Update the misleading comment for cifar10.py's softmax_linear layer (#5259)
* Update the misunderstanding comment for cifar10.py A fix for the issue #5251, make the comment more meaningful. * Update comment to be a bit more precise. * wrap to 80
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@ -256,7 +256,10 @@ def inference(images):
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local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name)
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_activation_summary(local4)
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# softmax, i.e. softmax(WX + b)
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# linear layer(WX + b),
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# We don't apply softmax here because
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# tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits
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# and performs the softmax internally for efficiency.
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with tf.variable_scope('softmax_linear') as scope:
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weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES],
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stddev=1/192.0, wd=0.0)
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