Firsh Push at 20241207

all_models_tools/all_model_tools.py

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+from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
+from keras.layers import GlobalAveragePooling2D, Dense, Reshape, Multiply
+from Load_process.file_processing import Process_File
+import datetime
+
+def attention_block(input):
+    channel = input.shape[-1]
+
+    GAP = GlobalAveragePooling2D()(input)
+
+    block = Dense(units = channel // 16, activation = "relu")(GAP)
+    block = Dense(units = channel, activation = "sigmoid")(block)
+    block = Reshape((1, 1, channel))(block)
+
+    block = Multiply()([input, block])
+
+    return block
+
+def call_back(model_name, index): 
+    File = Process_File()
+
+    model_dir = '../Result/save_the_best_model/' + model_name
+    File.JudgeRoot_MakeDir(model_dir)
+    modelfiles = File.Make_Save_Root('best_model( ' + str(datetime.date.today()) + " )-" +  str(index) + ".weights.h5", model_dir)
+    
+    model_mckp = ModelCheckpoint(modelfiles, monitor='val_loss', save_best_only=True, save_weights_only = True, mode='auto')
+
+    earlystop = EarlyStopping(monitor='val_loss', patience=74, verbose=1) # 提早停止
+
+    reduce_lr = ReduceLROnPlateau(
+                        monitor = 'val_loss',
+                        factor = 0.94,           # 學習率降低的量。 new_lr = lr * factor
+                        patience = 2,                # 沒有改進的時期數，之後學習率將降低
+                        verbose = 0,
+                        mode = 'auto',
+                        min_lr = 0                   # 學習率下限
+                    )
+
+    callbacks_list = [model_mckp, earlystop, reduce_lr]
+    return callbacks_list