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Alpha b #225

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merged 10 commits into from
Mar 19, 2024
Merged

Alpha b #225

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aeadc11
Merge pull request #218 from Aydinhamedi/Alpha-b
Aydinhamedi Mar 19, 2024
c8e8a3c
Merge branch 'main' into Beta-b
Aydinhamedi Mar 19, 2024
a4dba39
new file: ruff_check_results.txt
Aydinhamedi Mar 19, 2024
f64b484
deleted: ruff_check_results.txt
Aydinhamedi Mar 19, 2024
c19c304
Merge branches 'Beta-b' and 'Beta-b' of https://github.com/Aydinhamed…
Aydinhamedi Mar 19, 2024
7d08ad2
modified: BETA_E_Model_T&T.ipynb
Aydinhamedi Mar 19, 2024
64e2ac2
modified: BETA_E_Model_T&T.ipynb
Aydinhamedi Mar 19, 2024
4b37954
modified: BETA_E_Model_T&T.ipynb
Aydinhamedi Mar 19, 2024
8fc3120
Merge branch 'Alpha-b' of https://github.com/Aydinhamedi/Pneumonia-De…
Aydinhamedi Mar 19, 2024
dda68fe
Merge pull request #224 from Aydinhamedi/Beta-b
Aydinhamedi Mar 19, 2024
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4 changes: 2 additions & 2 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -100,5 +100,5 @@ Samples/*

