Advanced analysis

Here we present a tutorial of the advanced analysis of STASCAN. We slide6 on the human intestinal dataset (Fawkner-Corbett et al., Cell, 2021), which including 346 raw ST spots.

This tutorial demonstrates how to use STASCAN with advanced operations. We applied STASCAN on a comprehensive planarian dataset (Cui et al., Nat Commun, 2023), which employed a base model (which including 7,689 raw ST spots) followed by a section-specific model (which including 657 raw ST spots on section 25).

The multi-section data are available at https://github.com/AbbyWY/STASCAN/tree/master/Vignettes/Cui_Planarian_Multisections, and the adjacent image are available at https://github.com/AbbyWY/STASCAN/tree/master/Vignettes/Cui_Planarian_Adjacentimages.

For reference, we employed STASCAN on this dataset with three modules in 83 minutes. The experiment was conducted on the GPU platform (NVIDIA Tesla V100s with 32 GB memory).

Preparation

import sys
import os
import shutil
import glob

import STASCAN

vignettes_path = "../../Vignettes/Cui_Planarian_Multisections/" # the path of downloaded multi-section data data.
output_path = "./"
adjacent_path = "../../Vignettes/Cui_Planarian_Adjacentimages/" # the path of downloaded adjacent image.

label_list = ['Epidermal', 'Gut', 'Muscle', 'Neoblast', 'Neuronal', 'Parenchymal', 'Secretory'] # The list of cell types.
color_list = ["blue", "green", "red", "grey", "orange", "pink", "purple"]
dict_label = dict(zip(label_list, color_list))
spot_label = [[x] for x in label_list]

crop_size = 30 # Pixel radius of spot images.
imagetype = "png"
grey_level, white_threshold = 200, 0.7

Pre-training followed by section specific training

run = STASCAN.run_STASCAN.Optional()
# You can optionally set the parameter 'epochs=2' to quickly test the pipeline, but you should set the parameter 'epochs=50' for the official run in this tutorial.
run.SectionSpecificTraining(output_path, vignettes_path, imagetype, crop_size, epoch1=50, epoch2=50)

Running reports

The number of raw spots :  683
The strategy of pre-labelling is: joint
The number of prior spots :  370
The number of raw spots :  803
The strategy of pre-labelling is: joint
The number of prior spots :  548
The number of raw spots :  657
The strategy of pre-labelling is: joint
The number of prior spots :  366
The number of raw spots :  625
The strategy of pre-labelling is: joint
The number of prior spots :  272
The number of raw spots :  920
The strategy of pre-labelling is: joint
The number of prior spots :  612
The number of raw spots :  978
The strategy of pre-labelling is: joint
The number of prior spots :  470
The number of raw spots :  535
The strategy of pre-labelling is: joint
The number of prior spots :  209
The number of raw spots :  756
The strategy of pre-labelling is: joint
The number of prior spots :  457
The number of raw spots :  932
The strategy of pre-labelling is: joint
The number of prior spots :  552
The number of raw spots :  800
The strategy of pre-labelling is: joint
The number of prior spots :  594
Found 3563 images belonging to 7 classes.
Found 887 images belonging to 7 classes.
{'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
{'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}

