python – 如何將Keras .h5導出到tensorflow .pb？

問題描述

我有一個使用新數據集的調優的初始模型，並在Keras中將其保存為”.h5″模型。現在我的目標是在僅接受”.pb”擴展的android Tensorflow上運行我的模型。問題是在Keras或tensorflow中是否有任何庫可以進行此轉換？

最佳方法

Keras本身不包括將TensorFlow圖導出為協議緩衝區文件(.pb, Protocol Buffer)的任何方法，但是您可以使用常規TensorFlow實用程序來實現。 這裏是一篇博客文章，解釋了如何使用TensorFlow中包含的實用程序腳本freeze_graph.py做到這一點，一種典型實現方式。

但是，我個人覺得必須創建一個檢查點，然後運行一個外部腳本來獲取模型，並且我更喜歡從自己的Python代碼中執行此操作，所以我使用了這樣的函數：

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    """
    Freezes the state of a session into a pruned computation graph.

    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    pruned so subgraphs that are not necessary to compute the requested
    outputs are removed.
    @param session The TensorFlow session to be frozen.
    @param keep_var_names A list of variable names that should not be frozen,
                          or None to freeze all the variables in the graph.
    @param output_names Names of the relevant graph outputs.
    @param clear_devices Remove the device directives from the graph for better portability.
    @return The frozen graph definition.
    """
    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 = tf.graph_util.convert_variables_to_constants(
            session, input_graph_def, output_names, freeze_var_names)
        return frozen_graph

該實現從上述博客中的代碼文件freeze_graph.py中受到了啟發，參數也跟這個文件中的腳本類似。[注: session是TensorFlow會話對象]。當您要保持某些變量不凍結時(例如，對於有狀態模型)，才需要keep_var_names，而通常情況下是不需要的。 output_names是一個列表，其中包含產生所需輸出的操作的名稱。 clear_devices隻是刪除了所有設備指令以使圖形更易於移植。因此，對於具有一個輸出的典型Keras model，執行以下操作：

from keras import backend as K

# Create, compile and train model...

frozen_graph = freeze_session(K.get_session(),
                              output_names=[out.op.name for out in model.outputs])

然後，您可以像往常一樣使用tf.train.write_graph將圖形寫入文件：

tf.train.write_graph(frozen_graph, "some_directory", "my_model.pb", as_text=False)

次佳方法

freeze_session方法可以正常工作。但是與保存到檢查點文件相比，使用TensorFlow隨附的freeze_graph工具對我來說似乎更簡單，因為它更易於維護。您需要做的隻是以下兩個步驟：

首先，在您的Keras代碼model.fit(...)之後添加並訓練模型：

from keras import backend as K
import tensorflow as tf
print(model.output.op.name)
saver = tf.train.Saver()
saver.save(K.get_session(), '/tmp/keras_model.ckpt')

然後cd到您的TensorFlow根目錄，運行：

python tensorflow/python/tools/freeze_graph.py \
--input_meta_graph=/tmp/keras_model.ckpt.meta \
--input_checkpoint=/tmp/keras_model.ckpt \
--output_graph=/tmp/keras_frozen.pb \
--output_node_names="<output_node_name_printed_in_step_1>" \
--input_binary=true

第三種方法

以下簡單示例(XOR示例)顯示了如何導出Keras模型(采用h5格式和pb格式)，以及如何在Python和C++中使用該模型：

train.py：

import numpy as np
import tensorflow as tf


def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    """
    Freezes the state of a session into a pruned computation graph.

