tensorflow不知代码情况下获取graph的placeholder和output

最新推荐文章于 2023-01-29 17:32:54 发布

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最新推荐文章于 2023-01-29 17:32:54 发布

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本文介绍了一种使用TensorFlow 1.6版本从.meta文件中查找正确占位符操作名称的方法，并提供了如何仅使用ckpt文件进行模型推断而无需重建模型的详细步骤。

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Here is my way to find out the correct placeholder op name in the Graphdef part of the .meta file:

saver = tf.train.import_meta_graph('some_path/model.ckpt.meta')
imported_graph = tf.get_default_graph()
graph_op = imported_graph.get_operations()
with open('output.txt', 'w') as f:
    for i in graph_op:
        f.write(str(i))

In the output.txt file, we can easily find out the placeholder’s correct op names and other attrs. Here is part of my output file:

name: “input/input_image”
op: “Placeholder”
attr {
key: “dtype”
value {
type: DT_FLOAT
}
}
attr {
key: “shape”
value {
shape {
dim {
size: -1
}
dim {
size: 112
}
dim {
size: 112
}
dim {
size: 3
}
}
}
}
Obviously, in my tensorflow version(1.6), the correct placeholder op name is Placeholder. Now return back to mrry’s solution. Use [x for x in tf.get_default_graph().get_operations() if x.type == “Placeholder”] to get a list of all the placeholder ops.

Thus it’s easy and convenient to perform the inference operation with only the ckpt files without needing to reconstruct the model. For example:

input_x = … # prepare the model input


saver = tf.train.import_meta_graph('some_path/model.ckpt.meta')
graph_x = tf.get_default_graph().get_tensor_by_name('input/input_image:0')
graph_y = tf.get_default_graph().get_tensor_by_name('layer19/softmax:0')
sess = tf.Session()
saver.restore(sess, 'some_path/model.ckpt')

output_y = sess.run(graph_y, feed_dict={graph_x: input_x})