 Epoch 1/50
 112/112 [==============================] - 167s 1s/step - loss: 1.1223 - accuracy: 0.6941 - val_loss: 0.9450 - val_accuracy: 0.7035
 Epoch 2/50
 112/112 [==============================] - 46s 406ms/step - loss: 0.9433 - accuracy: 0.7022 - val_loss: 0.8766 - val_accuracy: 0.7046
 Epoch 3/50
 112/112 [==============================] - 45s 404ms/step - loss: 0.7750 - accuracy: 0.7457 - val_loss: 0.7478 - val_accuracy: 0.7678
 Epoch 4/50
 112/112 [==============================] - 45s 403ms/step - loss: 0.6286 - accuracy: 0.8033 - val_loss: 0.4783 - val_accuracy: 0.8512
 Epoch 5/50
 112/112 [==============================] - 46s 406ms/step - loss: 0.5309 - accuracy: 0.8274 - val_loss: 0.7367 - val_accuracy: 0.7869
 Epoch 6/50
 112/112 [==============================] - 45s 401ms/step - loss: 0.5111 - accuracy: 0.8330 - val_loss: 0.5453 - val_accuracy: 0.8320
 Epoch 7/50
 112/112 [==============================] - 45s 400ms/step - loss: 0.4781 - accuracy: 0.8409 - val_loss: 0.3811 - val_accuracy: 0.8670
 Epoch 8/50
 112/112 [==============================] - 45s 399ms/step - loss: 0.4073 - accuracy: 0.8687 - val_loss: 0.6688 - val_accuracy: 0.8388
 Epoch 9/50
 112/112 [==============================] - 45s 404ms/step - loss: 0.4228 - accuracy: 0.8585 - val_loss: 0.3358 - val_accuracy: 0.8850
 Epoch 10/50
 112/112 [==============================] - 45s 403ms/step - loss: 0.3990 - accuracy: 0.8661 - val_loss: 0.3051 - val_accuracy: 0.9008
 Epoch 11/50
 112/112 [==============================] - 45s 403ms/step - loss: 0.3849 - accuracy: 0.8751 - val_loss: 0.4098 - val_accuracy: 0.8703
 Epoch 12/50
 112/112 [==============================] - 45s 399ms/step - loss: 0.3702 - accuracy: 0.8768 - val_loss: 0.2940 - val_accuracy: 0.9064
 Epoch 13/50
 112/112 [==============================] - 45s 400ms/step - loss: 0.3576 - accuracy: 0.8849 - val_loss: 0.5126 - val_accuracy: 0.8670
 Epoch 14/50
 112/112 [==============================] - 45s 397ms/step - loss: 0.3468 - accuracy: 0.8905 - val_loss: 0.2725 - val_accuracy: 0.9076
 Epoch 15/50
 112/112 [==============================] - 45s 399ms/step - loss: 0.3289 - accuracy: 0.8956 - val_loss: 0.2594 - val_accuracy: 0.9200
 Epoch 16/50
 112/112 [==============================] - 45s 397ms/step - loss: 0.3372 - accuracy: 0.8908 - val_loss: 0.2828 - val_accuracy: 0.8985
 Epoch 17/50
 112/112 [==============================] - 45s 402ms/step - loss: 0.3319 - accuracy: 0.8880 - val_loss: 0.2652 - val_accuracy: 0.9154
 Epoch 18/50
 112/112 [==============================] - 45s 399ms/step - loss: 0.3258 - accuracy: 0.8931 - val_loss: 0.2418 - val_accuracy: 0.9211
 Epoch 19/50
 112/112 [==============================] - 45s 402ms/step - loss: 0.3288 - accuracy: 0.8948 - val_loss: 0.3031 - val_accuracy: 0.8895
 Epoch 20/50
 112/112 [==============================] - 45s 398ms/step - loss: 0.3231 - accuracy: 0.8919 - val_loss: 0.3001 - val_accuracy: 0.9008
 Epoch 21/50
 112/112 [==============================] - 45s 402ms/step - loss: 0.2967 - accuracy: 0.8953 - val_loss: 0.3391 - val_accuracy: 0.8963
 Epoch 22/50
 112/112 [==============================] - 45s 398ms/step - loss: 0.3009 - accuracy: 0.9012 - val_loss: 0.2379 - val_accuracy: 0.9188
 Epoch 23/50
 112/112 [==============================] - 45s 403ms/step - loss: 0.2777 - accuracy: 0.9116 - val_loss: 0.3404 - val_accuracy: 0.9008
 Epoch 24/50
 112/112 [==============================] - 45s 402ms/step - loss: 0.2608 - accuracy: 0.9119 - val_loss: 0.2651 - val_accuracy: 0.9143
 Epoch 25/50
 112/112 [==============================] - 45s 397ms/step - loss: 0.2697 - accuracy: 0.9077 - val_loss: 0.3457 - val_accuracy: 0.9008
 Epoch 26/50
 112/112 [==============================] - 45s 398ms/step - loss: 0.2738 - accuracy: 0.9127 - val_loss: 0.2492 - val_accuracy: 0.9233
 Epoch 27/50
 112/112 [==============================] - 45s 398ms/step - loss: 0.2564 - accuracy: 0.9161 - val_loss: 0.3345 - val_accuracy: 0.9109
 Epoch 28/50
 112/112 [==============================] - 45s 403ms/step - loss: 0.2740 - accuracy: 0.9082 - val_loss: 0.2843 - val_accuracy: 0.9030
 Epoch 29/50
 112/112 [==============================] - 45s 399ms/step - loss: 0.2428 - accuracy: 0.9200 - val_loss: 0.2345 - val_accuracy: 0.9200
 Epoch 30/50
 112/112 [==============================] - 45s 402ms/step - loss: 0.2357 - accuracy: 0.9209 - val_loss: 0.2539 - val_accuracy: 0.9222
 Epoch 31/50
 112/112 [==============================] - 45s 403ms/step - loss: 0.2572 - accuracy: 0.9180 - val_loss: 0.2527 - val_accuracy: 0.9233
 Epoch 32/50
 112/112 [==============================] - 46s 405ms/step - loss: 0.2261 - accuracy: 0.9194 - val_loss: 0.2744 - val_accuracy: 0.9154
 Epoch 33/50
 112/112 [==============================] - 45s 404ms/step - loss: 0.2382 - accuracy: 0.9192 - val_loss: 0.3402 - val_accuracy: 0.9109
 Epoch 34/50
 112/112 [==============================] - 45s 400ms/step - loss: 0.2306 - accuracy: 0.9245 - val_loss: 0.2252 - val_accuracy: 0.9278
 Epoch 35/50
 112/112 [==============================] - 46s 406ms/step - loss: 0.2338 - accuracy: 0.9234 - val_loss: 0.2442 - val_accuracy: 0.9166
 Epoch 36/50
 112/112 [==============================] - 45s 397ms/step - loss: 0.2109 - accuracy: 0.9251 - val_loss: 0.2635 - val_accuracy: 0.9154
 Epoch 37/50
 112/112 [==============================] - 45s 401ms/step - loss: 0.2405 - accuracy: 0.9175 - val_loss: 0.2030 - val_accuracy: 0.9324
 Epoch 38/50
 112/112 [==============================] - 45s 403ms/step - loss: 0.2103 - accuracy: 0.9318 - val_loss: 0.3040 - val_accuracy: 0.9019
 Epoch 39/50
 112/112 [==============================] - 45s 404ms/step - loss: 0.2089 - accuracy: 0.9315 - val_loss: 0.2473 - val_accuracy: 0.9121
 Epoch 40/50
 112/112 [==============================] - 45s 400ms/step - loss: 0.2104 - accuracy: 0.9310 - val_loss: 0.2569 - val_accuracy: 0.9087
 Epoch 41/50
 112/112 [==============================] - 45s 404ms/step - loss: 0.2064 - accuracy: 0.9307 - val_loss: 0.3204 - val_accuracy: 0.8985
 Epoch 42/50
 112/112 [==============================] - 45s 402ms/step - loss: 0.2166 - accuracy: 0.9267 - val_loss: 0.2839 - val_accuracy: 0.8985
 Epoch 43/50
 112/112 [==============================] - 45s 399ms/step - loss: 0.2147 - accuracy: 0.9279 - val_loss: 0.2536 - val_accuracy: 0.9177
 Epoch 44/50
 112/112 [==============================] - 45s 400ms/step - loss: 0.2204 - accuracy: 0.9296 - val_loss: 0.2578 - val_accuracy: 0.9087
 Epoch 45/50
 112/112 [==============================] - 46s 410ms/step - loss: 0.1829 - accuracy: 0.9369 - val_loss: 0.2621 - val_accuracy: 0.9188
 Epoch 46/50
 112/112 [==============================] - 45s 401ms/step - loss: 0.2074 - accuracy: 0.9307 - val_loss: 0.2359 - val_accuracy: 0.9267
 Epoch 47/50
 112/112 [==============================] - 45s 404ms/step - loss: 0.1979 - accuracy: 0.9310 - val_loss: 0.3590 - val_accuracy: 0.9121
 Epoch 48/50
 112/112 [==============================] - 45s 402ms/step - loss: 0.1785 - accuracy: 0.9357 - val_loss: 0.2407 - val_accuracy: 0.9278
 Epoch 49/50
 112/112 [==============================] - 46s 406ms/step - loss: 0.2017 - accuracy: 0.9332 - val_loss: 0.2541 - val_accuracy: 0.9278
 Epoch 50/50
 112/112 [==============================] - 45s 404ms/step - loss: 0.1865 - accuracy: 0.9399 - val_loss: 0.2715 - val_accuracy: 0.9290
 Found 322 images belonging to 7 classes.
 Found 81 images belonging to 7 classes.
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 Epoch 1/50
 11/11 [==============================] - 25s 2s/step - loss: 0.2312 - accuracy: 0.9286 - val_loss: 0.3046 - val_accuracy: 0.8889
 Epoch 2/50
 11/11 [==============================] - 4s 384ms/step - loss: 0.2351 - accuracy: 0.9379 - val_loss: 0.2111 - val_accuracy: 0.9136
 Epoch 3/50
 11/11 [==============================] - 4s 390ms/step - loss: 0.2018 - accuracy: 0.9441 - val_loss: 0.2406 - val_accuracy: 0.8889
 Epoch 4/50
 11/11 [==============================] - 4s 393ms/step - loss: 0.2541 - accuracy: 0.9286 - val_loss: 0.2017 - val_accuracy: 0.9136
 Epoch 5/50
 11/11 [==============================] - 4s 387ms/step - loss: 0.1927 - accuracy: 0.9472 - val_loss: 0.1732 - val_accuracy: 0.9259
 Epoch 6/50
 11/11 [==============================] - 4s 388ms/step - loss: 0.1569 - accuracy: 0.9534 - val_loss: 0.1694 - val_accuracy: 0.9259
 Epoch 7/50
 11/11 [==============================] - 4s 384ms/step - loss: 0.1273 - accuracy: 0.9658 - val_loss: 0.1587 - val_accuracy: 0.9259
 Epoch 8/50
 11/11 [==============================] - 5s 397ms/step - loss: 0.1564 - accuracy: 0.9565 - val_loss: 0.1588 - val_accuracy: 0.9259
 Epoch 9/50
 11/11 [==============================] - 4s 393ms/step - loss: 0.1423 - accuracy: 0.9534 - val_loss: 0.1565 - val_accuracy: 0.9259
 Epoch 10/50
 11/11 [==============================] - 4s 387ms/step - loss: 0.1266 - accuracy: 0.9658 - val_loss: 0.1485 - val_accuracy: 0.9259
 Epoch 11/50
 11/11 [==============================] - 4s 384ms/step - loss: 0.1379 - accuracy: 0.9503 - val_loss: 0.1403 - val_accuracy: 0.9383
 Epoch 12/50
 11/11 [==============================] - 4s 381ms/step - loss: 0.1541 - accuracy: 0.9472 - val_loss: 0.1317 - val_accuracy: 0.9259
 Epoch 13/50
 11/11 [==============================] - 4s 386ms/step - loss: 0.1335 - accuracy: 0.9472 - val_loss: 0.1365 - val_accuracy: 0.9259
 Epoch 14/50
 11/11 [==============================] - 4s 393ms/step - loss: 0.1420 - accuracy: 0.9565 - val_loss: 0.1438 - val_accuracy: 0.9259
 Epoch 15/50
 11/11 [==============================] - 4s 387ms/step - loss: 0.1212 - accuracy: 0.9534 - val_loss: 0.1381 - val_accuracy: 0.9259
 Epoch 16/50
 11/11 [==============================] - 4s 387ms/step - loss: 0.1007 - accuracy: 0.9720 - val_loss: 0.1311 - val_accuracy: 0.9259
 Epoch 17/50
 11/11 [==============================] - 4s 382ms/step - loss: 0.1121 - accuracy: 0.9627 - val_loss: 0.1270 - val_accuracy: 0.9506
 Epoch 18/50
 11/11 [==============================] - 4s 385ms/step - loss: 0.1663 - accuracy: 0.9379 - val_loss: 0.4306 - val_accuracy: 0.8765
 Epoch 19/50
 11/11 [==============================] - 4s 395ms/step - loss: 0.2555 - accuracy: 0.9255 - val_loss: 0.1390 - val_accuracy: 0.9383
 Epoch 20/50
 11/11 [==============================] - 4s 385ms/step - loss: 0.1224 - accuracy: 0.9658 - val_loss: 0.1074 - val_accuracy: 0.9506
 Epoch 21/50
 11/11 [==============================] - 5s 395ms/step - loss: 0.1113 - accuracy: 0.9658 - val_loss: 0.1048 - val_accuracy: 0.9506
 Epoch 22/50
 11/11 [==============================] - 4s 388ms/step - loss: 0.1441 - accuracy: 0.9472 - val_loss: 0.1093 - val_accuracy: 0.9506
 Epoch 23/50
 11/11 [==============================] - 4s 393ms/step - loss: 0.1231 - accuracy: 0.9596 - val_loss: 0.1137 - val_accuracy: 0.9383
 Epoch 24/50
 11/11 [==============================] - 4s 385ms/step - loss: 0.1161 - accuracy: 0.9689 - val_loss: 0.1182 - val_accuracy: 0.9506
 Epoch 25/50
 11/11 [==============================] - 4s 379ms/step - loss: 0.0905 - accuracy: 0.9658 - val_loss: 0.1118 - val_accuracy: 0.9506
 Epoch 26/50
 11/11 [==============================] - 4s 418ms/step - loss: 0.0977 - accuracy: 0.9658 - val_loss: 0.1032 - val_accuracy: 0.9506
 Epoch 27/50
 11/11 [==============================] - 4s 381ms/step - loss: 0.0953 - accuracy: 0.9783 - val_loss: 0.1000 - val_accuracy: 0.9630
 Epoch 28/50
 11/11 [==============================] - 4s 387ms/step - loss: 0.1256 - accuracy: 0.9503 - val_loss: 0.0964 - val_accuracy: 0.9630
 Epoch 29/50
 11/11 [==============================] - 4s 390ms/step - loss: 0.1006 - accuracy: 0.9658 - val_loss: 0.1063 - val_accuracy: 0.9383
 Epoch 30/50
 11/11 [==============================] - 4s 371ms/step - loss: 0.1057 - accuracy: 0.9689 - val_loss: 0.1035 - val_accuracy: 0.9383
 Epoch 31/50
 11/11 [==============================] - 4s 420ms/step - loss: 0.0917 - accuracy: 0.9689 - val_loss: 0.0958 - val_accuracy: 0.9506
 Epoch 32/50
 11/11 [==============================] - 4s 383ms/step - loss: 0.0988 - accuracy: 0.9658 - val_loss: 0.0951 - val_accuracy: 0.9506
 Epoch 33/50
 11/11 [==============================] - 4s 389ms/step - loss: 0.1109 - accuracy: 0.9627 - val_loss: 0.0969 - val_accuracy: 0.9506
 Epoch 34/50
 11/11 [==============================] - 4s 387ms/step - loss: 0.0846 - accuracy: 0.9658 - val_loss: 0.0981 - val_accuracy: 0.9506
 Epoch 35/50
 11/11 [==============================] - 4s 385ms/step - loss: 0.0648 - accuracy: 0.9720 - val_loss: 0.1018 - val_accuracy: 0.9383
 Epoch 36/50
 11/11 [==============================] - 4s 386ms/step - loss: 0.0933 - accuracy: 0.9627 - val_loss: 0.1017 - val_accuracy: 0.9506
 Epoch 37/50
 11/11 [==============================] - 4s 370ms/step - loss: 0.0917 - accuracy: 0.9658 - val_loss: 0.1014 - val_accuracy: 0.9383
 Epoch 38/50
 11/11 [==============================] - 4s 396ms/step - loss: 0.0877 - accuracy: 0.9720 - val_loss: 0.0984 - val_accuracy: 0.9630
 Epoch 39/50
 11/11 [==============================] - 4s 389ms/step - loss: 0.0848 - accuracy: 0.9689 - val_loss: 0.0998 - val_accuracy: 0.9506
 Epoch 40/50
 11/11 [==============================] - 4s 384ms/step - loss: 0.0929 - accuracy: 0.9658 - val_loss: 0.1002 - val_accuracy: 0.9506
 Epoch 41/50
 11/11 [==============================] - 4s 387ms/step - loss: 0.1001 - accuracy: 0.9534 - val_loss: 0.0985 - val_accuracy: 0.9506
 Epoch 42/50
 11/11 [==============================] - 4s 375ms/step - loss: 0.1204 - accuracy: 0.9596 - val_loss: 0.0915 - val_accuracy: 0.9506
 Epoch 43/50
 11/11 [==============================] - 4s 390ms/step - loss: 0.1144 - accuracy: 0.9658 - val_loss: 0.0988 - val_accuracy: 0.9506
 Epoch 44/50
 11/11 [==============================] - 4s 384ms/step - loss: 0.1081 - accuracy: 0.9658 - val_loss: 0.1077 - val_accuracy: 0.9383
 Epoch 45/50
 11/11 [==============================] - 5s 447ms/step - loss: 0.0801 - accuracy: 0.9720 - val_loss: 0.1000 - val_accuracy: 0.9383
 Epoch 46/50
 11/11 [==============================] - 4s 383ms/step - loss: 0.0960 - accuracy: 0.9658 - val_loss: 0.0953 - val_accuracy: 0.9506
 Epoch 47/50
 11/11 [==============================] - 4s 390ms/step - loss: 0.0809 - accuracy: 0.9689 - val_loss: 0.0905 - val_accuracy: 0.9506
 Epoch 48/50
 11/11 [==============================] - 4s 442ms/step - loss: 0.0826 - accuracy: 0.9689 - val_loss: 0.0845 - val_accuracy: 0.9630
 Epoch 49/50
 11/11 [==============================] - 4s 382ms/step - loss: 0.0856 - accuracy: 0.9689 - val_loss: 0.0793 - val_accuracy: 0.9630
 Epoch 50/50
 11/11 [==============================] - 4s 374ms/step - loss: 0.0914 - accuracy: 0.9720 - val_loss: 0.0832 - val_accuracy: 0.9753
 Found 464 images belonging to 7 classes.
 Found 118 images belonging to 7 classes.
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 Epoch 1/50
 15/15 [==============================] - 23s 1s/step - loss: 0.1863 - accuracy: 0.9332 - val_loss: 0.2454 - val_accuracy: 0.9237
 Epoch 2/50
 15/15 [==============================] - 6s 409ms/step - loss: 0.1817 - accuracy: 0.9418 - val_loss: 0.1881 - val_accuracy: 0.9322
 Epoch 3/50
 15/15 [==============================] - 6s 395ms/step - loss: 0.1435 - accuracy: 0.9483 - val_loss: 0.1713 - val_accuracy: 0.9407
 Epoch 4/50
 15/15 [==============================] - 6s 398ms/step - loss: 0.1481 - accuracy: 0.9569 - val_loss: 0.1696 - val_accuracy: 0.9322
 Epoch 5/50
 15/15 [==============================] - 6s 395ms/step - loss: 0.1482 - accuracy: 0.9440 - val_loss: 0.1512 - val_accuracy: 0.9492
 Epoch 6/50
 15/15 [==============================] - 6s 391ms/step - loss: 0.1233 - accuracy: 0.9634 - val_loss: 0.1400 - val_accuracy: 0.9492
 Epoch 7/50
 15/15 [==============================] - 6s 398ms/step - loss: 0.1418 - accuracy: 0.9526 - val_loss: 0.1417 - val_accuracy: 0.9492
 Epoch 8/50
 15/15 [==============================] - 6s 399ms/step - loss: 0.1266 - accuracy: 0.9526 - val_loss: 0.1432 - val_accuracy: 0.9492
 Epoch 9/50
 15/15 [==============================] - 6s 398ms/step - loss: 0.1209 - accuracy: 0.9569 - val_loss: 0.1373 - val_accuracy: 0.9492
