AI模型：“ ValueError：您正在尝试将包含190层的权重文件加载到具有26层的模型中。”

Question

我有一个扩展名为.hdf5的模型，我想将其转换为.pb扩展名。

我的模型：mushroom.hdf5，model_mushroom_weights.buf和model_mushroom.json

所以我尝试了python脚本：

from keras.models import Model
from keras.layers import *
import os
import tensorflow as tf


def keras_to_tensorflow(keras_model, output_dir, 
model_name,out_prefix="output_", log_tensorboard=True):

if os.path.exists(output_dir) == False:
    os.mkdir(output_dir)

out_nodes = []

for i in range(len(keras_model.outputs)):
    out_nodes.append(out_prefix + str(i + 1))
    tf.identity(keras_model.output[i], out_prefix + str(i + 1))

sess = K.get_session()

from tensorflow.python.framework import graph_util, graph_io

init_graph = sess.graph.as_graph_def()

main_graph = graph_util.convert_variables_to_constants(sess, init_graph, 
out_nodes)

graph_io.write_graph(main_graph, output_dir, name=model_name, 
as_text=False)

if log_tensorboard:
    from tensorflow.python.tools import import_pb_to_tensorboard

    import_pb_to_tensorboard.import_to_tensorboard(
        os.path.join(output_dir, model_name),
        output_dir)


  """
  We explicitly redefine the Squeezent architecture since Keras has no 
  predefined Squeezenet
  """

 def squeezenet_fire_module(input, input_channel_small=16, 
 input_channel_large=64):

channel_axis = 3

input = Conv2D(input_channel_small, (1,1), padding="valid" )(input)
input = Activation("relu")(input)

input_branch_1 = Conv2D(input_channel_large, (1,1), padding="valid" ) 
(input)
input_branch_1 = Activation("relu")(input_branch_1)

input_branch_2 = Conv2D(input_channel_large, (3, 3), padding="same") 
(input)
input_branch_2 = Activation("relu")(input_branch_2)

input = concatenate([input_branch_1, input_branch_2], axis=channel_axis)

return input


def SqueezeNet(input_shape=(224,224,3)):



image_input = Input(shape=input_shape)


network = Conv2D(64, (3,3), strides=(2,2), padding="valid")(image_input)
network = Activation("relu")(network)
network = MaxPool2D( pool_size=(3,3) , strides=(2,2))(network)

network = squeezenet_fire_module(input=network, input_channel_small=16, 
input_channel_large=64)
network = squeezenet_fire_module(input=network, input_channel_small=16, 
input_channel_large=64)
network = MaxPool2D(pool_size=(3,3), strides=(2,2))(network)

network = squeezenet_fire_module(input=network, input_channel_small=32, 
input_channel_large=128)
network = squeezenet_fire_module(input=network, input_channel_small=32, 
input_channel_large=128)
network = MaxPool2D(pool_size=(3, 3), strides=(2, 2))(network)

network = squeezenet_fire_module(input=network, input_channel_small=48, 
input_channel_large=192)
network = squeezenet_fire_module(input=network, input_channel_small=48, 
input_channel_large=192)
network = squeezenet_fire_module(input=network, input_channel_small=64, 
input_channel_large=256)
network = squeezenet_fire_module(input=network, input_channel_small=64, 
input_channel_large=256)

#Remove layers like Dropout and BatchNormalization, they are only needed 
in training
#network = Dropout(0.5)(network)

network = Conv2D(1000, kernel_size=(1,1), padding="valid", 
name="last_conv")(network)
network = Activation("relu")(network)

network = GlobalAvgPool2D()(network)
network = Activation("softmax",name="output")(network)


input_image = image_input
model = Model(inputs=input_image, outputs=network)

return model


keras_model = SqueezeNet()

keras_model.load_weights("mushroom.hdf5")


output_dir = os.path.join(os.getcwd(),"checkpoint")

keras_to_tensorflow(keras_model,output_dir=output_dir,model_name=" 
squeezenet.pb")

print("MODEL SAVED") 

我跑步时出现此错误：

  

ValueError：您正在尝试加载包含190层的重量文件   进入具有26层的模型

我认为是因为扩展名错误，但是找不到hdf5。

有了.h5，脚本可以很好地运行，并给我一些.pb，但是请您告诉我如何使用.hdf5做同样的事情？

谢谢。