有办法将keras mobilenetv2模型划分为子模型吗？
有办法将keras mobilenetv2模型划分为子模型吗？
提问于 2020-03-17 19:56:02
我试图将mobilenetv2模型分为两部分。
我首先想要运行模型的第一部分，保存输出，然后出于某些原因将其提供给第二个模型。我尝试过代码找到了这里，但是我得到了以下错误：
ValueError: A merge layer should be called on a list of inputs.我认为这是因为模型不是顺序的。有人能帮忙吗？
回答 1
Stack Overflow用户
发布于 2020-03-18 15:34:50
正如我在评论中提到的，mobile_net_v2中的一些层期望有多个输入，这些输入是以前一些层的输出。因此，将它们添加到顺序模型中会导致错误。我有个解决办法给你。使用mobile_net_v2实现(我自己的)在这链接中，我能够创建您想要的模型：
import tensorflow as tf
from tensorflow.keras import layers, Model, Sequential
def conv_block(input_tensor, c, s, t, expand=True):
"""
Convolutional Block for mobile net v2
Args:
input_tensor (keras tensor): input tensor
c (int): output channels
s (int): stride size of first layer in the series
t (int): expansion factor
expand (bool): expand filters or not?
Returns: keras tensor
"""
first_conv_channels = input_tensor.get_shape()[-1]
if expand:
x = layers.Conv2D(
first_conv_channels*t,
1,
1,
padding='same',
use_bias=False
)(input_tensor)
x = layers.BatchNormalization()(x)
x = layers.ReLU(6.0)(x)
else:
x = input_tensor
x = layers.DepthwiseConv2D(
3,
s,
'same',
1,
use_bias=False
)(x)
x = layers.BatchNormalization()(x)
x = layers.ReLU(6.0)(x)
x = layers.Conv2D(
c,
1,
1,
padding='same',
use_bias=False
)(x)
x = layers.BatchNormalization()(x)
if input_tensor.get_shape() == x.get_shape() and s == 1:
return x+input_tensor
return x
def splitted_model(input_shape=(224,224,3)):
input = layers.Input(shape=input_shape)
x = layers.Conv2D(
32,
3,
2,
padding='same',
use_bias=False
)(input)
x = layers.BatchNormalization()(x)
x = layers.ReLU(6.0)(x)
x = conv_block(x, 16, 1, 1, expand=False)
x = conv_block(x, 24, 2, 6)
x = conv_block(x, 24, 1, 6)
x = conv_block(x, 32, 2, 6)
x = conv_block(x, 32, 1, 6)
x = conv_block(x, 32, 1, 6)
x = conv_block(x, 64, 2, 6)
x = conv_block(x, 64, 1, 6)
x = conv_block(x, 64, 1, 6)
x = conv_block(x, 64, 1, 6)
model_f = Model(inputs=input, outputs=x)
input_2 = layers.Input(shape=(x.shape[1:]))
x = conv_block(input_2, 96, 1, 6)
x = conv_block(x, 96, 1, 6)
x = conv_block(x, 96, 1, 6)
x = conv_block(x, 160, 2, 6)
x = conv_block(x, 160, 1, 6)
x = conv_block(x, 160, 1, 6)
x = conv_block(x, 320, 1, 6)
x = layers.Conv2D(
1280,
1,
1,
padding='same',
use_bias=False
)(x)
x = layers.BatchNormalization()(x)
x = layers.ReLU(6.0)(x)
x = layers.GlobalAveragePooling2D()(x)
model_h = Model(inputs=input_2, outputs=x)
return model_f, model_h您可以这样创建您的两个模型：
IMG_SIZE = 160
IMG_SHAPE = (IMG_SIZE, IMG_SIZE, 3)
model_f, model_h = splitted_model(input_shape=IMG_SHAPE)注意，权重是随机初始化的。如果希望在imagenet上培训来自mobilenet_v2的权重，可以运行以下代码来复制权重：
mobile_net = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')
layer_f_counter = 0
layer_h_counter = 0
for i in range(len(mobile_net.layers)):
if layer_f_counter<len(model_f.layers):
if len(mobile_net.layers[i].get_weights()) > 0:
if len(model_f.layers[layer_f_counter].get_weights()) > 0:
print(mobile_net.layers[i].name,'here', model_f.layers[layer_f_counter].name, layer_f_counter)
model_f.layers[layer_f_counter].set_weights(mobile_net.layers[i].get_weights())
layer_f_counter += 1
print(layer_f_counter)
else:
if len(model_f.layers[layer_f_counter].get_weights()) > 0:
continue
else:
layer_f_counter+=1
else:
if layer_h_counter<len(model_h.layers):
if len(mobile_net.layers[i].get_weights()) > 0:
if len(model_h.layers[layer_h_counter].get_weights()) > 0:
print(mobile_net.layers[i].name,'here', model_h.layers[layer_h_counter].name, layer_h_counter)
model_h.layers[layer_h_counter].set_weights(mobile_net.layers[i].get_weights())
layer_h_counter += 1
print(layer_h_counter)
else:
if len(model_h.layers[layer_h_counter].get_weights()) > 0:
continue
else:
layer_h_counter+=1它迭代从Keras加载的mobilenet_v2层，将第一部分的权重复制到model_f，其余部分复制到model_h。您可以通过从mobile_net中打印出一些随机层权重来检查权重是否正确复制，以及新模型如下：
print(model_f.layers[1].get_weights()) # printing weights of first conv layer in model_f
print(mobile_net.get_layer('Conv1').get_weights()) # printing weights of fist conv layer in mobile_net也适用于model_h：
print(model_h.layers[-4].get_weights()) # printing weights of last conv layer in model_h
print(mobile_net.get_layer('Conv_1').get_weights()) # printing weights of last conv layer in mobile_net注意，我随机选择了将moile_net分离为model_f和model_h的块，您可以编辑它以更改要拆分的位置。希望能帮上忙。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/60729336
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