 Epoch 10/50
 15/15 [==============================] - 6s 396ms/step - loss: 0.1044 - accuracy: 0.9655 - val_loss: 0.1309 - val_accuracy: 0.9492
 Epoch 11/50
 15/15 [==============================] - 6s 390ms/step - loss: 0.1230 - accuracy: 0.9569 - val_loss: 0.1369 - val_accuracy: 0.9407
 Epoch 12/50
 15/15 [==============================] - 6s 397ms/step - loss: 0.1079 - accuracy: 0.9634 - val_loss: 0.1284 - val_accuracy: 0.9492
 Epoch 13/50
 15/15 [==============================] - 6s 412ms/step - loss: 0.1160 - accuracy: 0.9612 - val_loss: 0.1277 - val_accuracy: 0.9492
 Epoch 14/50
 15/15 [==============================] - 6s 393ms/step - loss: 0.1057 - accuracy: 0.9612 - val_loss: 0.1266 - val_accuracy: 0.9492
 Epoch 15/50
 15/15 [==============================] - 6s 398ms/step - loss: 0.1107 - accuracy: 0.9569 - val_loss: 0.1296 - val_accuracy: 0.9492
 Epoch 16/50
 15/15 [==============================] - 6s 406ms/step - loss: 0.1118 - accuracy: 0.9547 - val_loss: 0.1311 - val_accuracy: 0.9407
 Epoch 17/50
 15/15 [==============================] - 6s 395ms/step - loss: 0.1288 - accuracy: 0.9526 - val_loss: 0.1306 - val_accuracy: 0.9492
 Epoch 18/50
 15/15 [==============================] - 6s 389ms/step - loss: 0.0895 - accuracy: 0.9720 - val_loss: 0.1265 - val_accuracy: 0.9576
 Epoch 19/50
 15/15 [==============================] - 6s 408ms/step - loss: 0.0905 - accuracy: 0.9655 - val_loss: 0.1307 - val_accuracy: 0.9576
 Epoch 20/50
 15/15 [==============================] - 6s 393ms/step - loss: 0.0948 - accuracy: 0.9677 - val_loss: 0.1375 - val_accuracy: 0.9492
 Epoch 21/50
 15/15 [==============================] - 6s 398ms/step - loss: 0.1030 - accuracy: 0.9634 - val_loss: 0.1336 - val_accuracy: 0.9492
 Epoch 22/50
 15/15 [==============================] - 6s 409ms/step - loss: 0.1199 - accuracy: 0.9591 - val_loss: 0.1240 - val_accuracy: 0.9407
 Epoch 23/50
 15/15 [==============================] - 6s 398ms/step - loss: 0.1235 - accuracy: 0.9547 - val_loss: 0.1218 - val_accuracy: 0.9576
 Epoch 24/50
 15/15 [==============================] - 6s 396ms/step - loss: 0.1094 - accuracy: 0.9612 - val_loss: 0.1206 - val_accuracy: 0.9492
 Epoch 25/50
 15/15 [==============================] - 6s 394ms/step - loss: 0.0972 - accuracy: 0.9591 - val_loss: 0.1193 - val_accuracy: 0.9492
 Epoch 26/50
 15/15 [==============================] - 6s 395ms/step - loss: 0.0955 - accuracy: 0.9591 - val_loss: 0.1228 - val_accuracy: 0.9492
 Epoch 27/50
 15/15 [==============================] - 6s 397ms/step - loss: 0.0864 - accuracy: 0.9698 - val_loss: 0.1294 - val_accuracy: 0.9576
 Epoch 28/50
 15/15 [==============================] - 6s 397ms/step - loss: 0.0870 - accuracy: 0.9655 - val_loss: 0.1323 - val_accuracy: 0.9492
 Epoch 29/50
 15/15 [==============================] - 6s 397ms/step - loss: 0.0857 - accuracy: 0.9698 - val_loss: 0.1431 - val_accuracy: 0.9407
 Epoch 30/50
 15/15 [==============================] - 6s 397ms/step - loss: 0.0952 - accuracy: 0.9612 - val_loss: 0.1308 - val_accuracy: 0.9407
 Epoch 31/50
 15/15 [==============================] - 6s 397ms/step - loss: 0.1013 - accuracy: 0.9655 - val_loss: 0.1298 - val_accuracy: 0.9576
 Epoch 32/50
 15/15 [==============================] - 6s 401ms/step - loss: 0.0824 - accuracy: 0.9677 - val_loss: 0.1265 - val_accuracy: 0.9576
 Epoch 33/50
 15/15 [==============================] - 6s 390ms/step - loss: 0.0780 - accuracy: 0.9677 - val_loss: 0.1266 - val_accuracy: 0.9492
 Epoch 34/50
 15/15 [==============================] - 6s 412ms/step - loss: 0.0852 - accuracy: 0.9634 - val_loss: 0.1331 - val_accuracy: 0.9576
 Epoch 35/50
 15/15 [==============================] - 6s 406ms/step - loss: 0.0812 - accuracy: 0.9634 - val_loss: 0.1328 - val_accuracy: 0.9492
 Epoch 36/50
 15/15 [==============================] - 6s 399ms/step - loss: 0.0869 - accuracy: 0.9698 - val_loss: 0.1177 - val_accuracy: 0.9576
 Epoch 37/50
 15/15 [==============================] - 6s 396ms/step - loss: 0.1068 - accuracy: 0.9569 - val_loss: 0.1209 - val_accuracy: 0.9492
 Epoch 38/50
 15/15 [==============================] - 6s 398ms/step - loss: 0.0788 - accuracy: 0.9784 - val_loss: 0.1166 - val_accuracy: 0.9492
 Epoch 39/50
 15/15 [==============================] - 6s 393ms/step - loss: 0.0875 - accuracy: 0.9720 - val_loss: 0.1208 - val_accuracy: 0.9492
 Epoch 40/50
 15/15 [==============================] - 6s 401ms/step - loss: 0.0793 - accuracy: 0.9763 - val_loss: 0.1295 - val_accuracy: 0.9492
 Epoch 41/50
 15/15 [==============================] - 6s 402ms/step - loss: 0.0828 - accuracy: 0.9698 - val_loss: 0.1285 - val_accuracy: 0.9492
 Epoch 42/50
 15/15 [==============================] - 6s 406ms/step - loss: 0.0855 - accuracy: 0.9720 - val_loss: 0.1202 - val_accuracy: 0.9576
 Epoch 43/50
 15/15 [==============================] - 6s 388ms/step - loss: 0.0695 - accuracy: 0.9828 - val_loss: 0.1192 - val_accuracy: 0.9492
 Epoch 44/50
 15/15 [==============================] - 6s 390ms/step - loss: 0.0881 - accuracy: 0.9634 - val_loss: 0.1248 - val_accuracy: 0.9576
 Epoch 45/50
 15/15 [==============================] - 6s 396ms/step - loss: 0.0676 - accuracy: 0.9784 - val_loss: 0.1234 - val_accuracy: 0.9576
 Epoch 46/50
 15/15 [==============================] - 6s 405ms/step - loss: 0.0794 - accuracy: 0.9720 - val_loss: 0.1283 - val_accuracy: 0.9576
 Epoch 47/50
 15/15 [==============================] - 6s 399ms/step - loss: 0.0835 - accuracy: 0.9698 - val_loss: 0.1247 - val_accuracy: 0.9576
 Epoch 48/50
 15/15 [==============================] - 6s 392ms/step - loss: 0.0775 - accuracy: 0.9698 - val_loss: 0.1260 - val_accuracy: 0.9492
 Epoch 49/50
 15/15 [==============================] - 6s 403ms/step - loss: 0.0668 - accuracy: 0.9698 - val_loss: 0.1284 - val_accuracy: 0.9576
 Epoch 50/50
 15/15 [==============================] - 6s 399ms/step - loss: 0.0700 - accuracy: 0.9741 - val_loss: 0.1330 - val_accuracy: 0.9576
 Found 296 images belonging to 7 classes.
 Found 73 images belonging to 7 classes.
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 Epoch 1/50
 10/10 [==============================] - 15s 1s/step - loss: 0.3012 - accuracy: 0.8885 - val_loss: 0.2156 - val_accuracy: 0.9315
 Epoch 2/50
 10/10 [==============================] - 4s 384ms/step - loss: 0.2120 - accuracy: 0.9122 - val_loss: 0.2003 - val_accuracy: 0.9452
 Epoch 3/50
 10/10 [==============================] - 4s 383ms/step - loss: 0.1717 - accuracy: 0.9324 - val_loss: 0.2066 - val_accuracy: 0.9452
 Epoch 4/50
 10/10 [==============================] - 4s 369ms/step - loss: 0.1527 - accuracy: 0.9426 - val_loss: 0.2124 - val_accuracy: 0.9452
 Epoch 5/50
 10/10 [==============================] - 4s 380ms/step - loss: 0.1798 - accuracy: 0.9426 - val_loss: 0.2130 - val_accuracy: 0.9452
 Epoch 6/50
 10/10 [==============================] - 15s 2s/step - loss: 0.1828 - accuracy: 0.9223 - val_loss: 0.2115 - val_accuracy: 0.9452
 Epoch 7/50
 10/10 [==============================] - 4s 387ms/step - loss: 0.1352 - accuracy: 0.9426 - val_loss: 0.2115 - val_accuracy: 0.9452
 Epoch 8/50
 10/10 [==============================] - 4s 384ms/step - loss: 0.1660 - accuracy: 0.9223 - val_loss: 0.2219 - val_accuracy: 0.9452
 Epoch 9/50
 10/10 [==============================] - 4s 382ms/step - loss: 0.1504 - accuracy: 0.9426 - val_loss: 0.2106 - val_accuracy: 0.9452
 Epoch 10/50
 10/10 [==============================] - 4s 381ms/step - loss: 0.1481 - accuracy: 0.9459 - val_loss: 0.2074 - val_accuracy: 0.9452
 Epoch 11/50
 10/10 [==============================] - 4s 380ms/step - loss: 0.1523 - accuracy: 0.9392 - val_loss: 0.2072 - val_accuracy: 0.9452
 Epoch 12/50
 10/10 [==============================] - 4s 383ms/step - loss: 0.1477 - accuracy: 0.9392 - val_loss: 0.2173 - val_accuracy: 0.9589
 Epoch 13/50
 10/10 [==============================] - 4s 387ms/step - loss: 0.1429 - accuracy: 0.9493 - val_loss: 0.2184 - val_accuracy: 0.9452
 Epoch 14/50
 10/10 [==============================] - 4s 391ms/step - loss: 0.1288 - accuracy: 0.9358 - val_loss: 0.2183 - val_accuracy: 0.9452
 Epoch 15/50
 10/10 [==============================] - 4s 387ms/step - loss: 0.1159 - accuracy: 0.9628 - val_loss: 0.2210 - val_accuracy: 0.9452
 Epoch 16/50
 10/10 [==============================] - 4s 386ms/step - loss: 0.1106 - accuracy: 0.9662 - val_loss: 0.2315 - val_accuracy: 0.9452
 Epoch 17/50
 10/10 [==============================] - 4s 418ms/step - loss: 0.1292 - accuracy: 0.9561 - val_loss: 0.2361 - val_accuracy: 0.9452
 Epoch 18/50
 10/10 [==============================] - 4s 411ms/step - loss: 0.1327 - accuracy: 0.9426 - val_loss: 0.2492 - val_accuracy: 0.9452
 Epoch 19/50
 10/10 [==============================] - 4s 382ms/step - loss: 0.1359 - accuracy: 0.9493 - val_loss: 0.2306 - val_accuracy: 0.9452
 Epoch 20/50
 10/10 [==============================] - 4s 386ms/step - loss: 0.1307 - accuracy: 0.9493 - val_loss: 0.2221 - val_accuracy: 0.9452
 Epoch 21/50
 10/10 [==============================] - 4s 387ms/step - loss: 0.1072 - accuracy: 0.9595 - val_loss: 0.2216 - val_accuracy: 0.9452
 Epoch 22/50
 10/10 [==============================] - 4s 383ms/step - loss: 0.1007 - accuracy: 0.9662 - val_loss: 0.2189 - val_accuracy: 0.9589
 Epoch 23/50
 10/10 [==============================] - 4s 375ms/step - loss: 0.1332 - accuracy: 0.9628 - val_loss: 0.2266 - val_accuracy: 0.9452
 Epoch 24/50
 10/10 [==============================] - 4s 383ms/step - loss: 0.1140 - accuracy: 0.9527 - val_loss: 0.2307 - val_accuracy: 0.9589
 Epoch 25/50
 10/10 [==============================] - 4s 375ms/step - loss: 0.1075 - accuracy: 0.9628 - val_loss: 0.2329 - val_accuracy: 0.9589
 Epoch 26/50
 10/10 [==============================] - 4s 386ms/step - loss: 0.1173 - accuracy: 0.9595 - val_loss: 0.2390 - val_accuracy: 0.9315
 Epoch 27/50
 10/10 [==============================] - 4s 387ms/step - loss: 0.1014 - accuracy: 0.9730 - val_loss: 0.2442 - val_accuracy: 0.9315
 Epoch 28/50
 10/10 [==============================] - 4s 383ms/step - loss: 0.0821 - accuracy: 0.9730 - val_loss: 0.2452 - val_accuracy: 0.9315
 Epoch 29/50
 10/10 [==============================] - 4s 385ms/step - loss: 0.1378 - accuracy: 0.9493 - val_loss: 0.2523 - val_accuracy: 0.9315
 Epoch 30/50
 10/10 [==============================] - 4s 382ms/step - loss: 0.1047 - accuracy: 0.9628 - val_loss: 0.2332 - val_accuracy: 0.9452
 Epoch 31/50
 10/10 [==============================] - 4s 380ms/step - loss: 0.0852 - accuracy: 0.9797 - val_loss: 0.2363 - val_accuracy: 0.9452
 Epoch 32/50
 10/10 [==============================] - 4s 382ms/step - loss: 0.0776 - accuracy: 0.9764 - val_loss: 0.2332 - val_accuracy: 0.9452
 Epoch 33/50
 10/10 [==============================] - 4s 428ms/step - loss: 0.1016 - accuracy: 0.9628 - val_loss: 0.2330 - val_accuracy: 0.9315
 Epoch 34/50
 10/10 [==============================] - 4s 386ms/step - loss: 0.0813 - accuracy: 0.9730 - val_loss: 0.2246 - val_accuracy: 0.9315
 Epoch 35/50
 10/10 [==============================] - 4s 389ms/step - loss: 0.0875 - accuracy: 0.9628 - val_loss: 0.2276 - val_accuracy: 0.9315
 Epoch 36/50
 10/10 [==============================] - 4s 382ms/step - loss: 0.1167 - accuracy: 0.9696 - val_loss: 0.2369 - val_accuracy: 0.9452
 Epoch 37/50
 10/10 [==============================] - 4s 379ms/step - loss: 0.1273 - accuracy: 0.9561 - val_loss: 0.1887 - val_accuracy: 0.9452
 Epoch 38/50
 10/10 [==============================] - 4s 442ms/step - loss: 0.1065 - accuracy: 0.9628 - val_loss: 0.1876 - val_accuracy: 0.9452
 Epoch 39/50
 10/10 [==============================] - 4s 386ms/step - loss: 0.0856 - accuracy: 0.9696 - val_loss: 0.2151 - val_accuracy: 0.9452
 Epoch 40/50
 10/10 [==============================] - 4s 392ms/step - loss: 0.0850 - accuracy: 0.9662 - val_loss: 0.2351 - val_accuracy: 0.9452
 Epoch 41/50
 10/10 [==============================] - 4s 401ms/step - loss: 0.1294 - accuracy: 0.9628 - val_loss: 0.2393 - val_accuracy: 0.9452
 Epoch 42/50
 10/10 [==============================] - 4s 384ms/step - loss: 0.0957 - accuracy: 0.9628 - val_loss: 0.2532 - val_accuracy: 0.9452
 Epoch 43/50
 10/10 [==============================] - 4s 386ms/step - loss: 0.0906 - accuracy: 0.9595 - val_loss: 0.2540 - val_accuracy: 0.9452
 Epoch 44/50
 10/10 [==============================] - 4s 383ms/step - loss: 0.1372 - accuracy: 0.9358 - val_loss: 0.2154 - val_accuracy: 0.9452
 Epoch 45/50
 10/10 [==============================] - 4s 388ms/step - loss: 0.1091 - accuracy: 0.9561 - val_loss: 0.1797 - val_accuracy: 0.9452
 Epoch 46/50
 10/10 [==============================] - 4s 375ms/step - loss: 0.1044 - accuracy: 0.9662 - val_loss: 0.1889 - val_accuracy: 0.9452
 Epoch 47/50
 10/10 [==============================] - 4s 388ms/step - loss: 0.0699 - accuracy: 0.9797 - val_loss: 0.2081 - val_accuracy: 0.9452
 Epoch 48/50
 10/10 [==============================] - 4s 417ms/step - loss: 0.0884 - accuracy: 0.9662 - val_loss: 0.2182 - val_accuracy: 0.9452
 Epoch 49/50
 10/10 [==============================] - 4s 383ms/step - loss: 0.0878 - accuracy: 0.9662 - val_loss: 0.2268 - val_accuracy: 0.9452
 Epoch 50/50
 10/10 [==============================] - 4s 385ms/step - loss: 0.1292 - accuracy: 0.9493 - val_loss: 0.2375 - val_accuracy: 0.9452
 Found 220 images belonging to 7 classes.
 Found 55 images belonging to 7 classes.
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 Epoch 1/50
 7/7 [==============================] - 15s 2s/step - loss: 0.1499 - accuracy: 0.9500 - val_loss: 0.1116 - val_accuracy: 0.9636
 Epoch 2/50
 7/7 [==============================] - 3s 414ms/step - loss: 0.1458 - accuracy: 0.9591 - val_loss: 0.0970 - val_accuracy: 0.9636
 Epoch 3/50
 7/7 [==============================] - 3s 415ms/step - loss: 0.1257 - accuracy: 0.9636 - val_loss: 0.0953 - val_accuracy: 0.9818
 Epoch 4/50
 7/7 [==============================] - 3s 425ms/step - loss: 0.0959 - accuracy: 0.9727 - val_loss: 0.0987 - val_accuracy: 0.9818
 Epoch 5/50
 7/7 [==============================] - 3s 423ms/step - loss: 0.1056 - accuracy: 0.9636 - val_loss: 0.1020 - val_accuracy: 0.9818
 Epoch 6/50
 7/7 [==============================] - 3s 416ms/step - loss: 0.0859 - accuracy: 0.9727 - val_loss: 0.1021 - val_accuracy: 0.9818
 Epoch 7/50
 7/7 [==============================] - 3s 417ms/step - loss: 0.1034 - accuracy: 0.9636 - val_loss: 0.1024 - val_accuracy: 0.9818
 Epoch 8/50
 7/7 [==============================] - 3s 424ms/step - loss: 0.0876 - accuracy: 0.9864 - val_loss: 0.1036 - val_accuracy: 0.9818
 Epoch 9/50
 7/7 [==============================] - 3s 409ms/step - loss: 0.0806 - accuracy: 0.9773 - val_loss: 0.1059 - val_accuracy: 0.9818
 Epoch 10/50
 7/7 [==============================] - 3s 418ms/step - loss: 0.0861 - accuracy: 0.9636 - val_loss: 0.1080 - val_accuracy: 0.9818
 Epoch 11/50
 7/7 [==============================] - 3s 410ms/step - loss: 0.0888 - accuracy: 0.9636 - val_loss: 0.1084 - val_accuracy: 0.9818
 Epoch 12/50
 7/7 [==============================] - 3s 404ms/step - loss: 0.0982 - accuracy: 0.9682 - val_loss: 0.1115 - val_accuracy: 0.9818
 Epoch 13/50
 7/7 [==============================] - 3s 418ms/step - loss: 0.0784 - accuracy: 0.9773 - val_loss: 0.1132 - val_accuracy: 0.9818
 Epoch 14/50
 7/7 [==============================] - 3s 409ms/step - loss: 0.0927 - accuracy: 0.9636 - val_loss: 0.1107 - val_accuracy: 0.9818
 Epoch 15/50
 7/7 [==============================] - 3s 415ms/step - loss: 0.0795 - accuracy: 0.9682 - val_loss: 0.1079 - val_accuracy: 0.9818
 Epoch 16/50
 7/7 [==============================] - 3s 418ms/step - loss: 0.0685 - accuracy: 0.9818 - val_loss: 0.1065 - val_accuracy: 0.9818
 Epoch 17/50
 7/7 [==============================] - 3s 418ms/step - loss: 0.0645 - accuracy: 0.9818 - val_loss: 0.1057 - val_accuracy: 0.9818
 Epoch 18/50
 7/7 [==============================] - 3s 405ms/step - loss: 0.0812 - accuracy: 0.9727 - val_loss: 0.1060 - val_accuracy: 0.9818
 Epoch 19/50
 7/7 [==============================] - 3s 412ms/step - loss: 0.0618 - accuracy: 0.9773 - val_loss: 0.1072 - val_accuracy: 0.9818
 Epoch 20/50
