def reconstruct_model(checkpoint_filepath, ifMC):
"""
This function reconstruct models with input trained models. The model is
DenseNet 201 + DropOut layer. If it is a multi-class model, there are 5
nodes at the softmax layer. If it is a decision flow model, there are only 2
nodes at the softmax layer. Trained model is then returned.
"""
# Use DenseNet 201
denseNet = applications.DenseNet201(include_top=False,
weights=None,
input_shape=(128, 128, 3),
pooling='avg')
output = denseNet.output
output = layers.Dropout(0.5)(output) # Add Dropout layer
if ifMC == 0:
output = layers.Dense(2, activation='softmax')(output)
elif ifMC == 1:
output = layers.Dense(5, activation='softmax')(output)
model = tf.keras.models.Model(denseNet.input, output)
opt = tf.keras.optimizers.SGD(learning_rate=0.0002, momentum=0.7)
loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
model.compile(optimizer=opt, loss=loss, metrics=['accuracy', 'AUC'])
model.load_weights(checkpoint_filepath) # Load trained weights
return model
def dn201(img_size=(224,224), num_class=2, weights="imagenet", dtype=tf.float32):
input_layer = layers.Input(shape=(img_size[0],img_size[1],3), dtype=dtype)
base = models.DenseNet201(input_tensor=input_layer, include_top=False, weights=weights)
base.trainable = True
x = base.output
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(num_class)(x)
preds = layers.Activation("softmax", dtype=tf.float32)(x)
model = tf.keras.models.Model(inputs=input_layer, outputs=preds)
return model
def compute_mean_and_std(model_name, X, input_shape):
if model_name == 'Xception':
model = applications.Xception(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'VGG16':
model = applications.VGG16(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'VGG19':
model = applications.VGG19(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'ResNet50':
model = applications.ResNet50(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'InceptionResNetV2':
model = applications.InceptionResNetV2(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'InceptionV3':
model = applications.InceptionV3(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'MobileNet':
model = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'DenseNet121':
model = applications.DenseNet121(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'DenseNet169':
model = applications.DenseNet169(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'DenseNet201':
model = applications.DenseNet201(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'NASNetMobile':
model = applications.NASNetMobile(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif model_name == 'NASNetLarge':
model = applications.NASNetLarge(weights='imagenet',
include_top=False,
input_shape=input_shape)
else:
assert (False), "Specified base model is not available !"
features = model.predict(X)[:, 0, 0, :]
return features.mean(axis=0), features.std(axis=0)
def init_network(self):
model = applications.DenseNet201(include_top=False,
weights='imagenet',
input_shape=self.input_size)
x = model.output
x = AveragePooling2D(pool_size=(4, 4))(x)
x = Flatten()(x)
x = Dense(128, activation="relu")(x)
x = Dropout(0.5)(x)
output = Dense(len(self.CLASS_TARGETS), activation="softmax")(x)
for layer in model.layers:
layer.trainable = False
# create Model
self.model = Model(inputs=model.inputs, outputs=output)
# compile Model
self.model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
def reconstruct_model(checkpoint_filepath, ifMC):
denseNet = applications.DenseNet201(include_top=False,
weights=None,
input_shape=(128, 128, 3),
pooling='avg')
output = denseNet.output
output = layers.Dropout(0.5)(output)
if ifMC == 0:
output = layers.Dense(2, activation='softmax')(output)
elif ifMC == 1:
output = layers.Dense(5, activation='softmax')(output)
model = tf.keras.models.Model(denseNet.input, output)
opt = tf.keras.optimizers.SGD(learning_rate=0.0002, momentum=0.7)
loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
model.compile(optimizer=opt, loss=loss, metrics=['accuracy', 'AUC'])
model.load_weights(checkpoint_filepath)
return model
y_train = y_train.reshape(len(y_train), 1)
x_test = x_test.reshape(len(x_test), 128, 128, 3) / 255.0
y_test = y_test.reshape(len(y_test), 1)
x_validation = x_validation.reshape(len(x_validation), 128, 128, 3) / 255.0
y_validation = y_validation.reshape(len(y_validation), 1)
"""
Convert to one hot encoding
"""
y_train = tf.keras.utils.to_categorical(y_train, 2)
y_test = tf.keras.utils.to_categorical(y_test, 2)
y_validation = tf.keras.utils.to_categorical(y_validation, 2)
"""
Model setup
"""
denseNet = applications.DenseNet201(include_top=False,
weights="imagenet",
input_shape=(128, 128, 3),
pooling='avg')
output = denseNet.output
output = layers.Dropout(0.5)(output)
output = layers.Dense(2, activation='softmax')(output)
model = tf.keras.models.Model(denseNet.input, output)
model.trainable = True
regularizer = tf.keras.regularizers.l2(0.0001)
for layer in model.layers:
for attr in ['kernel_regularizer']:
if hasattr(layer, attr):
setattr(layer, attr, regularizer)
def estimate(X_train, y_train, back_bone):
IMAGE_WIDTH = 224 # Image width
IMAGE_HEIGHT = 224 # Image height
input_shape = (IMAGE_WIDTH, IMAGE_HEIGHT, 3) # (width, height, channel) channel = 3 ---> RGB
batch_size = 8
epochs = 1 # Number of epochs
ntrain = 0.8 * len(X_train) # split data with 80/20 train/validation
nval = 0.2 * len(X_train)
back_bone = str(back_bone)
print("Using " + back_bone + "...")
