问题描述
限时送ChatGPT账号..我已经通过 https://www.tensorflow/get_started/mnist/专业人士.阅读请注意,您可以使用 feed_dict 替换计算图中的任何张量——它不仅限于占位符",我尝试使用 feed_dict 为变量赋值,如下所示:
I'm through https://www.tensorflow/get_started/mnist/pros. Reading "Note that you can replace any tensor in your computation graph using feed_dict -- it's not restricted to just placeholders," I tried to give values to a Variable using feed_dict as follows:
print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels,
W[:, :]: np.zeros((784, 10))}))
然而,它给出了原始准确度 0.9149(我预计约为 0.1).我可以在使用 feed_dict 初始化后为变量赋予常量值吗?
However, it gave the original accuracy 0.9149 (I expected around 0.1). Can I give constant values to Variables after initialization using feed_dict?
推荐答案
在您的回答中,您已经将常量零传递给 W ,这是一个变量.并在声明中说
In your answer you have already passed the constants zeros to W which is a variable. And in the statement that
请注意,您可以使用 feed_dict 替换计算图中的任何张量——它不仅限于占位符
Note that you can replace any tensor in your computation graph using feed_dict -- it's not restricted to just placeholders
您通过 feed_dict 传递到图中的所有内容都是(通常是 numpy)常量,因此您也可以获得肯定的答案.
All what you pass into the graph by feed_dict are (often numpy) constants, so you can also get a positive answer.
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