tf.estimator.train_and_evaluate 出错了 评估精度和损失

I use tf.estimator.train_and_evaluate训练和评估我的模型。这是我的代码：

import tensorflow as tf
import numpy as np
from tensorflow.contrib.slim.nets import resnet_v2
import tensorflow.contrib.slim as slim

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data(path='mnist.npz')
x_train = np.expand_dims(x_train, 3).astype(np.float32)[:5000]
y_train = y_train.astype(np.int32)[:5000]
x_test = np.expand_dims(x_test, 3).astype(np.float32)[:1000]
y_test = y_test.astype(np.int32)[:1000]

tf.logging.set_verbosity(tf.logging.INFO)

cls_num = 10


def model_fn(features, labels, mode):
    is_training = False
    if mode == tf.estimator.ModeKeys.TRAIN:
        is_training = True
    with slim.arg_scope(resnet_v2.resnet_arg_scope()):
        logits, endpoints = resnet_v2.resnet_v2_50(features, 
                num_classes=cls_num,
                is_training=is_training,
                reuse=None)

    logits = tf.squeeze(logits, [1, 2])
    preds = tf.argmax(logits, 1)

    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

    accuracy = tf.metrics.accuracy(labels=labels, predictions=preds)
    metrics = {'accuracy': accuracy}

    if mode == tf.estimator.ModeKeys.EVAL:
        return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics)

    optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op) 


def process_fn(feature, label):
    feature = tf.expand_dims(feature, 3)
    return feature, label


def train_input_fn():
    dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    #dataset.map(process_fn)
    dataset = dataset.repeat(1).batch(8)
    return dataset

def eval_input_fn():
    dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))
    dataset = dataset.repeat(1).batch(8)
    return dataset


estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir='logs')
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn)
eval_specs = tf.estimator.EvalSpec(input_fn=eval_input_fn)
for _ in xrange(10):
    tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs)

训练步长没问题，loss变得很小（0.001左右），但是评估结果错误（以下是评估日志）：

...
INFO:tensorflow:Saving dict for global step 625: accuracy = 0.5, global_step = 625, loss = 1330830600000.0
...

任务很简单，就是二分类。我不认为这是过度拟合。我的评估代码有问题吗？