X = []
X_train = np.reshape(np.array(X_train), [len(X_train), ])
for img in list(range(0, len(X_train))):
if X_train[img].ndim >= 3:
X.append(cv2.resize(
X_train[img][:, :, :3], (IMAGE_WIDTH, IMAGE_HEIGHT), interpolation=cv2.INTER_CUBIC))
else:
smimg = cv2.cvtColor(X_train[img][0], cv2.COLOR_GRAY2RGB)
X.append(cv2.resize(smimg, (IMAGE_WIDTH, IMAGE_HEIGHT),
interpolation=cv2.INTER_CUBIC))
if y_train[img] == 'COVID':
y_train[img] = 1
elif y_train[img] == 'NonCOVID':
y_train[img] = 0
else:
continue
x = np.array(X)
X_train, X_val, y_train, y_val = train_test_split( # 20% validation set
x, y_train, test_size=0.20, random_state=2)
# data generator
if back_bone == 'ResNet50V2' or back_bone =='1':
train_datagen = ImageDataGenerator(
preprocessing_function=resnet_preprocess,
rotation_range=15,
shear_range=0.1,
zoom_range=0.2,
horizontal_flip=True,
width_shift_range=0.1,
height_shift_range=0.1
)
val_datagen = ImageDataGenerator(preprocessing_function=resnet_preprocess)
elif back_bone == 'Xception' or back_bone =='2':
train_datagen = ImageDataGenerator(
preprocessing_function=xception_preprocess,
rotation_range=15,
shear_range=0.1,
zoom_range=0.2,
horizontal_flip=True,
width_shift_range=0.1,
height_shift_range=0.1
)
val_datagen = ImageDataGenerator(preprocessing_function=xception_preprocess)
elif back_bone == "DenseNet201" or back_bone =='3':
train_datagen = ImageDataGenerator(
preprocessing_function=denset_preprocess,
rotation_range=15,
shear_range=0.1,
zoom_range=0.2,
horizontal_flip=True,
width_shift_range=0.1,
height_shift_range=0.1
)
val_datagen = ImageDataGenerator(preprocessing_function=denset_preprocess)
elif back_bone == "MobileNetV2" or back_bone == '4':
train_datagen = ImageDataGenerator(
preprocessing_function= mobile_preprocess,
rotation_range=15,
shear_range=0.1,
zoom_range=0.2,
horizontal_flip=True,
width_shift_range=0.1,
height_shift_range=0.1
)
val_datagen = ImageDataGenerator(preprocessing_function=mobile_preprocess)
else:
raise ValueError('Please select transfer learning model!')
train_generator = train_datagen.flow(
X_train, y_train, batch_size=batch_size, shuffle=True)
val_generator = val_datagen.flow(
X_val, y_val, batch_size=batch_size, shuffle=True)
# model
model = Sequential()
if back_bone == 'ResNet50V2' or back_bone == '1':
base_model = applications.resnet_v2.ResNet50V2(
include_top=False, pooling='avg', weights='imagenet', input_shape=input_shape)
elif back_bone == 'Xception' or back_bone == '2':
base_model = applications.Xception(
include_top=False, pooling='avg', weights='imagenet', input_shape=input_shape)
elif back_bone == 'DenseNet201' or back_bone =='3':
base_model = applications.DenseNet201(
include_top=False, pooling='avg', weights='imagenet', input_shape=input_shape)
elif back_bone == 'MobileNetV2' or back_bone =='4':
base_model = applications.MobileNetV2(
include_top=False, pooling='avg', weights='imagenet', input_shape=input_shape)
else:
raise ValueError('Please select transfer learning model!')
