将keras的h5模型转换为tensorflow的pb模型操作

1. Introduction

In this tutorial, we will discuss how to convert a Keras h5 model to a TensorFlow pb model. The h5 file format is commonly used to save Keras models, while the pb file format is used for TensorFlow models. Converting the model to pb format can be useful when deploying the model in TensorFlow serving or other TensorFlow-related applications.

2. Prerequisites

To follow along with this tutorial, you need to have the following installed:

Keras - a high-level neural network API

TensorFlow - an open-source machine learning framework

In addition, you should have a trained Keras model saved in h5 format that you want to convert to pb.

3. Loading the Keras h5 Model

The first step is to load the pre-trained Keras model from the h5 file. The model should be trained and saved using Keras API. We can use the load_model function from the keras.models module to load the model.

from keras.models import load_model

model = load_model('model.h5')

4. Converting the Model to pb Format

To convert the Keras h5 model to pb format, we need to create a TensorFlow session and then save the model using the tf.saved_model module. We also need to specify the input and output nodes of the model.

4.1 Creating the TensorFlow session

To create a TensorFlow session, we can use the tf.compat.v1.Session class.

import tensorflow as tf

sess = tf.compat.v1.Session()

4.2 Converting the Keras model

Before converting the model, we need to define the input and output nodes of the model. We can do this by inspecting the Keras model. The input node can be found using the model.inputs attribute, and the output node can be found using the model.outputs attribute.

input_node = model.inputs[0]

output_node = model.outputs[0]

Now, we can use the TensorFlow's saved_model.simple_save function to save the model in pb format.

inputs = {'input': input_node}

outputs = {'output': output_node}
tf.compat.v1.saved_model.simple_save(sess, 'saved_model', inputs=inputs, outputs=outputs)

5. Using the pb Model

Now that we have converted the Keras h5 model to pb format, we can use the pb model in TensorFlow.

5.1 Loading the pb model

To load the pb model, we can use the tf.saved_model.loader.load function.

graph = tf.compat.v1.Graph()

with graph.as_default():
    tf.compat.v1.saved_model.loader.load(sess, ['serve'], 'saved_model')

5.2 Making predictions with the pb model

To make predictions with the pb model, we need to create a TensorFlow session and run the model using the appropriate input data.

# Create a TensorFlow session

with tf.compat.v1.Session(graph=graph) as sess:
    # Get the input and output nodes of the model
    input_node = graph.get_tensor_by_name('input:0')
    output_node = graph.get_tensor_by_name('output:0')
    
    # Make predictions
    predictions = sess.run(output_node, feed_dict={input_node: input_data})

6. Conclusion

In this tutorial, we have learned how to convert a Keras h5 model to a TensorFlow pb model. We have seen how to load the Keras model, convert it to pb format, and use the pb model for making predictions. Converting the model to pb format can be useful in various TensorFlow-related applications. It allows us to utilize the power of TensorFlow while still benefitting from the ease of use of Keras.

Note: The temperature parameter mentioned in the title is not directly related to the model conversion process, but it can be applied when making predictions with the pb model. The temperature parameter controls the randomness of the predictions generated by a neural network. A higher temperature value (e.g., 0.6) leads to more random and diverse predictions, while a lower temperature value (e.g., 0.1) leads to more deterministic and confident predictions.