x_b=np.asarray(map(np.flipud,x))
pred = model.predictions(x,x_b,is_train)
else:
pred = model.predictions(x,is_train)
# print("Prediction done....")
if residual>0:
if(minibatch_index==n_test_batches-1):
pred = pred[0:(len(pred)-residual)]
y=y[0:(len(y)-residual)]
n_list=n_list[0:(len(n_list)-residual)]
# du.write_predictions(params,pred,n_list)
#u.write_pred(pred,minibatch_index,G_list,params)
loss=np.nanmean(np.abs(pred -y))*2
(loss3d,l_list,s_list) =u.get_loss_bb(y,pred)
#print(s_list)
sq_loss_lst.append(s_list)
loss_list=loss_list+l_list
batch_loss += loss
batch_loss3d += loss3d
sq_loss_lst=np.nanmean(sq_loss_lst,axis=0)
batch_loss/=n_test_batches
batch_loss3d/=n_test_batches
print "============================================================================"
print sq_loss_lst
s ='error %f, %f, %f,%f'%(batch_loss,batch_loss3d,n_test_batches,len(loss_list))
print (s)
pu.plot_histograms(loss_list)
pu.plot_error_frame(loss_list)
#pu.plot_cumsum(loss_list)
for sp in range(seq_length):
ls=full_lost[sp]
#print len(ls)
if(all==0):
if(sp==0):
final_loss[0:seq_length]=ls[0:seq_length]
for index in range(seq_length,len(ls),seq_length):
f_index=sp+index
final_loss[f_index-7]=ls[index-7]
else:
print(sp)
for index in range(0,len(ls)):
f_index=sp+index
final_loss[f_index]=final_loss[f_index]+ls[index]
final_loss=final_loss[seq_length:len(final_loss)-seq_length]
print len(final_loss)
if(all==1):
final_loss=[l/seq_length for l in final_loss]
print len(final_loss)
final_mean=np.mean(final_loss)
s ='Final mean error %f'%(final_mean)
print(s)
pu.plot_histograms(final_loss)
pu.plot_error_frame(final_loss)
#pu.plot_cumsum(loss_list)
final_loss = [0] * (len(full_lost[0]) + seq_length)
for sp in range(seq_length):
ls = full_lost[sp]
# print len(ls)
if all == 0:
if sp == 0:
final_loss[0:seq_length] = ls[0:seq_length]
for index in range(seq_length, len(ls), seq_length):
f_index = sp + index
final_loss[f_index - 7] = ls[index - 7]
else:
print (sp)
for index in range(0, len(ls)):
f_index = sp + index
final_loss[f_index] = final_loss[f_index] + ls[index]
final_loss = final_loss[seq_length : len(final_loss) - seq_length]
print len(final_loss)
if all == 1:
final_loss = [l / seq_length for l in final_loss]
print len(final_loss)
final_mean = np.mean(final_loss)
s = "Final mean error %f" % (final_mean)
print (s)
pu.plot_histograms(final_loss)
pu.plot_error_frame(final_loss)
# pu.plot_cumsum(loss_list)