base_model.trainable = False
model.add(base_model)
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=optimizers.Adam(lr=1e-4),
metrics=['acc'])
# callbacks
lr_reducer = ReduceLROnPlateau(
monitor='val_loss',
factor=0.8,
patience=5,
verbose=1,
mode='auto',
min_delta=0.0001,
cooldown=3,
min_lr=1e-14)
best_loss_path = "Model.h5"
ck_loss_model = ModelCheckpoint(
best_loss_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks = [ck_loss_model, lr_reducer]
history = model.fit_generator(train_generator,
steps_per_epoch=ntrain//batch_size,
epochs=epochs,
validation_data=val_generator,
validation_steps=nval // batch_size,
callbacks=callbacks)
return model
def build_autoencoder(base_model_name, input_shape, imagenet_mean,
imagenet_std, hidden_layer_size, n_classes,
weight_decay):
if base_model_name == 'Xception':
base_model = applications.Xception(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'VGG16':
base_model = applications.VGG16(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'VGG19':
base_model = applications.VGG19(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'ResNet50':
base_model = applications.ResNet50(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'InceptionResNetV2':
base_model = applications.InceptionResNetV2(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'InceptionV3':
base_model = applications.InceptionV3(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'MobileNet':
base_model = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'DenseNet121':
base_model = applications.DenseNet121(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'DenseNet169':
base_model = applications.DenseNet169(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'DenseNet201':
base_model = applications.DenseNet201(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'NASNetMobile':
base_model = applications.NASNetMobile(weights='imagenet',
include_top=False,
input_shape=input_shape)
elif base_model_name == 'NASNetLarge':
base_model = applications.NASNetLarge(weights='imagenet',
include_top=False,
input_shape=input_shape)
else:
assert (False), "Specified base model is not available !"
n_features = base_model.output.shape[-1]
x = base_model.output
x = tf.keras.layers.Lambda(lambda x: (x - imagenet_mean) / imagenet_std)(
x) # normalization
x = tf.keras.layers.Activation(activation='sigmoid',
name='encoder')(x) # sigmoid
x = tf.keras.layers.Dense(units=hidden_layer_size,
activation=None)(x) # encoding
x = tf.keras.layers.Activation(activation='relu')(x) # relu
x = tf.keras.layers.Dense(units=n_features,
activation=None,
name='decoder')(x) # decoding
x = tf.keras.layers.Dense(units=n_classes, activation='sigmoid')(
x) # x = tf.keras.layers.Activation(activation='sigmoid')(x) # sigmoid
model = tf.keras.Model(inputs=base_model.input, outputs=x[:, 0, 0, :])
for layer in model.layers:
if isinstance(layer, tf.keras.layers.Conv2D) or isinstance(
layer, tf.keras.layers.Dense):
layer.add_loss(
tf.keras.regularizers.l2(weight_decay)(layer.kernel))
if hasattr(layer, 'bias_regularizer') and layer.use_bias:
layer.add_loss(tf.keras.regularizers.l2(weight_decay)(layer.bias))
return model
return image_fingerprint_vector
tf.compat.v1.set_random_seed(1234)
model1 = applications.EfficientNetB7(weights='imagenet',
include_top=False,
pooling='avg')
model2 = applications.EfficientNetB6(weights='imagenet',
include_top=False,
pooling='avg')
model3 = applications.inception_resnet_v2.InceptionResNetV2(weights='imagenet',
include_top=False,
pooling='avg')
model4 = applications.DenseNet201(weights='imagenet',
include_top=False,
pooling='avg')
model5 = applications.inception_v3.InceptionV3(weights='imagenet',
include_top=False,
pooling='avg')
model6 = applications.NASNetLarge(weights='imagenet',
include_top=False,
pooling='avg')
model7 = applications.ResNet152V2(weights='imagenet',
include_top=False,
pooling='avg')
img = image.load_img('Ardee_Arollado__Number_08.png', target_size=(224, 224))
x = image.img_to_array(img) # convert image to numpy array
x = np.expand_dims(
x, axis=0
