def build_model(self, input_shape, n_classes):
## input layer
input_layer = Input(input_shape)
## convolutional layers
conv_layer1 = Conv2D(filters=8, kernel_size=(3, 3),
activation='relu')(input_layer)
conv_layer2 = Conv2D(filters=16, kernel_size=(3, 3),
activation='relu')(conv_layer1)
## add max pooling to obtain the most imformatic features
pooling_layer1 = MaxPool2D(pool_size=(2, 2))(conv_layer2)
conv_layer3 = Conv2D(filters=32, kernel_size=(3, 3),
activation='relu')(pooling_layer1)
conv_layer4 = Conv2D(filters=64, kernel_size=(3, 3),
activation='relu')(conv_layer3)
pooling_layer2 = MaxPool2D(pool_size=(2, 2))(conv_layer4)
## perform batch normalization on the convolution outputs before feeding it to MLP architecture
pooling_layer2 = BatchNormalization()(pooling_layer2)
flatten_layer = Flatten()(pooling_layer2)
## create an MLP architecture with dense layers : 4096 -> 512 -> 10
## add dropouts to avoid overfitting / perform regularization
dense_layer1 = Dense(units=2048, activation='relu')(flatten_layer)
dense_layer1 = Dropout(0.4)(dense_layer1)
dense_layer2 = Dense(units=512, activation='relu')(dense_layer1)
dense_layer2 = Dropout(0.4)(dense_layer2)
output_layer = Dense(units=n_classes,
activation='softmax')(dense_layer2)
## define the model with input layer and output layer
model = Model(inputs=input_layer, outputs=output_layer)
model.summary()
plot_model(model,
to_file=self.output_directory + '/model_graph.png',
show_shapes=True,
show_layer_names=True)
model.compile(loss=categorical_crossentropy,
optimizer=Adadelta(),
metrics=['acc'])
# model save
file_path = self.output_directory + '/best_model.hdf5'
model_checkpoint = callbacks.ModelCheckpoint(filepath=file_path,
monitor='loss',
save_best_only=True)
# Tensorboard log
log_dir = self.output_directory + '/tf_logs'
chk_n_mkdir(log_dir)
tb_cb = TrainValTensorBoard(log_dir=log_dir)
self.callbacks = [model_checkpoint, tb_cb]
return model
def build_model(self, input_shape, n_classes):
xception_1 = self.xception(include_top=False, pooling='max')
for layer in xception_1.layers:
layer.trainable = False
input_1 = xception_1.input
output_1 = xception_1.output
xception_2 = self.xception(include_top=False, pooling='max')
for layer in xception_2.layers:
layer.trainable = False
input_2 = xception_2.input
output_2 = xception_2.output
xception_3 = self.xception(include_top=False, pooling='max')
for layer in xception_3.layers:
layer.trainable = False
input_3 = xception_3.input
output_3 = xception_3.output
xception_4 = self.xception(include_top=False, pooling='max')
for layer in xception_4.layers:
layer.trainable = False
input_4 = xception_4.input
output_4 = xception_4.output
concat_layer = Maximum()([output_1, output_2, output_3, output_4])
concat_layer.trainable = False
# concat_layer = Dropout(0.25)(concat_layer)
# dense_layer1 = Dense(units=1024, activation='relu')(concat_layer)
dense_layer1 = Dropout(0.5)(concat_layer)
output_layer = Dense(n_classes,
activation='softmax',
name='predictions')(dense_layer1)
model = Model(inputs=[input_1, input_2, input_3, input_4],
outputs=[output_layer])
model.summary()
plot_model(model,
to_file=self.output_directory + '/model_graph.png',
show_shapes=True,
show_layer_names=True)
model.compile(loss=categorical_crossentropy,
optimizer=Adam(lr=0.01),
metrics=['acc'])
# model save
file_path = self.output_directory + '/best_model.hdf5'
model_checkpoint = keras.callbacks.ModelCheckpoint(filepath=file_path,
