mode='auto',
epsilon=0.0001,
cooldown=0,
min_lr=0)
# 创建一个 LossHistory 实例
history = LossHistory()
# compile
model.compile(optimizer=adam(lr=LEARNING_RATE),
loss='binary_crossentropy',
metrics=['accuracy'])
# fit
model.fit_generator(train_generator,
steps_per_epoch=train_generator.samples // BATCH_SIZE,
epochs=EPOCHS,
validation_data=valid_generator,
validation_steps=valid_generator.samples // BATCH_SIZE,
callbacks=[check_point, early_stopping, history])
# 绘制 loss 曲线和 batch 曲线
history.loss_plot('batch',
os.path.join(RESULT_PATH, 'inception_v1_loss_batch.png'))
history.acc_plot('batch',
os.path.join(RESULT_PATH, 'inception_v1_acc_batch.png'))
history.loss_plot('epoch',
os.path.join(RESULT_PATH, 'inception_v1_loss_epoch.png'))
history.acc_plot('epoch',
os.path.join(RESULT_PATH, 'inception_v1_acc_epoch.png'))
def mergeFinetuneModel():
X_train = []
X_valid = []
filenames = [
os.path.join(OUTPUT_PATH, 'inceptionv3-finetune-output.hdf5'),
os.path.join(OUTPUT_PATH, 'resnet50-finetune-output.hdf5'),
os.path.join(OUTPUT_PATH, 'xception-finetune-output.hdf5'),
os.path.join(OUTPUT_PATH, 'vgg16-finetune-output.hdf5')
]
for filename in filenames:
with h5py.File(filename, 'r') as h:
X_train.append(np.array(h['X_train']))
X_valid.append(np.array(h['X_val']))
y_train = np.array(h['y_train'])
y_valid = np.array(h['y_val'])
for x in X_train:
print(x.shape)
for x in X_valid:
print(x.shape)
X_train = np.concatenate(X_train, axis=1)
X_valid = np.concatenate(X_valid, axis=1)
# check
print('X_train shape:', X_train.shape)
print('X_valid shape:', X_valid.shape)
print('y_train shape:', y_train.shape)
print('y_valid shape:', y_valid.shape)
X_train, y_train = shuffle(X_train, y_train)
y_train = to_categorical(y_train)
X_valid, y_valid = shuffle(X_valid, y_valid)
y_valid = to_categorical(y_valid)
print('X_train shape:', X_train.shape)
print('X_valid shape:', X_valid.shape)
print('y_train shape:', y_train.shape)
print('y_valid shape:', y_valid.shape)
inputs = Input(X_train.shape[1:])
x = Dense(2048, activation='relu')(inputs)
x = Dropout(DROP_RATE)(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(CLASS_NUM, activation='softmax')(inputs)
model = Model(inputs, predictions)
check_point = ModelCheckpoint(filepath=os.path.join(
MODEL_PATH, 'merge-model-01.hdf5'),
verbose=1,
save_best_only=True)
early_stopping = EarlyStopping(monitor='val_loss',
min_delta=0.0001,
patience=EARLY_STOPPING_PATIENCE,
verbose=1,
mode='auto')
# 创建一个 LossHistory 实例
history = LossHistory()
model.compile(loss='binary_crossentropy',
optimizer=Adam(lr=LEARNING_RATE),
metrics=['accuracy'])
model.fit(X_train,
y_train,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
validation_data=(X_valid, y_valid),
callbacks=[early_stopping, check_point, history])
# 绘制 loss 曲线和 batch 曲线
history.loss_plot('batch',
os.path.join(RESULT_PATH, 'merge_all_loss_batch.png'))
history.acc_plot('batch',
os.path.join(RESULT_PATH, 'merge_all_acc_batch.png'))
history.loss_plot('epoch',
os.path.join(RESULT_PATH, 'merge_all_loss_epoch.png'))
history.acc_plot('epoch',
os.path.join(RESULT_PATH, 'merge_all_acc_epoch.png'))
def create_model():
train_gen = ImageDataGenerator()
valid_gen = ImageDataGenerator()
train_generator = train_gen.flow_from_directory(TRAIN_DATA_PATH,
IMAGE_SIZE,
shuffle=True,
batch_size=BATCH_SIZE,
color_mode='grayscale')
valid_generator = valid_gen.flow_from_directory(VALID_DATA_PATH,
IMAGE_SIZE,
batch_size=BATCH_SIZE,
color_mode='grayscale')
inputs = Input((*IMAGE_SIZE, 1))
x_input = Lambda(my_preprocess)(inputs)
# block1
x = Conv2D(64, (3, 3),
input_shape=(*IMAGE_SIZE, 1),
strides=(1, 1),
padding='same',
activation='relu',
