def create_model():
base_model = ResNet152V2(include_top=False,
pooling="max",
input_shape=input_shape,
classes=15)
print(base_model.summary())
model = Sequential()
model.add(base_model)
model.add(Dense(580))
model.add(Dropout(0.5))
model.add(Dense(len(finding_labels), activation='sigmoid'))
METRICS = [
# keras.metrics.TruePositives(name='tp'),
# keras.metrics.FalsePositives(name='fp'),
# keras.metrics.TrueNegatives(name='tn'),
# keras.metrics.FalseNegatives(name='fn'),
keras.metrics.Precision(name='precision'),
keras.metrics.Recall(name='recall'),
keras.metrics.AUC(name='AUC'),
]
model.compile(loss=keras.losses.binary_crossentropy,
optimizer=SGD(lr=0.01, momentum=0.01),
metrics=METRICS)
return model
def get_model(model_name):
if model_name == 'VGG16':
from keras.applications import VGG16
model = VGG16(weights="imagenet", include_top=False, pooling='avg')
size = 512 # if pooling is 'avg', else 512 * 7 * 7 if pooling is 'None'
elif model_name == 'ResNet50':
from keras.applications import ResNet50
model = ResNet50(weights="imagenet", include_top=False, pooling='avg')
size = 2048, # if pooling is 'avg', 2048 * 7 * 7 if pooling is 'None'
elif model_name == 'ResNet152':
from keras.applications import ResNet152
model = ResNet152(weights="imagenet", include_top=False, pooling='avg')
size = 2048 # if pooling is 'avg', 2048 * 7 * 7 # if pooling is 'None'
elif model_name == 'ResNet152V2':
from keras.applications import ResNet152V2
model = ResNet152V2(weights="imagenet",
include_top=False,
pooling='avg')
size = 2048 # if pooling is 'avg', 2048 * 7 * 7 # if pooling is 'None'
elif model_name == 'DenseNet121':
from keras.applications import DenseNet121
model = DenseNet121(weights="imagenet",
include_top=False,
pooling='avg'),
size = 1024 # if pooling is 'avg'
elif model_name == 'Custom':
## CUSTOM MODEL
from keras.models import load_model
model = load_model(config.FINE_TUNED_MODEL)
size = 2048 # our trained models are based on ResNet152
else:
raise ValueError(
"Model needs to be defined. Examples: VGG16 or ResNet50.")
return model, size
def train(self):
# re-size all the images to this
IMAGE_SIZE = [224, 224]
# add preprocessing layer to the front of VGG
resnet = ResNet152V2(input_shape=IMAGE_SIZE + [3],
weights='imagenet',
include_top=False)
# don't train existing weights
for layer in resnet.layers:
layer.trainable = False
# useful for getting number of classes
folders = glob(self.train_path + '*')
# our layers - you can add more if you want
x = Flatten()(resnet.output)
prediction = Dense(len(folders), activation='sigmoid')(x)
# create a model object
model = Model(inputs=resnet.input, outputs=prediction)
# view the structure of the model
model.summary()
# tell the model what cost and optimization method to use
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# Use the Image Data Generator to import the images from the dataset
train_datagen = ImageDataGenerator(rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
training_set = train_datagen.flow_from_directory(
self.train_path,
target_size=(224, 224),
batch_size=32,
class_mode='categorical')
test_set = test_datagen.flow_from_directory(self.train_path,
target_size=(224, 224),
batch_size=32,
class_mode='categorical')
# fit the model
r = model.fit_generator(training_set,
validation_data=test_set,
epochs=10,
steps_per_epoch=2,
validation_steps=len(test_set))
model.save(self.model_save_path)
def get_model():
conv_base = ResNet152V2(weights='imagenet',
include_top=False,
input_shape=(width, height, 3))
conv_base.trainable = True
x = conv_base.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.4)(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.4)(x)
