y) = async_b.get() # get the return value from your function. if (minibatch_index == n_train_batches - 1): pred, H, C = model.predictions(x, is_train, H, C) loss3d = u.get_loss(params, y, pred) batch_loss3d.append(loss3d) batch_loss3d = np.nanmean(batch_loss3d) if (batch_loss3d < best_loss): best_loss = batch_loss3d ext = str(epoch_counter) + "_" + str(batch_loss3d) + "_best.p" u.write_params(model.params, params, ext) else: ext = str(val_counter % 2) + ".p" u.write_params(model.params, params, ext) val_counter += 1 s = 'VAL--> epoch %i | error %f, %f' % (val_counter, batch_loss3d, n_test_batches) u.log_write(s, params) params = config.get_params() parser = argparse.ArgumentParser(description='Training the module') parser.add_argument('-m', '--model', help='Model: lstm, erd current(' + params["model"] + ')', default=params["model"]) args = vars(parser.parse_args()) params["model"] = args["model"] params = config.update_params(params) train_rnn(params)
loss3d =u.get_loss(params,y,pred) batch_loss3d.append(loss3d) x=[] y=[] (sid,H,C,x,y) = async_b.get() # get the return value from your function. if(minibatch_index==n_train_batches-1): pred,H,C= model.predictions(x,is_train,H,C) loss3d =u.get_loss(params,y,pred) batch_loss3d.append(loss3d) batch_loss3d=np.nanmean(batch_loss3d) if(batch_loss3d<best_loss): best_loss=batch_loss3d ext=str(epoch_counter)+"_"+str(batch_loss3d)+"_best.p" u.write_params(model.params,params,ext) else: ext=str(val_counter%2)+".p" u.write_params(model.params,params,ext) val_counter+=1#0.08 s ='VAL--> epoch %i | error %f, %f'%(val_counter,batch_loss3d,n_test_batches) u.log_write(s,params) params= config.get_params() parser = argparse.ArgumentParser(description='Training the module') parser.add_argument('-m','--model',help='Model: lstm, lstm2, erd current('+params["model"]+')',default=params["model"]) args = vars(parser.parse_args()) params["model"]=args["model"] params=config.update_params(params) train_rnn(params)
params["mfile"]='/mnt/Data1/hc/tt/cp/lstm_nostate1/cp/' # adding more values to params# rnn_keep_prob=0.8 input_keep_prob=1.0 params['rnn_keep_prob']=rnn_keep_prob params['input_keep_prob']=input_keep_prob seq=50 #what does this value signify? res=5 #what does this value signify? with tf.Graph().as_default(): print "seq: ============== %s ============" % seq print "reset_state: ============== %s ============" % res print "rnn_keep_prob: ============== %s ============" % rnn_keep_prob params['normalise_data'] = 4 # adding more values to params, what does this value signify? # params['reset_state']=res # adding more values to params, what does this value signify? # params['seq_length']=seq # adding more values to params, what does this value signify? # params["reload_data"] = 0 # adding more values to params, what does this value signify? # params = config.update_params(params) # New param values are updated# #params["model"] = "kfl_QRf" if params["model"] == "lstm": Model = lstm(params=params) elif params["model"] == "kfl_QRf": Model = kfl_QRf(params=params) elif params["model"] == "kfl_Rf": Model = kfl_Rf(params=params) elif params["model"] == "kfl_QRFf": Model = kfl_QRFf(params=params) elif params["model"] == "kfl_K": Model = kfl_K(params=params) params["rn_id"]="dobuleloss081500_nrm4_seq%i_res%i_keep%f_lr%f"%(seq,res,rnn_keep_prob,params["lr"]) # adding more values to params, what does this value signify? # params=config.update_params(params) # New param values are updated# # (params, X_train, Y_train, F_list_train, G_list_train, S_Train_list, R_L_Train_list, # X_test, Y_test, F_list_test, G_list_test, S_Test_list, R_L_Test_list) = \