diff --git a/models/ResNet50/class_names.json b/models/ResNet50/class_names.json
deleted file mode 100644
index 57874547cdef98b4f08dd1eebb09a2dcedc3ed4e..0000000000000000000000000000000000000000
--- a/models/ResNet50/class_names.json
+++ /dev/null
@@ -1 +0,0 @@
-["Abo", "Abra", "Akwakwak", "Alakazam", "Amonistar", "Amonita", "Aquali", "Arbok", "Arcanin", "Artikodin", "Aspicot", "A\u00e9romite", "Boustiflor", "Bulbizarre", "Caninos", "Carabaffe", "Carapuce", "Chenipan", "Chrysacier", "Ch\u00e9tiflor", "Coconfort", "Colossinge", "Crustabri", "Dardargnan", "Dodrio", "Doduo", "Dracaufeu", "Draco", "Dracolosse", "Ectoplasma", "Empiflor", "Excelangue", "Fantominus", "Farfetchd", "Feunard", "Flagadoss", "Florizarre", "F\u00e9rosinge", "Galopa", "Goupix", "Gravalanch", "Grodoudou", "Grolem", "Grotadmorv", "Herbizarre", "Hypnomade", "Hypoc\u00e9an", "Hypotrempe", "Ins\u00e9cateur", "Kabuto", "Kabutops", "Kadabra", "Kangourex", "Kicklee", "Kokiyas", "Krabboss", "Krabby", "Lamantine", "Leveinard", "Lippoutou", "Lokhlass", "L\u00e9viator", "M. Mime", "Machoc", "Machopeur", "Mackogneur", "Magicarpe", "Magmar", "Magn\u00e9ti", "Magn\u00e9ton", "Mew", "Mewtwo", "Miaouss", "Mimitoss", "Minidraco", "Mystherbe", "M\u00e9lodelfe", "M\u00e9lof\u00e9e", "M\u00e9tamorph", "Nidoking", "Nidoqueen", "Nidoran_femelle", "Nidoran_male", "Nidorina", "Nidorino", "Noadkoko", "Noeunoeuf", "Nosferalto", "Nosferapti", "Onix", "Ortide", "Ossatueur", "Osselait", "Otaria", "Papilusion", "Paras", "Parasect", "Persian", "Piafabec", "Pikachu", "Poissir\u00e8ne", "Poissoroy", "Ponyta", "Porygon", "Psykokwak", "Ptitard", "Pt\u00e9ra", "Pyroli", "Racaillou", "Rafflesia", "Raichu", "Ramoloss", "Rapasdepic", "Rattata", "Rattatac", "Reptincel", "Rhinocorne", "Rhinof\u00e9ros", "Rondoudou", "Ronflex", "Roucarnage", "Roucool", "Roucoups", "Sabelette", "Sablaireau", "Salam\u00e8che", "Saquedeneu", "Scarabrute", "Smogo", "Smogogo", "Soporifik", "Spectrum", "Stari", "Staross", "Sulfura", "Tadmorv", "Tartard", "Taupiqueur", "Tauros", "Tentacool", "Tentacruel", "Tortank", "Triopikeur", "Tygnon", "T\u00eatarte", "Voltali", "Voltorbe", "\u00c9lecthor", "\u00c9lectrode", "\u00c9lektek", "\u00c9voli"]
\ No newline at end of file
diff --git a/models/ResNet50/pokedex_ResNet50.h5 b/models/ResNet50/pokedex_ResNet50.h5
deleted file mode 100644
index a126216f80da6a9506e385e5c7b060c7471179c7..0000000000000000000000000000000000000000
Binary files a/models/ResNet50/pokedex_ResNet50.h5 and /dev/null differ
diff --git a/models/ResNet50/saved_model/fingerprint.pb b/models/ResNet50/saved_model/fingerprint.pb
deleted file mode 100644
index 2378f7e80c93a3c4204936a29dfbf4cd872d4a8c..0000000000000000000000000000000000000000
--- a/models/ResNet50/saved_model/fingerprint.pb
+++ /dev/null
@@ -1 +0,0 @@
-�٭���������������������� �������(�ɔ����� 2
\ No newline at end of file
diff --git a/models/ResNet50/saved_model/saved_model.pb b/models/ResNet50/saved_model/saved_model.pb
deleted file mode 100644
index 5bacac02853fc4439551aaa220bb5392d186b2c5..0000000000000000000000000000000000000000
Binary files a/models/ResNet50/saved_model/saved_model.pb and /dev/null differ
diff --git a/models/ResNet50/saved_model/variables/variables.data-00000-of-00001 b/models/ResNet50/saved_model/variables/variables.data-00000-of-00001
deleted file mode 100644
index 62847711cfdef2a5814449403869126581437a30..0000000000000000000000000000000000000000
Binary files a/models/ResNet50/saved_model/variables/variables.data-00000-of-00001 and /dev/null differ
diff --git a/models/ResNet50/saved_model/variables/variables.index b/models/ResNet50/saved_model/variables/variables.index
deleted file mode 100644
index bd1ff13d8c2f3c0a8473c14290ecd4d00c82f28a..0000000000000000000000000000000000000000
Binary files a/models/ResNet50/saved_model/variables/variables.index and /dev/null differ
diff --git a/models/Xception/pokedex_Xception.h5 b/models/Xception/pokedex_Xception.h5
deleted file mode 100644
index a2efede14ecec2aa20f1b5624c9efdf946529e71..0000000000000000000000000000000000000000
Binary files a/models/Xception/pokedex_Xception.h5 and /dev/null differ
diff --git a/models/Xception/saved_model/fingerprint.pb b/models/Xception/saved_model/fingerprint.pb
deleted file mode 100644
index 26ad768edd58b0a52130abab08c00ee2282c3862..0000000000000000000000000000000000000000
Binary files a/models/Xception/saved_model/fingerprint.pb and /dev/null differ
diff --git a/models/Xception/saved_model/saved_model.pb b/models/Xception/saved_model/saved_model.pb
deleted file mode 100644
index 26317ea4520cbcfb121998839db71b0e8ee25bf4..0000000000000000000000000000000000000000
Binary files a/models/Xception/saved_model/saved_model.pb and /dev/null differ
diff --git a/models/Xception/saved_model/variables/variables.data-00000-of-00001 b/models/Xception/saved_model/variables/variables.data-00000-of-00001
deleted file mode 100644
index bc96ae0060135c6077285b3134830352cf691a43..0000000000000000000000000000000000000000
Binary files a/models/Xception/saved_model/variables/variables.data-00000-of-00001 and /dev/null differ
diff --git a/models/Xception/saved_model/variables/variables.index b/models/Xception/saved_model/variables/variables.index
deleted file mode 100644
index a8dbb599d87680d6232a2c222a71b354595e6d12..0000000000000000000000000000000000000000
Binary files a/models/Xception/saved_model/variables/variables.index and /dev/null differ
diff --git a/slurm/ResNet50_16519109.out b/slurm/ResNet50_16519109.out
new file mode 100644
index 0000000000000000000000000000000000000000..7360036e5bc8c739bc9ae934715bae5ed4568304
--- /dev/null
+++ b/slurm/ResNet50_16519109.out
@@ -0,0 +1,14 @@
+2025-04-09 18:20:13.977433: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2025-04-09 18:20:19.184935: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:20:19.224471: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:20:19.224774: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:20:19.225595: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2025-04-09 18:20:19.225943: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:20:19.226273: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:20:19.226482: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:20:19.441911: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:20:19.442190: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:20:19.442518: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:20:19.442797: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 79383 MB memory: -> device: 0, name: NVIDIA A100 80GB PCIe, pci bus id: 0000:81:00.0, compute capability: 8.0
diff --git a/slurm/Xception_16519103.out b/slurm/Xception_16519103.out
new file mode 100644
index 0000000000000000000000000000000000000000..4e095aeacfa1613b00b36a89c116f30c82b789b6
--- /dev/null
+++ b/slurm/Xception_16519103.out
@@ -0,0 +1,352 @@
+2025-04-09 18:14:12.010126: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2025-04-09 18:14:16.572607: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:14:16.611967: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:14:16.612273: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:14:16.613091: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2025-04-09 18:14:16.613286: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:14:16.613513: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:14:16.613720: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:14:16.831847: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:14:16.832119: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:14:16.832452: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2025-04-09 18:14:16.832729: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 79383 MB memory: -> device: 0, name: NVIDIA A100 80GB PCIe, pci bus id: 0000:81:00.0, compute capability: 8.0
+2025-04-09 18:15:31.217324: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: known incorrect sRGB profile
+2025-04-09 18:16:19.014768: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: known incorrect sRGB profile
+WARNING:tensorflow:From /opt/ebsofts/TensorFlow/2.11.0-foss-2022a-CUDA-11.7.0/lib/python3.10/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
+Instructions for updating:
+Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
+WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2 cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter cause there is no registered converter for this op.
+WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+2025-04-09 18:16:36.185973: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:784] AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy because of the following reason: Found an unshardable source dataset: name: "TensorSliceDataset/_1"
+op: "TensorSliceDataset"
+input: "Placeholder/_0"
+attr {
+ key: "Toutput_types"
+ value {
+ list {
+ type: DT_STRING
+ }
+ }
+}
+attr {
+ key: "_cardinality"
+ value {
+ i: 20409
+ }
+}
+attr {
+ key: "is_files"
+ value {
+ b: false
+ }
+}
+attr {
+ key: "metadata"
+ value {
+ s: "\n\024TensorSliceDataset:0"
+ }
+}
+attr {
+ key: "output_shapes"
+ value {
+ list {
+ shape {
+ }
+ }
+ }
+}
+attr {
+ key: "replicate_on_split"
+ value {
+ b: false
+ }
+}
+experimental_type {
+ type_id: TFT_PRODUCT
+ args {
+ type_id: TFT_DATASET
+ args {
+ type_id: TFT_PRODUCT
+ args {
+ type_id: TFT_TENSOR
+ args {
+ type_id: TFT_STRING
+ }
+ }
+ }
+ }
+}
+
+Number of GPUs: 1
+Found 25511 files belonging to 151 classes.
+Using 20409 files for training.
+Found 25511 files belonging to 151 classes.
+Using 5102 files for validation.
+Detected 151 Pokémon classes.
+Computing class weights...
+Class weights ready.