x = BatchNormalization()(x)
preds = Dense(1, activation='sigmoid')(x)
model = Model(inputs=conv_base.input, outputs=preds)
model.compile(optimizer=optimizers.Adam(learning_rate=2e-5),
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def load(self,
model_path,
number_classes=3,
resnet_type=ModelType.RESNET50):
if not os.path.isfile(model_path):
raise Exception(
"model with '{0}' path not found".format(model_path))
base_model = None
if resnet_type == ModelType.RESNET150:
base_model = ResNet152V2(weights="imagenet", include_top=False)
elif resnet_type == ModelType.RESNET50:
base_model = ResNet50(weights="imagenet", include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1000, activation="relu")(x)
x = Dropout(0.5)(x)
pred = Dense(number_classes, activation="softmax")(x)
model = Model(inputs=base_model.input, outputs=pred)
model.load_weights(model_path)
return model
def Resnet_Net(trainable=None, net="ResNet50"):
netold = ['ResNet50', 'ResNet101', 'ResNet152']
# Preprocessing the dataset into keras feedable format
if net not in netold:
train_datagen = ImageDataGenerator(rotation_range=rotation,
width_shift_range=width_shift,
height_shift_range=height_shift,
rescale=scale,
shear_range=shear,
zoom_range=zoom,
horizontal_flip=horizontal,
fill_mode=fill,
validation_split=validation)
test_datagen = ImageDataGenerator(rescale=scale, )
if net in netold:
train_datagen = ImageDataGenerator(
dtype='float32',
preprocessing_function=preprocess_input,
validation_split=validation)
test_datagen = ImageDataGenerator(
dtype='float32', preprocessing_function=preprocess_input)
train_generator = train_datagen.flow_from_directory(
path,
target_size=target,
batch_size=batch,
class_mode='categorical',
subset='training',
)
validation_generator = train_datagen.flow_from_directory(
path,
target_size=target,
batch_size=batch,
class_mode='categorical',
subset='validation')
models_list = [
'ResNet50', 'ResNet101', 'ResNet152', 'ResNet50V2', 'ResNet101V2',
'ResNet152V2'
]
# Loading the ResNet50 Model
if net == "ResNet50":
resnet = ResNet50(include_top=False,
weights='imagenet',
input_shape=input_sh,
pooling=pooling_model)
if net == "ResNet101":
resnet = ResNet101(include_top=False,
weights='imagenet',
input_shape=input_sh,
pooling=pooling_model)
if net == "ResNet152":
resnet = ResNet152(include_top=False,
weights='imagenet',
input_shape=input_sh,
pooling=pooling_model)
if net == "ResNet50V2":
resnet = ResNet50V2(include_top=False,
weights='imagenet',
input_shape=input_sh,
pooling=pooling_model)
if net == "ResNet101V2":
resnet = ResNet101V2(include_top=False,
weights='imagenet',
input_shape=input_sh,
pooling=pooling_model)
if net == "ResNet152V2":
resnet = ResNet152V2(include_top=False,
weights='imagenet',
input_shape=input_sh,
pooling=pooling_model)
if net not in models_list:
raise ValueError('Please provide the raise model ')
output = resnet.layers[-1].output
if pooling_model is None:
output = keras.layers.Flatten()(output)
resnet = Model(resnet.input, output=output)
print(resnet.summary())
print('\n\n\n')
# If you chose not for fine tuning
if trainable is None:
model = Sequential()
model.add(resnet)
model.add(Dense(hidden, activation='relu', input_dim=input_sh))
model.add(Dropout(dropout_num))
model.add(Dense(hidden, activation='relu'))
model.add(Dropout(dropout_num))
if classes == 1:
model.add(Dense(classes, activation='sigmoid', name='Output'))
else:
model.add(Dense(classes, activation='softmax', name='Output'))
for layer in resnet.layers:
layer.trainable = False
print("The model summary of Resnet -->\n\n\n"
) # In this the Resnet50 layers are not trainable
