def build_model(classes=2):
inputs = Input(shape=(IMAGE_SIZE, IMAGE_SIZE, 3))
x = preprocess_input(inputs)
x = DenseNet201(weights=None, classes=classes)(x)
model = Model(inputs=inputs, outputs=x)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'])
return model
def create_model(model_name):
if model_name == 'efn_b4':
model = efn.EfficientNetB4(weights=None, classes=4)
elif model_name == 'efn_b4_p':
model = tf.keras.models.Sequential()
model.add(efn.EfficientNetB4(input_shape=(380, 380, 3), weights='imagenet', include_top=False))
elif model_name == 'efn_b5_p':
model = tf.keras.models.Sequential()
model.add(efn.EfficientNetB5(input_shape=(456, 456, 3), weights='imagenet', include_top=False))
elif model_name == 'resnet18':
model = ResNet([2, 2, 2, 2], input_shape=(224, 224, 3))
elif model_name == 'densenet121_p':
model = tf.keras.models.Sequential()
model.add(DenseNet121(input_shape=(224, 224, 3), weights='imagenet', include_top=False))
elif model_name == 'densenet201_p':
model = tf.keras.models.Sequential()
model.add(DenseNet201(input_shape=(224, 224, 3), weights='imagenet', include_top=False))
if model_name.split('_')[-1] == 'p':
model.add(GlobalAveragePooling2D())
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(4, activation='softmax'))
model.summary()
return model
def transferlearning(modelname, learning_rate, image_shape, training_path, epoch):
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
validation_split=0.2)
train_generator = train_datagen.flow_from_directory(
training_path,
target_size=(image_shape, image_shape),
batch_size=128,
class_mode='categorical',
subset='training',
shuffle=True)
validation_generator = train_datagen.flow_from_directory(
training_path,
target_size=(image_shape, image_shape),
batch_size=128,
class_mode='categorical',
subset='validation')
num_classes = train_generator.num_classes
if modelname == 'VGG19':
base_model = VGG19(input_shape=(image_shape, image_shape, 3),
include_top=False, weights='imagenet')
elif modelname == 'MobileNetV2':
base_model = MobileNetV2(
input_shape=(image_shape, image_shape, 3), include_top=False, weights='imagenet')
elif modelname == 'DenseNet201':
base_model = DenseNet201(
input_shape=(image_shape, image_shape, 3), include_top=False, weights='imagenet')
elif modelname == 'InceptionV3':
base_model = InceptionV3(
input_shape=(image_shape, image_shape, 3), include_top=False, weights='imagenet')
elif modelname == 'ResNet50':
base_model = ResNet50(input_shape=(image_shape, image_shape, 3),
include_top=False, weights='imagenet')
elif modelname == 'Xception':
base_model = Xception(input_shape=(image_shape, image_shape, 3),
include_top=False, weights='imagenet')
base_model.output
global_layer = keras.layers.GlobalAveragePooling2D()(base_model.output)
prediction_layer = keras.layers.Dense(
num_classes, activation='softmax')(global_layer)
model = keras.models.Model(
inputs=base_model.input, outputs=prediction_layer)
model.summary()
model.compile(optimizer=Adam(lr=learning_rate),
loss='categorical_crossentropy', metrics=["acc"])
history = model.fit_generator(
train_generator,
epochs=epoch,
validation_data=validation_generator,
validation_steps=1000)
return history
def __init__(self, model_name=None):
if model_name == 'Xception':
base_model = Xception(weights='imagenet')
self.preprocess_input = xception.preprocess_input
elif model_name == 'VGG19':
base_model = VGG19(weights='imagenet')
self.preprocess_input = vgg19.preprocess_input
elif model_name == 'ResNet50':
base_model = ResNet50(weights='imagenet')
self.preprocess_input = resnet.preprocess_input
elif model_name == 'ResNet101':
base_model = ResNet101(weights='imagenet')
self.preprocess_input = resnet.preprocess_input
elif model_name == 'ResNet152':
base_model = ResNet152(weights='imagenet')
self.preprocess_input = resnet.preprocess_input
elif model_name == 'ResNet50V2':
base_model = ResNet50V2(weights='imagenet')
