def generate_data_encode(self, objData, normalize=False, csv_save=False, path_save='', csv_name='encode'):
if normalize is True:
objData.normalization(self.normalization_max)
total = objData.total_inputs
cost_total = 0
result = []
if csv_save is True:
print('\n# GENERATE DATA ENCODE')
for i in range(objData.total_batchs_complete):
x_, label = objData.generate_batch()
cost, layer = self.sess.run([self.AEGlobal.cost, self.AEGlobal.net['encodeFC_1']], feed_dict={self.x_batch: x_})
utils.save_layer_output(layer, label, name=csv_name, dir=path_save)
cost_total = cost_total + cost
objData.next_batch_test()
print(cost_total, cost_total / total)
else:
print('\n# GENERATE DATA ENCODE')
for i in range(objData.total_batchs_complete):
x_, label = objData.generate_batch()
layer = self.sess.run(self.AEGlobal.z, feed_dict={self.x_batch: x_})
objData.next_batch_test()
result.append(np.concatenate((layer[0], [label]), axis=0))
return result
def test_model_save(net, sess_test, objData, xdir, xname):
total = objData.total_inputs
cost_total = 0
for i in range(objData.total_batchs_complete):
x_, label = objData.generate_batch()
cost, layer = sess_test.run([net.cost, net.z], feed_dict={x_batch: x_})
utils.save_layer_output(layer, label, name=xname, dir=xdir)
cost_total = cost_total + cost
objData.next_batch_test()
return cost_total, cost_total / total
def test_model(net, sess_test, objData, objLabel, size):
total_acc = 0
total_loss= 0
total_itera = int(np.ceil(size/net.minibatch))
for step in range(total_itera):
x, y = fill_feed_dict(objData, objLabel, step, net.minibatch)
accuracy, layer, loss = sess_test.run([net.accuracy, net.pool3, net.loss], feed_dict={svhn_batch: x, svhn_label: y, train_mode: False})
layer = np.around(layer)
# save output of a layer
# print(step)
utils.save_layer_output(layer, np.argmax(y, axis=1), name='train_SVHN', dir='../data/SVHN_data/train1152/')
# utils.save_layer_output_by_class(layer, np.argmax(y, axis=1), name='train_SVHN', dir='../data/SVHN_data/train1152/')
total_acc = total_acc + accuracy
total_loss = total_loss + loss
acc = total_acc / total_itera
xloss = total_loss / total_itera
print('Test Accuracy: %.5f%%' % acc, '- Test Loss: %.5f%%' % xloss)