name='block1_conv1')(x_input)
x = Conv2D(64, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='block1_conv2')(x)
x = MaxPooling2D((2, 2),
strides=(2, 2),
padding='same',
name='block1_pool')(x)
# block2
x = Conv2D(128, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='block2_conv1')(x)
x = Conv2D(128, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='block2_conv2')(x)
x = MaxPooling2D((2, 2),
strides=(2, 2),
padding='same',
name='block2_pool')(x)
# block3
x = Conv2D(256, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='block3_conv1')(x)
x = Conv2D(256, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='block3_conv2')(x)
x = Conv2D(256, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='block3_conv3')(x)
x = MaxPooling2D((2, 2),
strides=(2, 2),
padding='same',
name='block3_pool')(x)
# side1
x_side1 = SeparableConv2D(512, (3, 3),
padding='same',
use_bias=False,
name='side1_sepconv1')(x)
x_side1 = BatchNormalization(name='side1_bn1')(x_side1)
x_side1 = Activation('relu', name='side1_act1')(x_side1)
x_side1 = SeparableConv2D(512, (3, 3),
padding='same',
use_bias=False,
name='side1_sepconv2')(x_side1)
x_side1 = BatchNormalization(name='side1_bn2')(x_side1)
x_side1 = Activation('relu', name='side1_act2')(x_side1)
x_side1 = MaxPooling2D((2, 2),
strides=(2, 2),
padding='same',
name='side1_pool')(x_side1)
x_side1 = SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='side1_sepconv3')(x_side1)
x_side1 = BatchNormalization(name='side1_bn3')(x_side1)
x_side1 = Activation('relu', name='side1_act3')(x_side1)
x_side1 = SeparableConv2D(728, (3, 3),
padding='same',
activation='relu',
name='side1_sepconv4')(x_side1)
x_side1 = GlobalAveragePooling2D(name='side1_gap')(x_side1)
# side2
x_side2_1_1 = Conv2D(256, (1, 1),
strides=(1, 1),
padding='same',
activation='relu',
name='side2_1_conv1')(x)
x_side2_1_2 = Conv2D(256, (1, 1),
strides=(1, 1),
padding='same',
activation='relu',
name='side2_2_conv1')(x)
x_side2_1_2 = Conv2D(256, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='side2_2_conv2')(x_side2_1_2)
x_side2_1_3 = Conv2D(256, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='side2_3_conv1')(x)
x_side2_1_3 = Conv2D(256, (1, 1),
strides=(1, 1),
padding='same',
activation='relu',
name='side2_3_conv2')(x_side2_1_3)
x_side2_1 = keras.layers.concatenate(
[x_side2_1_1, x_side2_1_2, x_side2_1_3])
x_side2_2_1 = Conv2D(256, (1, 1),
strides=(1, 1),
padding='same',
activation='relu',
name='side3_1_conv1')(x_side2_1)
x_side2_2_2 = Conv2D(256, (1, 1),
strides=(1, 1),
padding='same',
activation='relu',
name='side3_2_conv1')(x_side2_1)
x_side2_2_2 = Conv2D(256, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='side3_2_conv2')(x_side2_2_2)
x_side2_2_3 = Conv2D(256, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='side3_3_conv1')(x_side2_1)
x_side2_2_3 = Conv2D(256, (1, 1),
strides=(1, 1),
padding='same',
activation='relu',
name='side3_3_conv2')(x_side2_2_3)
x_side2_2 = keras.layers.concatenate(
[x_side2_2_1, x_side2_2_2, x_side2_2_3])
x_side2 = GlobalAveragePooling2D(name='side2_gap')(x_side2_2)
# block4
x = Conv2D(512, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='block4_conv1')(x)
x = Conv2D(512, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='block4_conv2')(x)
x = Conv2D(512, (3, 3),
strides=(1, 1),
padding='same',
activation='relu',
name='block4_conv3')(x)
x = GlobalAveragePooling2D(name='gap')(x)
x = keras.layers.concatenate([x, x_side1, x_side2])
x = Dropout(DROP_RATE, name='dropout1')(x)
predictions = Dense(CLASS_NUM, activation='softmax', name='dense1')(x)
model = Model(inputs=inputs, outputs=predictions)
model.summary()
plot_model(model,
