mxnet框架下超全手寫字體識別—從數據預處理到網絡的訓練—模型及日誌的保存

        Mxnet框架深度學習框架越來越受到大家的歡迎。但是如何正確的使用這一框架,很多人並不是很清楚。從訓練數據的預處理,數據的生成(網絡真正需要的數據格式,網絡模型的保存,網絡訓練日誌的保存,等等,雖然網上有很多的trick,但是大多數比較零散),這裏,博主就從零開始,教大家訓練手寫字體(mnist)識別的一個完整的系統。


一、python、mxnet 如何安裝。

trickwindows下使用pip安裝Mxnet可能會錯出,因爲windows下的mxnet可能已來VC++2015或其他版本,linux下就不存在這種情況。


二、手寫字體數據集如何獲取。

import mxnet as mx
mnist = mx.test_utils.get_mnist()  # 得到手寫字體數據集

    運行這行代碼就可以下載到mnist數據集。mnist數據集主要包含四個壓縮文件,截圖如下:

說明:t10k-images-idx3-ubyte.gz:測試集圖片二進制壓縮文件

            t10k-labels-idx1-ubyte.gz:測試集圖片對應的標籤二進制壓縮文件

            train-images-idx3-ubyte.gz:訓練集圖片二進制壓縮文件

             train-labels-idx1-ubyte.gz: 訓練集圖片對應的標籤二進制壓縮文件

        經常有人問我,爲什麼下載下來的圖片打不開,根本不知道里面的圖片長什麼樣子如何讀取,如何進行訓練,我只能說,這些圖片已經被轉成了2進制文件,並不是原始的圖片。那麼這些文件裏面的圖片到底是如何組織的呢?,通過下面的代碼您就能完全瞭解。

由於我之前就已經下載過這4個壓縮文件,所以直接從本地讀取就可以了,沒有必要重複下載,並且有時候並不能完全下載下來。

train_data_path = 'mnist_data/train-images-idx3-ubyte.gz'
train_label_path = 'mnist_data/train-labels-idx1-ubyte.gz'
test_data_path = 'mnist_data/t10k-images-idx3-ubyte.gz'
test_label_path = 'mnist_data/t10k-labels-idx1-ubyte.gz'
train_label, train_data = read_data(image_url=train_data_path, label_url=train_label_path)
test_label, test_data = read_data(image_url=test_data_path, label_url=test_label_path)

print('shape of train_data:', train_data.shape)
print('shape of train_label:', train_label.shape)
print('shape of test_data:', test_data.shape)
print('shape of test_label:', test_label.shape)

輸出結果:

shape of train_data: (60000, 1, 28, 28)
shape of train_label: (60000,)
shape of test_data: (10000, 1, 28, 28)
shape of test_label: (10000,)
如果大家是初次下載,運行
mnist = mx.test_utils.get_mnist() 

後就已經得到了一個完整的手寫字體對象mnist。我們就可以直接通過下面的方式得到訓練集以及測試的數據,代碼如下:

train_image = mnist['train_data']
train_image_label = mnist['train_label']
test_image = mnist['test_data']
testimage_label = mnist['test_label']

三、數據如何處理。

        下載了mnist數據集,並且得到其具體的數據,該如何把這些數據轉換成我們訓練階段真正需要的格式?從上面的print的信息中我們已經可以知道圖片的大小已經是28×28、單通道灰度圖。如果我們不對圖片進行縮放的話,網絡的輸入應該是(batch_size, channel, height, width),所以我們需要把60000張訓練集圖片,10000張測試集圖片轉換成 (60000//batch_size)×(batch_size, channel, height, width)、(10000//batch_size)×(batch_size, channel, height, width)的迭代的形式。

          爲什麼mxnet裏面的訓練數據必須是以迭代器的形式傳入的?1)簡單,簡單,簡單!!! 2)mxnet框架中,用戶是不能像tensorflow框架那樣寫個for循環來顯示的將數據送入到網絡裏面。那麼如何正確的使用mxnet框架提供的迭代器呢?有的時候mxnet提供的迭代器類並不能滿足所有的需求,我們還需要重寫這個類。

          熟悉mxnet框架的小夥伴,應該知道,mxnet框架中網絡的輸入主要包含兩種:1)img,2)ndarray

          一般來說,對於前者我們可以很方便的使用mxnet提供的img2rec.py這個文件,將所有的圖片轉換成rec文件,然後將這個rec文件作爲網絡的輸入,其實也是一個迭代器對象。然而生成rec文件耗時,並且需要很大的額外空間,但是有沒有一種辦法不生成rec文件呢?當然有,就是上文提到的,重寫DataIter類,返回一個迭代器對象,每一次迭代都是(batch_size, channel, height, width)的完整數據快,這樣就可以將數據源源不斷的送入到網絡裏面去。完整代碼如下:

class Batch(object):
    def __init__(self, data, label):
        self.data = data
        self.label = label

class Inter(mx.io.DataIter):
    def __init__(self, batch_size, train_data, train_label):
        super(Inter, self).__init__()
        self.batch_size = batch_size
        self.begin = 0
        self.index = 0
        self.train_data = train_data
        self.train_label = train_label
        self.train_count = len(train_data)
        assert len(train_data) == len(train_label), 'Error'
        assert (self.train_count >= self.batch_size) and (self.batch_size > 0), 'Error'
        self.train_batches = self.train_count // self.batch_size

    def __iter__(self):
        return self

    def reset(self):
        self.begin = 0
        self.index = 0

    def next(self):
        if self.iter_next():
            return self.getdata()
        else:
            raise StopIteration

