The expected value range of the output activations wouldn’t match, if you are dropping activations during training with the specified p value and are using them during evaluation.
From the dropout paper:

At test time, it is not feasible to explicitly average the predictions from exponentially
many thinned models. However, a very simple approximate averaging method works well in
practice. The idea is to use a single neural net at test time without dropout. The weights
of this network are scaled-down versions of the trained weights. If a unit is retained with
probability p during training, the outgoing weights of that unit are multiplied by p at test
time as shown in Figure 2. This ensures that for any hidden unit the expected output (under
the distribution used to drop units at training time) is the same as the actual output at
test time.