monitor='loss',
save_best_only=True)
# Tensorboard log
log_dir = self.output_directory + '/tf_logs'
chk_n_mkdir(log_dir)
tb_cb = TrainValTensorBoard(log_dir=log_dir)
self.callbacks = [model_checkpoint, tb_cb]
return model
def build_model(self, input_shape, n_classes):
# Load the VGG model
xception_conv = Xception(weights='imagenet',
include_top=False,
input_shape=input_shape)
# Freeze the layers except the last 4 layers
for layer in xception_conv.layers:
layer.trainable = False
# Create the model
model = models.Sequential()
# Add the vgg convolutional base model
model.add(xception_conv)
# Add new layers
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation='softmax', name='predictions'))
# define the model with input layer and output layer
model.summary()
plot_model(model,
to_file=self.output_directory + '/model_graph.png',
show_shapes=True,
show_layer_names=True)
model.compile(loss=categorical_crossentropy,
optimizer=Adam(lr=0.01),
metrics=['acc'])
# model save
file_path = self.output_directory + '/best_model.hdf5'
model_checkpoint = callbacks.ModelCheckpoint(filepath=file_path,
monitor='loss',
save_best_only=True)
# Tensorboard log
log_dir = self.output_directory + '/tf_logs'
chk_n_mkdir(log_dir)
tb_cb = TrainValTensorBoard(log_dir=log_dir)
self.callbacks = [model_checkpoint, tb_cb]
return model
def build_model(self, input_shape, n_classes):
## input layer
input_layer = Input(input_shape)
## convolutional layers
conv_layer1 = Conv2D(filters=16, kernel_size=(3, 3), activation='relu')(input_layer)
pooling_layer1 = MaxPool2D(pool_size=(2, 2), strides=(2, 2))(conv_layer1)
conv_layer2 = Conv2D(filters=32, kernel_size=(3, 3), activation='relu')(pooling_layer1)
conv_layer3 = Conv2D(filters=32, kernel_size=(3, 3), activation='relu')(conv_layer2)
pooling_layer2 = MaxPool2D(pool_size=(2, 2), strides=(2, 2))(conv_layer3)
dropout_layer =Dropout(0.5)(pooling_layer2)
dense_layer = Dense(units=2048, activation='relu')(dropout_layer)
output_layer = Dense(units=n_classes, activation='softmax')(dense_layer)
## define the model with input layer and output layer
model = Model(inputs=input_layer, outputs=output_layer)
model.summary()
plot_model(model, to_file=self.output_directory + '/model_graph.png', show_shapes=True, show_layer_names=True)
model.compile(loss=categorical_crossentropy, optimizer=Adam(), metrics=['acc'])
# model save
file_path = self.output_directory + '/best_model.hdf5'
model_checkpoint = callbacks.ModelCheckpoint(filepath=file_path, monitor='loss', save_best_only=True)
# Tensorboard log
log_dir = self.output_directory + '/tf_logs'
chk_n_mkdir(log_dir)
tb_cb = TrainValTensorBoard(log_dir=log_dir)
self.callbacks = [model_checkpoint, tb_cb]
return model
def build_model(self, input_shape, n_classes):
# input layer
input_layer = Input(input_shape)
channel_axis = -1 # channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1
# Block 1
x = Conv3D(8, (3, 3, 3), use_bias=False,
name='block1_conv1')(input_layer)
x = BatchNormalization(axis=channel_axis, name='block1_conv1_bn')(x)
x = Activation('relu', name='block1_conv1_act')(x)
x = Conv3D(8, (3, 3, 2), use_bias=False, name='block1_conv2')(x)
x = BatchNormalization(axis=channel_axis, name='block1_conv2_bn')(x)
x = Activation('relu', name='block1_conv2_act')(x)
residual = Conv3D(16, (1, 1, 1),
strides=(2, 2, 1),
padding='same',
use_bias=False)(x)
residual = BatchNormalization(axis=channel_axis)(residual)