+Unique labels in training set: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
+ 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
+ 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
+ 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
+ 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
+ 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
+ 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
+ 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
+ 144 145 146 147 148 149 150]
+Class Names (index -> name):
+0: Abo
+1: Abra
+2: Akwakwak
+3: Alakazam
+4: Amonistar
+5: Amonita
+6: Aquali
+7: Arbok
+8: Arcanin
+9: Artikodin
+10: Aspicot
+11: Aéromite
+12: Boustiflor
+13: Bulbizarre
+14: Caninos
+15: Carabaffe
+16: Carapuce
+17: Chenipan
+18: Chrysacier
+19: Chétiflor
+20: Coconfort
+21: Colossinge
+22: Crustabri
+23: Dardargnan
+24: Dodrio
+25: Doduo
+26: Dracaufeu
+27: Draco
+28: Dracolosse
+29: Ectoplasma
+30: Empiflor
+31: Excelangue
+32: Fantominus
+33: Farfetchd
+34: Feunard
+35: Flagadoss
+36: Florizarre
+37: Férosinge
+38: Galopa
+39: Goupix
+40: Gravalanch
+41: Grodoudou
+42: Grolem
+43: Grotadmorv
+44: Herbizarre
+45: Hypnomade
+46: Hypocéan
+47: Hypotrempe
+48: Insécateur
+49: Kabuto
+50: Kabutops
+51: Kadabra
+52: Kangourex
+53: Kicklee
+54: Kokiyas
+55: Krabboss
+56: Krabby
+57: Lamantine
+58: Leveinard
+59: Lippoutou
+60: Lokhlass
+61: Léviator
+62: M. Mime
+63: Machoc
+64: Machopeur
+65: Mackogneur
+66: Magicarpe
+67: Magmar
+68: Magnéti
+69: Magnéton
+70: Mew
+71: Mewtwo
+72: Miaouss
+73: Mimitoss
+74: Minidraco
+75: Mystherbe
+76: Mélodelfe
+77: Mélofée
+78: Métamorph
+79: Nidoking
+80: Nidoqueen
+81: Nidoran_femelle
+82: Nidoran_male
+83: Nidorina
+84: Nidorino
+85: Noadkoko
+86: Noeunoeuf
+87: Nosferalto
+88: Nosferapti
+89: Onix
+90: Ortide
+91: Ossatueur
+92: Osselait
+93: Otaria
+94: Papilusion
+95: Paras
+96: Parasect
+97: Persian
+98: Piafabec
+99: Pikachu
+100: Poissirène
+101: Poissoroy
+102: Ponyta
+103: Porygon
+104: Psykokwak
+105: Ptitard
+106: Ptéra
+107: Pyroli
+108: Racaillou
+109: Rafflesia
+110: Raichu
+111: Ramoloss
+112: Rapasdepic
+113: Rattata
+114: Rattatac
+115: Reptincel
+116: Rhinocorne
+117: Rhinoféros
+118: Rondoudou
+119: Ronflex
+120: Roucarnage
+121: Roucool
+122: Roucoups
+123: Sabelette
+124: Sablaireau
+125: Salamèche
+126: Saquedeneu
+127: Scarabrute
+128: Smogo
+129: Smogogo
+130: Soporifik
+131: Spectrum
+132: Stari
+133: Staross
+134: Sulfura
+135: Tadmorv
+136: Tartard
+137: Taupiqueur
+138: Tauros
+139: Tentacool
+140: Tentacruel
+141: Tortank
+142: Triopikeur
+143: Tygnon
+144: Têtarte
+145: Voltali
+146: Voltorbe
+147: Électhor
+148: Électrode
+149: Élektek
+150: Évoli
+Epoch 1/20
+2025-04-09 18:16:45.917455: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8401
+2025-04-09 18:16:50.308046: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:630] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
+2025-04-09 18:16:50.336746: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x14de1ef42810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
+2025-04-09 18:16:50.336763: I tensorflow/compiler/xla/service/service.cc:181] StreamExecutor device (0): NVIDIA A100 80GB PCIe, Compute Capability 8.0
+2025-04-09 18:16:50.406696: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
+2025-04-09 18:16:50.833080: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
+
1/638 [..............................] - ETA: 3:22:15 - loss: 5.1084 - accuracy: 0.0000e+00
2/638 [..............................] - ETA: 54s - loss: 5.3410 - accuracy: 0.0000e+00
3/638 [..............................] - ETA: 53s - loss: 5.2624 - accuracy: 0.0000e+00
4/638 [..............................] - ETA: 53s - loss: 5.2625 - accuracy: 0.0156
5/638 [..............................] - ETA: 52s - loss: 5.2983 - accuracy: 0.0250
6/638 [..............................] - ETA: 52s - loss: 5.3428 - accuracy: 0.0208
7/638 [..............................] - ETA: 52s - loss: 5.3869 - accuracy: 0.0223
8/638 [..............................] - ETA: 59s - loss: 5.4310 - accuracy: 0.0234
9/638 [..............................] - ETA: 1:13 - loss: 5.4144 - accuracy: 0.0208
10/638 [..............................] - ETA: 1:23 - loss: 5.4346 - accuracy: 0.0188
11/638 [..............................] - ETA: 1:34 - loss: 5.4806 - accuracy: 0.0199
12/638 [..............................] - ETA: 1:41 - loss: 5.4661 - accuracy: 0.0208
13/638 [..............................] - ETA: 1:46 - loss: 5.4505 - accuracy: 0.0216
14/638 [..............................] - ETA: 1:49 - loss: 5.4635 - accuracy: 0.0201
15/638 [..............................] - ETA: 1:54 - loss: 5.4814 - accuracy: 0.0208
16/638 [..............................] - ETA: 1:58 - loss: 5.4691 - accuracy: 0.0234
17/638 [..............................] - ETA: 2:01 - loss: 5.4608 - accuracy: 0.0239
18/638 [..............................] - ETA: 2:04 - loss: 5.4555 - accuracy: 0.0226
19/638 [..............................] - ETA: 2:06 - loss: 5.4504 - accuracy: 0.0230
20/638 [..............................] - ETA: 2:08 - loss: 5.4440 - accuracy: 0.0234
21/638 [..............................] - ETA: 2:10 - loss: 5.4201 - accuracy: 0.0223
22/638 [>.............................] - ETA: 2:11 - loss: 5.4159 - accuracy: 0.0213
23/638 [>.............................] - ETA: 2:12 - loss: 5.3900 - accuracy: 0.0245
24/638 [>.............................] - ETA: 2:13 - loss: 5.4015 - accuracy: 0.0234
25/638 [>.............................] - ETA: 2:15 - loss: 5.3799 - accuracy: 0.0262
26/638 [>.............................] - ETA: 2:16 - loss: 5.3587 - accuracy: 0.0252
27/638 [>.............................] - ETA: 2:17 - loss: 5.3371 - accuracy: 0.0243
28/638 [>.............................] - ETA: 2:17 - loss: 5.2997 - accuracy: 0.0234
29/638 [>.............................] - ETA: 2:18 - loss: 5.3075 - accuracy: 0.0226
30/638 [>.............................] - ETA: 2:18 - loss: 5.3082 - accuracy: 0.0229
31/638 [>.............................] - ETA: 2:19 - loss: 5.2822 - accuracy: 0.0242
32/638 [>.............................] - ETA: 2:19 - loss: 5.2676 - accuracy: 0.0234
33/638 [>.............................] - ETA: 2:19 - loss: 5.2583 - accuracy: 0.0237
34/638 [>.............................] - ETA: 2:20 - loss: 5.2379 - accuracy: 0.0239
35/638 [>.............................] - ETA: 2:20 - loss: 5.2289 - accuracy: 0.0259
36/638 [>.............................] - ETA: 2:20 - loss: 5.2263 - accuracy: 0.0260
37/638 [>.............................] - ETA: 2:20 - loss: 5.2156 - accuracy: 0.0253
38/638 [>.............................] - ETA: 2:21 - loss: 5.2029 - accuracy: 0.0247
39/638 [>.............................] - ETA: 2:21 - loss: 5.1938 - accuracy: 0.0264
40/638 [>.............................] - ETA: 2:21 - loss: 5.1845 - accuracy: 0.0266
41/638 [>.............................] - ETA: 2:21 - loss: 5.1801 - accuracy: 0.0267
42/638 [>.............................] - ETA: 2:21 - loss: 5.1841 - accuracy: 0.0260
43/638 [=>............................] - ETA: 2:21 - loss: 5.1784 - accuracy: 0.0262
44/638 [=>............................] - ETA: 2:21 - loss: 5.1588 - accuracy: 0.0277
45/638 [=>............................] - ETA: 2:21 - loss: 5.1535 - accuracy: 0.0271
46/638 [=>............................] - ETA: 2:21 - loss: 5.1449 - accuracy: 0.0279
47/638 [=>............................] - ETA: 2:21 - loss: 5.1421 - accuracy: 0.0293
48/638 [=>............................] - ETA: 2:21 - loss: 5.1261 - accuracy: 0.0293
49/638 [=>............................] - ETA: 2:21 - loss: 5.1225 - accuracy: 0.0293
50/638 [=>............................] - ETA: 2:21 - loss: 5.1141 - accuracy: 0.0294
51/638 [=>............................] - ETA: 2:21 - loss: 5.1117 - accuracy: 0.0288
52/638 [=>............................] - ETA: 2:22 - loss: 5.0960 - accuracy: 0.0300
53/638 [=>............................] - ETA: 2:22 - loss: 5.0929 - accuracy: 0.0301
54/638 [=>............................] - ETA: 2:22 - loss: 5.0743 - accuracy: 0.0312
55/638 [=>............................] - ETA: 2:22 - loss: 5.0699 - accuracy: 0.0324
56/638 [=>............................] - ETA: 2:22 - loss: 5.0599 - accuracy: 0.0324
57/638 [=>............................] - ETA: 2:22 - loss: 5.0524 - accuracy: 0.0329
58/638 [=>............................] - ETA: 2:21 - loss: 5.0496 - accuracy: 0.0323
59/638 [=>............................] - ETA: 2:21 - loss: 5.0452 - accuracy: 0.0323
60/638 [=>............................] - ETA: 2:21 - loss: 5.0351 - accuracy: 0.0328
61/638 [=>............................] - ETA: 2:21 - loss: 5.0222 - accuracy: 0.0328
62/638 [=>............................] - ETA: 2:21 - loss: 5.0207 - accuracy: 0.0323
63/638 [=>............................] - ETA: 2:22 - loss: 5.0203 - accuracy: 0.0317
64/638 [==>...........................] - ETA: 2:21 - loss: 5.0069 - accuracy: 0.0322
65/638 [==>...........................] - ETA: 2:21 - loss: 4.9901 - accuracy: 0.0337
66/638 [==>...........................] - ETA: 2:21 - loss: 4.9794 - accuracy: 0.0346
67/638 [==>...........................] - ETA: 2:21 - loss: 4.9687 - accuracy: 0.0345
68/638 [==>...........................] - ETA: 2:21 - loss: 4.9615 - accuracy: 0.0345
69/638 [==>...........................] - ETA: 2:21 - loss: 4.9520 - accuracy: 0.0353
70/638 [==>...........................] - ETA: 2:21 - loss: 4.9554 - accuracy: 0.0353
71/638 [==>...........................] - ETA: 2:20 - loss: 4.9460 - accuracy: 0.0348
72/638 [==>...........................] - ETA: 2:20 - loss: 4.9443 - accuracy: 0.0352
73/638 [==>...........................] - ETA: 2:20 - loss: 4.9416 - accuracy: 0.0360
