if args.resume:
print('resume from checkpoint')
model = keras.models.load_model(save_file)
else:
print('train from start')
model = models.Sequential()
if '121' in args_model:
base_model = DenseNet121(weights=None,
include_top=False,
input_shape=(32, 32, 3),
pooling='avg')
elif '169' in args_model:
base_model = DenseNet169(weights=None,
include_top=False,
input_shape=(32, 32, 3),
pooling='avg')
elif '201' in args_model:
base_model = DenseNet201(weights=None,
include_top=False,
input_shape=(32, 32, 3),
pooling='avg')
model.add(base_model)
#model.add(layers.Flatten())
#model.add(layers.BatchNormalization())
#model.add(layers.Dense(128, activation='relu'))
#model.add(layers.Dropout(0.5))
#model.add(layers.BatchNormalization())
#model.add(layers.Dense(64, activation='relu'))
#model.add(layers.Dropout(0.5))
def densenet_169(weights=densenet169_path):
weights = weights
pretrained_model = DenseNet169(weights=weights, include_top=False)
return (pretrained_model)
#!/usr/bin/env python
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.densenet import preprocess_input
from keras.applications.densenet import decode_predictions
from keras.applications.densenet import DenseNet169
# iteration count
_iter = 1
"""
Main
"""
if __name__ == '__main__':
# load the model
model = DenseNet169()
# load an image from file
image = load_img('mug.jpg', target_size=(224, 224))
# convert the image pixels to a numpy array
image = img_to_array(image)
# reshape data for the model
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
# prepare the image for the VGG model
image = preprocess_input(image)
# predict the probability across all output classes
for i in range(_iter):
raw_input('{} iteration, press any key to perform...'.format(str(i)))
yhat = model.predict(image)
# return if not iter
if not _iter: exit()
from keras.applications.mobilenet import preprocess_input
preprocessing_function = preprocess_input
base_model = MobileNet(weights='imagenet',
include_top=False,
input_shape=(HEIGHT, WIDTH, 3))
elif args.model == "DenseNet121":
from keras.applications.densenet import preprocess_input
preprocessing_function = preprocess_input
base_model = DenseNet121(weights='imagenet',
include_top=False,
input_shape=(HEIGHT, WIDTH, 3))
elif args.model == "DenseNet169":
from keras.applications.densenet import preprocess_input
preprocessing_function = preprocess_input
base_model = DenseNet169(weights='imagenet',
include_top=False,
input_shape=(HEIGHT, WIDTH, 3))
elif args.model == "DenseNet201":
from keras.applications.densenet import preprocess_input
preprocessing_function = preprocess_input
base_model = DenseNet201(weights='imagenet',
include_top=False,
input_shape=(HEIGHT, WIDTH, 3))
elif args.model == "NASNetLarge":
from keras.applications.nasnet import preprocess_input
preprocessing_function = preprocess_input
base_model = NASNetLarge(weights='imagenet',
include_top=True,
input_shape=(HEIGHT, WIDTH, 3))
elif args.model == "NASNetMobile":
from keras.applications.nasnet import preprocess_input
def TransferNet(input_shape: Tuple[int, int],
num_classes: int,
feature_extractor: FeatureExtractor = None,
dense_layers: int = 3,
dropout_rate: float = 0.3) -> Model:
"""
Exploits a pre-trained model as feature extractor, feeding its output into a fully-connected NN.
The feature extractor model is NOT fine-tuned for the specific task.
Dropout and batch normalization are used throughout the trainable portion of the network.
:param input_shape: Shape of the input tensor as a 2-dimensional int tuple
:param num_classes: Number of classes for the final FC layer
:param feature_extractor: FeatureExtractor instance representing which pre-trained model to use as feature extractor
:param dense_layers: Number of layers for the FC NN
:param dropout_rate: Dropout rate
:return: a Keras model
"""
adam_opt = Adam(lr=0.1)
model_input = Input(shape=input_shape)
# load pre-trained model on ImageNet
if feature_extractor == FeatureExtractor.Dense121:
fe_model = DenseNet121(weights="imagenet",
include_top=False,
input_tensor=model_input)
elif feature_extractor == FeatureExtractor.Dense169:
fe_model = DenseNet169(weights="imagenet",
include_top=False,
input_tensor=model_input)
elif feature_extractor == FeatureExtractor.Dense201:
fe_model = DenseNet201(weights="imagenet",
include_top=False,
input_tensor=model_input)
elif feature_extractor == FeatureExtractor.NASNetLarge:
fe_model = NASNetLarge(weights="imagenet",
include_top=False,
input_tensor=model_input)
else:
# default: NASNetMobile
fe_model = NASNetMobile(weights="imagenet",
include_top=False,
input_tensor=model_input)
fe_model = Model(input=model_input,
output=fe_model.output,
name="FeatureExtractor")
fe_model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=adam_opt,
metrics=["accuracy"])
# get handles to the model (input, output tensors)
fe_input = fe_model.get_layer(index=0).input
fe_output = fe_model.get_layer(index=-1).output
# freeze layers
for _, layer in enumerate(fe_model.layers):
layer.trainable = False
# final fully-connected layers
dense = Flatten()(fe_output)
dense = BatchNormalization()(dense)
dense = Dropout(rate=dropout_rate)(dense)
num_units = 128
for i in range(1, dense_layers + 1):
dense = Dense(units=int(num_units / i), activation="relu")(dense)
dense = BatchNormalization()(dense)
dense = Dropout(rate=dropout_rate)(dense)
output_dense = Dense(units=num_classes, activation="softmax")(dense)
model = Model(input=fe_input, output=output_dense, name="TransferNet")
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=adam_opt,
metrics=["accuracy"])
return model
weights=None,
include_top=False)
batch_size = 32 * gpu_number
elif model_name == 'nasnet':
base_model = NASNetLarge(input_tensor=input_tensor,
weights=None,
include_top=False)
batch_size = 24 * gpu_number
elif model_name == 'densenet121':
base_model = DenseNet121(input_tensor=input_tensor,
weights=None,
include_top=False)
batch_size = 48 * gpu_number
elif model_name == 'densenet169':
base_model = DenseNet169(input_tensor=input_tensor,
weights=None,
include_top=False)
batch_size = 48 * gpu_number
elif model_name == 'densenet201':
base_model = DenseNet201(input_tensor=input_tensor,
weights=None,
include_top=False)
batch_size = 32 * gpu_number
elif model_name == 'seresnext':
base_model = SEResNeXt(input_tensor=input_tensor,
num_classes=num_classes,
include_top=False).model
batch_size = 4 * gpu_number
elif model_name == 'seresnet50':
base_model = SEResNet50(input_shape=input_shape, include_top=False)
batch_size = 32 * gpu_number
