我想在张量板上查看测试的准确性，但是看来训练数据可以使准确性提高。我在控制台上打印测试精度，它显示了约70％，但是在张量板上，曲线表明精度在增长，最终接近100％。
这是我的代码：
title我向张量板添加精度，如下所示：
def train_crack_captcha_cnn(is_train, checkpoint_dir):
global max_acc
X = tf.placeholder(tf.float32, [None, dr.ROWS, dr.COLS, dr.CHANNELS])
Y = tf.placeholder(tf.float32, [None, 1, 1, 2])
output, end_points = resnet_v2_50(X, num_classes = 2)
global_steps = tf.Variable(1, trainable=False)
learning_rate = tf.train.exponential_decay(0.001, global_steps, 100, 0.9)
with tf.device('/device:GPU:0'):
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=Y, logits=output))
# optimizer 为了加快训练 learning_rate应该开始大，然后慢慢衰
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss, global_step=global_steps)
predict = tf.argmax(output, axis = 3)
l = tf.argmax(Y, axis = 3)
correct_pred = tf.equal(predict, l)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
## tensorboard
tf.summary.scalar('test_accuracy', accuracy)
tf.summary.scalar("loss", loss)
tf.summary.scalar("learning_rate", learning_rate)
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement = True)) as sess:
if is_train:
writer = tf.summary.FileWriter("/tmp/cnn_log/log", graph = sess.graph)
sess.run(tf.global_variables_initializer())
step_value = sess.run(global_steps)
while step_value < 100000:
step_value = sess.run(global_steps)
merged = tf.summary.merge_all()
batch_x, batch_y = get_next_batch()
result, _, _loss= sess.run([merged, optimizer, loss], feed_dict={X: batch_x, Y: batch_y})
writer.add_summary(result, step_value)
print('step : {} loss : {}'.format(step_value, _loss))
# 每100 step计算一次准确率
if step_value % 20 == 0:
acc = sess.run(accuracy, feed_dict={X: validation, Y: validation_labels})
print('accuracy : {}'.format(acc))
# 如果准确率大于max_acc,保存模型,完成训练
if acc > max_acc:
max_acc = float(acc) #转换类型防止变为同一个引用
saver.save(sess, checkpoint_dir + "/" + str(step_value) + '-' + str(acc) + "/model.ckpt", global_step=global_steps)
##### predict #####
# predict_y = sess.run(output, feed_dict={X: test})
# data = pd.DataFrame([i for i in range(1, len(predict_y) + 1)], columns = ['id'])
# predict_y = np.argmax(predict_y, axis = 3)
# predict_y = np.reshape(predict_y,(-1))
# print(predict_y)
# predict_y = pd.Series(predict_y, name='label')
# data['label'] = predict_y
# data.to_csv("gender_submission.csv" + str(step), index=False)
##### end #####
writer.close()
else:
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
acc = sess.run(accuracy, feed_dict={X: validation, Y: validation_labels})
print('accuracy : {}'.format(acc))
每隔20步，我就会获得关于测试数据的准确性，并将结果打印到控制台，这与张量板上显示的数据不同。
为什么？