path_to_home = os.path.expanduser("~")
path_to_split_datasets = path_to_split_datasets.replace("~", path_to_home)
path_to_train = os.path.join(path_to_split_datasets, "train")
path_to_validation = os.path.join(path_to_split_datasets, "validation")
# get number of classes
sub_dirs = [
sub_dir for sub_dir in os.listdir(path_to_train)
if os.path.isdir(os.path.join(path_to_train, sub_dir))
]
num_classes = len(sub_dirs)
# parameters for CNN
if use_vgg:
base_model = VGG(include_top=False,
weights='imagenet',
input_shape=(64, 64, 3))
else:
base_model = DenseNet(include_top=False,
weights='imagenet',
input_shape=(64, 64, 3))
# add a global spatial average pooling layer
top_model = base_model.output
top_model = GlobalAveragePooling2D()(top_model)
# or just flatten the layers
# top_model = Flatten()(top_model)
# let's add a fully-connected layer
if use_vgg:
# only in VGG19 a fully connected nn is added for classfication
# DenseNet tends to overfitting if using additionally dense layers
top_model = Dense(2048, activation='relu')(top_model)
# contruct path
path_to_home = os.path.expanduser("~")
path_to_split_datasets = path_to_split_datasets.replace("~", path_to_home)
path_to_train = os.path.join(path_to_split_datasets, "train")
path_to_validation = os.path.join(path_to_split_datasets, "validation")
# get number of classes
sub_dirs = [
sub_dir for sub_dir in os.listdir(path_to_train)
if os.path.isdir(os.path.join(path_to_train, sub_dir))
]
num_classes = len(sub_dirs)
# parameters for CNN
if use_vgg:
base_model = VGG(include_top=False, weights=None, input_shape=(64, 64, 3))
else:
base_model = DenseNet(include_top=False,
weights=None,
input_shape=(64, 64, 3))
# add a global spatial average pooling layer
top_model = base_model.output
top_model = GlobalAveragePooling2D()(top_model)
# or just flatten the layers
# top_model = Flatten()(top_model)
# let's add a fully-connected layer
if use_vgg:
# only in VGG19 a fully connected nn is added for classfication
# DenseNet tends to overfitting if using additionally dense layers
top_model = Dense(2048, activation='relu')(top_model)
top_model = Dense(2048, activation='relu')(top_model)
'SeaLake': 9}
num_classes = len(class_indices)
# contruct path
path_to_home = os.path.expanduser("~")
path_to_split_datasets = path_to_split_datasets.replace("~", path_to_home)
path_to_train = os.path.join(path_to_split_datasets, "train")
path_to_validation = os.path.join(path_to_split_datasets, "validation")
# parameters for CNN
input_tensor = Input(shape=(64, 64, 13))
# introduce a additional layer to get from 13 to 3 input channels
input_tensor = Conv2D(3, (1, 1))(input_tensor)
if use_vgg:
base_model_imagenet = VGG(include_top=False,
weights='imagenet',
input_shape=(64, 64, 3))
base_model = VGG(include_top=False,
weights=None,
input_tensor=input_tensor)
for i, layer in enumerate(base_model_imagenet.layers):
# we must skip input layer, which has no weights
if i == 0:
continue
base_model.layers[i+1].set_weights(layer.get_weights())
else:
base_model_imagenet = DenseNet(include_top=False,
weights='imagenet',
input_shape=(64, 64, 3))
base_model = DenseNet(include_top=False,
weights=None,