def create_model(self, model_type='xception', load_weights=None):
if (model_type == 'inceptionv3' or model_type == 1):
base = InceptionV3(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'inceptionv3'
pred = base.output
elif (model_type == 'resnet50' or model_type == 2):
base = ResNet50(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'resnet50'
pred = base.output
elif (model_type == 'vgg19' or model_type == 3):
base = VGG19(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'vgg19'
pred = base.output
elif (model_type == 'vgg16' or model_type == 4):
base = VGG16(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'vgg16'
pred = base.output
elif (model_type == 'resnet152' or model_type == 5):
resbuild = ResnetBuilder()
base = resbuild.build_resnet_152(self.input_shape,
self.output_size)
model_name = 'resnet152'
pred = base.output
elif (model_type == 'resnet50MOD' or model_type == 6):
resbuild = ResnetBuilder()
base = resbuild.build_resnet_50(self.input_shape, self.output_size)
model_name = 'resnet50MOD'
pred = base.output
elif (model_type == 'inceptionv3MOD' or model_type == 7):
base = InceptionV3MOD(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'inceptionv3MOD'
pred = base.output
else:
base = Xception(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'xception'
pred = base.output
pred = Dense(self.output_size,
activation='sigmoid',
name='predictions')(pred)
self.model = Model(base.input, pred, name=model_name)
if load_weights != None:
self.model.load_weights(load_weights)
for layer in base.layers:
layer.trainable = True
self.model.compile(loss=losses.binary_crossentropy,
optimizer='adam',
metrics=[FScore2])
# create test
input_ds = tf.data.Dataset.from_tensor_slices(X_test)
target_ds = tf.data.Dataset.from_tensor_slices(Y_test)
test_ds = tf.data.Dataset.zip((input_ds, target_ds)).batch(batch_size).repeat()
test_ds = test_ds.prefetch(autotune)
'''
train and test
'''
results_dict = {}
num_try = 3
for try_i in range(num_try):
builder = ResnetBuilder(name='ResNet18', act='FReLU', classes=num_label,
include_top=True, input_shape=(img_size, img_size, 3))
frelu_model = builder.builder()
frelu_model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
hist = frelu_model.fit(train_ds,
steps_per_epoch=int(np.ceil(X_train.shape[0]//32)),
validation_data=test_ds,
validation_steps=int(np.ceil(X_test.shape[0]/32)),
epochs=200,
verbose=1,
)
results_dict["FReLU-try" +
str(try_i)] = hist.history['accuracy'], hist.history['loss'], hist.history['val_accuracy'], hist.history['val_loss']
def create_model(self, model_type='xception', load_weights=None):
if (model_type == 'inceptionv3' or model_type == 1):
base = InceptionV3(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'inceptionv3'
pred = base.output
elif (model_type == 'resnet50' or model_type == 2):
base = ResNet50(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'resnet50'
pred = base.output
elif (model_type == 'vgg19' or model_type == 3):
base = VGG19(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'vgg19'
pred = base.output
elif (model_type == 'vgg16' or model_type == 4):
base = VGG16(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'vgg16'
pred = base.output
elif (model_type == 'resnet152' or model_type == 5):
sys.path.append(os.path.join(PATH, "resnet", "keras-resnet"))
from resnet import ResnetBuilder
resbuild = ResnetBuilder()
base = resbuild.build_resnet_152(self.input_shape,
self.output_size)
model_name = 'resnet152'
pred = base.output
elif (model_type == 'resnet50MOD' or model_type == 6):
sys.path.append(os.path.join(PATH, "resnet", "keras-resnet"))
from resnet import ResnetBuilder
resbuild = ResnetBuilder()
base = resbuild.build_resnet_50(self.input_shape, self.output_size)
model_name = 'resnet50MOD'
pred = base.output
elif (model_type == 'inceptionv3MOD' or model_type == 7):
from keras.applications.inception_v3_mod import InceptionV3MOD
base = InceptionV3MOD(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'inceptionv3MOD'
pred = base.output
else:
base = Xception(include_top=False,
weights='imagenet',
input_tensor=self.input_tensor,
classes=self.output_size,
pooling='avg')
model_name = 'xception'
pred = base.output
pred = Dense(self.output_size,
activation='sigmoid',
name='predictions')(pred)
self.model = Model(base.input, pred, name=model_name)
if load_weights != None:
self.model.load_weights(load_weights)
for layer in base.layers:
layer.trainable = True
self.compile()