 7/7 [==============================] - 3s 408ms/step - loss: 0.0648 - accuracy: 0.9864 - val_loss: 0.1086 - val_accuracy: 0.9818
 Epoch 21/50
 7/7 [==============================] - 3s 413ms/step - loss: 0.0697 - accuracy: 0.9727 - val_loss: 0.1115 - val_accuracy: 0.9818
 Epoch 22/50
 7/7 [==============================] - 3s 409ms/step - loss: 0.0679 - accuracy: 0.9773 - val_loss: 0.1122 - val_accuracy: 0.9818
 Epoch 23/50
 7/7 [==============================] - 3s 419ms/step - loss: 0.0459 - accuracy: 0.9864 - val_loss: 0.1123 - val_accuracy: 0.9818
 Epoch 24/50
 7/7 [==============================] - 3s 414ms/step - loss: 0.0673 - accuracy: 0.9818 - val_loss: 0.1106 - val_accuracy: 0.9818
 Epoch 25/50
 7/7 [==============================] - 3s 417ms/step - loss: 0.0677 - accuracy: 0.9727 - val_loss: 0.1105 - val_accuracy: 0.9818
 Epoch 26/50
 7/7 [==============================] - 3s 417ms/step - loss: 0.0651 - accuracy: 0.9909 - val_loss: 0.1107 - val_accuracy: 0.9818
 Epoch 27/50
 7/7 [==============================] - 3s 426ms/step - loss: 0.0953 - accuracy: 0.9773 - val_loss: 0.1131 - val_accuracy: 0.9818
 Epoch 28/50
 7/7 [==============================] - 3s 412ms/step - loss: 0.1139 - accuracy: 0.9636 - val_loss: 0.1153 - val_accuracy: 0.9818
 Epoch 29/50
 7/7 [==============================] - 3s 416ms/step - loss: 0.0878 - accuracy: 0.9682 - val_loss: 0.1158 - val_accuracy: 0.9818
 Epoch 30/50
 7/7 [==============================] - 3s 410ms/step - loss: 0.0657 - accuracy: 0.9773 - val_loss: 0.1145 - val_accuracy: 0.9818
 Epoch 31/50
 7/7 [==============================] - 3s 418ms/step - loss: 0.0711 - accuracy: 0.9727 - val_loss: 0.1137 - val_accuracy: 0.9818
 Epoch 32/50
 7/7 [==============================] - 3s 425ms/step - loss: 0.0963 - accuracy: 0.9682 - val_loss: 0.1113 - val_accuracy: 0.9818
 Epoch 33/50
 7/7 [==============================] - 3s 412ms/step - loss: 0.0660 - accuracy: 0.9727 - val_loss: 0.1097 - val_accuracy: 0.9818
 Epoch 34/50
 7/7 [==============================] - 3s 407ms/step - loss: 0.0693 - accuracy: 0.9818 - val_loss: 0.1107 - val_accuracy: 0.9818
 Epoch 35/50
 7/7 [==============================] - 3s 418ms/step - loss: 0.0435 - accuracy: 0.9864 - val_loss: 0.1131 - val_accuracy: 0.9818
 Epoch 36/50
 7/7 [==============================] - 3s 411ms/step - loss: 0.0760 - accuracy: 0.9773 - val_loss: 0.1146 - val_accuracy: 0.9818
 Epoch 37/50
 7/7 [==============================] - 3s 413ms/step - loss: 0.0534 - accuracy: 0.9818 - val_loss: 0.1140 - val_accuracy: 0.9818
 Epoch 38/50
 7/7 [==============================] - 3s 420ms/step - loss: 0.0422 - accuracy: 0.9909 - val_loss: 0.1130 - val_accuracy: 0.9818
 Epoch 39/50
 7/7 [==============================] - 3s 408ms/step - loss: 0.0584 - accuracy: 0.9818 - val_loss: 0.1139 - val_accuracy: 0.9818
 Epoch 40/50
 7/7 [==============================] - 3s 417ms/step - loss: 0.0682 - accuracy: 0.9773 - val_loss: 0.1180 - val_accuracy: 0.9818
 Epoch 41/50
 7/7 [==============================] - 3s 409ms/step - loss: 0.0603 - accuracy: 0.9909 - val_loss: 0.1198 - val_accuracy: 0.9818
 Epoch 42/50
 7/7 [==============================] - 3s 415ms/step - loss: 0.0671 - accuracy: 0.9864 - val_loss: 0.1187 - val_accuracy: 0.9818
 Epoch 43/50
 7/7 [==============================] - 3s 403ms/step - loss: 0.0500 - accuracy: 0.9864 - val_loss: 0.1186 - val_accuracy: 0.9818
 Epoch 44/50
 7/7 [==============================] - 3s 409ms/step - loss: 0.0481 - accuracy: 0.9818 - val_loss: 0.1186 - val_accuracy: 0.9818
 Epoch 45/50
 7/7 [==============================] - 3s 433ms/step - loss: 0.0426 - accuracy: 0.9909 - val_loss: 0.1179 - val_accuracy: 0.9818
 Epoch 46/50
 7/7 [==============================] - 3s 414ms/step - loss: 0.0631 - accuracy: 0.9818 - val_loss: 0.1192 - val_accuracy: 0.9818
 Epoch 47/50
 7/7 [==============================] - 3s 415ms/step - loss: 0.0503 - accuracy: 0.9909 - val_loss: 0.1212 - val_accuracy: 0.9818
 Epoch 48/50
 7/7 [==============================] - 3s 429ms/step - loss: 0.0743 - accuracy: 0.9773 - val_loss: 0.1187 - val_accuracy: 0.9818
 Epoch 49/50
 7/7 [==============================] - 3s 408ms/step - loss: 0.0480 - accuracy: 0.9909 - val_loss: 0.1193 - val_accuracy: 0.9818
 Epoch 50/50
 7/7 [==============================] - 3s 411ms/step - loss: 0.0651 - accuracy: 0.9773 - val_loss: 0.1224 - val_accuracy: 0.9818
 Found 493 images belonging to 7 classes.
 Found 122 images belonging to 7 classes.
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 Epoch 1/50
 16/16 [==============================] - 36s 2s/step - loss: 0.1396 - accuracy: 0.9533 - val_loss: 0.1371 - val_accuracy: 0.9672
 Epoch 2/50
 16/16 [==============================] - 6s 397ms/step - loss: 0.1350 - accuracy: 0.9493 - val_loss: 0.1422 - val_accuracy: 0.9672
 Epoch 3/50
 16/16 [==============================] - 6s 392ms/step - loss: 0.1475 - accuracy: 0.9493 - val_loss: 0.1395 - val_accuracy: 0.9672
 Epoch 4/50
 16/16 [==============================] - 6s 397ms/step - loss: 0.1284 - accuracy: 0.9574 - val_loss: 0.1339 - val_accuracy: 0.9672
 Epoch 5/50
 16/16 [==============================] - 6s 390ms/step - loss: 0.1211 - accuracy: 0.9615 - val_loss: 0.1369 - val_accuracy: 0.9672
 Epoch 6/50
 16/16 [==============================] - 7s 399ms/step - loss: 0.1291 - accuracy: 0.9615 - val_loss: 0.1365 - val_accuracy: 0.9672
 Epoch 7/50
 16/16 [==============================] - 6s 395ms/step - loss: 0.1305 - accuracy: 0.9574 - val_loss: 0.1273 - val_accuracy: 0.9672
 Epoch 8/50
 16/16 [==============================] - 6s 396ms/step - loss: 0.1136 - accuracy: 0.9675 - val_loss: 0.1279 - val_accuracy: 0.9672
 Epoch 9/50
 16/16 [==============================] - 6s 410ms/step - loss: 0.1161 - accuracy: 0.9615 - val_loss: 0.1346 - val_accuracy: 0.9672
 Epoch 10/50
 16/16 [==============================] - 7s 398ms/step - loss: 0.1335 - accuracy: 0.9493 - val_loss: 0.1363 - val_accuracy: 0.9672
 Epoch 11/50
 16/16 [==============================] - 6s 393ms/step - loss: 0.1090 - accuracy: 0.9655 - val_loss: 0.1272 - val_accuracy: 0.9590
 Epoch 12/50
 16/16 [==============================] - 6s 413ms/step - loss: 0.1136 - accuracy: 0.9675 - val_loss: 0.1352 - val_accuracy: 0.9590
 Epoch 13/50
 16/16 [==============================] - 6s 393ms/step - loss: 0.1160 - accuracy: 0.9554 - val_loss: 0.1280 - val_accuracy: 0.9672
 Epoch 14/50
 16/16 [==============================] - 6s 391ms/step - loss: 0.1192 - accuracy: 0.9554 - val_loss: 0.1275 - val_accuracy: 0.9590
 Epoch 15/50
 16/16 [==============================] - 6s 388ms/step - loss: 0.1031 - accuracy: 0.9635 - val_loss: 0.1295 - val_accuracy: 0.9672
 Epoch 16/50
 16/16 [==============================] - 6s 395ms/step - loss: 0.1075 - accuracy: 0.9594 - val_loss: 0.1292 - val_accuracy: 0.9672
 Epoch 17/50
 16/16 [==============================] - 6s 391ms/step - loss: 0.1085 - accuracy: 0.9574 - val_loss: 0.1370 - val_accuracy: 0.9672
 Epoch 18/50
 16/16 [==============================] - 7s 404ms/step - loss: 0.0906 - accuracy: 0.9675 - val_loss: 0.1298 - val_accuracy: 0.9672
 Epoch 19/50
 16/16 [==============================] - 6s 394ms/step - loss: 0.0992 - accuracy: 0.9675 - val_loss: 0.1325 - val_accuracy: 0.9672
 Epoch 20/50
 16/16 [==============================] - 6s 391ms/step - loss: 0.1162 - accuracy: 0.9554 - val_loss: 0.1238 - val_accuracy: 0.9672
 Epoch 21/50
 16/16 [==============================] - 6s 411ms/step - loss: 0.1092 - accuracy: 0.9594 - val_loss: 0.1230 - val_accuracy: 0.9672
 Epoch 22/50
 16/16 [==============================] - 6s 396ms/step - loss: 0.0977 - accuracy: 0.9615 - val_loss: 0.1308 - val_accuracy: 0.9672
 Epoch 23/50
 16/16 [==============================] - 6s 394ms/step - loss: 0.1031 - accuracy: 0.9615 - val_loss: 0.1207 - val_accuracy: 0.9672
 Epoch 24/50
 16/16 [==============================] - 6s 391ms/step - loss: 0.1010 - accuracy: 0.9696 - val_loss: 0.1229 - val_accuracy: 0.9672
 Epoch 25/50
 16/16 [==============================] - 6s 392ms/step - loss: 0.0842 - accuracy: 0.9696 - val_loss: 0.1258 - val_accuracy: 0.9672
 Epoch 26/50
 16/16 [==============================] - 6s 398ms/step - loss: 0.1099 - accuracy: 0.9574 - val_loss: 0.1250 - val_accuracy: 0.9672
 Epoch 27/50
 16/16 [==============================] - 6s 394ms/step - loss: 0.1054 - accuracy: 0.9615 - val_loss: 0.1163 - val_accuracy: 0.9672
 Epoch 28/50
 16/16 [==============================] - 7s 398ms/step - loss: 0.1071 - accuracy: 0.9533 - val_loss: 0.1089 - val_accuracy: 0.9672
 Epoch 29/50
 16/16 [==============================] - 6s 392ms/step - loss: 0.0935 - accuracy: 0.9757 - val_loss: 0.1186 - val_accuracy: 0.9672
 Epoch 30/50
 16/16 [==============================] - 7s 418ms/step - loss: 0.0812 - accuracy: 0.9696 - val_loss: 0.1173 - val_accuracy: 0.9672
 Epoch 31/50
 16/16 [==============================] - 6s 395ms/step - loss: 0.0941 - accuracy: 0.9675 - val_loss: 0.1163 - val_accuracy: 0.9672
 Epoch 32/50
 16/16 [==============================] - 7s 391ms/step - loss: 0.1027 - accuracy: 0.9655 - val_loss: 0.1081 - val_accuracy: 0.9672
 Epoch 33/50
 16/16 [==============================] - 6s 396ms/step - loss: 0.1138 - accuracy: 0.9594 - val_loss: 0.1107 - val_accuracy: 0.9672
 Epoch 34/50
 16/16 [==============================] - 6s 398ms/step - loss: 0.0807 - accuracy: 0.9736 - val_loss: 0.1173 - val_accuracy: 0.9672
 Epoch 35/50
 16/16 [==============================] - 6s 392ms/step - loss: 0.0866 - accuracy: 0.9736 - val_loss: 0.1215 - val_accuracy: 0.9672
 Epoch 36/50
 16/16 [==============================] - 6s 392ms/step - loss: 0.1039 - accuracy: 0.9696 - val_loss: 0.1148 - val_accuracy: 0.9590
 Epoch 37/50
 16/16 [==============================] - 6s 391ms/step - loss: 0.0958 - accuracy: 0.9675 - val_loss: 0.1161 - val_accuracy: 0.9672
 Epoch 38/50
 16/16 [==============================] - 6s 391ms/step - loss: 0.0942 - accuracy: 0.9635 - val_loss: 0.1147 - val_accuracy: 0.9672
 Epoch 39/50
 16/16 [==============================] - 6s 391ms/step - loss: 0.1058 - accuracy: 0.9594 - val_loss: 0.1202 - val_accuracy: 0.9672
 Epoch 40/50
 16/16 [==============================] - 6s 390ms/step - loss: 0.0893 - accuracy: 0.9675 - val_loss: 0.1153 - val_accuracy: 0.9672
 Epoch 41/50
 16/16 [==============================] - 7s 398ms/step - loss: 0.0819 - accuracy: 0.9635 - val_loss: 0.1109 - val_accuracy: 0.9672
 Epoch 42/50
 16/16 [==============================] - 6s 392ms/step - loss: 0.0843 - accuracy: 0.9716 - val_loss: 0.1091 - val_accuracy: 0.9672
 Epoch 43/50
 16/16 [==============================] - 6s 389ms/step - loss: 0.0841 - accuracy: 0.9736 - val_loss: 0.1147 - val_accuracy: 0.9672
 Epoch 44/50
 16/16 [==============================] - 7s 398ms/step - loss: 0.0894 - accuracy: 0.9675 - val_loss: 0.1327 - val_accuracy: 0.9590
 Epoch 45/50
 16/16 [==============================] - 6s 391ms/step - loss: 0.0947 - accuracy: 0.9655 - val_loss: 0.1178 - val_accuracy: 0.9590
 Epoch 46/50
 16/16 [==============================] - 6s 396ms/step - loss: 0.0842 - accuracy: 0.9716 - val_loss: 0.1096 - val_accuracy: 0.9672
 Epoch 47/50
 16/16 [==============================] - 6s 390ms/step - loss: 0.0700 - accuracy: 0.9797 - val_loss: 0.1143 - val_accuracy: 0.9672
 Epoch 48/50
 16/16 [==============================] - 6s 415ms/step - loss: 0.0908 - accuracy: 0.9655 - val_loss: 0.1179 - val_accuracy: 0.9672
 Epoch 49/50
 16/16 [==============================] - 7s 398ms/step - loss: 0.0790 - accuracy: 0.9736 - val_loss: 0.1096 - val_accuracy: 0.9672
 Epoch 50/50
 16/16 [==============================] - 6s 387ms/step - loss: 0.0869 - accuracy: 0.9655 - val_loss: 0.1067 - val_accuracy: 0.9672
 Found 435 images belonging to 7 classes.
 Found 104 images belonging to 7 classes.
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 Epoch 1/50
 14/14 [==============================] - 22s 2s/step - loss: 0.4075 - accuracy: 0.8690 - val_loss: 0.3835 - val_accuracy: 0.8654
 Epoch 2/50
 14/14 [==============================] - 6s 398ms/step - loss: 0.3029 - accuracy: 0.9103 - val_loss: 0.2683 - val_accuracy: 0.8942
 Epoch 3/50
 14/14 [==============================] - 6s 392ms/step - loss: 0.2923 - accuracy: 0.9126 - val_loss: 0.2457 - val_accuracy: 0.9231
 Epoch 4/50
 14/14 [==============================] - 11s 785ms/step - loss: 0.3170 - accuracy: 0.8966 - val_loss: 0.2599 - val_accuracy: 0.8942
 Epoch 5/50
 14/14 [==============================] - 6s 395ms/step - loss: 0.2569 - accuracy: 0.9172 - val_loss: 0.2546 - val_accuracy: 0.8942
 Epoch 6/50
 14/14 [==============================] - 6s 391ms/step - loss: 0.2520 - accuracy: 0.9103 - val_loss: 0.2442 - val_accuracy: 0.9038
 Epoch 7/50
 14/14 [==============================] - 6s 399ms/step - loss: 0.2503 - accuracy: 0.9218 - val_loss: 0.2346 - val_accuracy: 0.9231
 Epoch 8/50
 14/14 [==============================] - 6s 407ms/step - loss: 0.2333 - accuracy: 0.9264 - val_loss: 0.2454 - val_accuracy: 0.9135
 Epoch 9/50
 14/14 [==============================] - 6s 394ms/step - loss: 0.2352 - accuracy: 0.9264 - val_loss: 0.2443 - val_accuracy: 0.9038
 Epoch 10/50
 14/14 [==============================] - 6s 393ms/step - loss: 0.2375 - accuracy: 0.9264 - val_loss: 0.2379 - val_accuracy: 0.9231
 Epoch 11/50
 14/14 [==============================] - 6s 401ms/step - loss: 0.2405 - accuracy: 0.9172 - val_loss: 0.2302 - val_accuracy: 0.9135
 Epoch 12/50
 14/14 [==============================] - 6s 404ms/step - loss: 0.2155 - accuracy: 0.9333 - val_loss: 0.2332 - val_accuracy: 0.9327
 Epoch 13/50
 14/14 [==============================] - 6s 397ms/step - loss: 0.2243 - accuracy: 0.9379 - val_loss: 0.2505 - val_accuracy: 0.9135
 Epoch 14/50
 14/14 [==============================] - 6s 398ms/step - loss: 0.2249 - accuracy: 0.9448 - val_loss: 0.2440 - val_accuracy: 0.9135
 Epoch 15/50
 14/14 [==============================] - 6s 398ms/step - loss: 0.2337 - accuracy: 0.9264 - val_loss: 0.2455 - val_accuracy: 0.9038
 Epoch 16/50
 14/14 [==============================] - 6s 389ms/step - loss: 0.2294 - accuracy: 0.9333 - val_loss: 0.2364 - val_accuracy: 0.9135
 Epoch 17/50
 14/14 [==============================] - 6s 401ms/step - loss: 0.2320 - accuracy: 0.9241 - val_loss: 0.2408 - val_accuracy: 0.9231
 Epoch 18/50
 14/14 [==============================] - 6s 397ms/step - loss: 0.2242 - accuracy: 0.9264 - val_loss: 0.2248 - val_accuracy: 0.9231
 Epoch 19/50
 14/14 [==============================] - 6s 400ms/step - loss: 0.2337 - accuracy: 0.9218 - val_loss: 0.2346 - val_accuracy: 0.9135
 Epoch 20/50
 14/14 [==============================] - 6s 397ms/step - loss: 0.2050 - accuracy: 0.9494 - val_loss: 0.2453 - val_accuracy: 0.9135
 Epoch 21/50
 14/14 [==============================] - 6s 396ms/step - loss: 0.2020 - accuracy: 0.9356 - val_loss: 0.2278 - val_accuracy: 0.9231
 Epoch 22/50
 14/14 [==============================] - 6s 400ms/step - loss: 0.2157 - accuracy: 0.9264 - val_loss: 0.2220 - val_accuracy: 0.9231
 Epoch 23/50
 14/14 [==============================] - 6s 428ms/step - loss: 0.2095 - accuracy: 0.9218 - val_loss: 0.2388 - val_accuracy: 0.9135
 Epoch 24/50
 14/14 [==============================] - 6s 396ms/step - loss: 0.2292 - accuracy: 0.9310 - val_loss: 0.2356 - val_accuracy: 0.9231
 Epoch 25/50
 14/14 [==============================] - 6s 395ms/step - loss: 0.1922 - accuracy: 0.9448 - val_loss: 0.2270 - val_accuracy: 0.9231
 Epoch 26/50
 14/14 [==============================] - 6s 401ms/step - loss: 0.1657 - accuracy: 0.9425 - val_loss: 0.2243 - val_accuracy: 0.9231
 Epoch 27/50
 14/14 [==============================] - 6s 396ms/step - loss: 0.2130 - accuracy: 0.9333 - val_loss: 0.2368 - val_accuracy: 0.9135
 Epoch 28/50
 14/14 [==============================] - 6s 395ms/step - loss: 0.1910 - accuracy: 0.9379 - val_loss: 0.2265 - val_accuracy: 0.9231
 Epoch 29/50
 14/14 [==============================] - 6s 400ms/step - loss: 0.1862 - accuracy: 0.9333 - val_loss: 0.2452 - val_accuracy: 0.9038
 Epoch 30/50
 14/14 [==============================] - 6s 402ms/step - loss: 0.1748 - accuracy: 0.9517 - val_loss: 0.2419 - val_accuracy: 0.9135
