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?