74/638 [==>...........................] - ETA: 2:20 - loss: 4.9374 - accuracy: 0.0359
75/638 [==>...........................] - ETA: 2:20 - loss: 4.9302 - accuracy: 0.0367
76/638 [==>...........................] - ETA: 2:20 - loss: 4.9309 - accuracy: 0.0374
77/638 [==>...........................] - ETA: 2:19 - loss: 4.9234 - accuracy: 0.0377
78/638 [==>...........................] - ETA: 2:19 - loss: 4.9196 - accuracy: 0.0377
79/638 [==>...........................] - ETA: 2:19 - loss: 4.9204 - accuracy: 0.0372
80/638 [==>...........................] - ETA: 2:19 - loss: 4.9100 - accuracy: 0.0375
81/638 [==>...........................] - ETA: 2:19 - loss: 4.9041 - accuracy: 0.0370
82/638 [==>...........................] - ETA: 2:19 - loss: 4.9017 - accuracy: 0.0373
83/638 [==>...........................] - ETA: 2:19 - loss: 4.8957 - accuracy: 0.0380
84/638 [==>...........................] - ETA: 2:18 - loss: 4.8913 - accuracy: 0.0383
85/638 [==>...........................] - ETA: 2:18 - loss: 4.8898 - accuracy: 0.0390
86/638 [===>..........................] - ETA: 2:18 - loss: 4.8836 - accuracy: 0.0392
87/638 [===>..........................] - ETA: 2:18 - loss: 4.8749 - accuracy: 0.0402
88/638 [===>..........................] - ETA: 2:18 - loss: 4.8671 - accuracy: 0.0405
89/638 [===>..........................] - ETA: 2:18 - loss: 4.8626 - accuracy: 0.0400
90/638 [===>..........................] - ETA: 2:18 - loss: 4.8616 - accuracy: 0.0396
91/638 [===>..........................] - ETA: 2:17 - loss: 4.8582 - accuracy: 0.0398
92/638 [===>..........................] - ETA: 2:17 - loss: 4.8539 - accuracy: 0.0404
93/638 [===>..........................] - ETA: 2:17 - loss: 4.8514 - accuracy: 0.0403
94/638 [===>..........................] - ETA: 2:17 - loss: 4.8467 - accuracy: 0.0402
95/638 [===>..........................] - ETA: 2:17 - loss: 4.8450 - accuracy: 0.0405
96/638 [===>..........................] - ETA: 2:17 - loss: 4.8407 - accuracy: 0.0407
97/638 [===>..........................] - ETA: 2:16 - loss: 4.8336 - accuracy: 0.0409
98/638 [===>..........................] - ETA: 2:16 - loss: 4.8270 - accuracy: 0.0411
99/638 [===>..........................] - ETA: 2:16 - loss: 4.8233 - accuracy: 0.0417
100/638 [===>..........................] - ETA: 2:16 - loss: 4.8159 - accuracy: 0.0425
101/638 [===>..........................] - ETA: 2:16 - loss: 4.8122 - accuracy: 0.0430
102/638 [===>..........................] - ETA: 2:15 - loss: 4.8089 - accuracy: 0.0429
103/638 [===>..........................] - ETA: 2:15 - loss: 4.8028 - accuracy: 0.0428
104/638 [===>..........................] - ETA: 2:15 - loss: 4.7976 - accuracy: 0.0433
105/638 [===>..........................] - ETA: 2:15 - loss: 4.7892 - accuracy: 0.0429
106/638 [===>..........................] - ETA: 2:15 - loss: 4.7866 - accuracy: 0.0425
107/638 [====>.........................] - ETA: 2:15 - loss: 4.7823 - accuracy: 0.0426
108/638 [====>.........................] - ETA: 2:14 - loss: 4.7777 - accuracy: 0.0428
109/638 [====>.........................] - ETA: 2:14 - loss: 4.7759 - accuracy: 0.0430
110/638 [====>.........................] - ETA: 2:14 - loss: 4.7719 - accuracy: 0.0432
111/638 [====>.........................] - ETA: 2:14 - loss: 4.7687 - accuracy: 0.0431
112/638 [====>.........................] - ETA: 2:14 - loss: 4.7657 - accuracy: 0.0427
113/638 [====>.........................] - ETA: 2:13 - loss: 4.7638 - accuracy: 0.0431
114/638 [====>.........................] - ETA: 2:13 - loss: 4.7598 - accuracy: 0.0430
115/638 [====>.........................] - ETA: 2:13 - loss: 4.7595 - accuracy: 0.0427
116/638 [====>.........................] - ETA: 2:13 - loss: 4.7577 - accuracy: 0.0434
117/638 [====>.........................] - ETA: 2:12 - loss: 4.7528 - accuracy: 0.0438
118/638 [====>.........................] - ETA: 2:12 - loss: 4.7522 - accuracy: 0.0437
119/638 [====>.........................] - ETA: 2:12 - loss: 4.7494 - accuracy: 0.0439
120/638 [====>.........................] - ETA: 2:12 - loss: 4.7436 - accuracy: 0.0443
121/638 [====>.........................] - ETA: 2:11 - loss: 4.7405 - accuracy: 0.0449
122/638 [====>.........................] - ETA: 2:11 - loss: 4.7338 - accuracy: 0.0451
123/638 [====>.........................] - ETA: 2:11 - loss: 4.7295 - accuracy: 0.0447
124/638 [====>.........................] - ETA: 2:11 - loss: 4.7226 - accuracy: 0.0449
125/638 [====>.........................] - ETA: 2:10 - loss: 4.7215 - accuracy: 0.0445
126/638 [====>.........................] - ETA: 2:10 - loss: 4.7147 - accuracy: 0.0451
127/638 [====>.........................] - ETA: 2:10 - loss: 4.7121 - accuracy: 0.0453
128/638 [=====>........................] - ETA: 2:10 - loss: 4.7052 - accuracy: 0.0449
129/638 [=====>........................] - ETA: 2:10 - loss: 4.7027 - accuracy: 0.0448
130/638 [=====>........................] - ETA: 2:09 - loss: 4.6969 - accuracy: 0.0447
131/638 [=====>........................] - ETA: 2:09 - loss: 4.6949 - accuracy: 0.0446
132/638 [=====>........................] - ETA: 2:09 - loss: 4.6995 - accuracy: 0.0445
133/638 [=====>........................] - ETA: 2:09 - loss: 4.6903 - accuracy: 0.0456
134/638 [=====>........................] - ETA: 2:08 - loss: 4.6930 - accuracy: 0.0452
135/638 [=====>........................] - ETA: 2:08 - loss: 4.6925 - accuracy: 0.0449
136/638 [=====>........................] - ETA: 2:08 - loss: 4.6913 - accuracy: 0.0448
137/638 [=====>........................] - ETA: 2:08 - loss: 4.6927 - accuracy: 0.0454
138/638 [=====>........................] - ETA: 2:07 - loss: 4.6911 - accuracy: 0.0453
139/638 [=====>........................] - ETA: 2:07 - loss: 4.6943 - accuracy: 0.0452
140/638 [=====>........................] - ETA: 2:07 - loss: 4.6962 - accuracy: 0.0451
141/638 [=====>........................] - ETA: 2:07 - loss: 4.6894 - accuracy: 0.0454
142/638 [=====>........................] - ETA: 2:06 - loss: 4.6864 - accuracy: 0.0458
143/638 [=====>........................] - ETA: 2:06 - loss: 4.6882 - accuracy: 0.0455
144/638 [=====>........................] - ETA: 2:06 - loss: 4.6841 - accuracy: 0.0456
145/638 [=====>........................] - ETA: 2:06 - loss: 4.6814 - accuracy: 0.0459
146/638 [=====>........................] - ETA: 2:06 - loss: 4.6794 - accuracy: 0.0462
147/638 [=====>........................] - ETA: 2:05 - loss: 4.6748 - accuracy: 0.0472
148/638 [=====>........................] - ETA: 2:05 - loss: 4.6715 - accuracy: 0.0479
149/638 [======>.......................] - ETA: 2:05 - loss: 4.6692 - accuracy: 0.0478
150/638 [======>.......................] - ETA: 2:05 - loss: 4.6690 - accuracy: 0.0477
151/638 [======>.......................] - ETA: 2:04 - loss: 4.6648 - accuracy: 0.0476
152/638 [======>.......................] - ETA: 2:04 - loss: 4.6603 - accuracy: 0.0481
153/638 [======>.......................] - ETA: 2:04 - loss: 4.6610 - accuracy: 0.0482
154/638 [======>.......................] - ETA: 2:04 - loss: 4.6588 - accuracy: 0.0481
155/638 [======>.......................] - ETA: 2:03 - loss: 4.6550 - accuracy: 0.0480
156/638 [======>.......................] - ETA: 2:03 - loss: 4.6518 - accuracy: 0.0481
157/638 [======>.......................] - ETA: 2:03 - loss: 4.6454 - accuracy: 0.0494
158/638 [======>.......................] - ETA: 2:03 - loss: 4.6416 - accuracy: 0.0496
159/638 [======>.......................] - ETA: 2:03 - loss: 4.6399 - accuracy: 0.0497
160/638 [======>.......................] - ETA: 2:03 - loss: 4.6340 - accuracy: 0.0500
161/638 [======>.......................] - ETA: 2:02 - loss: 4.6284 - accuracy: 0.0505
162/638 [======>.......................] - ETA: 2:02 - loss: 4.6246 - accuracy: 0.0503
163/638 [======>.......................] - ETA: 2:02 - loss: 4.6230 - accuracy: 0.0504
164/638 [======>.......................] - ETA: 2:02 - loss: 4.6205 - accuracy: 0.0501
165/638 [======>.......................] - ETA: 2:02 - loss: 4.6203 - accuracy: 0.0502
166/638 [======>.......................] - ETA: 2:01 - loss: 4.6186 - accuracy: 0.0499
167/638 [======>.......................] - ETA: 2:01 - loss: 4.6142 - accuracy: 0.0503
168/638 [======>.......................] - ETA: 2:01 - loss: 4.6100 - accuracy: 0.0510
169/638 [======>.......................] - ETA: 2:01 - loss: 4.6095 - accuracy: 0.0510
170/638 [======>.......................] - ETA: 2:00 - loss: 4.6085 - accuracy: 0.0511
171/638 [=======>......................] - ETA: 2:00 - loss: 4.6040 - accuracy: 0.0514
172/638 [=======>......................] - ETA: 2:00 - loss: 4.6016 - accuracy: 0.0518
173/638 [=======>......................] - ETA: 1:59 - loss: 4.5986 - accuracy: 0.0518
174/638 [=======>......................] - ETA: 1:59 - loss: 4.5970 - accuracy: 0.0519
175/638 [=======>......................] - ETA: 1:59 - loss: 4.5952 - accuracy: 0.0529
176/638 [=======>......................] - ETA: 1:59 - loss: 4.5929 - accuracy: 0.0527
177/638 [=======>......................] - ETA: 1:58 - loss: 4.5931 - accuracy: 0.0530
178/638 [=======>......................] - ETA: 1:58 - loss: 4.5887 - accuracy: 0.0534
179/638 [=======>......................] - ETA: 1:58 - loss: 4.5838 - accuracy: 0.0534
180/638 [=======>......................] - ETA: 1:58 - loss: 4.5802 - accuracy: 0.0536
181/638 [=======>......................] - ETA: 1:57 - loss: 4.5790 - accuracy: 0.0540
182/638 [=======>......................] - ETA: 1:57 - loss: 4.5784 - accuracy: 0.0541
183/638 [=======>......................] - ETA: 1:57 - loss: 4.5766 - accuracy: 0.0541
184/638 [=======>......................] - ETA: 1:57 - loss: 4.5754 - accuracy: 0.0542
