過去，您必須克隆模型才能讓新的 dropout 接受。我最近沒試過。
# This code allows you to change the dropout
# Load model from .json
model.load_weights(filenameToModelWeights) # Load weights
model.layers[-2].rate = 0.04 # layer[-2] is my dropout layer, rate is dropout attribute
model = keras.models.clone(model) # If I do not clone, the new rate is never used. Weights are re-init now.
model.load_weights(filenameToModelWeights) # Load weights
model.predict(x)
歸功于
http://www.gergltd.com/home/2018/03/changing-dropout-on-the-fly-during-training-time-test-time-in-keras/
如果模型一開始就沒有 dropout 層，就像 Keras 的預(yù)訓(xùn)練移動(dòng)網(wǎng)絡(luò)一樣，您必須使用方法添加它們。這是您可以做到的一種方法。
用于添加單層
def insert_single_layer_in_keras(model, layer_name, new_layer):
layers = [l for l in model.layers]
x = layers[0].output
for i in range(1, len(layers)):
x = layers[i](x)
# add layer afterward
if layers[i].name == layer_name:
x = new_layer(x)
new_model = Model(inputs=layers[0].input, outputs=x)
return new_model
用于系統(tǒng)地添加層
def insert_layers_in_model(model, layer_common_name, new_layer):
import re
layers = [l for l in model.layers]
x = layers[0].output
layer_config = new_layer.get_config()
base_name = layer_config['name']
layer_class = type(dropout_layer)
for i in range(1, len(layers)):
x = layers[i](x)
match = re.match(".+" + layer_common_name + "+", layers[i].name)
# add layer afterward
if match:
layer_config['name'] = base_name + "_" + str(i) # no duplicate names, could be done different
layer_copy = layer_class.from_config(layer_config)
x = layer_copy(x)
new_model = Model(inputs=layers[0].input, outputs=x)
return new_model
像這樣跑
import tensorflow as tf
from tensorflow.keras.applications.mobilenet import MobileNet
from tensorflow.keras.layers import Dropout
from tensorflow.keras.models import Model
base_model = MobileNet(weights='imagenet', include_top=False, input_shape=(192, 192, 3), dropout=.15)
dropout_layer = Dropout(0.5)
# add single layer after last dropout
mobile_net_with_dropout = insert_single_layer_in_model(base_model, "conv_pw_13_bn", dropout_layer)
# systematically add layers after any batchnorm layer
mobile_net_with_multi_dropout = insert_layers_in_model(base_model, "bn", dropout_layer)
順便說一句，您絕對(duì)應(yīng)該進(jìn)行實(shí)驗(yàn)，但您不太可能希望在 batchnorm 之上對(duì)像 mobilenet 這樣的小型網(wǎng)絡(luò)進(jìn)行額外的正則化。
