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keras实现theano和tensorflow训练的模型相互转换代码示例
时间:2020-06-19 编辑:袖梨 来源:一聚教程网
本篇文章小编给大家分享一下keras实现theano和tensorflow训练的模型相互转换代码示例,代码介绍的很详细,小编觉得挺不错的,现在分享给大家供大家参考,有需要的小伙伴们可以来看看。
代码如下
# 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 theanon1-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
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