185/638 [=======>......................] - ETA: 1:56 - loss: 4.5735 - accuracy: 0.0544
186/638 [=======>......................] - ETA: 1:56 - loss: 4.5713 - accuracy: 0.0543
187/638 [=======>......................] - ETA: 1:56 - loss: 4.5690 - accuracy: 0.0546
188/638 [=======>......................] - ETA: 1:56 - loss: 4.5680 - accuracy: 0.0547
189/638 [=======>......................] - ETA: 1:55 - loss: 4.5644 - accuracy: 0.0554
190/638 [=======>......................] - ETA: 1:55 - loss: 4.5682 - accuracy: 0.0553
191/638 [=======>......................] - ETA: 1:55 - loss: 4.5669 - accuracy: 0.0553
192/638 [========>.....................] - ETA: 1:55 - loss: 4.5630 - accuracy: 0.0555
193/638 [========>.....................] - ETA: 1:55 - loss: 4.5617 - accuracy: 0.0555
194/638 [========>.....................] - ETA: 1:54 - loss: 4.5603 - accuracy: 0.0561
195/638 [========>.....................] - ETA: 1:54 - loss: 4.5590 - accuracy: 0.0564
196/638 [========>.....................] - ETA: 1:54 - loss: 4.5586 - accuracy: 0.0563
197/638 [========>.....................] - ETA: 1:54 - loss: 4.5527 - accuracy: 0.0565
198/638 [========>.....................] - ETA: 1:54 - loss: 4.5493 - accuracy: 0.0567
199/638 [========>.....................] - ETA: 1:53 - loss: 4.5463 - accuracy: 0.0573
200/638 [========>.....................] - ETA: 1:53 - loss: 4.5434 - accuracy: 0.0572
201/638 [========>.....................] - ETA: 1:53 - loss: 4.5401 - accuracy: 0.0571
202/638 [========>.....................] - ETA: 1:52 - loss: 4.5389 - accuracy: 0.0568
203/638 [========>.....................] - ETA: 1:52 - loss: 4.5374 - accuracy: 0.0570
204/638 [========>.....................] - ETA: 1:52 - loss: 4.5366 - accuracy: 0.0571
205/638 [========>.....................] - ETA: 1:52 - loss: 4.5329 - accuracy: 0.0578
206/638 [========>.....................] - ETA: 1:52 - loss: 4.5296 - accuracy: 0.0575
207/638 [========>.....................] - ETA: 1:51 - loss: 4.5260 - accuracy: 0.0575
208/638 [========>.....................] - ETA: 1:51 - loss: 4.5222 - accuracy: 0.0577
209/638 [========>.....................] - ETA: 1:51 - loss: 4.5189 - accuracy: 0.0579
210/638 [========>.....................] - ETA: 1:51 - loss: 4.5173 - accuracy: 0.0579
211/638 [========>.....................] - ETA: 1:50 - loss: 4.5161 - accuracy: 0.0582
212/638 [========>.....................] - ETA: 1:50 - loss: 4.5114 - accuracy: 0.0584
213/638 [=========>....................] - ETA: 1:50 - loss: 4.5128 - accuracy: 0.0585
214/638 [=========>....................] - ETA: 1:50 - loss: 4.5121 - accuracy: 0.0584
215/638 [=========>....................] - ETA: 1:49 - loss: 4.5108 - accuracy: 0.0587
216/638 [=========>....................] - ETA: 1:49 - loss: 4.5121 - accuracy: 0.0589
217/638 [=========>....................] - ETA: 1:49 - loss: 4.5092 - accuracy: 0.0589
218/638 [=========>....................] - ETA: 1:49 - loss: 4.5080 - accuracy: 0.0588
219/638 [=========>....................] - ETA: 1:48 - loss: 4.5064 - accuracy: 0.0585
220/638 [=========>....................] - ETA: 1:48 - loss: 4.5053 - accuracy: 0.0587
221/638 [=========>....................] - ETA: 1:48 - loss: 4.5031 - accuracy: 0.0587
222/638 [=========>....................] - ETA: 1:48 - loss: 4.5008 - accuracy: 0.0584
223/638 [=========>....................] - ETA: 1:47 - loss: 4.5001 - accuracy: 0.0584
224/638 [=========>....................] - ETA: 1:47 - loss: 4.4978 - accuracy: 0.0585
225/638 [=========>....................] - ETA: 1:47 - loss: 4.4947 - accuracy: 0.0583
226/638 [=========>....................] - ETA: 1:47 - loss: 4.4926 - accuracy: 0.0584
227/638 [=========>....................] - ETA: 1:46 - loss: 4.4894 - accuracy: 0.0581
228/638 [=========>....................] - ETA: 1:46 - loss: 4.4854 - accuracy: 0.0587
229/638 [=========>....................] - ETA: 1:46 - loss: 4.4839 - accuracy: 0.0585
230/638 [=========>....................] - ETA: 1:45 - loss: 4.4814 - accuracy: 0.0584
231/638 [=========>....................] - ETA: 1:45 - loss: 4.4782 - accuracy: 0.05842025-04-09 18:17:57.661351: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: known incorrect sRGB profile
+
232/638 [=========>....................] - ETA: 1:45 - loss: 4.4787 - accuracy: 0.0585
233/638 [=========>....................] - ETA: 1:45 - loss: 4.4783 - accuracy: 0.0589
234/638 [==========>...................] - ETA: 1:45 - loss: 4.4792 - accuracy: 0.0592
235/638 [==========>...................] - ETA: 1:44 - loss: 4.4779 - accuracy: 0.0590
236/638 [==========>...................] - ETA: 1:44 - loss: 4.4746 - accuracy: 0.0592
237/638 [==========>...................] - ETA: 1:44 - loss: 4.4737 - accuracy: 0.0589
238/638 [==========>...................] - ETA: 1:44 - loss: 4.4723 - accuracy: 0.0592
239/638 [==========>...................] - ETA: 1:43 - loss: 4.4730 - accuracy: 0.0592
240/638 [==========>...................] - ETA: 1:43 - loss: 4.4691 - accuracy: 0.0599
241/638 [==========>...................] - ETA: 1:43 - loss: 4.4662 - accuracy: 0.0602
242/638 [==========>...................] - ETA: 1:43 - loss: 4.4657 - accuracy: 0.0604
243/638 [==========>...................] - ETA: 1:42 - loss: 4.4615 - accuracy: 0.0608
244/638 [==========>...................] - ETA: 1:42 - loss: 4.4584 - accuracy: 0.0612
245/638 [==========>...................] - ETA: 1:42 - loss: 4.4546 - accuracy: 0.0612
246/638 [==========>...................] - ETA: 1:41 - loss: 4.4516 - accuracy: 0.0612
247/638 [==========>...................] - ETA: 1:41 - loss: 4.4506 - accuracy: 0.0612
248/638 [==========>...................] - ETA: 1:41 - loss: 4.4489 - accuracy: 0.0615
249/638 [==========>...................] - ETA: 1:41 - loss: 4.4454 - accuracy: 0.0616
250/638 [==========>...................] - ETA: 1:40 - loss: 4.4441 - accuracy: 0.0617
251/638 [==========>...................] - ETA: 1:40 - loss: 4.4427 - accuracy: 0.0618
252/638 [==========>...................] - ETA: 1:40 - loss: 4.4410 - accuracy: 0.0620
253/638 [==========>...................] - ETA: 1:40 - loss: 4.4386 - accuracy: 0.0624
254/638 [==========>...................] - ETA: 1:39 - loss: 4.4364 - accuracy: 0.0625
255/638 [==========>...................] - ETA: 1:39 - loss: 4.4347 - accuracy: 0.0625
256/638 [===========>..................] - ETA: 1:39 - loss: 4.4329 - accuracy: 0.0625
257/638 [===========>..................] - ETA: 1:39 - loss: 4.4312 - accuracy: 0.0626
258/638 [===========>..................] - ETA: 1:38 - loss: 4.4264 - accuracy: 0.0625
259/638 [===========>..................] - ETA: 1:38 - loss: 4.4258 - accuracy: 0.0626
260/638 [===========>..................] - ETA: 1:38 - loss: 4.4262 - accuracy: 0.0632
261/638 [===========>..................] - ETA: 1:38 - loss: 4.4240 - accuracy: 0.0631
262/638 [===========>..................] - ETA: 1:37 - loss: 4.4192 - accuracy: 0.0631
263/638 [===========>..................] - ETA: 1:37 - loss: 4.4172 - accuracy: 0.0632
264/638 [===========>..................] - ETA: 1:37 - loss: 4.4131 - accuracy: 0.0636
265/638 [===========>..................] - ETA: 1:37 - loss: 4.4123 - accuracy: 0.0637
266/638 [===========>..................] - ETA: 1:36 - loss: 4.4113 - accuracy: 0.0637
267/638 [===========>..................] - ETA: 1:36 - loss: 4.4100 - accuracy: 0.0637
268/638 [===========>..................] - ETA: 1:36 - loss: 4.4065 - accuracy: 0.0637
269/638 [===========>..................] - ETA: 1:36 - loss: 4.4018 - accuracy: 0.0641
270/638 [===========>..................] - ETA: 1:35 - loss: 4.4017 - accuracy: 0.0644
271/638 [===========>..................] - ETA: 1:35 - loss: 4.4011 - accuracy: 0.0643
272/638 [===========>..................] - ETA: 1:35 - loss: 4.3971 - accuracy: 0.0645
273/638 [===========>..................] - ETA: 1:35 - loss: 4.3973 - accuracy: 0.0643
274/638 [===========>..................] - ETA: 1:34 - loss: 4.3939 - accuracy: 0.0646
275/638 [===========>..................] - ETA: 1:34 - loss: 4.3910 - accuracy: 0.0648
276/638 [===========>..................] - ETA: 1:34 - loss: 4.3894 - accuracy: 0.0649
277/638 [============>.................] - ETA: 1:34 - loss: 4.3871 - accuracy: 0.0649
278/638 [============>.................] - ETA: 1:33 - loss: 4.3861 - accuracy: 0.0651
279/638 [============>.................] - ETA: 1:33 - loss: 4.3829 - accuracy: 0.0656
280/638 [============>.................] - ETA: 1:33 - loss: 4.3801 - accuracy: 0.0661
281/638 [============>.................] - ETA: 1:32 - loss: 4.3789 - accuracy: 0.0661
282/638 [============>.................] - ETA: 1:32 - loss: 4.3758 - accuracy: 0.0665
283/638 [============>.................] - ETA: 1:32 - loss: 4.3727 - accuracy: 0.0666
284/638 [============>.................] - ETA: 1:32 - loss: 4.3699 - accuracy: 0.0670
285/638 [============>.................] - ETA: 1:31 - loss: 4.3689 - accuracy: 0.0671
286/638 [============>.................] - ETA: 1:31 - loss: 4.3673 - accuracy: 0.0673
287/638 [============>.................] - ETA: 1:31 - loss: 4.3656 - accuracy: 0.0676
288/638 [============>.................] - ETA: 1:31 - loss: 4.3649 - accuracy: 0.0675
289/638 [============>.................] - ETA: 1:30 - loss: 4.3628 - accuracy: 0.0676
290/638 [============>.................] - ETA: 1:30 - loss: 4.3620 - accuracy: 0.0677
291/638 [============>.................] - ETA: 1:30 - loss: 4.3585 - accuracy: 0.0680
292/638 [============>.................] - ETA: 1:30 - loss: 4.3577 - accuracy: 0.0677
293/638 [============>.................] - ETA: 1:29 - loss: 4.3544 - accuracy: 0.0678