 Epoch 31/50
 14/14 [==============================] - 6s 400ms/step - loss: 0.1587 - accuracy: 0.9540 - val_loss: 0.2373 - val_accuracy: 0.9231
 Epoch 32/50
 14/14 [==============================] - 6s 406ms/step - loss: 0.1890 - accuracy: 0.9310 - val_loss: 0.2704 - val_accuracy: 0.9135
 Epoch 33/50
 14/14 [==============================] - 6s 392ms/step - loss: 0.1705 - accuracy: 0.9402 - val_loss: 0.2392 - val_accuracy: 0.9231
 Epoch 34/50
 14/14 [==============================] - 6s 401ms/step - loss: 0.1743 - accuracy: 0.9471 - val_loss: 0.2135 - val_accuracy: 0.9231
 Epoch 35/50
 14/14 [==============================] - 6s 394ms/step - loss: 0.1936 - accuracy: 0.9379 - val_loss: 0.2156 - val_accuracy: 0.9231
 Epoch 36/50
 14/14 [==============================] - 6s 393ms/step - loss: 0.1664 - accuracy: 0.9471 - val_loss: 0.2334 - val_accuracy: 0.9135
 Epoch 37/50
 14/14 [==============================] - 6s 395ms/step - loss: 0.1862 - accuracy: 0.9402 - val_loss: 0.2228 - val_accuracy: 0.9231
 Epoch 38/50
 14/14 [==============================] - 6s 397ms/step - loss: 0.1724 - accuracy: 0.9494 - val_loss: 0.2451 - val_accuracy: 0.9135
 Epoch 39/50
 14/14 [==============================] - 6s 400ms/step - loss: 0.1828 - accuracy: 0.9379 - val_loss: 0.2327 - val_accuracy: 0.9135
 Epoch 40/50
 14/14 [==============================] - 6s 403ms/step - loss: 0.1606 - accuracy: 0.9402 - val_loss: 0.2367 - val_accuracy: 0.9135
 Epoch 41/50
 14/14 [==============================] - 6s 401ms/step - loss: 0.1839 - accuracy: 0.9356 - val_loss: 0.2280 - val_accuracy: 0.9231
 Epoch 42/50
 14/14 [==============================] - 6s 393ms/step - loss: 0.1646 - accuracy: 0.9494 - val_loss: 0.2326 - val_accuracy: 0.9231
 Epoch 43/50
 14/14 [==============================] - 6s 394ms/step - loss: 0.1560 - accuracy: 0.9494 - val_loss: 0.2359 - val_accuracy: 0.9327
 Epoch 44/50
 14/14 [==============================] - 6s 396ms/step - loss: 0.1727 - accuracy: 0.9379 - val_loss: 0.2455 - val_accuracy: 0.9231
 Epoch 45/50
 14/14 [==============================] - 6s 396ms/step - loss: 0.1480 - accuracy: 0.9609 - val_loss: 0.2602 - val_accuracy: 0.9231
 Epoch 46/50
 14/14 [==============================] - 6s 398ms/step - loss: 0.1527 - accuracy: 0.9425 - val_loss: 0.2284 - val_accuracy: 0.9327
 Epoch 47/50
 14/14 [==============================] - 6s 397ms/step - loss: 0.1628 - accuracy: 0.9471 - val_loss: 0.2471 - val_accuracy: 0.9231
 Epoch 48/50
 14/14 [==============================] - 6s 399ms/step - loss: 0.1589 - accuracy: 0.9517 - val_loss: 0.2571 - val_accuracy: 0.9135
 Epoch 49/50
 14/14 [==============================] - 6s 396ms/step - loss: 0.1589 - accuracy: 0.9471 - val_loss: 0.2446 - val_accuracy: 0.9327
 Epoch 50/50
 14/14 [==============================] - 6s 399ms/step - loss: 0.1759 - accuracy: 0.9471 - val_loss: 0.2487 - val_accuracy: 0.9231
 Found 169 images belonging to 7 classes.
 Found 44 images belonging to 7 classes.
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 Epoch 1/50
 6/6 [==============================] - 10s 2s/step - loss: 0.4352 - accuracy: 0.8580 - val_loss: 0.1337 - val_accuracy: 0.9545
 Epoch 2/50
 6/6 [==============================] - 2s 366ms/step - loss: 0.2891 - accuracy: 0.9172 - val_loss: 0.1216 - val_accuracy: 0.9773
 Epoch 3/50
 6/6 [==============================] - 2s 370ms/step - loss: 0.3721 - accuracy: 0.8876 - val_loss: 0.1159 - val_accuracy: 0.9773
 Epoch 4/50
 6/6 [==============================] - 2s 371ms/step - loss: 0.2688 - accuracy: 0.9231 - val_loss: 0.1155 - val_accuracy: 0.9773
 Epoch 5/50
 6/6 [==============================] - 2s 368ms/step - loss: 0.3116 - accuracy: 0.8994 - val_loss: 0.1153 - val_accuracy: 0.9773
 Epoch 6/50
 6/6 [==============================] - 2s 367ms/step - loss: 0.2611 - accuracy: 0.9290 - val_loss: 0.1144 - val_accuracy: 0.9773
 Epoch 7/50
 6/6 [==============================] - 2s 372ms/step - loss: 0.2915 - accuracy: 0.8994 - val_loss: 0.1143 - val_accuracy: 0.9773
 Epoch 8/50
 6/6 [==============================] - 2s 365ms/step - loss: 0.3109 - accuracy: 0.8935 - val_loss: 0.1123 - val_accuracy: 0.9773
 Epoch 9/50
 6/6 [==============================] - 10s 2s/step - loss: 0.2384 - accuracy: 0.9290 - val_loss: 0.1084 - val_accuracy: 0.9773
 Epoch 10/50
 6/6 [==============================] - 2s 439ms/step - loss: 0.2653 - accuracy: 0.9172 - val_loss: 0.1070 - val_accuracy: 0.9773
 Epoch 11/50
 6/6 [==============================] - 2s 364ms/step - loss: 0.2094 - accuracy: 0.9408 - val_loss: 0.1075 - val_accuracy: 0.9773
 Epoch 12/50
 6/6 [==============================] - 2s 370ms/step - loss: 0.2310 - accuracy: 0.9231 - val_loss: 0.1084 - val_accuracy: 0.9773
 Epoch 13/50
 6/6 [==============================] - 2s 365ms/step - loss: 0.2597 - accuracy: 0.9112 - val_loss: 0.1070 - val_accuracy: 0.9773
 Epoch 14/50
 6/6 [==============================] - 2s 432ms/step - loss: 0.2304 - accuracy: 0.9290 - val_loss: 0.1064 - val_accuracy: 0.9773
 Epoch 15/50
 6/6 [==============================] - 2s 367ms/step - loss: 0.2500 - accuracy: 0.9290 - val_loss: 0.1053 - val_accuracy: 0.9773
 Epoch 16/50
 6/6 [==============================] - 2s 354ms/step - loss: 0.2300 - accuracy: 0.9112 - val_loss: 0.1048 - val_accuracy: 0.9773
 Epoch 17/50
 6/6 [==============================] - 2s 374ms/step - loss: 0.2453 - accuracy: 0.9172 - val_loss: 0.1031 - val_accuracy: 0.9773
 Epoch 18/50
 6/6 [==============================] - 2s 375ms/step - loss: 0.1898 - accuracy: 0.9349 - val_loss: 0.1014 - val_accuracy: 0.9773
 Epoch 19/50
 6/6 [==============================] - 2s 369ms/step - loss: 0.2101 - accuracy: 0.9408 - val_loss: 0.0990 - val_accuracy: 0.9773
 Epoch 20/50
 6/6 [==============================] - 2s 372ms/step - loss: 0.1985 - accuracy: 0.9172 - val_loss: 0.0973 - val_accuracy: 0.9773
 Epoch 21/50
 6/6 [==============================] - 2s 380ms/step - loss: 0.2401 - accuracy: 0.9231 - val_loss: 0.0967 - val_accuracy: 0.9773
 Epoch 22/50
 6/6 [==============================] - 2s 370ms/step - loss: 0.2276 - accuracy: 0.8994 - val_loss: 0.0978 - val_accuracy: 0.9773
 Epoch 23/50
 6/6 [==============================] - 2s 367ms/step - loss: 0.2296 - accuracy: 0.9112 - val_loss: 0.0986 - val_accuracy: 0.9773
 Epoch 24/50
 6/6 [==============================] - 2s 367ms/step - loss: 0.2235 - accuracy: 0.9112 - val_loss: 0.1063 - val_accuracy: 0.9773
 Epoch 25/50
 6/6 [==============================] - 2s 369ms/step - loss: 0.2382 - accuracy: 0.9172 - val_loss: 0.1082 - val_accuracy: 0.9773
 Epoch 26/50
 6/6 [==============================] - 2s 366ms/step - loss: 0.2144 - accuracy: 0.9172 - val_loss: 0.1064 - val_accuracy: 0.9773
 Epoch 27/50
 6/6 [==============================] - 2s 372ms/step - loss: 0.1862 - accuracy: 0.9231 - val_loss: 0.0987 - val_accuracy: 0.9773
 Epoch 28/50
 6/6 [==============================] - 2s 369ms/step - loss: 0.1858 - accuracy: 0.9408 - val_loss: 0.0945 - val_accuracy: 0.9773
 Epoch 29/50
 6/6 [==============================] - 2s 352ms/step - loss: 0.2029 - accuracy: 0.9172 - val_loss: 0.0959 - val_accuracy: 0.9773
 Epoch 30/50
 6/6 [==============================] - 2s 455ms/step - loss: 0.1866 - accuracy: 0.9349 - val_loss: 0.0956 - val_accuracy: 0.9773
 Epoch 31/50
 6/6 [==============================] - 2s 441ms/step - loss: 0.1753 - accuracy: 0.9349 - val_loss: 0.0947 - val_accuracy: 0.9773
 Epoch 32/50
 6/6 [==============================] - 2s 372ms/step - loss: 0.1799 - accuracy: 0.9408 - val_loss: 0.0943 - val_accuracy: 0.9773
 Epoch 33/50
 6/6 [==============================] - 2s 434ms/step - loss: 0.1718 - accuracy: 0.9349 - val_loss: 0.0937 - val_accuracy: 0.9773
 Epoch 34/50
 6/6 [==============================] - 2s 438ms/step - loss: 0.1665 - accuracy: 0.9467 - val_loss: 0.0923 - val_accuracy: 0.9773
 Epoch 35/50
 6/6 [==============================] - 2s 368ms/step - loss: 0.1751 - accuracy: 0.9408 - val_loss: 0.0913 - val_accuracy: 0.9773
 Epoch 36/50
 6/6 [==============================] - 2s 353ms/step - loss: 0.1740 - accuracy: 0.9645 - val_loss: 0.0912 - val_accuracy: 0.9773
 Epoch 37/50
 6/6 [==============================] - 2s 437ms/step - loss: 0.1691 - accuracy: 0.9408 - val_loss: 0.0920 - val_accuracy: 0.9773
 Epoch 38/50
 6/6 [==============================] - 2s 375ms/step - loss: 0.1599 - accuracy: 0.9349 - val_loss: 0.0939 - val_accuracy: 0.9773
 Epoch 39/50
 6/6 [==============================] - 2s 374ms/step - loss: 0.1462 - accuracy: 0.9467 - val_loss: 0.0954 - val_accuracy: 0.9773
 Epoch 40/50
 6/6 [==============================] - 2s 436ms/step - loss: 0.1579 - accuracy: 0.9467 - val_loss: 0.0961 - val_accuracy: 0.9773
 Epoch 41/50
 6/6 [==============================] - 2s 366ms/step - loss: 0.1463 - accuracy: 0.9408 - val_loss: 0.0967 - val_accuracy: 0.9773
 Epoch 42/50
 6/6 [==============================] - 2s 438ms/step - loss: 0.1727 - accuracy: 0.9467 - val_loss: 0.0965 - val_accuracy: 0.9773
 Epoch 43/50
 6/6 [==============================] - 2s 369ms/step - loss: 0.1943 - accuracy: 0.9231 - val_loss: 0.0945 - val_accuracy: 0.9773
 Epoch 44/50
 6/6 [==============================] - 2s 435ms/step - loss: 0.1681 - accuracy: 0.9349 - val_loss: 0.0909 - val_accuracy: 0.9773
 Epoch 45/50
 6/6 [==============================] - 2s 372ms/step - loss: 0.1417 - accuracy: 0.9527 - val_loss: 0.0892 - val_accuracy: 0.9773
 Epoch 46/50
 6/6 [==============================] - 2s 375ms/step - loss: 0.1693 - accuracy: 0.9290 - val_loss: 0.0889 - val_accuracy: 0.9773
 Epoch 47/50
 6/6 [==============================] - 2s 371ms/step - loss: 0.1540 - accuracy: 0.9349 - val_loss: 0.0915 - val_accuracy: 0.9773
 Epoch 48/50
 6/6 [==============================] - 2s 363ms/step - loss: 0.1351 - accuracy: 0.9763 - val_loss: 0.0936 - val_accuracy: 0.9773
 Epoch 49/50
 6/6 [==============================] - 2s 353ms/step - loss: 0.1783 - accuracy: 0.9467 - val_loss: 0.0950 - val_accuracy: 0.9773
 Epoch 50/50
 6/6 [==============================] - 2s 383ms/step - loss: 0.2081 - accuracy: 0.9112 - val_loss: 0.0924 - val_accuracy: 0.9773
 Found 368 images belonging to 7 classes.
 Found 90 images belonging to 7 classes.
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 Epoch 1/50
 12/12 [==============================] - 14s 1s/step - loss: 0.1777 - accuracy: 0.9538 - val_loss: 0.1319 - val_accuracy: 0.9556
 Epoch 2/50
 12/12 [==============================] - 5s 397ms/step - loss: 0.1562 - accuracy: 0.9647 - val_loss: 0.1276 - val_accuracy: 0.9667
 Epoch 3/50
 12/12 [==============================] - 5s 396ms/step - loss: 0.1314 - accuracy: 0.9538 - val_loss: 0.1278 - val_accuracy: 0.9667
 Epoch 4/50
 12/12 [==============================] - 5s 389ms/step - loss: 0.1282 - accuracy: 0.9620 - val_loss: 0.1249 - val_accuracy: 0.9667
 Epoch 5/50
 12/12 [==============================] - 5s 390ms/step - loss: 0.0851 - accuracy: 0.9701 - val_loss: 0.1218 - val_accuracy: 0.9667
 Epoch 6/50
 12/12 [==============================] - 5s 400ms/step - loss: 0.1146 - accuracy: 0.9565 - val_loss: 0.1215 - val_accuracy: 0.9667
 Epoch 7/50
 12/12 [==============================] - 5s 399ms/step - loss: 0.1076 - accuracy: 0.9592 - val_loss: 0.1246 - val_accuracy: 0.9667
 Epoch 8/50
 12/12 [==============================] - 5s 393ms/step - loss: 0.0923 - accuracy: 0.9647 - val_loss: 0.1242 - val_accuracy: 0.9667
 Epoch 9/50
 12/12 [==============================] - 5s 393ms/step - loss: 0.0981 - accuracy: 0.9620 - val_loss: 0.1235 - val_accuracy: 0.9667
 Epoch 10/50
 12/12 [==============================] - 5s 393ms/step - loss: 0.0881 - accuracy: 0.9810 - val_loss: 0.1195 - val_accuracy: 0.9667
 Epoch 11/50
 12/12 [==============================] - 5s 413ms/step - loss: 0.1049 - accuracy: 0.9620 - val_loss: 0.1124 - val_accuracy: 0.9667
 Epoch 12/50
 12/12 [==============================] - 5s 391ms/step - loss: 0.1090 - accuracy: 0.9565 - val_loss: 0.1104 - val_accuracy: 0.9778
 Epoch 13/50
 12/12 [==============================] - 5s 395ms/step - loss: 0.0969 - accuracy: 0.9647 - val_loss: 0.1099 - val_accuracy: 0.9778
 Epoch 14/50
 12/12 [==============================] - 5s 395ms/step - loss: 0.1046 - accuracy: 0.9647 - val_loss: 0.1112 - val_accuracy: 0.9667
 Epoch 15/50
 12/12 [==============================] - 5s 396ms/step - loss: 0.0872 - accuracy: 0.9728 - val_loss: 0.1099 - val_accuracy: 0.9778
 Epoch 16/50
 12/12 [==============================] - 5s 387ms/step - loss: 0.0788 - accuracy: 0.9701 - val_loss: 0.1098 - val_accuracy: 0.9778
 Epoch 17/50
 12/12 [==============================] - 5s 391ms/step - loss: 0.1108 - accuracy: 0.9592 - val_loss: 0.1098 - val_accuracy: 0.9778
 Epoch 18/50
 12/12 [==============================] - 5s 382ms/step - loss: 0.0866 - accuracy: 0.9620 - val_loss: 0.1084 - val_accuracy: 0.9778
 Epoch 19/50
 12/12 [==============================] - 5s 385ms/step - loss: 0.0834 - accuracy: 0.9701 - val_loss: 0.1098 - val_accuracy: 0.9778
 Epoch 20/50
 12/12 [==============================] - 5s 392ms/step - loss: 0.0969 - accuracy: 0.9592 - val_loss: 0.1077 - val_accuracy: 0.9778
 Epoch 21/50
 12/12 [==============================] - 5s 393ms/step - loss: 0.0811 - accuracy: 0.9674 - val_loss: 0.1069 - val_accuracy: 0.9778
 Epoch 22/50
 12/12 [==============================] - 5s 421ms/step - loss: 0.0725 - accuracy: 0.9783 - val_loss: 0.1085 - val_accuracy: 0.9778
 Epoch 23/50
 12/12 [==============================] - 5s 392ms/step - loss: 0.0922 - accuracy: 0.9647 - val_loss: 0.1084 - val_accuracy: 0.9667
 Epoch 24/50
 12/12 [==============================] - 5s 391ms/step - loss: 0.0871 - accuracy: 0.9674 - val_loss: 0.1067 - val_accuracy: 0.9667
 Epoch 25/50
 12/12 [==============================] - 5s 395ms/step - loss: 0.0829 - accuracy: 0.9647 - val_loss: 0.1063 - val_accuracy: 0.9778
 Epoch 26/50
 12/12 [==============================] - 5s 394ms/step - loss: 0.0750 - accuracy: 0.9674 - val_loss: 0.1063 - val_accuracy: 0.9778
 Epoch 27/50
 12/12 [==============================] - 5s 415ms/step - loss: 0.0855 - accuracy: 0.9674 - val_loss: 0.1090 - val_accuracy: 0.9778
 Epoch 28/50
 12/12 [==============================] - 5s 394ms/step - loss: 0.0852 - accuracy: 0.9674 - val_loss: 0.1085 - val_accuracy: 0.9667
 Epoch 29/50
 12/12 [==============================] - 5s 391ms/step - loss: 0.0775 - accuracy: 0.9728 - val_loss: 0.1087 - val_accuracy: 0.9778
 Epoch 30/50
 12/12 [==============================] - 5s 419ms/step - loss: 0.0720 - accuracy: 0.9783 - val_loss: 0.1104 - val_accuracy: 0.9667
 Epoch 31/50
 12/12 [==============================] - 5s 395ms/step - loss: 0.0796 - accuracy: 0.9728 - val_loss: 0.1098 - val_accuracy: 0.9667
 Epoch 32/50
 12/12 [==============================] - 5s 396ms/step - loss: 0.0706 - accuracy: 0.9783 - val_loss: 0.1112 - val_accuracy: 0.9667
 Epoch 33/50
 12/12 [==============================] - 5s 400ms/step - loss: 0.0746 - accuracy: 0.9674 - val_loss: 0.1090 - val_accuracy: 0.9667
 Epoch 34/50
 12/12 [==============================] - 5s 398ms/step - loss: 0.0822 - accuracy: 0.9674 - val_loss: 0.1065 - val_accuracy: 0.9667
 Epoch 35/50
 12/12 [==============================] - 5s 392ms/step - loss: 0.0818 - accuracy: 0.9701 - val_loss: 0.1048 - val_accuracy: 0.9778
 Epoch 36/50
 12/12 [==============================] - 5s 390ms/step - loss: 0.0645 - accuracy: 0.9755 - val_loss: 0.1052 - val_accuracy: 0.9778
 Epoch 37/50
 12/12 [==============================] - 5s 398ms/step - loss: 0.0770 - accuracy: 0.9647 - val_loss: 0.1065 - val_accuracy: 0.9778
 Epoch 38/50
 12/12 [==============================] - 5s 393ms/step - loss: 0.0733 - accuracy: 0.9674 - val_loss: 0.1119 - val_accuracy: 0.9778
 Epoch 39/50
 12/12 [==============================] - 5s 385ms/step - loss: 0.0808 - accuracy: 0.9647 - val_loss: 0.1122 - val_accuracy: 0.9778
 Epoch 40/50
 12/12 [==============================] - 5s 400ms/step - loss: 0.0712 - accuracy: 0.9783 - val_loss: 0.1117 - val_accuracy: 0.9667
 Epoch 41/50