# Exclude backup load
/Backup_*_COPY.ipynb
/ruff_check_results.txt
/ruff_format_results.txt
/ruff_check_results.txt
/ruff_format_results.txt
34 changes: 17 additions & 17 deletions BETA_E_Model_T&T.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -83,8 +83,8 @@
"import tensorflow as tf\n",
"from keras.models import Model\n",
"import matplotlib.pyplot as plt\n",
"from keras.optimizers import * # noqa: F403\n",
"from adabelief_tf import AdaBeliefOptimizer # noqa: F401\n",
"from keras.optimizers import * # noqa: F403\n",
"from adabelief_tf import AdaBeliefOptimizer # noqa: F401\n",
"\n",
"# from tensorflow_addons.optimizers import Yogi\n",
"from imblearn.over_sampling import SMOTE\n",
Expand Down Expand Up @@ -837,7 +837,7 @@
"model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n",
"\n",
"# compile model\n",
"opt = SGD(momentum=0.9)\n",
"opt = SGD(momentum=0.9) # noqa: F405\n",
"# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001)\n",
"# opt = Adam()\n",
"model.compile(optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
Expand Down Expand Up @@ -880,7 +880,7 @@
"model = Model(inputs=ConvNeXtLarge_M.inputs, outputs=ConvNeXtLarge_M.outputs)\n",
"\n",
"# compile model\n",
"opt = SGD(momentum=0.9)\n",
"opt = SGD(momentum=0.9) # noqa: F405\n",
"# opt = SGD(learning_rate=0.008, momentum=0.85, decay=0.001)\n",
"# opt = Adam()\n",
"model.compile(optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
Expand Down Expand Up @@ -3128,7 +3128,7 @@
" # opt = Adamax() # noqa: F405\n",
" # opt = RMSprop(momentum=0.9) # noqa: F405\n",
" # opt = Adagrad() # noqa: F405\n",
" opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, amsgrad=False) # noqa: F405\n",
" opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, amsgrad=False) # noqa: F405\n",
" # opt = Yogi() # noqa: F405\n",
" model_EfficientNetB7_NS.compile(\n",
" optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n",
Expand Down Expand Up @@ -3228,7 +3228,7 @@
" print(\"Total model layers: \", len(combo_model.layers))\n",
"\n",
" # OPT/compile\n",
" opt = SGD(momentum=0.9)\n",
" opt = SGD(momentum=0.9) # noqa: F405\n",
" combo_model.compile(optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
"\n",
" return combo_model\n",
Expand Down Expand Up @@ -7425,7 +7425,7 @@
"model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n",
"\n",
"# compile model\n",
"opt = SGD(momentum=0.9)\n",
"opt = SGD(momentum=0.9) # noqa: F405\n",
"# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False)\n",
"# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n",
"# opt = Adam()\n",
Expand Down Expand Up @@ -8764,7 +8764,7 @@
" model_EfficientNetB4_NS = Model(inputs=base_model.input, outputs=predictions)\n",
" print(\"Total model layers: \", len(model_EfficientNetB4_NS.layers))\n",
" # OPT/compile\n",
" opt = SGD(momentum=0.95, nesterov=False)\n",
" opt = SGD(momentum=0.95, nesterov=False) # noqa: F405\n",
" # opt = Nadam()\n",
" # opt = Adamax()\n",
" # opt = RMSprop(momentum=0.9)\n",
Expand Down Expand Up @@ -8851,7 +8851,7 @@
" model_EfficientNetB0_NS = Model(inputs=base_model.input, outputs=predictions)\n",
" print(\"Total model layers: \", len(model_EfficientNetB0_NS.layers))\n",
" # OPT/compile\n",
" opt = SGD(momentum=0.92, nesterov=False)\n",
" opt = SGD(momentum=0.92, nesterov=False) # noqa: F405\n",
" # opt = Nadam()\n",
" # opt = Adamax()\n",
" # opt = RMSprop(momentum=0.9)\n",
Expand Down Expand Up @@ -8923,7 +8923,7 @@
" model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions)\n",
" print(\"Total model layers: \", len(model_EfficientNetB7_NS.layers))\n",
" # OPT/compile\n",
" opt = SGD(momentum=0.9)\n",
" opt = SGD(momentum=0.9) # noqa: F405\n",
" # opt = Yogi()\n",
" model_EfficientNetB7_NS.compile(optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
"\n",
Expand Down Expand Up @@ -8998,7 +8998,7 @@
" model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions)\n",
" print(\"Total model layers: \", len(model_EfficientNetB7_NS.layers))\n",
" # OPT/compile\n",
" opt = SGD(momentum=0.9, nesterov=False)\n",
" opt = SGD(momentum=0.9, nesterov=False) # noqa: F405\n",
" # opt = Nadam()\n",
" # opt = Adamax()\n",
" # opt = RMSprop(momentum=0.9)\n",
Expand Down Expand Up @@ -9063,7 +9063,7 @@
" model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions)\n",
" print(\"Total model layers: \", len(model_EfficientNetB7_NS.layers))\n",
" # OPT/compile\n",
" opt = SGD(momentum=0.9)\n",
" opt = SGD(momentum=0.9) # noqa: F405\n",
" # opt = Yogi()\n",
" model_EfficientNetB7_NS.compile(optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
"\n",
Expand Down Expand Up @@ -9776,7 +9776,7 @@
" model_EfficientNetB4_NS = Model(inputs=base_model.input, outputs=predictions)\n",
" print(\"Total model layers: \", len(model_EfficientNetB4_NS.layers))\n",
" # OPT/compile\n",
" opt = SGD(momentum=0.92, nesterov=False)\n",
" opt = SGD(momentum=0.92, nesterov=False) # noqa: F405\n",
" # opt = Nadam()\n",
" # opt = Adamax()\n",
" # opt = RMSprop(momentum=0.9)\n",
Expand Down Expand Up @@ -9817,7 +9817,7 @@
"gc.collect()\n",
"tf.keras.backend.clear_session()\n",
"# CONF/Other\n",
"LRF_OPT = SGD(momentum=0.9)\n",
"LRF_OPT = SGD(momentum=0.9) # noqa: F405\n",
"LFR_batch_size = 1 # or any other batch size that fits in your memory\n",
"LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size)\n",
"# Instantiate LrFinder\n",
Expand Down Expand Up @@ -9875,7 +9875,7 @@
"# CEC_opt = Adagrad()\n",
"# CEC_opt = Yogi()\n",
"# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n",
"CEC_opt = SGD(momentum=0.9, nesterov=False)\n",
"CEC_opt = SGD(momentum=0.9, nesterov=False) # noqa: F405\n",
"# CEC_opt = Adam()\n",
"# Main\n",
"try:\n",
Expand Down Expand Up @@ -9986,7 +9986,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T07:04:23.573633300Z",
Expand All @@ -10001,7 +10001,7 @@
"Training the model...\n",
"\u001b[0;33m\n",
"Setup Verbose:\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m03_d19-h21_m07_s37]\u001b[0m\u001b[0;36m...\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m03_d19-h21_m24_s12]\u001b[0m\u001b[0;36m...\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mUse_OneCycleLr \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
Expand Down
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