self.preprocess_input = resnet_v2.preprocess_input
elif model_name == 'ResNet101V2':
base_model = ResNet101V2(weights='imagenet')
self.preprocess_input = resnet_v2.preprocess_input
elif model_name == 'ResNet152V2':
base_model = ResNet152V2(weights='imagenet')
self.preprocess_input = resnet_v2.preprocess_input
elif model_name == 'InceptionV3':
base_model = InceptionV3(weights='imagenet')
self.preprocess_input = inception_v3.preprocess_input
elif model_name == 'InceptionResNetV2':
base_model = InceptionResNetV2(weights='imagenet')
self.preprocess_input = inception_resnet_v2.preprocess_input
elif model_name == 'DenseNet121':
base_model = DenseNet121(weights='imagenet')
self.preprocess_input = densenet.preprocess_input
elif model_name == 'DenseNet169':
base_model = DenseNet169(weights='imagenet')
self.preprocess_input = densenet.preprocess_input
elif model_name == 'DenseNet201':
base_model = DenseNet201(weights='imagenet')
self.preprocess_input = densenet.preprocess_input
elif model_name == 'NASNetLarge':
base_model = NASNetLarge(weights='imagenet')
self.preprocess_input = nasnet.preprocess_input
elif model_name == 'NASNetMobile':
base_model = NASNetMobile(weights='imagenet')
self.preprocess_input = nasnet.preprocess_input
elif model_name == 'MobileNet':
base_model = MobileNet(weights='imagenet')
self.preprocess_input = mobilenet.preprocess_input
elif model_name == 'MobileNetV2':
base_model = MobileNetV2(weights='imagenet')
self.preprocess_input = mobilenet_v2.preprocess_input
else:
base_model = VGG16(weights='imagenet')
self.preprocess_input = vgg16.preprocess_input
self.model = Model(inputs=base_model.input,
outputs=base_model.layers[-2].output)
def create_model(model_name, input_shape=(IMG_SIZE, IMG_SIZE, 3)):
if model_name == 'efn_b4':
model = efn.EfficientNetB4(weights=None, classes=4)
elif model_name == 'efn_b4_p':
model = tf.keras.models.Sequential()
model.add(
efn.EfficientNetB4(input_shape=input_shape,
weights='imagenet',
include_top=False))
elif model_name == 'efn_b5_p':
model = tf.keras.models.Sequential()
model.add(
efn.EfficientNetB5(input_shape=input_shape,
weights='imagenet',
include_top=False))
elif model_name == 'efn_b6_p':
model = tf.keras.models.Sequential()
model.add(
efn.EfficientNetB6(input_shape=input_shape,
weights='imagenet',
include_top=False))
elif model_name == 'efn_b7_p':
model = tf.keras.models.Sequential()
model.add(
efn.EfficientNetB7(input_shape=input_shape,
weights='imagenet',
include_top=False))
elif model_name == 'densenet121_p':
model = tf.keras.models.Sequential()
model.add(
DenseNet121(input_shape=input_shape,
weights='imagenet',
include_top=False))
elif model_name == 'densenet201_p':
model = tf.keras.models.Sequential()
model.add(
DenseNet201(input_shape=input_shape,
weights='imagenet',
include_top=False))
elif model_name == 'inceptionResV2_p':
model = tf.keras.models.Sequential()
model.add(
InceptionResNetV2(input_shape=input_shape,
weights='imagenet',
include_top=False))
if model_name.split('_')[-1] == 'p':
model.add(GlobalAveragePooling2D())
#model.add(Dense(128, activation='relu'))
#model.add(Dense(64, activation='relu'))
model.add(Dense(4, activation='softmax'))
model.summary()
return model
def pretrainded_model(type: str, trainable=False):
with strategy.scope():
if type == 'VGG16':
pretrained_model = VGG16(weights='imagenet',
include_top=False,
input_shape=[*IMAGE_SIZE, 3])
elif type == 'VGG19':
pretrained_model = VGG19(weights='imagenet',
include_top=False,
input_shape=[*IMAGE_SIZE, 3])
elif type == 'DenseNet121':
pretrained_model = DenseNet121(weights='imagenet',
include_top=False,
input_shape=[*IMAGE_SIZE, 3])
elif type == 'DenseNet169':
pretrained_model = DenseNet169(weights='imagenet',
include_top=False,
input_shape=[*IMAGE_SIZE, 3])
elif type == 'DenseNet201':
pretrained_model = DenseNet201(weights='imagenet',
include_top=False,
input_shape=[*IMAGE_SIZE, 3])