for i, layer in enumerate(resnet.layers):
print(i, layer.name, layer.trainable)
model.compile(
loss=loss_param, # Change according to data
optimizer=optimizers.RMSprop(),
metrics=['accuracy'])
print("The summary of final Model \n\n\n")
print(model.summary())
print('\n\n\n')
fit_history = model.fit_generator(
train_generator,
steps_per_epoch=len(train_generator.filenames) // batch,
epochs=epoch,
shuffle=True,
validation_data=validation_generator,
validation_steps=len(train_generator.filenames) // batch,
class_weight=n,
callbacks=[
EarlyStopping(patience=patience_param,
restore_best_weights=True),
ReduceLROnPlateau(patience=patience_param)
])
os.chdir(output_path)
model.save("model.h5")
print(fit_history.history.keys())
plt.figure(1, figsize=(15, 8))
plt.subplot(221)
plt.plot(fit_history.history['accuracy'])
plt.plot(fit_history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'valid'])
plt.subplot(222)
plt.plot(fit_history.history['loss'])
plt.plot(fit_history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'valid'])
plt.show()
if trainable is not None:
# Make last block of the conv_base trainable:
for layer in resnet.layers[:trainable]:
layer.trainable = False
for layer in resnet.layers[trainable:]:
layer.trainable = True
print('Last block of the conv_base is now trainable')
for i, layer in enumerate(resnet.layers):
print(i, layer.name, layer.trainable)
model = Sequential()
model.add(resnet)
model.add(Dense(hidden, activation='relu', input_dim=input_sh))
model.add(Dropout(dropout_num))
model.add(Dense(hidden, activation='relu'))
model.add(Dropout(dropout_num))
model.add(Dense(hidden, activation='relu'))
model.add(Dropout(dropout_num))
if classes == 1:
model.add(Dense(classes, activation='sigmoid', name='Output'))
else:
model.add(Dense(classes, activation='softmax', name='Output'))
for layer in resnet.layers:
layer.trainable = False
print("The model summary of Resnet -->\n\n\n"
) # In this the Resnet50 layers are not trainable
model.compile(
loss=loss_param, # Change according to data
optimizer=optimizers.RMSprop(),
metrics=['accuracy'])
print("The summary of final Model \n\n\n")
print(model.summary())
print('\n\n\n')
fit_history = model.fit_generator(
train_generator,
steps_per_epoch=len(train_generator.filenames) // batch,
epochs=epoch,
shuffle=True,
validation_data=validation_generator,
validation_steps=len(train_generator.filenames) // batch,
class_weight=n,
callbacks=[
EarlyStopping(patience=patience_param,
restore_best_weights=True),
ReduceLROnPlateau(patience=patience_param)
])
os.chdir(output_path)
model.save("model.h5")
print(fit_history.history.keys())
plt.figure(1, figsize=(15, 8))
plt.subplot(221)
plt.plot(fit_history.history['accuracy'])
plt.plot(fit_history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'valid'])
plt.subplot(222)
plt.plot(fit_history.history['loss'])
plt.plot(fit_history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'valid'])
plt.show()
def design_model(train, test, ytrain, ytest, epoch, nclas, value, val):
#intialize time for training and testing
tr = 0.0
tt = 0.0
#start time
st = time.time()
#input shape
image_input = Input(shape=(None, None, 3))
#adding a lambda layer to reshape to size 224 by 224
#this make sure that image are converted to 224,224 before entering the model
prep = Lambda(lambda x: tf.image.resize(x, (224, 224)))(image_input)
#using pretained weights from imagnet for transfer learning
#using InceptionResNetV2 model
dmodel = InceptionResNetV2(include_top=False,
weights='imagenet',
input_shape=(224, 224, 3))(prep)
dx = GlobalAveragePooling2D()(dmodel)
i_model = Model(image_input, dx)
i_model.summary()
