如何使用Keras将Theano训练的模型转换为TensorFlow格式？

本文共计433个文字，预计阅读时间需要2分钟。

我尽量简洁地改写以下内容，不超过100字：

我就不多说了，大家直接看代码吧！

pythoncode_snippet_id=1947416 snippet_file_name=blog_20161025_1_3331239 name=code class=python

我就废话不多说了，大家还是直接看代码吧~

</pre><pre code_snippet_id="1947416" snippet_file_name="blog_20161025_1_3331239" name="code" class="python">

# coding:utf-8 """ If you want to load pre-trained weights that include convolutions (layers Convolution2D or Convolution1D), be mindful of this: Theano and TensorFlow implement convolution in different ways (TensorFlow actually implements correlation, much like Caffe), and thus, convolution kernels trained with Theano (resp. TensorFlow) need to be converted before being with TensorFlow (resp. Theano). """ from keras import backend as K from keras.utils.np_utils import convert_kernel from text_classifier import keras_text_classifier import sys def th2tf( model): import tensorflow as tf ops = [] for layer in model.layers: if layer.__class__.__name__ in ['Convolution1D', 'Convolution2D']: original_w = K.get_value(layer.W) converted_w = convert_kernel(original_w) ops.append(tf.assign(layer.W, converted_w).op) K.get_session().run(ops) return model def tf2th(model): for layer in model.layers: if layer.__class__.__name__ in ['Convolution1D', 'Convolution2D']: original_w = K.get_value(layer.W) converted_w = convert_kernel(original_w) K.set_value(layer.W, converted_w) return model def conv_layer_converted(tf_weights, th_weights, m = 0): """ :param tf_weights: :param th_weights: :param m: 0-tf2th, 1-th2tf :return: """ if m == 0: # tf2th tc = keras_text_classifier(weights_path=tf_weights) model = tc.loadmodel() model = tf2th(model) model.save_weights(th_weights) elif m == 1: # th2tf tc = keras_text_classifier(weights_path=th_weights) model = tc.loadmodel() model = th2tf(model) model.save_weights(tf_weights) else: print("0-tf2th, 1-th2tf") return if __name__ == '__main__': if len(sys.argv) < 4: print("python tf_weights th_weights <0|1>\n0-tensorflow to theano\n1-theano to tensorflow") sys.exit(0) tf_weights = sys.argv[1] th_weights = sys.argv[2] m = int(sys.argv[3]) conv_layer_converted(tf_weights, th_weights, m)

补充知识：keras学习之修改底层为TensorFlow还是theano

我们知道，keras的底层是TensorFlow或者theano

要知道我们是用的哪个为底层，只需要import keras即可显示

修改方法：

打开

修改

以上这篇keras实现theano和tensorflow训练的模型相互转换就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持易盾网络。