pretrained_model.trainable = trainable
model = Sequential([
# To a base pretrained on ImageNet to extract features from images...
pretrained_model,
# ... attach a new head to act as a classifier.
Flatten(),
Dense(256, activation='relu'),
BatchNormalization(),
Dropout(0.2),
Dense(256, activation='relu'),
BatchNormalization(),
Dropout(0.2),
Dense(256, activation='relu'),
BatchNormalization(),
Dropout(0.2),
Dense(256, activation='relu'),
BatchNormalization(),
Dropout(0.2),
Dense(256, activation='relu'),
BatchNormalization(),
Dropout(0.2),
tf.keras.layers.Dense(len(CLASSES),
activation='softmax',
use_bias=False)
])
return model
def build_model():
model = Sequential([
DenseNet201(weights='imagenet',
include_top=False,
input_shape=(224, 224, 3)),
layers.GlobalAveragePooling2D(),
layers.Dropout(0.5),
layers.BatchNormalization(),
layers.Dense(classes, activation='softmax'),
])
model.compile(loss='binary_crossentropy',
optimizer=Adam(learning_rate=1e-4),
metrics=['accuracy'])
return model
def download_model():
model = Sequential()
conv_base = DenseNet201(input_shape=(224, 224, 3),
include_top=False,
pooling='max',
weights='imagenet')
model.add(conv_base)
model.add(BatchNormalization())
model.add(Dense(2048, activation='relu', kernel_regularizer=l1_l2(0.01)))
model.add(BatchNormalization())
model.add(Dense(8, activation='softmax'))
train_layers = [layer for layer in conv_base.layers[::-1][:5]]
for layer in conv_base.layers:
if layer in train_layers:
layer.trainable = True
model.save("model/model.h5")
def train(self):
model = Sequential()
model.add(
DenseNet201(weights="imagenet",
include_top=False,
input_shape=self.input_shape))
model.add(Flatten())
model.add(Dense(1024, activation="relu"))
model.add(Dense(1, activation="sigmoid"))
plot_model(model)
model.summary()
model.compile(optimizer=Adam(learning_rate=1e-3),
loss="binary_crossentropy",
metrics=['accuracy'])
history = model.fit(self.train_data,
epochs=100,
verbose=1,
validation_data=self.valid_data)
return model, history
def densenet(shape, class_num):
base_model = DenseNet201(
include_top=False, weights='imagenet',
pooling='avg') #, input_tensor=Input(shape=shape))
nw = base_model.output
nw = Dense(512, activation='relu')(nw)
nw = Dropout(.4)(nw)
nw = Dense(512, activation='relu')(nw)
if class_num <= 2:
output = Dense(class_num, activation='sigmoid', name='output')(nw)
else:
output = Dense(class_num, activation='softmax', name='output')(nw)
base_model.trainable = False
'''#for train part of model
layer_names = [l.name for l in base_model.layers]
idx = layer_names.index('block7a_expand_conv')
for layer in base_model.layers[:idx]:
layer.trainable = False
'''
return Model(inputs=base_model.input, outputs=output)
def get_model(model_name):
if model_name == 'MobileNet':
base_model = MobileNet(weights='imagenet',
include_top=False,
input_shape=(img_size, img_size, 3))
elif model_name == 'VGG16':
base_model = vgg16.VGG16(weights='imagenet',
include_top=False,
input_shape=(img_size, img_size, 3))
elif model_name == 'DenseNet':
base_model = DenseNet121(weights='imagenet',
include_top=False,
input_shape=(img_size, img_size, 3))
elif model_name == 'DenseNet201':
base_model = DenseNet201(weights='imagenet',
include_top=False,
input_shape=(img_size, img_size, 3))
