def test_vgg(self, objData, normalize=False):
if normalize is True:
objData.normalization(self.normalization_max)
count_success = 0
prob_predicted = []
plot_predicted = []
label_total = []
prob_total = np.random.random((0, self.num_class))
print('\n# TEST VGG TRAINED')
for i in range(objData.total_batchs_complete):
batch, label = objData.generate_batch()
prob = self.sess.run(self.probVGG, feed_dict={self.x_batch: batch})
label_total = np.concatenate((label_total, label), axis=0)
prob_total = np.concatenate((prob_total, prob), axis=0)
# Acumulamos la presicion de cada iteracion, para despues hacer un promedio
count, prob_predicted, plot_predicted = utils.process_prob(
label,
prob,
predicted=prob_predicted,
plot_predicted=plot_predicted)
count_success = count_success + count
objData.next_batch_test()
# promediamos la presicion total
print('\n# STATUS:')
y_true = objData.labels
y_prob = prob_predicted
utils.metrics_multiclass(y_true, y_prob)
def test_model(net, sess_test, objData):
total = objData.total_images
count_success = 0
count_by_class = np.zeros([net.num_class, net.num_class])
prob_predicted = []
plot_predicted = []
label_total = []
prob_total = np.random.random((0, net.num_class))
print('\n# PHASE: Test classification')
for i in range(objData.total_batchs_complete):
batch, label = objData.generate_batch()
prob, layer = sess_test.run([net.prob, net.relu6], feed_dict={vgg_batch: batch, train_mode: False})
label_total = np.concatenate((label_total, label), axis=0)
prob_total = np.concatenate((prob_total, prob), axis=0)
# save output of a layer
# utils.save_layer_output(layer, label, name='Train_SNC4_relu6', dir='../data/features/')
# utils.save_layer_output_by_class(layer, label, name='Train_SNC4', dir='../data/features/')
count, prob_predicted, plot_predicted = utils.process_prob(label, prob, predicted=prob_predicted,
plot_predicted=plot_predicted)
count_success = count_success + count
objData.next_batch_test()
# promediamos la presicion total
print('\n# STATUS:')
y_true = objData.labels
y_prob = prob_predicted
accuracy_final = utils.metrics_multiclass(y_true, y_prob)
return accuracy_final
def test_model(net, sess_test, objData, plot_result=False):
count_success = 0
prob_predicted = []
plot_predicted = []
label_total = []
prob_total = np.random.random((0, net.num_class))
# Iteraciones por Batch, en cada iteracion la session de tensorflow procesa los 'n' datos de entrada
print('\n# PHASE: Test classification')
for i in range(objData.total_batchs_complete):
batch, label = objData.generate_batch()
prob = sess_test.run(net.net['prob'],
feed_dict={
mlp_batch: batch,
train_mode: False
})
label_total = np.concatenate((label_total, label), axis=0)
prob_total = np.concatenate((prob_total, prob), axis=0)
# Acumulamos la presicion de cada iteracion, para despues hacer un promedio
count, prob_predicted, plot_predicted = utils.process_prob(
label,
prob,
predicted=prob_predicted,
plot_predicted=plot_predicted)
count_success = count_success + count
objData.next_batch_test()
# promediamos la presicion total
print('\n# STATUS:')
y_true = objData.labels
y_prob = prob_predicted
accuracy_total = utils.metrics(y_true, y_prob, plot_predicted, plot_result)
# if plot_result is True:
# utils.precision_recall(y_true=label_total, y_prob=prob_total, num_class=net.num_class)
return accuracy_total