294/638 [============>.................] - ETA: 1:29 - loss: 4.3517 - accuracy: 0.0685
295/638 [============>.................] - ETA: 1:29 - loss: 4.3512 - accuracy: 0.0683
296/638 [============>.................] - ETA: 1:29 - loss: 4.3490 - accuracy: 0.0685
297/638 [============>.................] - ETA: 1:28 - loss: 4.3468 - accuracy: 0.0685
298/638 [=============>................] - ETA: 1:28 - loss: 4.3466 - accuracy: 0.0687
299/638 [=============>................] - ETA: 1:28 - loss: 4.3449 - accuracy: 0.0688
300/638 [=============>................] - ETA: 1:28 - loss: 4.3440 - accuracy: 0.0686
301/638 [=============>................] - ETA: 1:27 - loss: 4.3420 - accuracy: 0.0691
302/638 [=============>................] - ETA: 1:27 - loss: 4.3413 - accuracy: 0.0691
303/638 [=============>................] - ETA: 1:27 - loss: 4.3401 - accuracy: 0.0692
304/638 [=============>................] - ETA: 1:27 - loss: 4.3375 - accuracy: 0.0692
305/638 [=============>................] - ETA: 1:26 - loss: 4.3349 - accuracy: 0.0695
306/638 [=============>................] - ETA: 1:26 - loss: 4.3329 - accuracy: 0.0696
307/638 [=============>................] - ETA: 1:26 - loss: 4.3320 - accuracy: 0.0700
308/638 [=============>................] - ETA: 1:26 - loss: 4.3297 - accuracy: 0.0700
309/638 [=============>................] - ETA: 1:25 - loss: 4.3273 - accuracy: 0.0702
310/638 [=============>................] - ETA: 1:25 - loss: 4.3265 - accuracy: 0.0704
311/638 [=============>................] - ETA: 1:25 - loss: 4.3250 - accuracy: 0.0708
312/638 [=============>................] - ETA: 1:25 - loss: 4.3221 - accuracy: 0.0707
313/638 [=============>................] - ETA: 1:24 - loss: 4.3196 - accuracy: 0.0707
314/638 [=============>................] - ETA: 1:24 - loss: 4.3174 - accuracy: 0.0712
315/638 [=============>................] - ETA: 1:24 - loss: 4.3158 - accuracy: 0.0710
316/638 [=============>................] - ETA: 1:24 - loss: 4.3152 - accuracy: 0.0711
317/638 [=============>................] - ETA: 1:23 - loss: 4.3148 - accuracy: 0.0711
318/638 [=============>................] - ETA: 1:23 - loss: 4.3143 - accuracy: 0.0711
319/638 [==============>...............] - ETA: 1:23 - loss: 4.3135 - accuracy: 0.0713
320/638 [==============>...............] - ETA: 1:23 - loss: 4.3177 - accuracy: 0.0715
321/638 [==============>...............] - ETA: 1:22 - loss: 4.3159 - accuracy: 0.0713
322/638 [==============>...............] - ETA: 1:22 - loss: 4.3153 - accuracy: 0.0714
323/638 [==============>...............] - ETA: 1:22 - loss: 4.3141 - accuracy: 0.0715
324/638 [==============>...............] - ETA: 1:22 - loss: 4.3144 - accuracy: 0.0716
325/638 [==============>...............] - ETA: 1:21 - loss: 4.3143 - accuracy: 0.0715
326/638 [==============>...............] - ETA: 1:21 - loss: 4.3143 - accuracy: 0.0717
327/638 [==============>...............] - ETA: 1:21 - loss: 4.3136 - accuracy: 0.0718
328/638 [==============>...............] - ETA: 1:20 - loss: 4.3117 - accuracy: 0.0719
329/638 [==============>...............] - ETA: 1:20 - loss: 4.3104 - accuracy: 0.0723
330/638 [==============>...............] - ETA: 1:20 - loss: 4.3082 - accuracy: 0.0725
331/638 [==============>...............] - ETA: 1:20 - loss: 4.3070 - accuracy: 0.0726
332/638 [==============>...............] - ETA: 1:19 - loss: 4.3048 - accuracy: 0.0727
333/638 [==============>...............] - ETA: 1:19 - loss: 4.3032 - accuracy: 0.0728
334/638 [==============>...............] - ETA: 1:19 - loss: 4.3037 - accuracy: 0.0728
335/638 [==============>...............] - ETA: 1:19 - loss: 4.3021 - accuracy: 0.0729
336/638 [==============>...............] - ETA: 1:18 - loss: 4.2997 - accuracy: 0.0729
337/638 [==============>...............] - ETA: 1:18 - loss: 4.3002 - accuracy: 0.0729
338/638 [==============>...............] - ETA: 1:18 - loss: 4.3004 - accuracy: 0.0729
339/638 [==============>...............] - ETA: 1:18 - loss: 4.2990 - accuracy: 0.0729
340/638 [==============>...............] - ETA: 1:17 - loss: 4.2974 - accuracy: 0.0730
341/638 [===============>..............] - ETA: 1:17 - loss: 4.2976 - accuracy: 0.0729
342/638 [===============>..............] - ETA: 1:17 - loss: 4.2955 - accuracy: 0.0733
343/638 [===============>..............] - ETA: 1:17 - loss: 4.2938 - accuracy: 0.0733
344/638 [===============>..............] - ETA: 1:16 - loss: 4.2950 - accuracy: 0.0736
345/638 [===============>..............] - ETA: 1:16 - loss: 4.2920 - accuracy: 0.0740
346/638 [===============>..............] - ETA: 1:16 - loss: 4.2897 - accuracy: 0.0742
347/638 [===============>..............] - ETA: 1:16 - loss: 4.2882 - accuracy: 0.0741
348/638 [===============>..............] - ETA: 1:15 - loss: 4.2862 - accuracy: 0.0742
349/638 [===============>..............] - ETA: 1:15 - loss: 4.2856 - accuracy: 0.0744
350/638 [===============>..............] - ETA: 1:15 - loss: 4.2842 - accuracy: 0.0744
351/638 [===============>..............] - ETA: 1:15 - loss: 4.2843 - accuracy: 0.0743
352/638 [===============>..............] - ETA: 1:14 - loss: 4.2830 - accuracy: 0.0745
353/638 [===============>..............] - ETA: 1:14 - loss: 4.2823 - accuracy: 0.0745
354/638 [===============>..............] - ETA: 1:14 - loss: 4.2797 - accuracy: 0.0749
355/638 [===============>..............] - ETA: 1:14 - loss: 4.2786 - accuracy: 0.0747
356/638 [===============>..............] - ETA: 1:13 - loss: 4.2783 - accuracy: 0.0750
357/638 [===============>..............] - ETA: 1:13 - loss: 4.2793 - accuracy: 0.0749
358/638 [===============>..............] - ETA: 1:13 - loss: 4.2768 - accuracy: 0.0752
359/638 [===============>..............] - ETA: 1:13 - loss: 4.2753 - accuracy: 0.0751
360/638 [===============>..............] - ETA: 1:12 - loss: 4.2731 - accuracy: 0.0753
361/638 [===============>..............] - ETA: 1:12 - loss: 4.2718 - accuracy: 0.0755
362/638 [================>.............] - ETA: 1:12 - loss: 4.2709 - accuracy: 0.0754
363/638 [================>.............] - ETA: 1:12 - loss: 4.2696 - accuracy: 0.0755
364/638 [================>.............] - ETA: 1:11 - loss: 4.2685 - accuracy: 0.0756
365/638 [================>.............] - ETA: 1:11 - loss: 4.2705 - accuracy: 0.0757
366/638 [================>.............] - ETA: 1:11 - loss: 4.2684 - accuracy: 0.0756
367/638 [================>.............] - ETA: 1:11 - loss: 4.2672 - accuracy: 0.0760
368/638 [================>.............] - ETA: 1:10 - loss: 4.2640 - accuracy: 0.0762
369/638 [================>.............] - ETA: 1:10 - loss: 4.2607 - accuracy: 0.0767
370/638 [================>.............] - ETA: 1:10 - loss: 4.2602 - accuracy: 0.0769
371/638 [================>.............] - ETA: 1:10 - loss: 4.2579 - accuracy: 0.0773
372/638 [================>.............] - ETA: 1:09 - loss: 4.2575 - accuracy: 0.0774
373/638 [================>.............] - ETA: 1:09 - loss: 4.2561 - accuracy: 0.0774
374/638 [================>.............] - ETA: 1:09 - loss: 4.2563 - accuracy: 0.0775
375/638 [================>.............] - ETA: 1:08 - loss: 4.2537 - accuracy: 0.0776
376/638 [================>.............] - ETA: 1:08 - loss: 4.2518 - accuracy: 0.0776
377/638 [================>.............] - ETA: 1:08 - loss: 4.2511 - accuracy: 0.0775
378/638 [================>.............] - ETA: 1:08 - loss: 4.2500 - accuracy: 0.0774
379/638 [================>.............] - ETA: 1:07 - loss: 4.2493 - accuracy: 0.0773
380/638 [================>.............] - ETA: 1:07 - loss: 4.2474 - accuracy: 0.0776
381/638 [================>.............] - ETA: 1:07 - loss: 4.2472 - accuracy: 0.0776
382/638 [================>.............] - ETA: 1:07 - loss: 4.2465 - accuracy: 0.0778
383/638 [=================>............] - ETA: 1:06 - loss: 4.2441 - accuracy: 0.0778
384/638 [=================>............] - ETA: 1:06 - loss: 4.2418 - accuracy: 0.0779
385/638 [=================>............] - ETA: 1:06 - loss: 4.2408 - accuracy: 0.0779
386/638 [=================>............] - ETA: 1:06 - loss: 4.2394 - accuracy: 0.0778
387/638 [=================>............] - ETA: 1:05 - loss: 4.2387 - accuracy: 0.0778
388/638 [=================>............] - ETA: 1:05 - loss: 4.2396 - accuracy: 0.0778
389/638 [=================>............] - ETA: 1:05 - loss: 4.2389 - accuracy: 0.0779
390/638 [=================>............] - ETA: 1:05 - loss: 4.2376 - accuracy: 0.0780
391/638 [=================>............] - ETA: 1:04 - loss: 4.2358 - accuracy: 0.0782
392/638 [=================>............] - ETA: 1:04 - loss: 4.2349 - accuracy: 0.0783
393/638 [=================>............] - ETA: 1:04 - loss: 4.2331 - accuracy: 0.0784
394/638 [=================>............] - ETA: 1:04 - loss: 4.2312 - accuracy: 0.0786
395/638 [=================>............] - ETA: 1:03 - loss: 4.2289 - accuracy: 0.0789
396/638 [=================>............] - ETA: 1:03 - loss: 4.2266 - accuracy: 0.0792
397/638 [=================>............] - ETA: 1:03 - loss: 4.2253 - accuracy: 0.0792
398/638 [=================>............] - ETA: 1:02 - loss: 4.2241 - accuracy: 0.0792
399/638 [=================>............] - ETA: 1:02 - loss: 4.2214 - accuracy: 0.0796
400/638 [=================>............] - ETA: 1:02 - loss: 4.2215 - accuracy: 0.0795
401/638 [=================>............] - ETA: 1:02 - loss: 4.2204 - accuracy: 0.0793
402/638 [=================>............] - ETA: 1:01 - loss: 4.2196 - accuracy: 0.0795
403/638 [=================>............] - ETA: 1:01 - loss: 4.2175 - accuracy: 0.0798
404/638 [=================>............] - ETA: 1:01 - loss: 4.2153 - accuracy: 0.0803