elif model_name == 'Inception':
base_model = InceptionV3(weights='imagenet',
include_top=False,
input_shape=(img_size, img_size, 3))
elif model_name == 'ResNet':
base_model = ResNet50(weights='imagenet',
include_top=False,
input_shape=(img_size, img_size, 3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.3)(x)
x = Dense(256, activation='relu')(x) #dense layer 2
preds = Dense(4, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=preds, name=model_name)
model.compile(optimizer='Adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def create_model(
model_name, log_dir, args
): # optimizer, learning rate, activation, neurons, batch size, epochs...
input_shape = input_size(model_name, args)
if args.head == 'max' or (args.base_trainable
and args.head != 't_complex'):
pool = 'max'
else:
pool = 'none'
if model_name == 'VGG16':
conv_base = VGG16(weights='imagenet',
include_top=False,
pooling=pool,
input_shape=input_shape)
elif model_name == 'VGG19':
conv_base = VGG19(weights='imagenet',
include_top=False,
pooling=pool,
input_shape=input_shape)
elif model_name == 'ResNet50':
conv_base = ResNet50(weights='imagenet',
include_top=False,
pooling=pool,
input_shape=input_shape)
elif model_name == 'InceptionV3':
conv_base = InceptionV3(weights='imagenet',
include_top=False,
pooling=pool,
input_shape=input_shape)
elif model_name == 'Xception':
conv_base = Xception(weights='imagenet',
include_top=False,
pooling=pool,
input_shape=input_shape)
elif model_name == 'InceptionResNetV2':
conv_base = InceptionResNetV2(weights='imagenet',
include_top=False,
pooling=pool,
input_shape=input_shape)
elif model_name == 'NASNetMobile':
conv_base = NASNetMobile(weights='imagenet',
include_top=False,
pooling=pool,
input_shape=input_shape)
elif model_name == 'NASNetLarge':
conv_base = NASNetLarge(weights='imagenet',
include_top=False,
pooling=pool,
input_shape=input_shape)
elif model_name == 'DenseNet201':
conv_base = DenseNet201(weights='imagenet',
include_top=False,
pooling=pool,
input_shape=input_shape)
elif model_name == 'MobileNetV2':
conv_base = MobileNetV2(weights='imagenet',
include_top=False,
pooling=pool,
input_shape=input_shape)
else:
conv_base = None
print("Model name not known!")
exit()
conv_base.trainable = args.base_trainable
model = models.Sequential()
if args.base_trainable:
if args.head == 't_complex':
model = models.Sequential()
model.add(conv_base)
model.add(
layers.Conv2D(filters=1024,
kernel_size=(3, 3),
padding='same',
strides=1))
model.add(layers.Flatten()) # ??
model.add(layers.Dense(1024, activation='sigmoid'))
model.add(layers.Dense(256, activation='sigmoid'))
model.add(layers.Dense(args.CLASSES_NO, activation='softmax')
) # (samples, new_rows, new_cols, filters)
else:
model.add(conv_base)
model.add(layers.Dense(args.CLASSES_NO, activation='softmax'))
elif args.head == 'dense':
# outside only?
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dense(args.CLASSES_NO, activation='softmax'))
elif args.head == 'max':
model.add(conv_base)
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(args.CLASSES_NO, activation='softmax'))
elif args.head == 'mod':
model = models.Sequential()
model.add(conv_base)
model.add(
layers.Conv2D(filters=2048, kernel_size=(3, 3), padding='valid'))
model.add(layers.Flatten()) # ??
