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如何在Keras中计算Mobilenet FLOP

run_meta = tf.RunMetadata()
enter codwith tf.Session(graph=tf.Graph()) as sess:
K.set_session(sess)


with tf.device('/cpu:0'):
    base_model = MobileNet(alpha=1, weights=None, input_tensor=tf.placeholder('float32', shape=(1,224,224,3)))




opts = tf.profiler.ProfileOptionBuilder.float_operation()    
flops = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts)

opts = tf.profiler.ProfileOptionBuilder.trainable_variables_parameter()    
params = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts)

print("{:,} --- {:,}".format(flops.total_float_ops, params.total_parameters))
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当我运行上面的代码时,我得到了下面的结果

1,137,481,704 --- 4,253,864
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这与本文所述的触发器不同。

移动网络:https ://arxiv.org/pdf/1704.04861.pdf

ShuffleNet:https://arxiv.org/pdf/1707.01083.pdf

如何计算论文中所述的确切触发器?

flops deep-learning keras

1
推荐指数
4
解决办法
3951
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标签 统计

deep-learning ×1

flops ×1

keras ×1