 12/12 [==============================] - 5s 401ms/step - loss: 0.0774 - accuracy: 0.9755 - val_loss: 0.1103 - val_accuracy: 0.9667
 Epoch 42/50
 12/12 [==============================] - 5s 393ms/step - loss: 0.0674 - accuracy: 0.9728 - val_loss: 0.1094 - val_accuracy: 0.9667
 Epoch 43/50
 12/12 [==============================] - 5s 392ms/step - loss: 0.0632 - accuracy: 0.9674 - val_loss: 0.1073 - val_accuracy: 0.9667
 Epoch 44/50
 12/12 [==============================] - 5s 398ms/step - loss: 0.0677 - accuracy: 0.9728 - val_loss: 0.1077 - val_accuracy: 0.9667
 Epoch 45/50
 12/12 [==============================] - 5s 390ms/step - loss: 0.0600 - accuracy: 0.9755 - val_loss: 0.1069 - val_accuracy: 0.9667
 Epoch 46/50
 12/12 [==============================] - 5s 403ms/step - loss: 0.0812 - accuracy: 0.9728 - val_loss: 0.1080 - val_accuracy: 0.9667
 Epoch 47/50
 12/12 [==============================] - 5s 394ms/step - loss: 0.0702 - accuracy: 0.9728 - val_loss: 0.1106 - val_accuracy: 0.9667
 Epoch 48/50
 12/12 [==============================] - 5s 398ms/step - loss: 0.0662 - accuracy: 0.9783 - val_loss: 0.1113 - val_accuracy: 0.9667
 Epoch 49/50
 12/12 [==============================] - 5s 394ms/step - loss: 0.0839 - accuracy: 0.9674 - val_loss: 0.1088 - val_accuracy: 0.9778
 Epoch 50/50
 12/12 [==============================] - 5s 410ms/step - loss: 0.0833 - accuracy: 0.9620 - val_loss: 0.1095 - val_accuracy: 0.9778
 Found 446 images belonging to 7 classes.
 Found 107 images belonging to 7 classes.
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 Epoch 1/50
 14/14 [==============================] - 26s 2s/step - loss: 0.2006 - accuracy: 0.9283 - val_loss: 0.2316 - val_accuracy: 0.9346
 Epoch 2/50
 14/14 [==============================] - 6s 403ms/step - loss: 0.1899 - accuracy: 0.9260 - val_loss: 0.2590 - val_accuracy: 0.9065
 Epoch 3/50
 14/14 [==============================] - 6s 408ms/step - loss: 0.1674 - accuracy: 0.9484 - val_loss: 0.2633 - val_accuracy: 0.9159
 Epoch 4/50
 14/14 [==============================] - 6s 405ms/step - loss: 0.1488 - accuracy: 0.9529 - val_loss: 0.2335 - val_accuracy: 0.9159
 Epoch 5/50
 14/14 [==============================] - 6s 407ms/step - loss: 0.1544 - accuracy: 0.9462 - val_loss: 0.2555 - val_accuracy: 0.9159
 Epoch 6/50
 14/14 [==============================] - 6s 407ms/step - loss: 0.1531 - accuracy: 0.9507 - val_loss: 0.2451 - val_accuracy: 0.9252
 Epoch 7/50
 14/14 [==============================] - 6s 410ms/step - loss: 0.1355 - accuracy: 0.9596 - val_loss: 0.2340 - val_accuracy: 0.9252
 Epoch 8/50
 14/14 [==============================] - 6s 405ms/step - loss: 0.1461 - accuracy: 0.9596 - val_loss: 0.2576 - val_accuracy: 0.9252
 Epoch 9/50
 14/14 [==============================] - 6s 406ms/step - loss: 0.1492 - accuracy: 0.9641 - val_loss: 0.2576 - val_accuracy: 0.9252
 Epoch 10/50
 14/14 [==============================] - 6s 408ms/step - loss: 0.1463 - accuracy: 0.9529 - val_loss: 0.2713 - val_accuracy: 0.9252
 Epoch 11/50
 14/14 [==============================] - 6s 407ms/step - loss: 0.1325 - accuracy: 0.9596 - val_loss: 0.2654 - val_accuracy: 0.9252
 Epoch 12/50
 14/14 [==============================] - 6s 409ms/step - loss: 0.1421 - accuracy: 0.9529 - val_loss: 0.2621 - val_accuracy: 0.9252
 Epoch 13/50
 14/14 [==============================] - 6s 413ms/step - loss: 0.1378 - accuracy: 0.9596 - val_loss: 0.2571 - val_accuracy: 0.9252
 Epoch 14/50
 14/14 [==============================] - 6s 408ms/step - loss: 0.1414 - accuracy: 0.9641 - val_loss: 0.2742 - val_accuracy: 0.9252
 Epoch 15/50
 14/14 [==============================] - 6s 406ms/step - loss: 0.1090 - accuracy: 0.9664 - val_loss: 0.3017 - val_accuracy: 0.9159
 Epoch 16/50
 14/14 [==============================] - 6s 405ms/step - loss: 0.1301 - accuracy: 0.9574 - val_loss: 0.3102 - val_accuracy: 0.9252
 Epoch 17/50
 14/14 [==============================] - 6s 409ms/step - loss: 0.1429 - accuracy: 0.9552 - val_loss: 0.2936 - val_accuracy: 0.9159
 Epoch 18/50
 14/14 [==============================] - 6s 418ms/step - loss: 0.1296 - accuracy: 0.9664 - val_loss: 0.2649 - val_accuracy: 0.9252
 Epoch 19/50
 14/14 [==============================] - 6s 407ms/step - loss: 0.1419 - accuracy: 0.9574 - val_loss: 0.2773 - val_accuracy: 0.9252
 Epoch 20/50
 14/14 [==============================] - 6s 419ms/step - loss: 0.1010 - accuracy: 0.9753 - val_loss: 0.2912 - val_accuracy: 0.9159
 Epoch 21/50
 14/14 [==============================] - 6s 406ms/step - loss: 0.1304 - accuracy: 0.9574 - val_loss: 0.2842 - val_accuracy: 0.9252
 Epoch 22/50
 14/14 [==============================] - 6s 410ms/step - loss: 0.1165 - accuracy: 0.9619 - val_loss: 0.2963 - val_accuracy: 0.9159
 Epoch 23/50
 14/14 [==============================] - 6s 407ms/step - loss: 0.1024 - accuracy: 0.9731 - val_loss: 0.3033 - val_accuracy: 0.9159
 Epoch 24/50
 14/14 [==============================] - 6s 406ms/step - loss: 0.1038 - accuracy: 0.9686 - val_loss: 0.2836 - val_accuracy: 0.9159
 Epoch 25/50
 14/14 [==============================] - 6s 404ms/step - loss: 0.0984 - accuracy: 0.9709 - val_loss: 0.2658 - val_accuracy: 0.9252
 Epoch 26/50
 14/14 [==============================] - 6s 406ms/step - loss: 0.0966 - accuracy: 0.9709 - val_loss: 0.2725 - val_accuracy: 0.9159
 Epoch 27/50
 14/14 [==============================] - 6s 411ms/step - loss: 0.0990 - accuracy: 0.9641 - val_loss: 0.2890 - val_accuracy: 0.9159
 Epoch 28/50
 14/14 [==============================] - 6s 405ms/step - loss: 0.0957 - accuracy: 0.9731 - val_loss: 0.2896 - val_accuracy: 0.9159
 Epoch 29/50
 14/14 [==============================] - 6s 405ms/step - loss: 0.0982 - accuracy: 0.9709 - val_loss: 0.2645 - val_accuracy: 0.9159
 Epoch 30/50
 14/14 [==============================] - 6s 415ms/step - loss: 0.0967 - accuracy: 0.9798 - val_loss: 0.3147 - val_accuracy: 0.9159
 Epoch 31/50
 14/14 [==============================] - 6s 408ms/step - loss: 0.1088 - accuracy: 0.9596 - val_loss: 0.3226 - val_accuracy: 0.9159
 Epoch 32/50
 14/14 [==============================] - 6s 405ms/step - loss: 0.0948 - accuracy: 0.9664 - val_loss: 0.2574 - val_accuracy: 0.9252
 Epoch 33/50
 14/14 [==============================] - 6s 409ms/step - loss: 0.1316 - accuracy: 0.9619 - val_loss: 0.2754 - val_accuracy: 0.9159
 Epoch 34/50
 14/14 [==============================] - 6s 414ms/step - loss: 0.1077 - accuracy: 0.9731 - val_loss: 0.3288 - val_accuracy: 0.9159
 Epoch 35/50
 14/14 [==============================] - 6s 407ms/step - loss: 0.1063 - accuracy: 0.9709 - val_loss: 0.2778 - val_accuracy: 0.9252
 Epoch 36/50
 14/14 [==============================] - 6s 420ms/step - loss: 0.1208 - accuracy: 0.9596 - val_loss: 0.2901 - val_accuracy: 0.9159
 Epoch 37/50
 14/14 [==============================] - 6s 410ms/step - loss: 0.1249 - accuracy: 0.9731 - val_loss: 0.3595 - val_accuracy: 0.9065
 Epoch 38/50
 14/14 [==============================] - 6s 403ms/step - loss: 0.1055 - accuracy: 0.9686 - val_loss: 0.3057 - val_accuracy: 0.9159
 Epoch 39/50
 14/14 [==============================] - 6s 406ms/step - loss: 0.0941 - accuracy: 0.9753 - val_loss: 0.3103 - val_accuracy: 0.9159
 Epoch 40/50
 14/14 [==============================] - 6s 409ms/step - loss: 0.1034 - accuracy: 0.9753 - val_loss: 0.3149 - val_accuracy: 0.9065
 Epoch 41/50
 14/14 [==============================] - 6s 438ms/step - loss: 0.0944 - accuracy: 0.9709 - val_loss: 0.3427 - val_accuracy: 0.9065
 Epoch 42/50
 14/14 [==============================] - 6s 422ms/step - loss: 0.0850 - accuracy: 0.9821 - val_loss: 0.3070 - val_accuracy: 0.9159
 Epoch 43/50
 14/14 [==============================] - 6s 411ms/step - loss: 0.1078 - accuracy: 0.9619 - val_loss: 0.2931 - val_accuracy: 0.9159
 Epoch 44/50
 14/14 [==============================] - 6s 404ms/step - loss: 0.1012 - accuracy: 0.9686 - val_loss: 0.3121 - val_accuracy: 0.9159
 Epoch 45/50
 14/14 [==============================] - 6s 407ms/step - loss: 0.0941 - accuracy: 0.9753 - val_loss: 0.3329 - val_accuracy: 0.9159
 Epoch 46/50
 14/14 [==============================] - 6s 410ms/step - loss: 0.0911 - accuracy: 0.9664 - val_loss: 0.3349 - val_accuracy: 0.9159
 Epoch 47/50
 14/14 [==============================] - 6s 404ms/step - loss: 0.1087 - accuracy: 0.9686 - val_loss: 0.2674 - val_accuracy: 0.9252
 Epoch 48/50
 14/14 [==============================] - 6s 408ms/step - loss: 0.0729 - accuracy: 0.9731 - val_loss: 0.3035 - val_accuracy: 0.9159
 Epoch 49/50
 14/14 [==============================] - 6s 405ms/step - loss: 0.0812 - accuracy: 0.9776 - val_loss: 0.3067 - val_accuracy: 0.9159
 Epoch 50/50
 14/14 [==============================] - 6s 409ms/step - loss: 0.0866 - accuracy: 0.9731 - val_loss: 0.3173 - val_accuracy: 0.9159
 Found 478 images belonging to 7 classes.
 Found 123 images belonging to 7 classes.
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 {'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
 Epoch 1/50
 15/15 [==============================] - 24s 1s/step - loss: 0.1265 - accuracy: 0.9603 - val_loss: 0.1472 - val_accuracy: 0.9593
 Epoch 2/50
 15/15 [==============================] - 6s 417ms/step - loss: 0.1059 - accuracy: 0.9665 - val_loss: 0.1453 - val_accuracy: 0.9512
 Epoch 3/50
 15/15 [==============================] - 6s 413ms/step - loss: 0.1137 - accuracy: 0.9644 - val_loss: 0.1353 - val_accuracy: 0.9593
 Epoch 4/50
 15/15 [==============================] - 6s 408ms/step - loss: 0.1004 - accuracy: 0.9707 - val_loss: 0.1251 - val_accuracy: 0.9512
 Epoch 5/50
 15/15 [==============================] - 6s 424ms/step - loss: 0.0809 - accuracy: 0.9728 - val_loss: 0.1126 - val_accuracy: 0.9675
 Epoch 6/50
 15/15 [==============================] - 6s 408ms/step - loss: 0.0715 - accuracy: 0.9833 - val_loss: 0.1125 - val_accuracy: 0.9593
 Epoch 7/50
 15/15 [==============================] - 6s 406ms/step - loss: 0.0820 - accuracy: 0.9749 - val_loss: 0.1119 - val_accuracy: 0.9593
 Epoch 8/50
 15/15 [==============================] - 6s 411ms/step - loss: 0.0827 - accuracy: 0.9770 - val_loss: 0.1046 - val_accuracy: 0.9593
 Epoch 9/50
 15/15 [==============================] - 6s 409ms/step - loss: 0.0879 - accuracy: 0.9686 - val_loss: 0.1061 - val_accuracy: 0.9593
 Epoch 10/50
 15/15 [==============================] - 6s 414ms/step - loss: 0.0732 - accuracy: 0.9770 - val_loss: 0.1055 - val_accuracy: 0.9593
 Epoch 11/50
 15/15 [==============================] - 6s 415ms/step - loss: 0.0713 - accuracy: 0.9791 - val_loss: 0.0944 - val_accuracy: 0.9675
 Epoch 12/50
 15/15 [==============================] - 6s 407ms/step - loss: 0.0780 - accuracy: 0.9728 - val_loss: 0.0857 - val_accuracy: 0.9756
 Epoch 13/50
 15/15 [==============================] - 6s 408ms/step - loss: 0.0574 - accuracy: 0.9812 - val_loss: 0.0840 - val_accuracy: 0.9756
 Epoch 14/50
 15/15 [==============================] - 6s 411ms/step - loss: 0.0847 - accuracy: 0.9707 - val_loss: 0.0862 - val_accuracy: 0.9675
 Epoch 15/50
 15/15 [==============================] - 6s 405ms/step - loss: 0.0768 - accuracy: 0.9770 - val_loss: 0.0874 - val_accuracy: 0.9675
 Epoch 16/50
 15/15 [==============================] - 6s 415ms/step - loss: 0.0574 - accuracy: 0.9854 - val_loss: 0.0822 - val_accuracy: 0.9756
 Epoch 17/50
 15/15 [==============================] - 6s 410ms/step - loss: 0.0647 - accuracy: 0.9707 - val_loss: 0.0839 - val_accuracy: 0.9756
 Epoch 18/50
 15/15 [==============================] - 6s 410ms/step - loss: 0.0668 - accuracy: 0.9812 - val_loss: 0.0840 - val_accuracy: 0.9756
 Epoch 19/50
 15/15 [==============================] - 6s 405ms/step - loss: 0.0664 - accuracy: 0.9728 - val_loss: 0.0834 - val_accuracy: 0.9756
 Epoch 20/50
 15/15 [==============================] - 6s 418ms/step - loss: 0.0747 - accuracy: 0.9812 - val_loss: 0.0774 - val_accuracy: 0.9756
 Epoch 21/50
 15/15 [==============================] - 6s 408ms/step - loss: 0.0652 - accuracy: 0.9770 - val_loss: 0.0762 - val_accuracy: 0.9837
 Epoch 22/50
 15/15 [==============================] - 6s 412ms/step - loss: 0.0545 - accuracy: 0.9770 - val_loss: 0.0727 - val_accuracy: 0.9837
 Epoch 23/50
 15/15 [==============================] - 6s 406ms/step - loss: 0.0745 - accuracy: 0.9686 - val_loss: 0.0728 - val_accuracy: 0.9837
 Epoch 24/50
 15/15 [==============================] - 6s 407ms/step - loss: 0.0704 - accuracy: 0.9749 - val_loss: 0.0709 - val_accuracy: 0.9837
 Epoch 25/50
 15/15 [==============================] - 6s 415ms/step - loss: 0.0491 - accuracy: 0.9874 - val_loss: 0.0704 - val_accuracy: 0.9837
 Epoch 26/50
 15/15 [==============================] - 6s 412ms/step - loss: 0.0701 - accuracy: 0.9791 - val_loss: 0.0715 - val_accuracy: 0.9756
 Epoch 27/50
 15/15 [==============================] - 6s 416ms/step - loss: 0.0612 - accuracy: 0.9749 - val_loss: 0.0713 - val_accuracy: 0.9756
 Epoch 28/50
 15/15 [==============================] - 6s 406ms/step - loss: 0.0621 - accuracy: 0.9728 - val_loss: 0.0706 - val_accuracy: 0.9837
 Epoch 29/50
 15/15 [==============================] - 6s 410ms/step - loss: 0.0509 - accuracy: 0.9854 - val_loss: 0.0683 - val_accuracy: 0.9837
 Epoch 30/50
 15/15 [==============================] - 6s 406ms/step - loss: 0.0576 - accuracy: 0.9812 - val_loss: 0.0664 - val_accuracy: 0.9837
 Epoch 31/50
 15/15 [==============================] - 6s 414ms/step - loss: 0.0531 - accuracy: 0.9874 - val_loss: 0.0663 - val_accuracy: 0.9837
 Epoch 32/50
 15/15 [==============================] - 6s 404ms/step - loss: 0.0558 - accuracy: 0.9833 - val_loss: 0.0641 - val_accuracy: 0.9837
 Epoch 33/50
 15/15 [==============================] - 6s 410ms/step - loss: 0.0675 - accuracy: 0.9854 - val_loss: 0.0624 - val_accuracy: 0.9837
 Epoch 34/50
 15/15 [==============================] - 6s 411ms/step - loss: 0.0532 - accuracy: 0.9812 - val_loss: 0.0606 - val_accuracy: 0.9837
 Epoch 35/50
 15/15 [==============================] - 6s 409ms/step - loss: 0.0434 - accuracy: 0.9895 - val_loss: 0.0596 - val_accuracy: 0.9837
 Epoch 36/50
 15/15 [==============================] - 6s 403ms/step - loss: 0.0521 - accuracy: 0.9791 - val_loss: 0.0605 - val_accuracy: 0.9837
 Epoch 37/50
 15/15 [==============================] - 6s 412ms/step - loss: 0.0635 - accuracy: 0.9749 - val_loss: 0.0595 - val_accuracy: 0.9837
 Epoch 38/50
 15/15 [==============================] - 7s 434ms/step - loss: 0.0502 - accuracy: 0.9770 - val_loss: 0.0594 - val_accuracy: 0.9837
 Epoch 39/50
 15/15 [==============================] - 6s 419ms/step - loss: 0.0452 - accuracy: 0.9895 - val_loss: 0.0604 - val_accuracy: 0.9837
 Epoch 40/50
 15/15 [==============================] - 6s 406ms/step - loss: 0.0607 - accuracy: 0.9791 - val_loss: 0.0595 - val_accuracy: 0.9837
 Epoch 41/50
 15/15 [==============================] - 7s 466ms/step - loss: 0.0536 - accuracy: 0.9791 - val_loss: 0.0570 - val_accuracy: 0.9837
 Epoch 42/50
 15/15 [==============================] - 6s 410ms/step - loss: 0.0486 - accuracy: 0.9812 - val_loss: 0.0540 - val_accuracy: 0.9919
 Epoch 43/50
 15/15 [==============================] - 6s 406ms/step - loss: 0.0459 - accuracy: 0.9874 - val_loss: 0.0538 - val_accuracy: 0.9919
 Epoch 44/50
 15/15 [==============================] - 6s 421ms/step - loss: 0.0594 - accuracy: 0.9812 - val_loss: 0.0556 - val_accuracy: 0.9837
 Epoch 45/50
 15/15 [==============================] - 6s 415ms/step - loss: 0.0584 - accuracy: 0.9791 - val_loss: 0.0570 - val_accuracy: 0.9837
 Epoch 46/50
 15/15 [==============================] - 6s 404ms/step - loss: 0.0420 - accuracy: 0.9937 - val_loss: 0.0558 - val_accuracy: 0.9837
 Epoch 47/50
 15/15 [==============================] - 6s 414ms/step - loss: 0.0558 - accuracy: 0.9833 - val_loss: 0.0523 - val_accuracy: 0.9837
 Epoch 48/50
 15/15 [==============================] - 6s 405ms/step - loss: 0.0481 - accuracy: 0.9854 - val_loss: 0.0510 - val_accuracy: 0.9919
 Epoch 49/50
 15/15 [==============================] - 6s 417ms/step - loss: 0.0500 - accuracy: 0.9791 - val_loss: 0.0505 - val_accuracy: 0.9919
 Epoch 50/50
 15/15 [==============================] - 6s 410ms/step - loss: 0.0440 - accuracy: 0.9874 - val_loss: 0.0494 - val_accuracy: 0.9919

