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) = \