def resnet50():
"""
resnet50的基础特征提取网络
:return:
"""
bn_axis = 3
input_tensor = Input(shape=(224, 224, 3))
# resnet50基础网络部分
x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(input_tensor)
x = layers.Conv2D(64, (7, 7),
strides=(2, 2),
padding='valid',
kernel_initializer='he_normal',
name='conv1')(x)
x = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
'''
# 选定trainable的层，默认全部训练
base_model = Model(inputs=img_input, outputs=x)
for layer in base_model.layers:
if isinstance(layer, BatchNormalization):
layer.trainable = True
else:
layer.trainable = False
'''
return input_tensor, x
def ResNet50_lr(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000):
"""Instantiates the ResNet50 architecture.
Optionally loads weights pre-trained
on ImageNet. Note that when using TensorFlow,
for best performance you should set
`image_data_format='channels_last'` in your Keras config
at ~/.keras/keras.json.
The model and the weights are compatible with both
TensorFlow and Theano. The data format
convention used by the model is the one
specified in your Keras config file.
# Arguments
include_top: whether to include the fully-connected
layer at the top of the network.
weights: one of `None` (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)` (with `channels_last` data format)
or `(3, 224, 224)` (with `channels_first` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 197.
E.g. `(200, 200, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
if not (weights in {'imagenet', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), '
'or the path to the weights file to be loaded.')
if weights == 'imagenet' and include_top and classes != 1000:
raise ValueError('If using `weights` as imagenet with `include_top`'
' as true, `classes` should be 1000')
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=64,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input)
x = Conv2D(64, (7, 7), strides=(2, 2), padding='valid', name='conv1')(x)
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
# x = AveragePooling2D((7, 7), name='avg_pool')(x)
if include_top:
x = Flatten()(x)
x = Dense(classes, activation='softmax', name='fc1000')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='resnet50')
# load weights
if weights == 'imagenet':
if include_top:
weights_path = get_file(
'resnet50_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
else:
weights_path = get_file(
'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='a268eb855778b3df3c7506639542a6af')
model.load_weights(weights_path)
if K.backend() == 'theano':
layer_utils.convert_all_kernels_in_model(model)
if include_top:
maxpool = model.get_layer(name='avg_pool')
shape = maxpool.output_shape[1:]
dense = model.get_layer(name='fc1000')
layer_utils.convert_dense_weights_data_format(
dense, shape, 'channels_first')
if K.image_data_format() == 'channels_first' and K.backend(
) == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image data format convention '
'(`image_data_format="channels_first"`). '
'For best performance, set '
'`image_data_format="channels_last"` in '
'your Keras config '
'at ~/.keras/keras.json.')
elif weights is not None:
model.load_weights(weights)
return model
def ResNet50(self):
classes = self.num_classes
img_input = Input(shape=self.input_shape)
x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input)
x = layers.Conv2D(64, (7, 7),
strides=(2, 2),
padding='valid',
name='conv1')(x)
x = layers.BatchNormalization(axis=3, name='bn_conv1')(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
x = layers.AveragePooling2D((7, 7), name='avg_pool')(x)
x = layers.Flatten()(x)
x = layers.Dense(classes, activation='softmax', name='fc1000')(x)
model = models.Model(img_input, x, name='resnet50')
return model
def KerasModelResNet(self, imgInput):
"""
Construct resNet. The image size is 150*150, which is suitable for image.
"""
bn_axis = 3
x = ZeroPadding2D((3, 3))(imgInput)
x = Convolution2D(8, 7, strides=(2, 2), name='conv1')(x)
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [8, 8, 16], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [8, 8, 16], stage=2, block='b')
x = identity_block(x, 3, [8, 8, 16], stage=2, block='c')
x = conv_block(x, 3, [16, 16, 32], stage=3, block='a')
x = identity_block(x, 3, [16, 16, 32], stage=3, block='b')
x = identity_block(x, 3, [16, 16, 32], stage=3, block='c')
x = identity_block(x, 3, [16, 16, 32], stage=3, block='d')
x = conv_block(x, 3, [32, 32, 64], stage=4, block='a')
x = identity_block(x, 3, [32, 32, 64], stage=4, block='b')
x = identity_block(x, 3, [32, 32, 64], stage=4, block='c')
x = identity_block(x, 3, [32, 32, 64], stage=4, block='d')
x = identity_block(x, 3, [32, 32, 64], stage=4, block='e')
x = identity_block(x, 3, [32, 32, 64], stage=4, block='f')
x = conv_block(x, 3, [64, 64, 128], stage=5, block='a')
x = identity_block(x, 3, [64, 64, 128], stage=5, block='b')
x = identity_block(x, 3, [64, 64, 128], stage=5, block='c')
x = conv_block(x, 3, [64, 64, 256], stage=6, block='a')
x = identity_block(x, 3, [64, 64, 256], stage=6, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=6, block='c')
x = GlobalAveragePooling2D()(x)
# x = Flatten()(x)
x = Dense(self.featureDim,
kernel_regularizer=regularizers.l2(0.0002),
activity_regularizer=regularizers.l1(0.0002),
name='fc_feature')(x)
x = PReLU()(x)
return x
def resnet50(inputs):
# Determine proper input shape
bn_axis = 3
# x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(inputs)
x = layers.Conv2D(64, (3, 3), strides=(1, 1), padding='same',
name='conv1')(inputs)
x = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 64], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 64], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 64], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 128], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 128], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 128], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 128], stage=3, block='d')
x = conv_block(x, 3, [256, 256, 256], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 256], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 256], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 256], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 256], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 256], stage=4, block='f')
x = conv_block(x, 3, [512, 512, 512], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 512], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 512], stage=5, block='c')
# # 确定精调层
# no_train_model = Model(inputs=img_input, outputs=x)
# for l in no_train_model.layers:
# if isinstance(l, layers.BatchNormalization):
# l.trainable = True
# else:
# l.trainable = False
# model = Model(input, x, name='resnet50')
x = layers.GlobalAveragePooling2D()(x)
# # 新增一个全连接层降维
# x = layers.Dense(units=512)(x)
return x
def resnet50_unet_sigmoid(
input_shape=(IMG_H, IMG_W, IMG_C), weights='imagenet'):
inp = Input(input_shape)
x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(inp)
x = layers.Conv2D(64, (7, 7),
strides=(2, 2),
padding='valid',
name='conv1')(x)
x = layers.BatchNormalization(axis=BN_AXIS, name='bn_conv1')(x)
x = layers.Activation('relu')(x)
c1 = x
# print("c1")
# print(c1.shape)
x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
c2 = x
# print("c2")
# print(c2.shape)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
c3 = x
# print("c3")
# print(c3.shape)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
c4 = x
# print("c4")
# print(c4.shape)
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
c5 = x
# print("c5")
# print(c5.shape)
u6 = conv_block_custom(UpSampling2D()(c5), 1024)
# print("u6")
# print(u6.shape)
u6 = concatenate([u6, c4], axis=-1)
u6 = conv_block_custom(u6, 1024)
u7 = conv_block_custom(UpSampling2D()(u6), 512)
# print("u7")
# print(u7.shape)
u7 = concatenate([u7, c3], axis=-1)
u7 = conv_block_custom(u7, 512)
u8 = conv_block_custom(UpSampling2D()(u7), 256)
# print("u8")
# print(u8.shape)
u8 = concatenate([u8, c2], axis=-1)
u8 = conv_block_custom(u8, 256)
u9 = conv_block_custom(UpSampling2D()(u8), 64)
# print("u9")
# print(u9.shape)
u9 = concatenate([u9, c1], axis=-1)
u9 = conv_block_custom(u9, 64)
u10 = conv_block_custom(UpSampling2D()(u9), 32)
u10 = conv_block_custom(u10, 32)
res = Conv2D(2, (1, 1), activation='sigmoid')(u10)
model = Model(inp, res)
if weights == "imagenet":
resnet50 = ResNet50(weights=weights,
include_top=False,
input_shape=(input_shape[0], input_shape[1], 3))
# resnet50.summary()
print("Loading imagenet weitghts ...")
for i in tqdm(range(3, len(resnet50.layers) - 2)):
try:
model.layers[i].set_weights(resnet50.layers[i].get_weights())
model.layers[i].trainable = False
except:
print(resnet50.layers[i].name)
exit()
print("imagenet weights have been loaded.")
del resnet50
return model
def custom_resnet(n=0, dp_rate=0):
WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
# Determine proper input shape
# input_shape = _obtain_input_shape(input_shape,default_size=224,min_size=197,data_format=K.image_data_format(),include_top=include_top)
img_input = Input(shape=(224, 224, 3))
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
x = Dropout(dp_rate)(x)
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
x = Dropout(dp_rate)(x)
x = AveragePooling2D((7, 7), name='avg_pool')(x)
x = Flatten()(x)
x = Dense(25, activation='softmax', name='fc1000')(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
inputs = img_input
# Create model.
model = Model(inputs, x, name='resnet50')
# load weights
weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='a268eb855778b3df3c7506639542a6af')
model.load_weights(weights_path, by_name=True)
split_value = True # len(model.layers) + 1 - n
for layer in model.layers[:split_value]:
layer.trainable = False
for layer in model.layers[split_value:]:
layer.trainable = True
return model
def resnet_dropout(include_top=False, weights='imagenet', input_tensor=None, pooling='avg', input_shape=(224, 224, 3),
classes=25, dp_rate=0., n_retrain_layers=0):
WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
if weights not in {'imagenet', None}:
raise ValueError('The `weights` argument should be either '
'`None` (random initialization) or `imagenet` '
'(pre-training on ImageNet).')
if weights == 'imagenet' and include_top and classes != 1000:
raise ValueError('If using `weights` as imagenet with `include_top`'
' as true, `classes` should be 1000')
# Determine proper input shape
# input_shape = _obtain_input_shape(input_shape,default_size=224,min_size=197,data_format=K.image_data_format(),include_top=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x = ZeroPadding2D((3, 3))(img_input)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = Dropout(dp_rate)(x)
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
x = AveragePooling2D((7, 7), name='avg_pool')(x)
if include_top:
x = Flatten()(x)
x = Dense(classes, activation='softmax', name='fc1000')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='resnet50')
# load weights
if weights == 'imagenet':
if include_top:
weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
else:
weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='a268eb855778b3df3c7506639542a6af')
model.load_weights(weights_path)
split_value = len(model.layers) + 1 - n_retrain_layers
for layer in model.layers[:split_value]:
layer.trainable = False
for layer in model.layers[split_value:]:
layer.trainable = True
return model