405/638 [==================>...........] - ETA: 1:01 - loss: 4.2146 - accuracy: 0.0803
406/638 [==================>...........] - ETA: 1:00 - loss: 4.2129 - accuracy: 0.0806
407/638 [==================>...........] - ETA: 1:00 - loss: 4.2104 - accuracy: 0.0809
408/638 [==================>...........] - ETA: 1:00 - loss: 4.2100 - accuracy: 0.0807
409/638 [==================>...........] - ETA: 1:00 - loss: 4.2088 - accuracy: 0.0809
410/638 [==================>...........] - ETA: 59s - loss: 4.2067 - accuracy: 0.0809
411/638 [==================>...........] - ETA: 59s - loss: 4.2053 - accuracy: 0.0812
412/638 [==================>...........] - ETA: 59s - loss: 4.2042 - accuracy: 0.0811
413/638 [==================>...........] - ETA: 59s - loss: 4.2034 - accuracy: 0.0811
414/638 [==================>...........] - ETA: 58s - loss: 4.2017 - accuracy: 0.0813
415/638 [==================>...........] - ETA: 58s - loss: 4.1999 - accuracy: 0.0816
416/638 [==================>...........] - ETA: 58s - loss: 4.1987 - accuracy: 0.0817
417/638 [==================>...........] - ETA: 58s - loss: 4.1976 - accuracy: 0.0818
418/638 [==================>...........] - ETA: 57s - loss: 4.1968 - accuracy: 0.0820
419/638 [==================>...........] - ETA: 57s - loss: 4.1966 - accuracy: 0.0821
420/638 [==================>...........] - ETA: 57s - loss: 4.1962 - accuracy: 0.0821
421/638 [==================>...........] - ETA: 56s - loss: 4.1967 - accuracy: 0.0823
422/638 [==================>...........] - ETA: 56s - loss: 4.1976 - accuracy: 0.0823
423/638 [==================>...........] - ETA: 56s - loss: 4.1966 - accuracy: 0.0822
424/638 [==================>...........] - ETA: 56s - loss: 4.1961 - accuracy: 0.0823
425/638 [==================>...........] - ETA: 55s - loss: 4.1931 - accuracy: 0.0825
426/638 [===================>..........] - ETA: 55s - loss: 4.1918 - accuracy: 0.0826
427/638 [===================>..........] - ETA: 55s - loss: 4.1911 - accuracy: 0.0826
428/638 [===================>..........] - ETA: 55s - loss: 4.1895 - accuracy: 0.0827
429/638 [===================>..........] - ETA: 54s - loss: 4.1877 - accuracy: 0.0828
430/638 [===================>..........] - ETA: 54s - loss: 4.1862 - accuracy: 0.0830
431/638 [===================>..........] - ETA: 54s - loss: 4.1859 - accuracy: 0.0830
432/638 [===================>..........] - ETA: 54s - loss: 4.1858 - accuracy: 0.0830
433/638 [===================>..........] - ETA: 53s - loss: 4.1852 - accuracy: 0.0830
434/638 [===================>..........] - ETA: 53s - loss: 4.1837 - accuracy: 0.0832
435/638 [===================>..........] - ETA: 53s - loss: 4.1826 - accuracy: 0.0833
436/638 [===================>..........] - ETA: 53s - loss: 4.1808 - accuracy: 0.0832
437/638 [===================>..........] - ETA: 52s - loss: 4.1795 - accuracy: 0.0832
438/638 [===================>..........] - ETA: 52s - loss: 4.1784 - accuracy: 0.0834
439/638 [===================>..........] - ETA: 52s - loss: 4.1786 - accuracy: 0.0835
440/638 [===================>..........] - ETA: 52s - loss: 4.1776 - accuracy: 0.0834
441/638 [===================>..........] - ETA: 51s - loss: 4.1759 - accuracy: 0.0835
442/638 [===================>..........] - ETA: 51s - loss: 4.1743 - accuracy: 0.0838
443/638 [===================>..........] - ETA: 51s - loss: 4.1730 - accuracy: 0.0839
444/638 [===================>..........] - ETA: 50s - loss: 4.1716 - accuracy: 0.0838
445/638 [===================>..........] - ETA: 50s - loss: 4.1701 - accuracy: 0.0839
446/638 [===================>..........] - ETA: 50s - loss: 4.1688 - accuracy: 0.0839
447/638 [====================>.........] - ETA: 50s - loss: 4.1670 - accuracy: 0.0842
448/638 [====================>.........] - ETA: 49s - loss: 4.1667 - accuracy: 0.0842
449/638 [====================>.........] - ETA: 49s - loss: 4.1656 - accuracy: 0.0844
450/638 [====================>.........] - ETA: 49s - loss: 4.1646 - accuracy: 0.0843
451/638 [====================>.........] - ETA: 49s - loss: 4.1630 - accuracy: 0.0845
452/638 [====================>.........] - ETA: 48s - loss: 4.1625 - accuracy: 0.0846
453/638 [====================>.........] - ETA: 48s - loss: 4.1630 - accuracy: 0.0844
454/638 [====================>.........] - ETA: 48s - loss: 4.1619 - accuracy: 0.0844
455/638 [====================>.........] - ETA: 48s - loss: 4.1605 - accuracy: 0.0845
456/638 [====================>.........] - ETA: 47s - loss: 4.1587 - accuracy: 0.0846
457/638 [====================>.........] - ETA: 47s - loss: 4.1568 - accuracy: 0.0849
458/638 [====================>.........] - ETA: 47s - loss: 4.1553 - accuracy: 0.0849
459/638 [====================>.........] - ETA: 47s - loss: 4.1548 - accuracy: 0.0848
460/638 [====================>.........] - ETA: 46s - loss: 4.1534 - accuracy: 0.0847
461/638 [====================>.........] - ETA: 46s - loss: 4.1533 - accuracy: 0.0848
462/638 [====================>.........] - ETA: 46s - loss: 4.1526 - accuracy: 0.0850
463/638 [====================>.........] - ETA: 45s - loss: 4.1512 - accuracy: 0.0852
464/638 [====================>.........] - ETA: 45s - loss: 4.1516 - accuracy: 0.0854
465/638 [====================>.........] - ETA: 45s - loss: 4.1505 - accuracy: 0.0856
466/638 [====================>.........] - ETA: 45s - loss: 4.1505 - accuracy: 0.0859
467/638 [====================>.........] - ETA: 44s - loss: 4.1503 - accuracy: 0.0859
468/638 [=====================>........] - ETA: 44s - loss: 4.1511 - accuracy: 0.0858
469/638 [=====================>........] - ETA: 44s - loss: 4.1501 - accuracy: 0.0859
470/638 [=====================>........] - ETA: 44s - loss: 4.1492 - accuracy: 0.0863
471/638 [=====================>........] - ETA: 43s - loss: 4.1475 - accuracy: 0.0865
472/638 [=====================>........] - ETA: 43s - loss: 4.1460 - accuracy: 0.0867
473/638 [=====================>........] - ETA: 43s - loss: 4.1457 - accuracy: 0.0868
474/638 [=====================>........] - ETA: 43s - loss: 4.1434 - accuracy: 0.0870
475/638 [=====================>........] - ETA: 42s - loss: 4.1436 - accuracy: 0.0870
476/638 [=====================>........] - ETA: 42s - loss: 4.1416 - accuracy: 0.0873
477/638 [=====================>........] - ETA: 42s - loss: 4.1413 - accuracy: 0.0873
478/638 [=====================>........] - ETA: 42s - loss: 4.1392 - accuracy: 0.0876
479/638 [=====================>........] - ETA: 41s - loss: 4.1378 - accuracy: 0.0878
480/638 [=====================>........] - ETA: 41s - loss: 4.1363 - accuracy: 0.0880
481/638 [=====================>........] - ETA: 41s - loss: 4.1362 - accuracy: 0.0882
482/638 [=====================>........] - ETA: 40s - loss: 4.1345 - accuracy: 0.0884
483/638 [=====================>........] - ETA: 40s - loss: 4.1321 - accuracy: 0.0887
484/638 [=====================>........] - ETA: 40s - loss: 4.1318 - accuracy: 0.0888
485/638 [=====================>........] - ETA: 40s - loss: 4.1303 - accuracy: 0.0890
486/638 [=====================>........] - ETA: 39s - loss: 4.1288 - accuracy: 0.0891
487/638 [=====================>........] - ETA: 39s - loss: 4.1281 - accuracy: 0.0891
488/638 [=====================>........] - ETA: 39s - loss: 4.1278 - accuracy: 0.0893
489/638 [=====================>........] - ETA: 39s - loss: 4.1278 - accuracy: 0.0895
490/638 [======================>.......] - ETA: 38s - loss: 4.1258 - accuracy: 0.0898
491/638 [======================>.......] - ETA: 38s - loss: 4.1237 - accuracy: 0.0901
492/638 [======================>.......] - ETA: 38s - loss: 4.1234 - accuracy: 0.0901
493/638 [======================>.......] - ETA: 38s - loss: 4.1220 - accuracy: 0.0902
494/638 [======================>.......] - ETA: 37s - loss: 4.1211 - accuracy: 0.0901
495/638 [======================>.......] - ETA: 37s - loss: 4.1210 - accuracy: 0.0900
496/638 [======================>.......] - ETA: 37s - loss: 4.1204 - accuracy: 0.0900
497/638 [======================>.......] - ETA: 37s - loss: 4.1187 - accuracy: 0.0901
498/638 [======================>.......] - ETA: 36s - loss: 4.1179 - accuracy: 0.0902
499/638 [======================>.......] - ETA: 36s - loss: 4.1160 - accuracy: 0.0904
500/638 [======================>.......] - ETA: 36s - loss: 4.1157 - accuracy: 0.0906
501/638 [======================>.......] - ETA: 35s - loss: 4.1144 - accuracy: 0.0908
502/638 [======================>.......] - ETA: 35s - loss: 4.1132 - accuracy: 0.0908
503/638 [======================>.......] - ETA: 35s - loss: 4.1116 - accuracy: 0.0908
504/638 [======================>.......] - ETA: 35s - loss: 4.1113 - accuracy: 0.0908
505/638 [======================>.......] - ETA: 34s - loss: 4.1103 - accuracy: 0.0909
506/638 [======================>.......] - ETA: 34s - loss: 4.1095 - accuracy: 0.0909
507/638 [======================>.......] - ETA: 34s - loss: 4.1073 - accuracy: 0.0912
508/638 [======================>.......] - ETA: 34s - loss: 4.1059 - accuracy: 0.0914
509/638 [======================>.......] - ETA: 33s - loss: 4.1051 - accuracy: 0.0915
510/638 [======================>.......] - ETA: 33s - loss: 4.1037 - accuracy: 0.0917
511/638 [=======================>......] - ETA: 33s - loss: 4.1021 - accuracy: 0.0919
512/638 [=======================>......] - ETA: 33s - loss: 4.1019 - accuracy: 0.0920
513/638 [=======================>......] - ETA: 32s - loss: 4.1004 - accuracy: 0.0922
514/638 [=======================>......] - ETA: 32s - loss: 4.0989 - accuracy: 0.0924
515/638 [=======================>......] - ETA: 32s - loss: 4.0979 - accuracy: 0.0924
516/638 [=======================>......] - ETA: 32s - loss: 4.0981 - accuracy: 0.0924