# Block 2
x = Conv3D(16, (3, 3, 1),
padding='same',
use_bias=False,
name='block2_conv1')(x)
x = BatchNormalization(axis=channel_axis, name='block2_conv1_bn')(x)
x = MaxPooling3D((3, 3, 1),
strides=(2, 2, 1),
padding='same',
name='block2_pool')(x)
x = add([x, residual])
residual = Conv3D(32, (1, 1, 1),
strides=(2, 2, 1),
padding='same',
use_bias=False)(x)
residual = BatchNormalization(axis=channel_axis)(residual)
# Block 3
x = Activation('relu', name='block3_conv1_act')(x)
x = Conv3D(32, (3, 3, 1),
padding='same',
use_bias=False,
name='block3_conv1')(x)
x = BatchNormalization(axis=channel_axis, name='block3_conv1_bn')(x)
x = MaxPooling3D((3, 3, 1),
strides=(2, 2, 1),
padding='same',
name='block3_pool')(x)
x = add([x, residual])
# Block 4
x = Conv3D(64, (3, 3, 1),
padding='same',
use_bias=False,
name='block4_conv1')(x)
x = BatchNormalization(axis=channel_axis, name='block4_conv1_bn')(x)
x = Activation('relu', name='block4_conv1_act')(x)
# Classification block
x = GlobalAveragePooling3D(name='avg_pool')(x)
output_layer = Dense(n_classes,
activation='softmax',
name='predictions')(x)
# ## create an MLP architecture with dense layers : 4096 -> 512 -> 10
# ## add dropouts to avoid overfitting / perform regularization
# dense_layer1 = Dense(units=2048, activation='relu')(x)
# dense_layer1 = Dropout(0.4)(dense_layer1)
# dense_layer2 = Dense(units=512, activation='relu')(dense_layer1)
# dense_layer2 = Dropout(0.4)(dense_layer2)
# output_layer = Dense(units=n_classes, activation='softmax')(dense_layer2)
# define the model with input layer and output layer
model = Model(inputs=input_layer, outputs=output_layer)
model.summary()
plot_model(model,
to_file=self.output_directory + '/model_graph.png',
show_shapes=True,
show_layer_names=True)
model.compile(loss=categorical_crossentropy,
optimizer=Adadelta(lr=0.1),
metrics=['acc'])
# model save
file_path = self.output_directory + '/best_model.hdf5'
model_checkpoint = keras.callbacks.ModelCheckpoint(filepath=file_path,
monitor='loss',
save_best_only=True)
# Tensorboard log
log_dir = self.output_directory + '/tf_logs'
chk_n_mkdir(log_dir)
tb_cb = TrainValTensorBoard(log_dir=log_dir)
self.callbacks = [model_checkpoint, tb_cb]
return model
def build_model(self, input_shape, n_classes):
# input layer
input_layer = Input(input_shape)
# Block 1
x = Conv3D(64, (3, 3, 3),
activation='relu',
padding='same',
name='block1_conv1')(input_layer)
x = Conv3D(64, (3, 3, 3),
activation='relu',
padding='same',
name='block1_conv2')(x)
x = MaxPool3D((2, 2, 2), strides=(2, 2, 1), name='block1_pool')(x)
# Block 2
x = Conv3D(128, (3, 3, 3),
activation='relu',
padding='same',
name='block2_conv1')(x)
x = Conv3D(128, (3, 3, 3),
activation='relu',
padding='same',
name='block2_conv2')(x)
x = MaxPool3D((2, 2, 2), strides=(2, 2, 1), name='block2_pool')(x)
# Block 3
x = Conv3D(256, (3, 3, 2),
activation='relu',
padding='same',
name='block3_conv1')(x)
x = Conv3D(256, (3, 3, 2),
activation='relu',
padding='same',
name='block3_conv2')(x)
x = Conv3D(256, (3, 3, 2),
activation='relu',
padding='same',
name='block3_conv3')(x)
x = Conv3D(256, (3, 3, 2),
activation='relu',
padding='same',
name='block3_conv4')(x)
x = MaxPool3D((2, 2, 2), strides=(2, 2, 1), name='block3_pool')(x)
# Block 4
x = Conv3D(512, (3, 3, 1),
activation='relu',
padding='same',
name='block4_conv1')(x)
x = Conv3D(512, (3, 3, 1),
activation='relu',
padding='same',
name='block4_conv2')(x)