    </pre>
</div>

Module 1: Cell annotation for unseen spots (Section-25, Fig.3b)

Preparation

step1 = STASCAN.preparation.Generator(output_path + "/section8/", vignettes_path + "/section8/tissue_positions_list.csv", vignettes_path + "/section8/Section-25.png", crop_size)
step1.RawSpot_generator()
step1.ImputedSpot_generator()

The number of raw spots :  657
The number of imputed spots :  1280

Prediction

step2 = STASCAN.model.BuildPrediction(output_path + "/section8/Models/finetuned_model.h5", output_path + "/section8/")
step2.prediction(spot_label, output_path + "/section8/ImputedSpot", output_path + "/section8/ImputedSpot/imputed_spot.txt", "Imputed")
step2.prediction(spot_label, output_path + "/section8/RawSpot", output_path + "/section8/ImputedSpot/adjust_raw_spot.txt", "Raw")

 {0: 'Epidermal', 1: 'Gut', 2: 'Muscle', 3: 'Neoblast', 4: 'Neuronal', 5: 'Parenchymal', 6: 'Secretory'}
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 1/1 [==============================] - 0s 29ms/step
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 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 33ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 33ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 33ms/step
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 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 33ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 33ms/step
 1/1 [==============================] - 0s 32ms/step
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 1/1 [==============================] - 0s 33ms/step
 1/1 [==============================] - 0s 33ms/step
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 1/1 [==============================] - 0s 32ms/step
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 1/1 [==============================] - 0s 32ms/step
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 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 {0: 'Epidermal', 1: 'Gut', 2: 'Muscle', 3: 'Neoblast', 4: 'Neuronal', 5: 'Parenchymal', 6: 'Secretory'}
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    </pre>
</div>

Model evaluation

# Metrics
fig = STASCAN.StatPlot.Metric()
fig.ROC_Curve(output_path + "/section8/PriorSpot/test/", output_path + "/section8/Predict/Rawpredict_detail.txt", label_list, color_list, output_path + "/section8/Models/ROC.pdf")
fig.Loss_Accuracy_Curve(output_path + "/section8/Models/Log_FinetunedModel.txt", output_path + "/section8/Models/")

# Check
fig = STASCAN.StatPlot.Check()
fig.Check_PriorSpot(output_path + "/section8/ImputedSpot/adjust_raw_spot.txt", output_path + "/section8/PriorSpot/prior_spot.txt", vignettes_path + "/section8/Section-25.png", dict_label, output_path + "/section8/")
fig.Check_ImputedSpot(output_path + "/section8/ImputedSpot/adjust_raw_spot.txt", output_path + "/section8/ImputedSpot/imputed_spot.txt", vignettes_path + "/section8/Section-25.png", output_path + "/section8/")

Visualization

# Visualization
fig = STASCAN.run_STASCAN.Visualization()
fig.EnhancedPlot(output_path + "/section8/Predict/Raw_predict.txt", output_path + "/section8/PriorSpot/prior_spot.txt", output_path + "/section8/Predict/Imputed_predict.txt", vignettes_path + "/section8/Section-25.png", dict_label, output_path + "/section8/", pointsize=18)

Module 2: Cell annotation for subdivided spots (Section-25, Fig.3c)

Pseudo-labeling (optional)

if not os.path.exists(output_path + "/section8/SubModule/"):
    os.makedirs(output_path + "/section8/SubModule/")

