def main(args):
print("------------Start Evaluation-----------")
print("CheckPoint : {}".format(FLAGS.ckpt_dir))
print("Network : {}".format(FLAGS.network))
print("data : {}".format(FLAGS.data))
print("---------------------------------------")
# load dataset
data = Load(FLAGS.data)
batch_size = 3000
iteration = data.x_test.shape[0] // batch_size
test = data.load_test(data.x_test, data.y_test, batch_size)
test_iter = test.make_initializable_iterator()
inputs, labels = test_iter.get_next()
model_set = set_model(data.output_dim)
model = eval(FLAGS.network)(model=model_set, name=FLAGS.network, out_dim=data.output_dim, lr=0, opt=None, trainable=False)
logits = model.inference(inputs)
logits = tf.identity(logits, name="output_logits")
correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.train.Saver()
with tf.Session() as sess:
sess.run([test_iter.initializer],feed_dict={data.test_placeholder: data.x_test,
data.test_labels_placeholder: data.y_test})
utils = Utils(sess=sess)
if utils.restore_model(FLAGS.ckpt_dir):
avg_accuracy = 0
for i in range(iteration):
test_accuracy = sess.run(accuracy)
avg_accuracy += test_accuracy
print("accuracy_{} : {}".format(i, test_accuracy))
print("average_accuracy : {}".format(avg_accuracy / iteration))
return
else:
return
def get_class(self, labels):
l = [(np.sum(labels == i), i) for i in set(labels)]
l = np.array(l, dtype=[('x', np.int16), ('y', np.int16)])
return np.sort(l, order='x')[::-1]
def predit(self, x):
topk = self.get_topk(x)
c = self.get_class(topk[1])
confidence = c[0][0] / self.k
clazz = c[0][1]
return clazz, confidence
if __name__ == '__main__':
load = Load()
train, test = load.load_train(2000), load.load_test(200)
print('获取数据成功：', train[0].shape, train[1].shape)
#预处理标签
train_labels, test_labels = np.argmax(train[1], axis=1), np.argmax(test[1],
axis=1)
classify = Classify(6, train[0], train_labels)
ylabel, y_label = np.array([]), np.array([])
for test_item, i in zip(test[0], np.arange(0, test[0].shape[0])):
x = test_item
# print(x.shape)
y = test_labels[i]
# topk=classify.get_topk(x)
#
# # print('k:',topk[1])
