move from .h5 model format to .keras format

haverland · Dec 22, 2024 · d2c44cb · d2c44cb
1 parent f1821b5
commit d2c44cb
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Showing 4 changed files with 250 additions and 608 deletions.
diff --git a/dig-class100-s2.ipynb b/dig-class100-s2.ipynb
diff --git a/dig-class100-s2_bw.ipynb b/dig-class100-s2_bw.ipynb
@@ -975,7 +975,7 @@
     "\n",
     "#annealer = LearningRateScheduler(lambda x: 1e-3 * 0.98 ** x, verbose=0)\n",
     "earlystop = EarlyStopping(monitor='val_loss', mode='min' , patience=25, verbose=1,  restore_best_weights=True)\n",
-    "mc = ModelCheckpoint(output_tmp_dir + model_filename + '_best.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True, initial_value_threshold=.9)\n",
+    "mc = ModelCheckpoint(output_tmp_dir + model_filename + '_best.keras', monitor='val_loss', mode='min', verbose=1, save_best_only=True, initial_value_threshold=.9)\n",
     "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.85, patience=3, min_lr=1e-5, verbose=1)\n",
     "history = model.fit(train_iterator, \n",
     "                validation_data=validation_iterator, \n",
@@ -985,7 +985,7 @@
     "                verbose=1)\n",
     "\n",
     "\n",
-    "#model.save(model_filename + '.h5')\n",
+    "#model.save(model_filename + '.keras')\n",
     "\n",
     "plot_acc_loss(history, \"Trainingsresults\")"
    ]
@@ -1039,7 +1039,7 @@
     }
    ],
    "source": [
-    "model = keras.models.load_model(output_tmp_dir + model_filename + '_best.h5')\n",
+    "model = keras.models.load_model(output_tmp_dir + model_filename + '_best.keras')\n",
     "false_predicted_files = predict_meter_digits(model, x_data=xz_data, y_data=yz_data, f_data=fz_data, \n",
     "                                            max_delta=0.11)\n",
     "fpf = pd.DataFrame(false_predicted_files)\n",
@@ -1143,7 +1143,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.10 ('mlp')",
+   "display_name": "mlp",
    "language": "python",
    "name": "python3"
   },
@@ -1170,11 +1170,6 @@
    "parameters": {},
    "start_time": "2022-05-05T11:23:55.373564",
    "version": "2.3.4"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "4976e0179d97dd6d59b1329a76e601e17b789c2571b41c8b57f5fd69821c0dd3"
-   }
   }
  },
  "nbformat": 4,

diff --git a/dig-class100_s0.ipynb b/dig-class100_s0.ipynb
@@ -392,7 +392,7 @@
     "\n",
     "#annealer = LearningRateScheduler(lambda x: 1e-3 * 0.98 ** x, verbose=0)\n",
     "earlystop = EarlyStopping(monitor='val_accuracy', mode='max' , patience=25, verbose=1,  restore_best_weights=True)\n",
-    "mc = ModelCheckpoint(model_filename + '_best.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)\n",
+    "mc = ModelCheckpoint(model_filename + '_best.keras', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)\n",
     "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.8, patience=5, min_lr=1e-5, verbose=1)\n",
     "history = model.fit(train_iterator, \n",
     "                validation_data=validation_iterator, \n",
@@ -402,7 +402,7 @@
     "                verbose=1)\n",
     "\n",
     "\n",
-    "#model.save(model_filename + '.h5')\n",
+    "#model.save(model_filename + '.keras')\n",
     "\n",
     "plot_acc_loss(history, \"Trainingsresults\")"
    ]
@@ -424,7 +424,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = keras.models.load_model(model_filename + '_best.h5')\n",
+    "model = keras.models.load_model(model_filename + '_best.keras')\n",
     "xz_data, yz_data, fz_data = shuffle(xz_data, yz_data, fz_data)\n",
     "false_predicted_files = predict_meter_digits(model, x_data=xz_data, y_data=yz_data, f_data=fz_data, \n",
     "                                            max_delta=0.)\n",

diff --git a/dig_class100-s1.ipynb b/dig_class100-s1.ipynb
@@ -1070,7 +1070,7 @@
     "\n",
     "#annealer = LearningRateScheduler(lambda x: 1e-3 * 0.98 ** x, verbose=0)\n",
     "earlystop = EarlyStopping(monitor='val_loss', mode='min' , patience=25, verbose=1,  restore_best_weights=True)\n",
-    "mc = ModelCheckpoint(output_tmp_dir + model_filename + '_best.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True, initial_value_threshold=.9)\n",
+    "mc = ModelCheckpoint(output_tmp_dir + model_filename + '_best.keras', monitor='val_loss', mode='min', verbose=1, save_best_only=True, initial_value_threshold=.9)\n",
     "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.85, patience=5, min_lr=1e-5, verbose=1)\n",
     "history = model.fit(train_iterator, \n",
     "                validation_data=validation_iterator, \n",
@@ -1080,7 +1080,7 @@
     "                verbose=1)\n",
     "\n",
     "\n",
-    "#model.save(model_filename + '.h5')\n",
+    "#model.save(model_filename + '.keras')\n",
     "\n",
     "plot_acc_loss(history, \"Trainingsresults\")"
    ]
@@ -1146,7 +1146,7 @@
     }
    ],
    "source": [
-    "model = keras.models.load_model(output_tmp_dir + model_filename + '_best.h5')\n",
+    "model = keras.models.load_model(output_tmp_dir + model_filename + '_best.keras')\n",
     "false_predicted_files = predict_meter_digits(model, x_data=xz_data, y_data=yz_data, f_data=fz_data, \n",
     "                                            max_delta=0.11)\n",
     "fpf = pd.DataFrame(false_predicted_files)\n",