  Machine learning (ML) has been well applied to studying equilibrium phase
transition models, by accurately predicating critical thresholds and some
critical exponents. Difficulty will be raised, however, for integrating ML into
non-equilibrium phase transitions. The extra dimension in a given
non-equilibrium system, namely time, can greatly slow down the procedure
towards the steady state. In this paper we find that by using some simple
techniques of ML, non-steady state configurations of directed percolation (DP)
suffice to capture its essential critical behaviors in both (1+1) and (2+1)
dimensions. With the supervised learning method, the framework of our binary
classification neural networks can identify the phase transition threshold, as
well as the spatial and temporal correlation exponents. The characteristic time
$t_{c}$, specifying the transition from active phases to absorbing ones, is
also a major product of the learning. Moreover, we employ the convolutional
autoencoder, an unsupervised learning technique, to extract dimensionality
reduction representations and cluster configurations of (1+1) bond DP. It is
quite appealing that such a method can yield a reasonable estimation of the
critical point.