# Optional
pseudo_spot = run.PseudoLabelling(output_path + "/section8/SubModule/", output_path + "/section8/RawSpot", output_path + "/section8/Predict/Rawpredict_detail.txt", label_list)

for each in label_list:
    if each not in list(set([x[-1] for x in pseudo_spot])):
            imgsLib = []
            imgsLib.extend(glob.glob(os.path.join(output_path + "/section8/PriorSpot/test/" + each + "/", "*.png")))
            for n in imgsLib:
                    shutil.copy(n, output_path + "/section8/SubModule/PseudoSpot/")

shutil.copytree(output_path + "/section8/SubModule/PseudoSpot/", output_path + "/section8/SubModule/PriorSpot/")
shutil.copytree(output_path + "/section8/RawSpot/", output_path + "/section8/SubModule/RawSpot/")
shutil.move(output_path + "/section8/SubModule/PriorSpot/pseudo_spot.txt",output_path + "/section8/SubModule/PriorSpot/prior_spot.txt")

The number of pseudo spots :  522
'.//section8/SubModule/PriorSpot/prior_spot.txt'

Preparation

# Preparation
step3 = STASCAN.preparation.Generator(output_path + "/section8/SubModule/", vignettes_path + "/section8/tissue_positions_list.csv", vignettes_path + "/section8/Section-25.png", crop_size)
step3.SubSpot_generator()

step4 = STASCAN.preparation.Shuffler(output_path + "/section8/SubModule/SubSpot/prior_divided/")
step4.Dataset_divider(spot_label)

for each in label_list:
    if not os.listdir(output_path + "/section8/SubModule/SubSpot/prior_divided/test/" + each):
            imgsLib = []
            imgsLib.extend(glob.glob(os.path.join(output_path + "/section8/SubModule/SubSpot/prior_divided/train/" + each + "/", "*.png")))
            shutil.move(imgsLib[0], output_path + "/section8/SubModule/SubSpot/prior_divided/test/" + each + "/")

The number of raw spots :  657

Model re-training

step5 = STASCAN.model.BuildModel()
traindata_path, testdata_path = output_path + "/section8/SubModule/SubSpot/prior_divided/train/", output_path + "/section8/SubModule/SubSpot/prior_divided/test/"
step5.finetuned_model(output_path + "/section8/Models/finetuned_model.h5", traindata_path, testdata_path, output_path + "/section8/SubModule/SubSpot/")

Found 1675 images belonging to 7 classes.
Found 417 images belonging to 7 classes.
{'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}
{'Epidermal': 0, 'Gut': 1, 'Muscle': 2, 'Neoblast': 3, 'Neuronal': 4, 'Parenchymal': 5, 'Secretory': 6}

Re-training reports

 Epoch 1/50
 53/53 [==============================] - 49s 904ms/step - loss: 0.6751 - accuracy: 0.7713 - val_loss: 0.4169 - val_accuracy: 0.8465
 Epoch 2/50
 53/53 [==============================] - 22s 405ms/step - loss: 0.5347 - accuracy: 0.8161 - val_loss: 0.3902 - val_accuracy: 0.8489
 Epoch 3/50
 53/53 [==============================] - 21s 394ms/step - loss: 0.4814 - accuracy: 0.8251 - val_loss: 0.3701 - val_accuracy: 0.8657
 Epoch 4/50
 53/53 [==============================] - 22s 404ms/step - loss: 0.4752 - accuracy: 0.8322 - val_loss: 0.3720 - val_accuracy: 0.8513
 Epoch 5/50
 53/53 [==============================] - 21s 399ms/step - loss: 0.4452 - accuracy: 0.8388 - val_loss: 0.3560 - val_accuracy: 0.8705
 Epoch 6/50
 53/53 [==============================] - 21s 398ms/step - loss: 0.4346 - accuracy: 0.8501 - val_loss: 0.3630 - val_accuracy: 0.8681
 Epoch 7/50
 53/53 [==============================] - 21s 398ms/step - loss: 0.4269 - accuracy: 0.8543 - val_loss: 0.3568 - val_accuracy: 0.8753
 Epoch 8/50
 53/53 [==============================] - 21s 398ms/step - loss: 0.4425 - accuracy: 0.8448 - val_loss: 0.3519 - val_accuracy: 0.8729
 Epoch 9/50
 53/53 [==============================] - 21s 400ms/step - loss: 0.4327 - accuracy: 0.8388 - val_loss: 0.3735 - val_accuracy: 0.8609
 Epoch 10/50
 53/53 [==============================] - 21s 396ms/step - loss: 0.4215 - accuracy: 0.8561 - val_loss: 0.3607 - val_accuracy: 0.8705
 Epoch 11/50
 53/53 [==============================] - 21s 396ms/step - loss: 0.4057 - accuracy: 0.8490 - val_loss: 0.3805 - val_accuracy: 0.8513
 Epoch 12/50
 53/53 [==============================] - 21s 402ms/step - loss: 0.4344 - accuracy: 0.8448 - val_loss: 0.3538 - val_accuracy: 0.8657
 Epoch 13/50
 53/53 [==============================] - 21s 396ms/step - loss: 0.4123 - accuracy: 0.8561 - val_loss: 0.3446 - val_accuracy: 0.8705
 Epoch 14/50
 53/53 [==============================] - 21s 399ms/step - loss: 0.3992 - accuracy: 0.8555 - val_loss: 0.3500 - val_accuracy: 0.8681
 Epoch 15/50
 53/53 [==============================] - 21s 394ms/step - loss: 0.4235 - accuracy: 0.8561 - val_loss: 0.3539 - val_accuracy: 0.8825
 Epoch 16/50
 53/53 [==============================] - 21s 402ms/step - loss: 0.3942 - accuracy: 0.8513 - val_loss: 0.3435 - val_accuracy: 0.8705
 Epoch 17/50
 53/53 [==============================] - 21s 399ms/step - loss: 0.4020 - accuracy: 0.8609 - val_loss: 0.3576 - val_accuracy: 0.8681
 Epoch 18/50
 53/53 [==============================] - 22s 404ms/step - loss: 0.3870 - accuracy: 0.8543 - val_loss: 0.3687 - val_accuracy: 0.8753
 Epoch 19/50
 53/53 [==============================] - 21s 397ms/step - loss: 0.3867 - accuracy: 0.8567 - val_loss: 0.3615 - val_accuracy: 0.8681
 Epoch 20/50
 53/53 [==============================] - 21s 394ms/step - loss: 0.4004 - accuracy: 0.8555 - val_loss: 0.3308 - val_accuracy: 0.8801
 Epoch 21/50
 53/53 [==============================] - 21s 394ms/step - loss: 0.3879 - accuracy: 0.8633 - val_loss: 0.3403 - val_accuracy: 0.8753
 Epoch 22/50
 53/53 [==============================] - 21s 393ms/step - loss: 0.3641 - accuracy: 0.8621 - val_loss: 0.3460 - val_accuracy: 0.8825
 Epoch 23/50
 53/53 [==============================] - 21s 398ms/step - loss: 0.3746 - accuracy: 0.8585 - val_loss: 0.3194 - val_accuracy: 0.8849
 Epoch 24/50
 53/53 [==============================] - 21s 398ms/step - loss: 0.3670 - accuracy: 0.8716 - val_loss: 0.3209 - val_accuracy: 0.8801
 Epoch 25/50
 53/53 [==============================] - 21s 401ms/step - loss: 0.3758 - accuracy: 0.8657 - val_loss: 0.3568 - val_accuracy: 0.8729
 Epoch 26/50
 53/53 [==============================] - 21s 396ms/step - loss: 0.3774 - accuracy: 0.8716 - val_loss: 0.3241 - val_accuracy: 0.8777
 Epoch 27/50
 53/53 [==============================] - 21s 398ms/step - loss: 0.3678 - accuracy: 0.8609 - val_loss: 0.3308 - val_accuracy: 0.8897
 Epoch 28/50
 53/53 [==============================] - 21s 394ms/step - loss: 0.3590 - accuracy: 0.8675 - val_loss: 0.3275 - val_accuracy: 0.8753
 Epoch 29/50
 53/53 [==============================] - 21s 397ms/step - loss: 0.3716 - accuracy: 0.8639 - val_loss: 0.3798 - val_accuracy: 0.8657
 Epoch 30/50
 53/53 [==============================] - 21s 401ms/step - loss: 0.3640 - accuracy: 0.8734 - val_loss: 0.3415 - val_accuracy: 0.8705
 Epoch 31/50
 53/53 [==============================] - 21s 398ms/step - loss: 0.3661 - accuracy: 0.8734 - val_loss: 0.3605 - val_accuracy: 0.8801
 Epoch 32/50
 53/53 [==============================] - 21s 394ms/step - loss: 0.3704 - accuracy: 0.8621 - val_loss: 0.3241 - val_accuracy: 0.8801
 Epoch 33/50
 53/53 [==============================] - 21s 392ms/step - loss: 0.3561 - accuracy: 0.8693 - val_loss: 0.3247 - val_accuracy: 0.8753
 Epoch 34/50
 53/53 [==============================] - 21s 395ms/step - loss: 0.3478 - accuracy: 0.8693 - val_loss: 0.3420 - val_accuracy: 0.8801
 Epoch 35/50
 53/53 [==============================] - 21s 396ms/step - loss: 0.3521 - accuracy: 0.8782 - val_loss: 0.3302 - val_accuracy: 0.8777
 Epoch 36/50
 53/53 [==============================] - 21s 397ms/step - loss: 0.3491 - accuracy: 0.8746 - val_loss: 0.3537 - val_accuracy: 0.8729
 Epoch 37/50
 53/53 [==============================] - 21s 398ms/step - loss: 0.3426 - accuracy: 0.8740 - val_loss: 0.3313 - val_accuracy: 0.8873
 Epoch 38/50
 53/53 [==============================] - 21s 398ms/step - loss: 0.3485 - accuracy: 0.8722 - val_loss: 0.3683 - val_accuracy: 0.8729
 Epoch 39/50
 53/53 [==============================] - 22s 404ms/step - loss: 0.3286 - accuracy: 0.8752 - val_loss: 0.3300 - val_accuracy: 0.8777
 Epoch 40/50
 53/53 [==============================] - 21s 399ms/step - loss: 0.3476 - accuracy: 0.8728 - val_loss: 0.3226 - val_accuracy: 0.8801
 Epoch 41/50
 53/53 [==============================] - 21s 397ms/step - loss: 0.3452 - accuracy: 0.8854 - val_loss: 0.3191 - val_accuracy: 0.8801
 Epoch 42/50
 53/53 [==============================] - 21s 397ms/step - loss: 0.3330 - accuracy: 0.8764 - val_loss: 0.3600 - val_accuracy: 0.8777
 Epoch 43/50
 53/53 [==============================] - 21s 395ms/step - loss: 0.3377 - accuracy: 0.8782 - val_loss: 0.3081 - val_accuracy: 0.8873
 Epoch 44/50
 53/53 [==============================] - 21s 399ms/step - loss: 0.3453 - accuracy: 0.8788 - val_loss: 0.3214 - val_accuracy: 0.8897
 Epoch 45/50
 53/53 [==============================] - 21s 395ms/step - loss: 0.3347 - accuracy: 0.8788 - val_loss: 0.3388 - val_accuracy: 0.8825
 Epoch 46/50
 53/53 [==============================] - 21s 399ms/step - loss: 0.3299 - accuracy: 0.8722 - val_loss: 0.3325 - val_accuracy: 0.8705
 Epoch 47/50
 53/53 [==============================] - 21s 396ms/step - loss: 0.3250 - accuracy: 0.8812 - val_loss: 0.3663 - val_accuracy: 0.8705
 Epoch 48/50
 53/53 [==============================] - 21s 396ms/step - loss: 0.3359 - accuracy: 0.8788 - val_loss: 0.3495 - val_accuracy: 0.8777
 Epoch 49/50
 53/53 [==============================] - 21s 396ms/step - loss: 0.3237 - accuracy: 0.8878 - val_loss: 0.3270 - val_accuracy: 0.8825
 Epoch 50/50
 53/53 [==============================] - 21s 394ms/step - loss: 0.3235 - accuracy: 0.8788 - val_loss: 0.3194 - val_accuracy: 0.8897

    </pre>
</div>

Prediction

step6 = STASCAN.model.BuildPrediction(output_path + "/section8/SubModule/SubSpot/Models/finetuned_model.h5", output_path + "/section8/SubModule/SubSpot/")
step6.prediction(spot_label, output_path + "/section8/SubModule/SubSpot/raw_divided/", output_path + "/section8/SubModule/SubSpot/raw_divided/subloc.txt", "Subdivided")

 {0: 'Epidermal', 1: 'Gut', 2: 'Muscle', 3: 'Neoblast', 4: 'Neuronal', 5: 'Parenchymal', 6: 'Secretory'}
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    </pre>
</div>

Model evaluation

# Metrics
fig = STASCAN.StatPlot.Metric()
fig.ROC_Curve(output_path + "/section8/SubModule/SubSpot/prior_divided/test/", output_path + "/section8/SubModule/SubSpot/Predict/Subdividedpredict_detail.txt", label_list, color_list, output_path + "/section8/SubModule/SubSpot/Models/ROC.pdf")
fig.Loss_Accuracy_Curve(output_path + "/section8/SubModule/SubSpot/Models/Log_FinetunedModel.txt", output_path + "/section8/SubModule/SubSpot/Models/")

Visualization

# Visualization
fig = STASCAN.run_STASCAN.Visualization()
fig.SubResolutionPlot(output_path + "/section8/Predict/Raw_predict.txt", output_path + "/section8/SubModule/SubSpot/Predict/Subdivided_predict.txt", vignettes_path+ "/section8/Section-25.png", dict_label, output_path + "/section8/SubModule/SubSpot/", pointsize=18)

Module 3: Cell annotation for unseen sections (Adjacent-22/Section-21, Fig.3e)

Prepatation

if not os.path.exists(output_path + "/section6/AdjacentModule/"):
    os.makedirs(output_path + "/section6/AdjacentModule/")

# Prepatation
step7 = STASCAN.preparation.Generator(output_path + "/section6/AdjacentModule/", vignettes_path + "/section6/tissue_positions_list.csv", adjacent_path+"/image22/img22.png", crop_size)
step7.RawSpot_generator()
step7.ImputedSpot_generator()

step8 = STASCAN.preparation.Detection()
step8.DetectWhiteRegion(output_path + "/section6/AdjacentModule/RawSpot/", grey_level, white_threshold)
step8.DetectWhiteRegion(output_path + "/section6/AdjacentModule/ImputedSpot/", grey_level, white_threshold)

The number of raw spots :  756
The number of imputed spots :  1472

Prediction

# Prediction
step9 = STASCAN.model.BuildPrediction(output_path + "/section6/Models/finetuned_model.h5", output_path + "/section6/AdjacentModule/")
step9.prediction(spot_label, output_path + "/section6/AdjacentModule/ImputedSpot/", output_path + "/section6/AdjacentModule/ImputedSpot/imputed_spot.txt", "Adjacent_imputed")
step9.prediction(spot_label, output_path + "/section6/AdjacentModule/RawSpot", output_path + "/section6/AdjacentModule/ImputedSpot/adjust_raw_spot.txt", "Adjacent_raw")

 {0: 'Epidermal', 1: 'Gut', 2: 'Muscle', 3: 'Neoblast', 4: 'Neuronal', 5: 'Parenchymal', 6: 'Secretory'}
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 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 28ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 29ms/step
 1/1 [==============================] - 0s 31ms/step
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 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 29ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 32ms/step
 1/1 [==============================] - 0s 31ms/step
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 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 30ms/step
 1/1 [==============================] - 0s 30ms/step
 1/1 [==============================] - 0s 30ms/step
 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 29ms/step
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 1/1 [==============================] - 0s 31ms/step
 1/1 [==============================] - 0s 31ms/step
 {0: 'Epidermal', 1: 'Gut', 2: 'Muscle', 3: 'Neoblast', 4: 'Neuronal', 5: 'Parenchymal', 6: 'Secretory'}
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    </pre>
</div>

Visualization

# Visualization
fig = STASCAN.run_STASCAN.Visualization()
fig.AdjacentSectionPlot(output_path + "/section6/AdjacentModule/Predict/Adjacent_raw_predict.txt", output_path + "/section6/AdjacentModule/Predict/Adjacent_imputed_predict.txt", adjacent_path+"/image22/img22.png", dict_label, output_path + "/section6/AdjacentModule/", pointsize=18)