Update models.py

models.py

@@ -1,27 +1,46 @@
-from tensorflow.keras.applications import VGG19, EfficientNetB0, DenseNet121
-from tensorflow.keras.models import Model
-from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Input
-
-def create_vgg19_model():
-    base_model = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-    x = Flatten()(base_model.output)
-    x = Dense(128, activation='relu')(x)
-    output = Dense(2, activation='softmax')(x)
-    model = Model(inputs=base_model.input, outputs=output)
-    return model
-
-def create_efficientnet_model():
-    base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-    x = GlobalAveragePooling2D()(base_model.output)
-    x = Dense(128, activation='relu')(x)
-    output = Dense(2, activation='softmax')(x)
-    model = Model(inputs=base_model.input, outputs=output)
-    return model
-
-def create_densenet_model():
-    base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-    x = GlobalAveragePooling2D()(base_model.output)
-    x = Dense(128, activation='relu')(x)
-    output = Dense(2, activation='softmax')(x)
-    model = Model(inputs=base_model.input, outputs=output)
-    return model
+# from tensorflow.keras.applications import VGG19, EfficientNetB0, DenseNet121
+# from tensorflow.keras.models import Model
+# from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Input
+
+# def create_vgg19_model():
+#     base_model = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+#     x = Flatten()(base_model.output)
+#     x = Dense(128, activation='relu')(x)
+#     output = Dense(2, activation='softmax')(x)
+#     model = Model(inputs=base_model.input, outputs=output)
+#     return model
+
+# def create_efficientnet_model():
+#     base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+#     x = GlobalAveragePooling2D()(base_model.output)
+#     x = Dense(128, activation='relu')(x)
+#     output = Dense(2, activation='softmax')(x)
+#     model = Model(inputs=base_model.input, outputs=output)
+#     return model
+
+# def create_densenet_model():
+#     base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+#     x = GlobalAveragePooling2D()(base_model.output)
+#     x = Dense(128, activation='relu')(x)
+#     output = Dense(2, activation='softmax')(x)
+#     model = Model(inputs=base_model.input, outputs=output)
+#     return model
+
+
+from tensorflow.keras.applications import VGG19, EfficientNetB0, DenseNet121
+from tensorflow.keras.models import Model
+
+def create_vgg19_model():
+    base_model = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+    model = Model(inputs=base_model.input, outputs=base_model.get_layer("block5_conv4").output)
+    return model
+
+def create_efficientnet_model():
+    base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+    model = Model(inputs=base_model.input, outputs=base_model.get_layer("top_conv").output)
+    return model
+
+def create_densenet_model():
+    base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+    model = Model(inputs=base_model.input, outputs=base_model.get_layer("conv5_block16_concat").output)
+    return model