    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    pruned so subgraphs that are not necessary to compute the requested
    outputs are removed.
    @param session The TensorFlow session to be frozen.
    @param keep_var_names A list of variable names that should not be frozen,
                          or None to freeze all the variables in the graph.
    @param output_names Names of the relevant graph outputs.
    @param clear_devices Remove the device directives from the graph for better portability.
    @return The frozen graph definition.
    """
    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 = tf.graph_util.convert_variables_to_constants(
            session, input_graph_def, output_names, freeze_var_names)
        return frozen_graph


X = np.array([[0,0], [0,1], [1,0], [1,1]], 'float32')
Y = np.array([[0], [1], [1], [0]], 'float32')

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(64, input_dim=2, activation='relu'))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

model.compile(loss='mean_squared_error', optimizer='adam', metrics=['binary_accuracy'])

model.fit(X, Y, batch_size=1, nb_epoch=100, verbose=0)

# inputs:  ['dense_input']
print('inputs: ', [input.op.name for input in model.inputs])

# outputs:  ['dense_4/Sigmoid']
print('outputs: ', [output.op.name for output in model.outputs])

model.save('./xor.h5')

frozen_graph = freeze_session(tf.keras.backend.get_session(), output_names=[out.op.name for out in model.outputs])
tf.train.write_graph(frozen_graph, './', 'xor.pbtxt', as_text=True)
tf.train.write_graph(frozen_graph, './', 'xor.pb', as_text=False)

預測：

import numpy as np
import tensorflow as tf

model = tf.keras.models.load_model('./xor.h5')

# 0 ^ 0 =  [[0.01974997]]
print('0 ^ 0 = ', model.predict(np.array([[0, 0]])))

# 0 ^ 1 =  [[0.99141496]]
print('0 ^ 1 = ', model.predict(np.array([[0, 1]])))

# 1 ^ 0 =  [[0.9897714]]
print('1 ^ 0 = ', model.predict(np.array([[1, 0]])))

# 1 ^ 1 =  [[0.00406971]]
print('1 ^ 1 = ', model.predict(np.array([[1, 1]])))

opencv-predict.py：

import numpy as np
import cv2 as cv


model = cv.dnn.readNetFromTensorflow('./xor.pb')

# 0 ^ 0 =  [[0.01974997]]
model.setInput(np.array([[0, 0]]), name='dense_input')
print('0 ^ 0 = ', model.forward(outputName='dense_4/Sigmoid'))

# 0 ^ 1 =  [[0.99141496]]
model.setInput(np.array([[0, 1]]), name='dense_input')
print('0 ^ 1 = ', model.forward(outputName='dense_4/Sigmoid'))

# 1 ^ 0 =  [[0.9897714]]
model.setInput(np.array([[1, 0]]), name='dense_input')
print('1 ^ 0 = ', model.forward(outputName='dense_4/Sigmoid'))

# 1 ^ 1 =  [[0.00406971]]
model.setInput(np.array([[1, 1]]), name='dense_input')
print('1 ^ 1 = ', model.forward(outputName='dense_4/Sigmoid'))

predict.cpp：

#include <cstdlib>
#include <iostream>
#include <opencv2/opencv.hpp>

int main(int argc, char **argv)
{
    cv::dnn::Net net;

    net = cv::dnn::readNetFromTensorflow("./xor.pb");

    // 0 ^ 0 = [0.018541215]
    float x0[] = { 0, 0 };
    net.setInput(cv::Mat(1, 2, CV_32F, x0), "dense_input");
    std::cout << "0 ^ 0 = " << net.forward("dense_4/Sigmoid") << std::endl;

    // 0 ^ 1 = [0.98295897]
    float x1[] = { 0, 1 };
    net.setInput(cv::Mat(1, 2, CV_32F, x1), "dense_input");
    std::cout << "0 ^ 1 = " << net.forward("dense_4/Sigmoid") << std::endl;

    // 1 ^ 0 = [0.98810625]
    float x2[] = { 1, 0 };
    net.setInput(cv::Mat(1, 2, CV_32F, x2), "dense_input");
    std::cout << "1 ^ 0 = " << net.forward("dense_4/Sigmoid") << std::endl;

    // 1 ^ 1 = [0.010002014]
    float x3[] = { 1, 1 };
    net.setInput(cv::Mat(1, 2, CV_32F, x3), "dense_input");
    std::cout << "1 ^ 1 = " << net.forward("dense_4/Sigmoid") << std::endl;

    return EXIT_SUCCESS;
}

參考資料

• How to export Keras .h5 to tensorflow .pb?