517/638 [=======================>......] - ETA: 31s - loss: 4.0982 - accuracy: 0.0928
518/638 [=======================>......] - ETA: 31s - loss: 4.0980 - accuracy: 0.0928
519/638 [=======================>......] - ETA: 31s - loss: 4.0964 - accuracy: 0.0931
520/638 [=======================>......] - ETA: 30s - loss: 4.0944 - accuracy: 0.0931
521/638 [=======================>......] - ETA: 30s - loss: 4.0925 - accuracy: 0.0932
522/638 [=======================>......] - ETA: 30s - loss: 4.0912 - accuracy: 0.0934
523/638 [=======================>......] - ETA: 30s - loss: 4.0902 - accuracy: 0.0934
524/638 [=======================>......] - ETA: 29s - loss: 4.0906 - accuracy: 0.0933
525/638 [=======================>......] - ETA: 29s - loss: 4.0907 - accuracy: 0.0933
526/638 [=======================>......] - ETA: 29s - loss: 4.0885 - accuracy: 0.0936
527/638 [=======================>......] - ETA: 29s - loss: 4.0878 - accuracy: 0.0937
528/638 [=======================>......] - ETA: 28s - loss: 4.0879 - accuracy: 0.0937
529/638 [=======================>......] - ETA: 28s - loss: 4.0858 - accuracy: 0.0940
530/638 [=======================>......] - ETA: 28s - loss: 4.0839 - accuracy: 0.0945
531/638 [=======================>......] - ETA: 28s - loss: 4.0830 - accuracy: 0.0944
532/638 [========================>.....] - ETA: 27s - loss: 4.0814 - accuracy: 0.0946
533/638 [========================>.....] - ETA: 27s - loss: 4.0802 - accuracy: 0.0946
534/638 [========================>.....] - ETA: 27s - loss: 4.0789 - accuracy: 0.0950
532025-04-09 18:19:17.520753: W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: known incorrect sRGB profile
+5/638 [========================>.....] - ETA: 27s - loss: 4.0773 - accuracy: 0.0954
536/638 [========================>.....] - ETA: 26s - loss: 4.0754 - accuracy: 0.0956
537/638 [========================>.....] - ETA: 26s - loss: 4.0759 - accuracy: 0.0955
538/638 [========================>.....] - ETA: 26s - loss: 4.0753 - accuracy: 0.0955
539/638 [========================>.....] - ETA: 25s - loss: 4.0733 - accuracy: 0.0958
540/638 [========================>.....] - ETA: 25s - loss: 4.0725 - accuracy: 0.0959
541/638 [========================>.....] - ETA: 25s - loss: 4.0718 - accuracy: 0.0961
542/638 [========================>.....] - ETA: 25s - loss: 4.0713 - accuracy: 0.0960
543/638 [========================>.....] - ETA: 24s - loss: 4.0695 - accuracy: 0.0963
544/638 [========================>.....] - ETA: 24s - loss: 4.0685 - accuracy: 0.0964
545/638 [========================>.....] - ETA: 24s - loss: 4.0690 - accuracy: 0.0963
546/638 [========================>.....] - ETA: 24s - loss: 4.0688 - accuracy: 0.0967
547/638 [========================>.....] - ETA: 23s - loss: 4.0681 - accuracy: 0.0968
548/638 [========================>.....] - ETA: 23s - loss: 4.0670 - accuracy: 0.0967
549/638 [========================>.....] - ETA: 23s - loss: 4.0659 - accuracy: 0.0967
550/638 [========================>.....] - ETA: 23s - loss: 4.0650 - accuracy: 0.0967
551/638 [========================>.....] - ETA: 22s - loss: 4.0628 - accuracy: 0.0969
552/638 [========================>.....] - ETA: 22s - loss: 4.0614 - accuracy: 0.0970
553/638 [=========================>....] - ETA: 22s - loss: 4.0609 - accuracy: 0.0970
554/638 [=========================>....] - ETA: 22s - loss: 4.0588 - accuracy: 0.0971
555/638 [=========================>....] - ETA: 21s - loss: 4.0575 - accuracy: 0.0972
556/638 [=========================>....] - ETA: 21s - loss: 4.0565 - accuracy: 0.0973
557/638 [=========================>....] - ETA: 21s - loss: 4.0558 - accuracy: 0.0976
558/638 [=========================>....] - ETA: 21s - loss: 4.0540 - accuracy: 0.0978
559/638 [=========================>....] - ETA: 20s - loss: 4.0531 - accuracy: 0.0980
560/638 [=========================>....] - ETA: 20s - loss: 4.0522 - accuracy: 0.0983
561/638 [=========================>....] - ETA: 20s - loss: 4.0512 - accuracy: 0.0983
562/638 [=========================>....] - ETA: 19s - loss: 4.0499 - accuracy: 0.0985
563/638 [=========================>....] - ETA: 19s - loss: 4.0511 - accuracy: 0.0985
564/638 [=========================>....] - ETA: 19s - loss: 4.0507 - accuracy: 0.0985
565/638 [=========================>....] - ETA: 19s - loss: 4.0491 - accuracy: 0.0985
566/638 [=========================>....] - ETA: 18s - loss: 4.0498 - accuracy: 0.0984
567/638 [=========================>....] - ETA: 18s - loss: 4.0492 - accuracy: 0.0985
568/638 [=========================>....] - ETA: 18s - loss: 4.0470 - accuracy: 0.0987
569/638 [=========================>....] - ETA: 18s - loss: 4.0454 - accuracy: 0.0989
570/638 [=========================>....] - ETA: 17s - loss: 4.0438 - accuracy: 0.0992
571/638 [=========================>....] - ETA: 17s - loss: 4.0432 - accuracy: 0.0992
572/638 [=========================>....] - ETA: 17s - loss: 4.0424 - accuracy: 0.0992
573/638 [=========================>....] - ETA: 17s - loss: 4.0410 - accuracy: 0.0995
574/638 [=========================>....] - ETA: 16s - loss: 4.0387 - accuracy: 0.0999
575/638 [==========================>...] - ETA: 16s - loss: 4.0384 - accuracy: 0.1001
576/638 [==========================>...] - ETA: 16s - loss: 4.0369 - accuracy: 0.1002
577/638 [==========================>...] - ETA: 16s - loss: 4.0356 - accuracy: 0.1004
578/638 [==========================>...] - ETA: 15s - loss: 4.0352 - accuracy: 0.1003
579/638 [==========================>...] - ETA: 15s - loss: 4.0336 - accuracy: 0.1005
580/638 [==========================>...] - ETA: 15s - loss: 4.0319 - accuracy: 0.1008
581/638 [==========================>...] - ETA: 15s - loss: 4.0313 - accuracy: 0.1006
582/638 [==========================>...] - ETA: 14s - loss: 4.0307 - accuracy: 0.1006
583/638 [==========================>...] - ETA: 14s - loss: 4.0295 - accuracy: 0.1008
584/638 [==========================>...] - ETA: 14s - loss: 4.0293 - accuracy: 0.1008
585/638 [==========================>...] - ETA: 13s - loss: 4.0290 - accuracy: 0.1008
586/638 [==========================>...] - ETA: 13s - loss: 4.0278 - accuracy: 0.1011
587/638 [==========================>...] - ETA: 13s - loss: 4.0265 - accuracy: 0.1011
588/638 [==========================>...] - ETA: 13s - loss: 4.0264 - accuracy: 0.1013
589/638 [==========================>...] - ETA: 12s - loss: 4.0257 - accuracy: 0.1015
590/638 [==========================>...] - ETA: 12s - loss: 4.0242 - accuracy: 0.1017
591/638 [==========================>...] - ETA: 12s - loss: 4.0232 - accuracy: 0.1019
592/638 [==========================>...] - ETA: 12s - loss: 4.0224 - accuracy: 0.1021
593/638 [==========================>...] - ETA: 11s - loss: 4.0223 - accuracy: 0.1021
594/638 [==========================>...] - ETA: 11s - loss: 4.0206 - accuracy: 0.1021
595/638 [==========================>...] - ETA: 11s - loss: 4.0202 - accuracy: 0.1023
596/638 [===========================>..] - ETA: 11s - loss: 4.0202 - accuracy: 0.1025
597/638 [===========================>..] - ETA: 10s - loss: 4.0186 - accuracy: 0.1025
598/638 [===========================>..] - ETA: 10s - loss: 4.0172 - accuracy: 0.1026
599/638 [===========================>..] - ETA: 10s - loss: 4.0163 - accuracy: 0.1026
600/638 [===========================>..] - ETA: 10s - loss: 4.0160 - accuracy: 0.1027
601/638 [===========================>..] - ETA: 9s - loss: 4.0139 - accuracy: 0.1028
602/638 [===========================>..] - ETA: 9s - loss: 4.0124 - accuracy: 0.1029
603/638 [===========================>..] - ETA: 9s - loss: 4.0113 - accuracy: 0.1031
604/638 [===========================>..] - ETA: 8s - loss: 4.0109 - accuracy: 0.1031
605/638 [===========================>..] - ETA: 8s - loss: 4.0094 - accuracy: 0.1032
606/638 [===========================>..] - ETA: 8s - loss: 4.0085 - accuracy: 0.1035
607/638 [===========================>..] - ETA: 8s - loss: 4.0085 - accuracy: 0.1035
608/638 [===========================>..] - ETA: 7s - loss: 4.0073 - accuracy: 0.1038
609/638 [===========================>..] - ETA: 7s - loss: 4.0070 - accuracy: 0.1038
610/638 [===========================>..] - ETA: 7s - loss: 4.0054 - accuracy: 0.1039
611/638 [===========================>..] - ETA: 7s - loss: 4.0040 - accuracy: 0.1040
612/638 [===========================>..] - ETA: 6s - loss: 4.0044 - accuracy: 0.1041
613/638 [===========================>..] - ETA: 6s - loss: 4.0036 - accuracy: 0.1043
614/638 [===========================>..] - ETA: 6s - loss: 4.0027 - accuracy: 0.1043
615/638 [===========================>..] - ETA: 6s - loss: 4.0026 - accuracy: 0.1043
616/638 [===========================>..] - ETA: 5s - loss: 4.0011 - accuracy: 0.1043
617/638 [============================>.] - ETA: 5s - loss: 3.9999 - accuracy: 0.1045
618/638 [============================>.] - ETA: 5s - loss: 3.9996 - accuracy: 0.1044
619/638 [============================>.] - ETA: 4s - loss: 3.9985 - accuracy: 0.1045
620/638 [============================>.] - ETA: 4s - loss: 3.9991 - accuracy: 0.1045
621/638 [============================>.] - ETA: 4s - loss: 3.9977 - accuracy: 0.1044
622/638 [============================>.] - ETA: 4s - loss: 3.9977 - accuracy: 0.1045
623/638 [============================>.] - ETA: 3s - loss: 3.9977 - accuracy: 0.1046
624/638 [============================>.] - ETA: 3s - loss: 3.9974 - accuracy: 0.1048
625/638 [============================>.] - ETA: 3s - loss: 3.9975 - accuracy: 0.1049
626/638 [============================>.] - ETA: 3s - loss: 3.9975 - accuracy: 0.1048
627/638 [============================>.] - ETA: 2s - loss: 3.9958 - accuracy: 0.1052
