Firsh Push at 20241207

all_models_tools/pre_train_model_construction.py

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+from all_models_tools.all_model_tools import attention_block
+from keras.activations import softmax, sigmoid
+from keras.applications import VGG16,VGG19, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, DenseNet121, NASNetLarge, Xception 
+from keras.layers import GlobalAveragePooling2D, Dense, Flatten
+from keras import regularizers
+from keras.layers import Add
+from application.Xception_indepentment import Xception_indepentment   
+
+def Original_VGG19_Model():
+    vgg19 = VGG19(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(vgg19.output)
+    dense = Dense(units = 4096, activation = "relu")(GAP)
+    dense = Dense(units = 4096, activation = "relu")(dense)
+    output = Dense(units = 2, activation = "softmax")(dense)
+
+    return vgg19.input, output
+
+def Original_ResNet50_model():
+    xception = ResNet50(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(xception.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return xception.input, dense
+
+def Original_NASNetLarge_model():
+    nasnetlarge = NASNetLarge(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(nasnetlarge.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return nasnetlarge.input, dense
+
+def Original_DenseNet121_model():
+    Densenet201 = DenseNet121(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(Densenet201.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return Densenet201.input, dense
+
+def Original_Xception_model():
+    xception = Xception(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(xception.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return xception.input, dense
+
+def Original_VGG16_Model():
+    vgg16 = VGG16(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    flatten = Flatten()(vgg16.output)
+    dense = Dense(units = 4096, activation = "relu")(flatten)
+    dense = Dense(units = 4096, activation = "relu")(dense)
+    output = Dense(units = 2, activation = "softmax")(dense)
+
+    return vgg16.input, output
+
+def Original_ResNet50v2_model():
+    resnet50v2 = ResNet50V2(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(resnet50v2.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return resnet50v2.input, dense
+
+def Original_ResNet101_model():
+    resnet101 = ResNet101(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(resnet101.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return resnet101.input, dense
+
+def Original_ResNet101V2_model():
+    resnet101v2 = ResNet101V2(include_top = False, weights = "imagenet", input_shape = (512, 512, 3))
+    GAP = GlobalAveragePooling2D()(resnet101v2.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return resnet101v2.input, dense
+
+def Original_ResNet152_model():
+    resnet152 = ResNet152(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(resnet152.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return resnet152.input, dense
+
+def Original_ResNet152V2_model():
+    resnet152v2 = ResNet152V2(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(resnet152v2.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return resnet152v2.input, dense
+
+def Original_InceptionV3_model():
+    inceptionv3 = InceptionV3(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(inceptionv3.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return inceptionv3.input, dense
+
+def Original_InceptionResNetV2_model():
+    inceptionResnetv2 = InceptionResNetV2(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(inceptionResnetv2.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return inceptionResnetv2.input, dense
+
+def Original_MobileNet_model():
+    mobilenet = MobileNet(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(mobilenet.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return mobilenet.input, dense
+
+def Original_MobileNetV2_model():
+    mobilenetv2 = MobileNetV2(include_top = False, weights = "imagenet", input_shape = (200, 200, 3))
+    GAP = GlobalAveragePooling2D()(mobilenetv2.output)
+    dense = Dense(units = 2, activation = "softmax")(GAP)
+
+    return mobilenetv2.input, dense