard --logdir=.

Then connect to [http://localhost:6006/](http://localhost:6006/) with your browser.
_____no_output_____

%cd /home/qzt/stardist/examples/2D_____no_output_____quick_demo = False

if quick_demo:
    print (
        "NOTE: This is only for a quick demonstration!\n"
        "      Please set the variable 'quick_demo = False' for proper (long) training.",
        file=sys.stderr, flush=True
    )
    model.train(X_trn, Y_trn, validation_data=(X_val,Y_val), augmenter=augmenter,
                epochs=2, steps_per_epoch=10)

    print("====> Stopping training and loading previously trained demo model from disk.", file=sys.stderr, flush=True)
    model = StarDist2D(None, name='2D_demo', basedir='../../models/examples')
    model.basedir = None # to prevent files of the demo model to be overwritten (not needed for your model)
else:
    model.train(X_trn, Y_trn, validation_data=(X_val,Y_val), augmenter=augmenter)
None;_____no_output_____

# Threshold optimization_____no_output_____While the default values for the probability and non-maximum suppression thresholds already yield good results in many cases, we still recommend to adapt the thresholds to your data. The optimized threshold values are saved to disk and will be automatically loaded with the model._____no_output_____

model.optimize_thresholds(X_val, Y_val)NMS threshold = 0.3:  80%|████████  | 16/20 [00:12<00:03,  1.24it/s, 0.475 -> 0.892]
NMS threshold = 0.4:  80%|████████  | 16/20 [00:12<00:03,  1.28it/s, 0.475 -> 0.892]
NMS threshold = 0.5:  80%|████████  | 16/20 [00:13<00:03,  1.15it/s, 0.475 -> 0.892]
%pwd_____no_output_____model.export_TF(fname='b8_yrange3-50')_____no_output_____# export_TF(self, fname=None, single_output=True, upsample_grid=True)
# tf.saved_model.save(model, "/home/qzt/data/")_____no_output_____