Keras模型转成tensorflow的.pb操作_tensorflow生成.h5文件资源-CSDN文库

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Keras模型转成模型转成tensorflow的的.pb操作操作

Keras的.h5模型转成tensorflow的.pb格式模型，方便后期的前端部署。直接上代码

from keras.models import Model

from keras.layers import Dense, Dropout

from keras.applications.mobilenet import MobileNet

from keras.applications.mobilenet import preprocess_input

from keras.preprocessing.image import load_img, img_to_array

import tensorflow as tf

from keras import backend as K

import os

base_model = MobileNet((None, None, 3), alpha=1, include_top=False, pooling='avg', weights=None)

x = Dropout(0.75)(base_model.output)

x = Dense(10, activation='softmax')(x)

model = Model(base_model.input, x)

model.load_weights('mobilenet_weights.h5')

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):

from tensorflow.python.framework.graph_util import convert_variables_to_constants

graph = session.graph

with graph.as_default():

freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))

output_names = output_names or [] output_names += [v.op.name for v in tf.global_variables()] input_graph_def =

graph.as_graph_def()

if clear_devices:

for node in input_graph_def.node:

node.device = ""

frozen_graph = convert_variables_to_constants(session, input_graph_def,

output_names, freeze_var_names)

return frozen_graph

output_graph_name = 'NIMA.pb'

output_fld = ''

#K.set_learning_phase(0)

print('input is :', model.input.name)

print ('output is:', model.output.name)

sess = K.get_session()

frozen_graph = freeze_session(K.get_session(), output_names=[model.output.op.name])

from tensorflow.python.framework import graph_io

graph_io.write_graph(frozen_graph, output_fld, output_graph_name, as_text=False)

print('saved the constant graph (ready for inference) at: ', os.path.join(output_fld, output_graph_name))

补充知识：补充知识：keras h5 model 转换为转换为tflite

在移动端的模型,若选择tensorflow或者keras最基本的就是生成tflite文件，以本文记录一次转换过程。

环境环境

tensorflow 1.12.0

python 3.6.5

h5 model saved by `model.save(‘tf.h5’)`

直接转换直接转换

`tflite_convert --output_file=tf.tflite --keras_model_file=tf.h5`

output

`TypeError: __init__() missing 2 required positional arguments: 'filters' and 'kernel_size'`

先转成先转成pb再转再转tflite

```

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