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1024, activation='sigmoid'))
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(
args.CLASSES_NO,
activation='softmax')) # (samples, new_rows, new_cols, filters)
if args.lr_decay:
lr_schedule = ExponentialDecay(args.INIT_LEARN_RATE,
decay_steps=args.DECAY_STEPS,
decay_rate=args.DECAY_RATE,
staircase=True)
model.compile(loss='categorical_crossentropy',
optimizer=SGD(lr_schedule),
metrics=['acc']) # To different optimisers?
else:
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=args.LEARNING_RATE),
metrics=['acc'])
with open(os.path.join(log_dir, 'modelsummary.txt'), 'w') as f:
with redirect_stdout(f):
model.summary()
print(model.summary())
return model
def load_pretrained_img_embedder(self):
self.img_embedder = DenseNet201(weights=None)
self.img_embedder.load_weights(
"Models/densenet201_weights_tf_dim_ordering_tf_kernels.h5")
self.img_embedder = Model(inputs=self.img_embedder.inputs,
outputs=self.img_embedder.layers[-2].output)
vgg16 = NASNetMobile()
# vgg16.summary()
print("NASNetMobile",len(vgg16.trainable_weights)/2)
print('----------------------------------------------------------------------------')
vgg16 = DenseNet121()
# vgg16.summary()
print("DenseNet121",len(vgg16.trainable_weights)/2)
print('----------------------------------------------------------------------------')
vgg16 = DenseNet169()
# vgg16.summary()
print("DenseNet169",len(vgg16.trainable_weights)/2)
print('----------------------------------------------------------------------------')
vgg16 = DenseNet201()
# vgg16.summary()
print("DenseNet201",len(vgg16.trainable_weights)/2)
print('----------------------------------------------------------------------------')
vgg16 = MobileNetV2()
# vgg16.summary()
print("MobileNetV2",len(vgg16.trainable_weights)/2)
print('----------------------------------------------------------------------------')
vgg16 = MobileNet()
# vgg16.summary()
print("MobileNet",len(vgg16.trainable_weights)/2)
headModel = baseModel.output
headModel = Dense(51, activation='softmax')(headModel)
elif network == "MobileNetV2":
print(network)
train, test, lb2, labelsTest = preprocessing(network)
baseModel = MobileNetV2(weights=pretraining,
include_top=False,
input_tensor=Input(shape=(224, 224, 3)),
pooling="avg")
headModel = baseModel.output
headModel = Dense(51, activation='softmax', use_bias=True)(headModel)
elif network == "DenseNet201":
print(network)
train, test, lb2, labelsTest = preprocessing_EfficcientNet()
baseModel = DenseNet201(weights=pretraining,
include_top=False,
input_tensor=Input(shape=(224, 224, 3)),
pooling="avg")
headModel = baseModel.output
headModel = Dense(51, activation='softmax')(headModel)
elif network == "NASNetMobile":
print(network)
train, test, lb2, labelsTest = preprocessing(network)
baseModel = NASNetMobile(weights=pretraining,
include_top=False,
input_tensor=Input(shape=(224, 224, 3)),
pooling="avg")
headModel = baseModel.output
headModel = Dense(51, activation='softmax')(headModel)
elif network == "EfficientNetB0":
print(network)
train, test, lb2, labelsTest = preprocessing_EfficcientNet()
print('Training data:')
train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
directory=train_dir,
