Can I apply calculated gradient in tensorflow?
When you create your network and attach some loss you call minimize on the optimizer, which (under the hood) calls "apply_gradients". This function adds gradient computing ops to your graph. All you have to do is now request the op responsible for your partial derivative and pass the precomputed partial derivative through feed_dict option. Use tensorboard to visualize your graph and investigate names of gradients you are interested in. By default they will be in the "gradient" namescope, and naming of each op will be analogous to your operations, so something among the lines of gradient/output_op:0 etc.
What I want to do is to simulate the back-propagation process on different machines, from one machine, I get the gradient from layer3 d(layer3_output)/d(layer2_output) as a numpy array, how am I able to get d(layer3_output)/d(layer1_output) efficiently given the gradient I received and passed to the previous layer?