628/638 [============================>.] - ETA: 2s - loss: 3.9949 - accuracy: 0.1052
629/638 [============================>.] - ETA: 2s - loss: 3.9937 - accuracy: 0.1053
630/638 [============================>.] - ETA: 2s - loss: 3.9925 - accuracy: 0.1054
631/638 [============================>.] - ETA: 1s - loss: 3.9908 - accuracy: 0.1055
632/638 [============================>.] - ETA: 1s - loss: 3.9903 - accuracy: 0.1056
633/638 [============================>.] - ETA: 1s - loss: 3.9893 - accuracy: 0.1057
634/638 [============================>.] - ETA: 1s - loss: 3.9884 - accuracy: 0.1058
635/638 [============================>.] - ETA: 0s - loss: 3.9872 - accuracy: 0.1059
636/638 [============================>.] - ETA: 0s - loss: 3.9864 - accuracy: 0.1060
637/638 [============================>.2025-04-09 18:19:46.595154: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:784] AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy because of the following reason: Found an unshardable source dataset: name: "TensorSliceDataset/_1"
+op: "TensorSliceDataset"
+input: "Placeholder/_0"
+attr {
+ key: "Toutput_types"
+ value {
+ list {
+ type: DT_STRING
+ }
+ }
+}
+attr {
+ key: "_cardinality"
+ value {
+ i: 5102
+ }
+}
+attr {
+ key: "is_files"
+ value {
+ b: false
+ }
+}
+attr {
+ key: "metadata"
+ value {
+ s: "\n\024TensorSliceDataset:7"
+ }
+}
+attr {
+ key: "output_shapes"
+ value {
+ list {
+ shape {
+ }
+ }
+ }
+}
+attr {
+ key: "replicate_on_split"
+ value {
+ b: false
+ }
+}
+experimental_type {
+ type_id: TFT_PRODUCT
+ args {
+ type_id: TFT_DATASET
+ args {
+ type_id: TFT_PRODUCT
+ args {
+ type_id: TFT_TENSOR
+ args {
+ type_id: TFT_STRING
+ }
+ }
+ }
+ }
+}
+
+] - ETA: 0s - loss: 3.9850 - accuracy: 0.1061
638/638 [==============================] - ETA: 0s - loss: 3.9849 - accuracy: 0.1061
638/638 [==============================] - 214s 307ms/step - loss: 3.9849 - accuracy: 0.1061 - val_loss: 4.0034 - val_accuracy: 0.1015
+Epoch 2/20
+
1/638 [..............................] - ETA: 9:35 - loss: 3.5540 - accuracy: 0.2188
2/638 [..............................] - ETA: 3:07 - loss: 3.5350 - accuracy: 0.1406
3/638 [..............................] - ETA: 3:05 - loss: 3.3892 - accuracy: 0.1354
4/638 [..............................] - ETA: 3:07 - loss: 3.3924 - accuracy: 0.1562
5/638 [..............................] - ETA: 3:04 - loss: 3.4678 - accuracy: 0.1688
6/638 [..............................] - ETA: 2:59 - loss: 3.4290 - accuracy: 0.1823
7/638 [..............................] - ETA: 2:56 - loss: 3.5196 - accuracy: 0.1696
8/638 [..............................] - ETA: 2:54 - loss: 3.4554 - accuracy: 0.1602
9/638 [..............................] - ETA: 3:15 - loss: 3.4445 - accuracy: 0.1632
10/638 [..............................] - ETA: 3:11 - loss: 3.4641 - accuracy: 0.1688
11/638 [..............................] - ETA: 3:16 - loss: 3.4603 - accuracy: 0.1676
12/638 [..............................] - ETA: 3:18 - loss: 3.4314 - accuracy: 0.1771
13/638 [..............................] - ETA: 3:14 - loss: 3.4881 - accuracy: 0.1779
14/638 [..............................] - ETA: 3:13 - loss: 3.5081 - accuracy: 0.1741
15/638 [..............................] - ETA: 3:11 - loss: 3.5045 - accuracy: 0.1729
16/638 [..............................] - ETA: 3:08 - loss: 3.5027 - accuracy: 0.1777
17/638 [..............................] - ETA: 3:08 - loss: 3.4930 - accuracy: 0.1820
18/638 [..............................] - ETA: 3:06 - loss: 3.4796 - accuracy: 0.1788
19/638 [..............................] - ETA: 3:04 - loss: 3.4973 - accuracy: 0.1776
20/638 [..............................] - ETA: 3:03 - loss: 3.4821 - accuracy: 0.1781
21/638 [..............................] - ETA: 3:01 - loss: 3.4756 - accuracy: 0.1786
22/638 [>.............................] - ETA: 3:00 - loss: 3.4676 - accuracy: 0.1776
23/638 [>.............................] - ETA: 2:59 - loss: 3.4938 - accuracy: 0.1726
24/638 [>.............................] - ETA: 2:58 - loss: 3.5010 - accuracy: 0.1732
25/638 [>.............................] - ETA: 2:57 - loss: 3.5025 - accuracy: 0.1700
26/638 [>.............................] - ETA: 2:57 - loss: 3.4877 - accuracy: 0.1671
27/638 [>.............................] - ETA: 2:56 - loss: 3.4575 - accuracy: 0.1759
28/638 [>.............................] - ETA: 2:55 - loss: 3.4444 - accuracy: 0.1730
29/638 [>.............................] - ETA: 2:54 - loss: 3.4284 - accuracy: 0.1756
30/638 [>.............................] - ETA: 2:54 - loss: 3.4387 - accuracy: 0.1740
31/638 [>.............................] - ETA: 2:53 - loss: 3.4542 - accuracy: 0.1754
32/638 [>.............................] - ETA: 2:52 - loss: 3.4632 - accuracy: 0.1748
33/638 [>.............................] - ETA: 2:54 - loss: 3.4725 - accuracy: 0.1752
34/638 [>.............................] - ETA: 2:54 - loss: 3.4688 - accuracy: 0.1737
35/638 [>.............................] - ETA: 2:53 - loss: 3.4536 - accuracy: 0.1768
36/638 [>.............................] - ETA: 2:52 - loss: 3.4438 - accuracy: 0.1753
37/638 [>.............................] - ETA: 2:51 - loss: 3.4313 - accuracy: 0.1824
38/638 [>.............................] - ETA: 2:50 - loss: 3.4356 - accuracy: 0.1826
39/638 [>.............................] - ETA: 2:50 - loss: 3.4525 - accuracy: 0.1827
40/638 [>.............................] - ETA: 2:49 - loss: 3.4402 - accuracy: 0.1852
41/638 [>.............................] - ETA: 2:49 - loss: 3.4516 - accuracy: 0.1852
42/638 [>.............................] - ETA: 2:48 - loss: 3.4436 - accuracy: 0.1853
43/638 [=>............................] - ETA: 2:47 - loss: 3.4370 - accuracy: 0.1846
44/638 [=>............................] - ETA: 2:47 - loss: 3.4387 - accuracy: 0.1868
45/638 [=>............................] - ETA: 2:46 - loss: 3.4353 - accuracy: 0.1896
46/638 [=>............................] - ETA: 2:45 - loss: 3.4485 - accuracy: 0.1875
47/638 [=>............................] - ETA: 2:45 - loss: 3.4363 - accuracy: 0.1868
48/638 [=>............................] - ETA: 2:44 - loss: 3.4435 - accuracy: 0.1849
49/638 [=>............................] - ETA: 2:44 - loss: 3.4460 - accuracy: 0.1849
50/638 [=>............................] - ETA: 2:44 - loss: 3.4360 - accuracy: 0.1869
51/638 [=>............................] - ETA: 2:44 - loss: 3.4362 - accuracy: 0.1857
52/638 [=>............................] - ETA: 2:44 - loss: 3.4324 - accuracy: 0.1881
53/638 [=>............................] - ETA: 2:43 - loss: 3.4325 - accuracy: 0.1869
54/638 [=>............................] - ETA: 2:43 - loss: 3.4434 - accuracy: 0.1840
55/638 [=>............................] - ETA: 2:43 - loss: 3.4383 - accuracy: 0.1841
56/638 [=>............................] - ETA: 2:42 - loss: 3.4272 - accuracy: 0.1858
57/638 [=>............................] - ETA: 2:42 - loss: 3.4318 - accuracy: 0.1842
58/638 [=>............................] - ETA: 2:42 - loss: 3.4262 - accuracy: 0.1853
59/638 [=>............................] - ETA: 2:42 - loss: 3.4196 - accuracy: 0.1843
60/638 [=>............................] - ETA: 2:42 - loss: 3.4292 - accuracy: 0.1828
61/638 [=>............................] - ETA: 2:41 - loss: 3.4300 - accuracy: 0.1819
62/638 [=>............................] - ETA: 2:42 - loss: 3.4265 - accuracy: 0.1815
63/638 [=>............................] - ETA: 2:42 - loss: 3.4284 - accuracy: 0.1806
64/638 [==>...........................] - ETA: 2:42 - loss: 3.4344 - accuracy: 0.1797
65/638 [==>...........................] - ETA: 2:41 - loss: 3.4470 - accuracy: 0.1798
66/638 [==>...........................] - ETA: 2:41 - loss: 3.4426 - accuracy: 0.1804
67/638 [==>...........................] - ETA: 2:40 - loss: 3.4319 - accuracy: 0.1814
68/638 [==>...........................] - ETA: 2:40 - loss: 3.4274 - accuracy: 0.1824
69/638 [==>...........................] - ETA: 2:40 - loss: 3.4222 - accuracy: 0.1816
70/638 [==>...........................] - ETA: 2:40 - loss: 3.4183 - accuracy: 0.1835
71/638 [==>...........................] - ETA: 2:39 - loss: 3.4153 - accuracy: 0.1818
72/638 [==>...........................] - ETA: 2:39 - loss: 3.4119 - accuracy: 0.1814
73/638 [==>...........................] - ETA: 2:39 - loss: 3.4205 - accuracy: 0.1819
74/638 [==>...........................] - ETA: 2:39 - loss: 3.4235 - accuracy: 0.1824
75/638 [==>...........................] - ETA: 2:38 - loss: 3.4219 - accuracy: 0.1829
76/638 [==>...........................] - ETA: 2:39 - loss: 3.4201 - accuracy: 0.1809
77/638 [==>...........................] - ETA: 2:39 - loss: 3.4234 - accuracy: 0.1802
78/638 [==>...........................] - ETA: 2:38 - loss: 3.4252 - accuracy: 0.1803
79/638 [==>...........................] - ETA: 2:38 - loss: 3.4272 - accuracy: 0.1804
80/638 [==>...........................] - ETA: 2:38 - loss: 3.4285 - accuracy: 0.1809
81/638 [==>...........................] - ETA: 2:37 - loss: 3.4192 - accuracy: 0.1825
82/638 [==>...........................] - ETA: 2:37 - loss: 3.4180 - accuracy: 0.1833
83/638 [==>...........................] - ETA: 2:37 - loss: 3.4117 - accuracy: 0.1834
84/638 [==>...........................] - ETA: 2:36 - loss: 3.4154 - accuracy: 0.1842
85/638 [==>...........................] - ETA: 2:36 - loss: 3.4148 - accuracy: 0.1835
86/638 [===>..........................] - ETA: 2:36 - loss: 3.4138 - accuracy: 0.1831
87/638 [===>..........................] - ETA: 2:35 - loss: 3.4093 - accuracy: 0.1839
88/638 [===>..........................] - ETA: 2:35 - loss: 3.4039 - accuracy: 0.1847
89/638 [===>..........................] - ETA: 2:35 - loss: 3.4032 - accuracy: 0.1847
\ No newline at end of file