shuffle=True,
target_size=(IMG_WIDTH, IMG_HEIGHT),
class_mode='binary')
print('Testing data:')
test_data_gen = test_image_generator.flow_from_directory(batch_size=batch_size,
directory=test_dir,
target_size=(IMG_WIDTH, IMG_HEIGHT),
class_mode='binary')
base_model = DenseNet201(input_shape=(IMG_WIDTH, IMG_HEIGHT, 3),
include_top=False,
weights='imagenet')
base_model.trainable = False
model = Sequential([
base_model,
GlobalAveragePooling2D(),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=[BinaryAccuracy(), Precision(), Recall()])
model.summary()
val_dataset = (tf.data.Dataset
.from_tensor_slices((val_paths, val_labels))
.map(decode_image, num_parallel_calls=AUTO)
.batch(batch_size)
)
test_dataset = (tf.data.Dataset
.from_tensor_slices(test_paths)
.map(decode_image, num_parallel_calls=AUTO)
.batch(batch_size)
)
model = tf.keras.Sequential([
# Input shape 300x300 image with 3 bytes color
DenseNet201(weights = 'imagenet',
include_top = False,
input_shape = (299, 299, 3)),
GlobalAveragePooling2D(),
Dense(1024, activation = 'relu'),
BatchNormalization(),
Dropout(0.5),
Dense(512, activation = 'relu'),
BatchNormalization(),
Dropout(0.5),
Dense(100, activation = 'softmax')
])
# create SGD optimizer
optim = tf.keras.optimizers.SGD()
def build_fc_densenet(n_classes,
h,
w,
n_layers=201,
use_bottleneck=False,
bottleneck_blocks=32):
"""
Build a Fully Convolutional Densenet model.
Parameters:
n_classes: Number of classes to predict
h: Height of input images
w: Width of input images
n_layers: Numbers of Densenet's layers. Values in [121,169,201]. Densenet201 is used by default or if the value is not in the valid set.
use_bottleneck: Whether or not use a bottleneck block as mentioned in the paper.
bottleneck_blocks: Number of blocks to use if use_bottleneck parameter is True
Return:
A tf.keras Model instance
"""
if n_layers == 121:
blocks = [6, 12, 24, 16]
base_model = DenseNet121(input_shape=[h, w, 3], include_top=False)
elif n_layers == 169:
blocks = [6, 12, 32, 32]
base_model = DenseNet169(input_shape=[h, w, 3], include_top=False)
else:
blocks = [6, 12, 48, 32]
base_model = DenseNet201(input_shape=[h, w, 3], include_top=False)
skips_n = 3
grown_factor = 32
#Encoder
skip_names = [
str.format('conv{0}_block{1}_concat', i + 2, blocks[i])
for i in range(skips_n + 1)
]
upsample_factors = [4, 2, 2, 2]
skip_layers = [base_model.get_layer(name).output for name in skip_names]
base = Model(inputs=base_model.inputs, outputs=skip_layers)
inputs = Input(shape=[h, w, 3])
skips = base(inputs)
x = skips[-1]
#bottleneck
if use_bottleneck:
x = dense_block(x, bottleneck_blocks, name='bottleneck')
#Upsample path
for i in range(1, 4):
print('upsampling', x, skips[-i - 1])
skip = skips[-i - 1]
x = transition_up(skip, x)
x = dense_block(x, blocks[-i], name='upsample' + str(i))
#4x upsampling
x = Conv2DTranspose(64,
3,
4,
padding='same',
kernel_initializer='he_uniform')(x)
x = score(x, n_classes)
#ending model
model = Model(inputs=inputs, outputs=x)
return model
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical
#1. 데이터
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2],
3).astype('float32') / 255.
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2],
3).astype('float32') / 255.