x = Conv3D(512, (3, 3, 1),
activation='relu',
padding='same',
name='block4_conv3')(x)
x = Conv3D(512, (3, 3, 1),
activation='relu',
padding='same',
name='block4_conv4')(x)
x = MaxPool3D((2, 2, 1), strides=(2, 2, 1), name='block4_pool')(x)
# Block 5
x = Conv3D(512, (3, 3, 1),
activation='relu',
padding='same',
name='block5_conv1')(x)
x = Conv3D(512, (3, 3, 1),
activation='relu',
padding='same',
name='block5_conv2')(x)
x = Conv3D(512, (3, 3, 1),
activation='relu',
padding='same',
name='block5_conv3')(x)
x = Conv3D(512, (3, 3, 1),
activation='relu',
padding='same',
name='block5_conv4')(x)
x = MaxPool3D((2, 2, 1), strides=(2, 2, 1), name='block5_pool')(x)
# Classification block
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dropout(0.4)(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dropout(0.4)(x)
output_layer = Dense(n_classes,
activation='softmax',
name='predictions')(x)
## define the model with input layer and output layer
model = Model(inputs=input_layer, outputs=output_layer)
model.summary()
plot_model(model,
to_file=self.output_directory + '/model_graph.png',
show_shapes=True,
show_layer_names=True)
model.compile(loss=categorical_crossentropy,
optimizer=Adadelta(lr=0.1),
metrics=['acc'])
# model save
file_path = self.output_directory + '/best_model.hdf5'
model_checkpoint = callbacks.ModelCheckpoint(filepath=file_path,
monitor='loss',
save_best_only=True)
# Tensorboard log
log_dir = self.output_directory + '/tf_logs'
chk_n_mkdir(log_dir)
tb_cb = TrainValTensorBoard(log_dir=log_dir)
self.callbacks = [model_checkpoint, tb_cb]
return model
def build_model(self, input_shape, n_classes):
# input layer
input_layer = Input(input_shape)
channel_axis = -1 # channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1
# Block 1
x = Conv2D(32, (3, 3),
strides=(2, 2),
use_bias=False,
name='block1_conv1')(input_layer)
x = BatchNormalization(axis=channel_axis, name='block1_conv1_bn')(x)
x = Activation('relu', name='block1_conv1_act')(x)
x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
x = BatchNormalization(axis=channel_axis, name='block1_conv2_bn')(x)
x = Activation('relu', name='block1_conv2_act')(x)
residual = Conv2D(128, (1, 1),
strides=(2, 2),
padding='same',
use_bias=False)(x)
residual = BatchNormalization(axis=channel_axis)(residual)
# Block 2
x = SeparableConv2D(128, (3, 3),
padding='same',
use_bias=False,
name='block2_sepconv1')(x)
x = BatchNormalization(axis=channel_axis, name='block2_sepconv1_bn')(x)
x = Activation('relu', name='block2_sepconv2_act')(x)
x = SeparableConv2D(128, (3, 3),
padding='same',
use_bias=False,
name='block2_sepconv2')(x)
x = BatchNormalization(axis=channel_axis, name='block2_sepconv2_bn')(x)
x = MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='block2_pool')(x)
x = add([x, residual])
residual = Conv2D(256, (1, 1),
strides=(2, 2),
padding='same',
use_bias=False)(x)
residual = BatchNormalization(axis=channel_axis)(residual)
# Block 3
x = Activation('relu', name='block3_sepconv1_act')(x)
x = SeparableConv2D(256, (3, 3),
padding='same',
use_bias=False,
name='block3_sepconv1')(x)
x = BatchNormalization(axis=channel_axis, name='block3_sepconv1_bn')(x)
x = Activation('relu', name='block3_sepconv2_act')(x)
x = SeparableConv2D(256, (3, 3),
padding='same',
use_bias=False,
name='block3_sepconv2')(x)
x = BatchNormalization(axis=channel_axis, name='block3_sepconv2_bn')(x)
x = MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='block3_pool')(x)
