examples we are using at each step the dropout variable represents threshold at which we eliminate some units at random we will be using dropout in our final hidden layer to give each unit chance of being eliminated at every training step this helps prevent overfitting we have now defined the architecture of our neural networkand the hyperparameters that impact the learning process the next step is to build the network as tensorflow graph step -building the tensorflow graph to build our networkwe will set up the network as computational graph for tensorflow to execute the core concept of tensorflow is the tensora data structure similar to an array or list initializedmanipulated as they are passed through the graphand updated through the learning process we'll start by defining three tensors as placeholderswhich are tensors that we'll feed values into later add the following to your filemain py tf placeholder("float"[nonen_input] tf placeholder("float"[nonen_output]keep_prob tf placeholder(tf float the only parameter that needs to be specified at its declaration is the size of the data we will be feeding in for we use shape of [none ]where none represents any amountas we will be feeding in an undefined number of -pixel images the shape of is [none as