# if niter > 0: # mfn = wts+"/iter_%06d.model.npz" % niter # sfn = wts+"/iter_%06d.state.npz" % niter # ut.mprint("Restoring model from " + mfn ) # ut.loadNet(mfn,model.weights,sess) # ut.mprint("Restoring state from " + sfn ) # ut.loadAdam(sfn,opt,model.weights,sess) # ut.mprint("Done!") ######################################################################### # Main Training loop stop = False ut.mprint("Starting from Iteration %d" % niter) sess.run(tset.fetchOp, feed_dict=tset.fdict()) while niter < MAXITER and not ut.stop: ## Validate model every so often if niter % VALFREQ == 0: ut.mprint("Validating model") val_iter = vset.ndata // BSZ vloss, vset.niter = [], 0 sess.run(vset.fetchOp, feed_dict=vset.fdict()) for its in range(val_iter): sess.run(swpV) outs = sess.run(lvals + [vset.fetchOp], feed_dict={ **vset.fdict(), is_training: False })
if niter > 0: mfn = wts+"/iter_%06d.model.npz" % niter sfn = wts+"/iter_%06d.state.npz" % niter ut.mprint("Restoring model from " + mfn ) ut.loadNet(mfn,model.weights,sess) ut.mprint("Restoring state from " + sfn ) ut.loadAdam(sfn,opt,model.weights,sess) ut.mprint("Done!") ######################################################################### # Main Training loop stop=False ut.mprint("Starting from Iteration %d" % niter) sess.run(tset.fetchOp,feed_dict=tset.fdict()) while niter < MAXITER and not ut.stop: ## Validate model every so often if niter % VALFREQ == 0: ut.mprint("Validating model") val_iter = vset.ndata // BSZ vloss, vset.niter = [], 0 sess.run(vset.fetchOp,feed_dict=vset.fdict()) for its in range(val_iter): sess.run(swpV) outs = sess.run( lvals+[vset.fetchOp], feed_dict={**vset.fdict(), is_training: False} )