x = add([x, residual])
residual = Conv2D(728, (1, 1),
strides=(2, 2),
padding='same',
use_bias=False)(x)
residual = BatchNormalization(axis=channel_axis)(residual)
# Block 4
x = Activation('relu', name='block4_sepconv1_act')(x)
x = SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block4_sepconv1')(x)
x = BatchNormalization(axis=channel_axis, name='block4_sepconv1_bn')(x)
x = Activation('relu', name='block4_sepconv2_act')(x)
x = SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block4_sepconv2')(x)
x = BatchNormalization(axis=channel_axis, name='block4_sepconv2_bn')(x)
x = MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='block4_pool')(x)
x = add([x, residual])
# Block 5-12
for i in range(8):
residual = x
prefix = 'block' + str(i + 5)
x = Activation('relu', name=prefix + '_sepconv1_act')(x)
x = SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name=prefix + '_sepconv1')(x)
x = BatchNormalization(axis=channel_axis,
name=prefix + '_sepconv1_bn')(x)
x = Activation('relu', name=prefix + '_sepconv2_act')(x)
x = SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name=prefix + '_sepconv2')(x)
x = BatchNormalization(axis=channel_axis,
name=prefix + '_sepconv2_bn')(x)
x = Activation('relu', name=prefix + '_sepconv3_act')(x)
x = SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name=prefix + '_sepconv3')(x)
x = BatchNormalization(axis=channel_axis,
name=prefix + '_sepconv3_bn')(x)
x = add([x, residual])
residual = Conv2D(1024, (1, 1),
strides=(2, 2),
padding='same',
use_bias=False)(x)
residual = BatchNormalization(axis=channel_axis)(residual)
# Block 13
x = Activation('relu', name='block13_sepconv1_act')(x)
x = SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block13_sepconv1')(x)
x = BatchNormalization(axis=channel_axis,
name='block13_sepconv1_bn')(x)
x = Activation('relu', name='block13_sepconv2_act')(x)
x = SeparableConv2D(1024, (3, 3),
padding='same',
use_bias=False,
name='block13_sepconv2')(x)
x = BatchNormalization(axis=channel_axis,
name='block13_sepconv2_bn')(x)
x = MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='block13_pool')(x)
x = add([x, residual])
# Block 14
x = SeparableConv2D(1536, (3, 3),
padding='same',
use_bias=False,
name='block14_sepconv1')(x)
x = BatchNormalization(axis=channel_axis,
name='block14_sepconv1_bn')(x)
x = Activation('relu', name='block14_sepconv1_act')(x)
x = SeparableConv2D(2048, (3, 3),
padding='same',
use_bias=False,
name='block14_sepconv2')(x)
x = BatchNormalization(axis=channel_axis,
name='block14_sepconv2_bn')(x)
x = Activation('relu', name='block14_sepconv2_act')(x)
# Classification block
x = GlobalAveragePooling2D(name='avg_pool')(x)
output_layer = Dense(n_classes,
activation='softmax',
name='predictions')(x)
# define the model with input layer and output layer
model = Model(inputs=input_layer, outputs=output_layer)
model.summary()
plot_model(model,
to_file=self.output_directory + '/model_graph.png',
show_shapes=True,
show_layer_names=True)
model.compile(loss=categorical_crossentropy,
optimizer=Adadelta(lr=0.1),
metrics=['acc'])
# model save
file_path = self.output_directory + '/best_model.hdf5'
model_checkpoint = callbacks.ModelCheckpoint(filepath=file_path,
monitor='loss',
save_best_only=True)
# Tensorboard log
log_dir = self.output_directory + '/tf_logs'
chk_n_mkdir(log_dir)
tb_cb = TrainValTensorBoard(log_dir=log_dir)
self.callbacks = [model_checkpoint, tb_cb]
return model