#get the deep feature from InceptionResNetV2, feature extraction from InceptionResNetV2 model
#get training deep features
df_train_i = i_model.predict(train,
batch_size=32,
workers=50,
use_multiprocessing=True,
verbose=1)
#get testing deep features
df_test_i = i_model.predict(test,
batch_size=32,
workers=50,
use_multiprocessing=True,
verbose=1)
#using pretained weights from imagnet for transfer learning
#using ResNet152V2 model
vmodel = ResNet152V2(include_top=False,
weights='imagenet',
input_shape=(224, 224, 3))(prep)
vx = GlobalAveragePooling2D()(vmodel)
r_model = Model(image_input, vx)
r_model.summary()
#get deep features from ResNet152V2, extraction of deep features
#get training features
df_train_r = r_model.predict(train,
batch_size=32,
workers=50,
use_multiprocessing=True,
verbose=1)
#get testing features
df_test_r = r_model.predict(test,
batch_size=32,
workers=50,
use_multiprocessing=True,
verbose=1)
#combining of deep features from both the InceptionResNetV2 and ResNet152V2 models, Feature fusion or feature combination
#fusion of deep features extracted from InceptionResNetV2 and ResNet152V2 model and creating final_train and final_test set of features
final_train = tf.keras.layers.Concatenate()([df_train_i, df_train_r])
final_test = tf.keras.layers.Concatenate()([df_test_i, df_test_r])
#my own fully connected classifier(ANN)
classifier = Sequential([
Dense(4096, activation='relu', input_shape=final_train[0].shape),
Dropout(0.5),
Dense(nclas, activation='softmax')
])
#optimizer
opt = Adam(lr=1e-4, decay=1e-4 / 50)
#compile my own classifier
classifier.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
#reduce_lr method is used to reduce the learning rate if the learning rate is stagnant or if there are no major improvements during training
reduce_lr = ReduceLROnPlateau(monitor='loss',
factor=0.2,
patience=5,
min_lr=0.001)
#early stopping method is used to montior the loss if there are no significant reductions in loss then halts the training
es = EarlyStopping(monitor='loss', patience=10)
#fit the model
history = classifier.fit(final_train,
ytrain,
epochs=epoch,
batch_size=32,
shuffle=True,
verbose=1,
workers=50,
use_multiprocessing=True,
callbacks=[reduce_lr, es])
#total time for training
tr = time.time() - st
#plot the graph of accuracy and loss for training
plotgraph(history, value, val)
#start time for testing
st = time.time()
#evalute the model
(loss, accuracy) = classifier.evaluate(final_test,
ytest,
batch_size=32,
verbose=1,
workers=50,
use_multiprocessing=True)
#total time for testing
tt = time.time() - st
#training and testing accuracy
train_acc = history.history["accuracy"][-1:][0]
test_acc = accuracy
#free memory
del i_model, df_train_i, df_test_i, r_model, df_train_r, df_test_r, final_train, final_test, classifier, history
return tr, tt, train_acc, test_acc
batch_size = 32
training_set = aug.flow_from_directory(train_dir,
target_size=(224, 224),
batch_size=batch_size,
class_mode="categorical",
subset="training")
test_set = aug.flow_from_directory(train_dir,
target_size=(224, 224),
batch_size=batch_size,
class_mode="categorical",
subset="validation")
# define architecture
baseModel = ResNet152V2(weights="imagenet",
include_top=False,
input_shape=(224, 224, 3))
headModel = baseModel.output
headModel = GlobalAveragePooling2D()(headModel)
headModel = Dropout(0.25)(headModel)
headModel = Dense(39, activation='sigmoid',
name="resnet152v2_dense")(headModel)
model = Model(inputs=baseModel.input, outputs=headModel, name="ResNet152V2")
model.trainable = True
print(model.summary())
# define criteria for stopping. we will stop training if validation accuracy got reached 98%
class myCallback(tf.keras.callbacks.Callback):