#2. 모델
t = DenseNet201(weights='imagenet',
include_top=False,
input_shape=(x_train.shape[1], x_train.shape[2], 3))
t.trainable = False #학습시키지 않겠다 이미지넷 가져다가 그대로 쓰겠다
# model.trainable=True
model = Sequential()
model.add(t)
model.add(Flatten())
model.add(Dense(256))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Activation('relu'))
model.add(Dense(256))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
mirrored_strategy = tf.distribute.MirroredStrategy(
devices=["/gpu:0", "/gpu:1"])
with mirrored_strategy.scope():
architectures = [("DenseNet121",
DenseNet121(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')),
("DenseNet169",
DenseNet169(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')),
("DenseNet201",
DenseNet201(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')),
("InceptionResNetV2",
InceptionResNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')),
("MobileNet",
MobileNet(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')),
("MobileNetV2",
MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')),
("ResNet101",
ResNet101(input_shape=IMG_SHAPE,
def train_model(path,
train_images=None,
train_labels=None,
test_images=None,
test_labels=None,
model_name=None,
epochs=80,
learning_rate=0.0001,
input_shape=(224, 224, 3),
classes=2,
batch_size=16,
classifier_activation='softmax',
callbacks=None):
'''
saves the model as .h5 file\n
path = directory for saving the files
train_images = a numpy array containing the image data for training\n
train_labels = a numpy array containing the labels for training\n
test_images = a numpy array containing the image data for test\n
test_labels = a numpy array containing the labels for test\n
model_name = a string, name of the model -> "vgg19", "resnet50_v2", "inception_resnet_v2", "densenet201", "inception_v3", "xception", "mobilenet_v2"\n
epochs\n
learning_rate\n
'''
base_model = None
if model_name == 'vgg19':
base_model = VGG19(weights=None,
include_top=False,
input_shape=input_shape)
if model_name == 'resnet50_v2':
base_model = ResNet50V2(weights=None,
include_top=False,
input_shape=input_shape)
if model_name == 'inception_resnet_v2':
base_model = InceptionResNetV2(weights=None,
include_top=False,
input_shape=input_shape)
if model_name == 'densenet201':
base_model = DenseNet201(weights=None,
include_top=False,
input_shape=input_shape)
if model_name == 'inception_v3':
base_model = InceptionV3(weights=None,
include_top=False,
input_shape=input_shape)
if model_name == 'xception':
base_model = Xception(weights=None,
include_top=False,
input_shape=input_shape)
if model_name == 'mobilenet_v2':
base_model = MobileNetV2(weights=None,
include_top=False,
input_shape=input_shape)
x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
output = tf.keras.layers.Dense(classes,
activation=classifier_activation)(x)
model = tf.keras.Model(inputs=base_model.input, outputs=output)
optimizer = Adam(learning_rate=learning_rate,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-07)
model.compile(
optimizer=optimizer,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
results = model.fit(train_images,
train_labels,
epochs=epochs,
validation_data=(test_images, test_labels),
batch_size=batch_size,
callbacks=callbacks)
#losses = pd.DataFrame(model.history.history)
#losses[['loss','val_loss']].plot()
save_model = path + model_name + '.h5'
model.save(save_model)
return results
def construct_model(pretrainedNN):
model = Sequential()
if (pretrainedNN == 'VGG16'):
model.add(
VGG16(weights=None, include_top=False, input_shape=(32, 32, 3)))
elif (pretrainedNN == 'VGG19'):
model.add(
VGG19(weights=None, include_top=False, input_shape=(32, 32, 3)))
elif (pretrainedNN == 'ResNet101'):
model.add(
ResNet101(weights=None, include_top=False,