to_file=os.path.join(RESULT_PATH, 'my_model.png'),
show_shapes=True)
check_point = ModelCheckpoint(monitor='val_loss',
filepath=os.path.join(
MODEL_PATH, MODEL_NAME),
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto')
# early stopping
early_stopping = EarlyStopping(monitor='val_loss',
patience=EARLY_STOPPING_PATIENCE,
verbose=0,
mode='auto')
# reduce lr
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=REDUCE_LR_PATIENCE,
verbose=0,
mode='auto',
epsilon=0.0001,
cooldown=0,
min_lr=0)
# 创建一个 LossHistory 实例
history = LossHistory()
# compile
model.compile(optimizer=adam(lr=LEARNING_RATE),
loss='binary_crossentropy',
metrics=['accuracy'])
# fit
model.fit_generator(train_generator,
steps_per_epoch=train_generator.samples // BATCH_SIZE,
epochs=EPOCHS,
validation_data=valid_generator,
validation_steps=valid_generator.samples // BATCH_SIZE,
callbacks=[check_point, early_stopping, history])
# 绘制 loss 曲线和 batch 曲线
history.loss_plot('batch', os.path.join(RESULT_PATH, 'my_loss_batch.png'))
history.acc_plot('batch', os.path.join(RESULT_PATH, 'my_batch.png'))
history.loss_plot('epoch', os.path.join(RESULT_PATH, 'my_loss_epoch.png'))
history.acc_plot('epoch', os.path.join(RESULT_PATH, 'my_acc_epoch.png'))
K.clear_session()
def finetuneModel():
train_gen = ImageDataGenerator()
valid_gen = ImageDataGenerator()
train_generator = train_gen.flow_from_directory(TRAIN_DATA_PATH,
IMAGE_SIZE,
shuffle=True,
batch_size=BATCH_SIZE,
color_mode='grayscale')
valid_generator = valid_gen.flow_from_directory(VALID_DATA_PATH,
IMAGE_SIZE,
batch_size=BATCH_SIZE,
color_mode='grayscale')
inputs = Input((*IMAGE_SIZE, 1))
x = Lambda(my_preprocess)(inputs)
base_model = InceptionV3(input_tensor=x, weights=None, include_top=False)
x = GlobalAveragePooling2D(name='my_global_average_pooling_layer_1')(
base_model.output)
x = Dropout(DROP_RATE, name='my_dropout_layer_1')(x)
predictions = Dense(CLASS_NUM,
activation='softmax',
name='my_dense_layer_1')(x)
model = Model(base_model.input, predictions)
plot_model(model,
to_file=os.path.join(RESULT_PATH, 'inceptionv3.png'),
show_shapes=True)
# set trainable layer
for layer in model.layers[:INCEPTIONV3_NO_TRAINABLE_LAYERS]:
layer.trainable = False
for layer in model.layers[INCEPTIONV3_NO_TRAINABLE_LAYERS:]:
layer.trainable = True
model.summary()
# check
for i, layer in enumerate(model.layers):
print('{}: {}, {}'.format(i, layer.name, layer.trainable))
print('=' * 100)
layers = zip(range(len(model.layers)), [x.name for x in model.layers])
for layer_num, layer_name in layers:
print('{}: {}'.format(layer_num + 1, layer_name))
# check point
check_point = ModelCheckpoint(monitor='val_loss',
filepath=os.path.join(
MODEL_PATH, MODEL_NAME),
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto')
# early stoppiing
early_stopping = EarlyStopping(monitor='val_loss',
patience=EARLY_STOPPING_PATIENCE,
verbose=0,
mode='auto')
# 创建一个 LossHistory 实例
history = LossHistory()
# compile
model.compile(optimizer=adam(lr=LEARNING_RATE),
loss='binary_crossentropy',
metrics=['accuracy'])
# fit
model.fit_generator(train_generator,
steps_per_epoch=train_generator.samples // BATCH_SIZE,
epochs=EPOCHS,
validation_data=valid_generator,
validation_steps=valid_generator.samples // BATCH_SIZE,
callbacks=[check_point, early_stopping, history])
# 绘制 loss 曲线和 batch 曲线
history.loss_plot('batch',
os.path.join(RESULT_PATH, 'inceptionv3_loss_batch.png'))
history.acc_plot('batch',
os.path.join(RESULT_PATH, 'inceptionv3_acc_batch.png'))
history.loss_plot('epoch',
os.path.join(RESULT_PATH, 'inceptionv3_loss_epoch.png'))
history.acc_plot('epoch',
os.path.join(RESULT_PATH, 'inceptionv3_acc_epoch.png'))