from keras.applications import VGG16, VGG19, Xception, ResNet101, ResNet101V2, ResNet152, ResNet152V2 from keras.applications import ResNet50, ResNet50V2, InceptionV3, InceptionResNetV2 from keras.applications import MobileNet, MobileNetV2, DenseNet121, DenseNet169, DenseNet201 from keras.applications import NASNetLarge, NASNetMobile from keras.models import Sequential from keras.layers import Dense, Conv2D, MaxPool2D, Flatten, BatchNormalization, Activation from keras.optimizers import Adam # vgg16 = VGG16() # (None, 224, 224, 3) # model = VGG19() model = Xception() model = ResNet101() model = ResNet101V2() model = ResNet152() model = ResNet152V2() model = ResNet50() model = ResNet50V2() model = InceptionV3() model = InceptionResNetV2() model = MobileNet() model = MobileNetV2() model = DenseNet121() model = DenseNet169() model = DenseNet201() model = NASNetLarge() model = NASNetMobile() # vgg16.summary() ''' model= Sequential() # model.add(vgg16)
random_img = random.choice(os.listdir(path))
img_path = os.path.join(path, random_img)
img = image.load_img(img_path, target_size=(img_width, img_height))
img_tensor = image.img_to_array(
img) # Image data encoded as integers in the 0–255 range
img_tensor /= 255. # Normalize to [0,1] for plt.imshow application
plt.imshow(img_tensor)
plt.show()
# Instantiate convolutional base
from keras.applications import ResNet152V2
conv_base = ResNet152V2(
weights='imagenet',
include_top=False,
input_shape=(img_width, img_height,
3)) # 3 = number of channels in RGB pictures
# Freeze all
for layer in conv_base.layers:
layer.trainable = False
# Fine-tuning the model
set_trainable = False
for layer in conv_base.layers:
if layer.name in ['block5_conv1', 'block4_conv1']: set_trainable = True
if set_trainable: layer.trainable = True
else: layer.trainable = False
conv_base.summary()
#pass our images through it for feature extraction
# Extract features
xy = (X_train, X_test, y_train, y_test)
#일반화
X_train = X_train.astype(float) / 255
X_test = X_test.astype(float) / 255
history = additional_model.fit(X_train, y_train,
batch_size=1,
epochs=1,
validation_data=(X_test, y_test))
input_tensor = Input(shape=(400, 300, 3), dtype='float32', name='input')
pre_trained_res = ResNet152V2(weights='imagenet', include_top=False, input_shape=(400, 300, 3))
pre_trained_res.trainable = False
pre_trained_res.summary()
additional_model = models.Sequential()
additional_model.add(pre_trained_res)
additional_model.add(layers.Flatten())
additional_model.add(layers.Dense(4096, activation='relu'))
additional_model.add(layers.Dense(2048, activation='relu'))
additional_model.add(layers.Dense(1024, activation='relu'))
additional_model.add(layers.Dense(3, activation='softmax'))
additional_model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
batch_size=256) # we can use much larger batches for evaluation
# used a fixed dataset for evaluating the algorithm
test_X, test_Y = next(
flow_from_dataframe(img_gen,
valid_df,
location_col='location',
y_col=disease,
target_size=IMG_SIZE,
color_mode='rgb',
batch_size=256)) # one big batch
t_x, t_y = next(train_gen)
base_model = ResNet152V2(input_shape=t_x.shape[1:],
include_top=False,
weights='imagenet')
base_model.trainable = False
# (Mader, 2018)
pt_features = Input(base_model.get_output_shape_at(0)[1:],
name='feature_input')
pt_depth = base_model.get_output_shape_at(0)[-1]
bn_features = BatchNormalization(name='Features_BN')(pt_features)
# (Mader, 2018)
attn_layer = Conv2D(180,
kernel_size=(1, 1),
padding='same',
activation='elu')(bn_features)
attn_layer = Conv2D(64,
kernel_size=(1, 1),
padding='same',