input_shape=(32, 32, 3)))
elif (pretrainedNN == 'ResNet152'):
model.add(
ResNet152(weights=None, include_top=False,
input_shape=(32, 32, 3)))
elif (pretrainedNN == 'ResNet50V2'):
model.add(
ResNet50V2(weights=None,
include_top=False,
input_shape=(32, 32, 3)))
elif (pretrainedNN == 'ResNet101V2'):
model.add(
ResNet101V2(weights=None,
include_top=False,
input_shape=(32, 32, 3)))
elif (pretrainedNN == 'ResNet152V2'):
model.add(
ResNet152V2(weights=None,
include_top=False,
input_shape=(32, 32, 3)))
elif (pretrainedNN == 'MobileNet'):
model.add(
MobileNet(weights=None, include_top=False,
input_shape=(32, 32, 3)))
elif (pretrainedNN == 'MobileNetV2'):
model.add(
MobileNetV2(weights=None,
include_top=False,
input_shape=(32, 32, 3)))
elif (pretrainedNN == 'DenseNet121'):
model.add(
DenseNet121(weights=None,
include_top=False,
input_shape=(32, 32, 3)))
elif (pretrainedNN == 'DenseNet169'):
model.add(
DenseNet169(weights=None,
include_top=False,
input_shape=(32, 32, 3)))
elif (pretrainedNN == 'DenseNet201'):
model.add(
DenseNet201(weights=None,
include_top=False,
input_shape=(32, 32, 3)))
else:
model.add(
ResNet50(weights=None, include_top=False, input_shape=(32, 32, 3)))
model.add(Flatten())
model.add(Dense(77, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
return model
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dropout(0.5))
model.add(layers.BatchNormalization())
model.add(layers.Dense(2, activation='softmax'))
model.compile(loss='binary_crossentropy',
optimizer=Adam(lr=lr),
metrics=['accuracy'])
return model
K.clear_session()
gc.collect()
resnet = DenseNet201(weights='imagenet')
model = build_model(resnet, lr=1e-4)
model.summary()
# Learning Rate Reducer
learn_control = ReduceLROnPlateau(monitor='val_acc',
patience=5,
verbose=1,
factor=0.2,
min_lr=1e-7)
# Checkpoint
filepath = "weights.best.hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
def DenseNet_greyscale(blocks,input_shape,pooling,trainable):
if blocks == 121:
blocks = [6, 12, 24, 16]
elif blocks == 169:
blocks == [6, 12, 32, 32]
elif blocks == 201:
blocks == [6, 12, 48, 32]
img_input = layers.Input(shape=input_shape)
bn_axis = 3
x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x)
x = layers.Activation('relu', name='conv1/relu')(x)
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)
x = dense_block(x, blocks[0], name='conv2')
x = transition_block(x, 0.5, name='pool2')
x = dense_block(x, blocks[1], name='conv3')
x = transition_block(x, 0.5, name='pool3')
x = dense_block(x, blocks[2], name='conv4')
x = transition_block(x, 0.5, name='pool4')
x = dense_block(x, blocks[3], name='conv5')
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
x = layers.Activation('relu', name='relu')(x)
if pooling == 'avg':
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D(name='max_pool')(x)
# Create model.
if blocks == [6, 12, 24, 16]:
model = models.Model(img_input, x, name='densenet121')
elif blocks == [6, 12, 32, 32]:
model = models.Model(img_input, x, name='densenet169')
elif blocks == [6, 12, 48, 32]:
model = models.Model(img_input, x, name='densenet201')
# Load weights
if blocks == [6, 12, 24, 16]:
pretrained_model = DenseNet121(include_top=False,pooling=pooling)
elif blocks == [6, 12, 32, 32]:
pretrained_model = DenseNet169(include_top=False,pooling=pooling)
elif blocks == [6, 12, 48, 32]:
pretrained_model = DenseNet201(include_top=False,pooling=pooling)
w = pretrained_model.layers[2].get_weights()[0].sum(2,keepdims=True)
model.layers[2].set_weights([w])
model.layers[2].trainable = trainable
model.trainable = trainable
for l1,l2 in zip(model.layers[3:],pretrained_model.layers[3:]):
l1.set_weights(l2.get_weights())
l1.trainable = trainable
return model
#test = DenseNet_greyscale(121,(224,224,1),'max',False)