    def __next__(self):
        return self.next()

    def iter_next(self):
        if self.begin < self.train_batches:
            return True
        else:
            return False

    def get_batch_images_labels(self):
        data = self.train_data[self.index:self.index + self.batch_size, :, :, :]
        label = self.train_label[self.index:self.index + self.batch_size]
        return data, label

    def getdata(self):
        images, labels = self.get_batch_images_labels()  # 順序的得到數據
        data_all = [mx.nd.array(images)]
        label_all = [mx.nd.array(labels)]
        self.index += self.batch_size
        self.begin += 1
        return Batch(data_all, label_all)

    def getlabel(self):
        pass

    def getindex(self):
        return None

    def getpad(self):
        pass

Inter這個類就簡單的重寫了原生的mxnet迭代器類DataIter。從我寫的代碼中就可以看出這個迭代器類每次都會返回一個Batch對象,數據(data)和標籤(label),其中data的shape爲:(batch_size, channel, height, width),label的shape爲(batch_size,)請注意迭代器裏面的reset方法。

四、神經網絡的構建

             mxnet框架裏面有兩個非常重要的包:symbol和gluon。我們完全可以通過這兩個組件構建神經網絡。當然也完全可以提通過ndarray對象構建神經網絡。這裏我會一一給出代碼。

首先我給出網絡上一張很經典的Lenet-5的網絡結構圖:


trick:有沒有發現網絡圖片的原始輸入是32×32,而我們的圖片矩陣卻是28×28的?所以我在具體實現的時候稍微調整了下網絡結構。

        1、使用symbol構建Lenet-5網絡結構:

def get_net(class_num, bn_mom=0.99, filter_list=(6, 16)):
    data = mx.sym.Variable('data')
    imput = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=1e-5, momentum=bn_mom, name='bn_imput')  # 批量標準化
    # layer_1 卷積
    layer_1 = mx.sym.Convolution(data=imput, num_filter=filter_list[0], kernel=(5, 5), stride=(2, 2), pad=(2, 2),
                                 no_bias=False, name="conv_layer_1")
    bn_layer_1 = mx.sym.BatchNorm(data=layer_1, fix_gamma=False, eps=1e-5, momentum=bn_mom, name='bn_layer_1')
    a_bn_layer_1 = mx.sym.Activation(data=bn_layer_1, act_type='relu', name='relu_a_bn_layer_1')
    # layer_2 卷積
    bn_layer_2 = mx.sym.BatchNorm(data=a_bn_layer_1, fix_gamma=True, eps=1e-5, momentum=bn_mom, name='bn_layer_2')
    conv_layer_2 = mx.sym.Convolution(data=bn_layer_2, num_filter=filter_list[1], kernel=(5, 5), stride=(1, 1),
                                      pad=(0, 0), no_bias=False, name="conv_layer_2")

    bn_layer_2_1 = mx.sym.BatchNorm(data=conv_layer_2, fix_gamma=False, eps=1e-5, momentum=bn_mom, name='bn_layer_2_1')
    a_bn_layer_2 = mx.sym.Activation(data=bn_layer_2_1, act_type='relu', name='relu_a_a_bn_layer_2')

    # 下采樣層
    pooling_layer_2 = mx.symbol.Pooling(data=a_bn_layer_2, kernel=(5, 5), stride=(2, 2), pad=(2, 2), pool_type='max',
                                        name='pooling_layer_2')
    # 全連接層
    fc = mx.symbol.FullyConnected(data=pooling_layer_2, num_hidden=120, flatten=True, no_bias=False, name='fc')
    bn1_fc = mx.sym.BatchNorm(data=fc, fix_gamma=False, eps=1e-5, momentum=bn_mom, name='bn1_fc')
    fc1 = mx.symbol.FullyConnected(data=bn1_fc, num_hidden=84, flatten=True, no_bias=False, name='fc1')
    bn1_fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=False, eps=1e-5, momentum=bn_mom, name='bn1_fc1')
    fc2 = mx.symbol.FullyConnected(data=bn1_fc1, num_hidden=class_num, flatten=True, no_bias=False, name='fc2')
    bn1_fc2 = mx.sym.BatchNorm(data=fc2, fix_gamma=False, eps=1e-5, momentum=bn_mom, name='bn1_fc2')
    return mx.symbol.SoftmaxOutput(data=bn1_fc2, name='softmax')

        2、使用gluon組件構建Lenet-5網絡結構:

def create_net():
    net = nn.Sequential()
    with net.name_scope(): 
        net.add(
            nn.BatchNorm(epsilon=1e-5, momentum=0.9),
            nn.Conv2D(channels=6, kernel_size=5, strides=2, padding=2, activation='relu'),
            nn.BatchNorm(epsilon=1e-5, momentum=0.9),
            nn.Conv2D(channels=16, kernel_size=5, strides=1, padding=0, activation='relu'),
            nn.BatchNorm(epsilon=1e-5, momentum=0.9),
            nn.AvgPool2D(pool_size=2, strides=2, padding=2),
            nn.Flatten(),
            nn.BatchNorm(epsilon=1e-5, momentum=0.9),
            nn.Dense(120, activation='relu'),
            nn.BatchNorm(epsilon=1e-5, momentum=0.9),
            nn.Dense(84, activation='relu'),
            nn.BatchNorm(epsilon=1e-5, momentum=0.9),
            nn.Dense(10)
        )
    return net

        3、使用mxnet的ndarray(區別於 numpy的 array)構建Lenet-5網絡結構:

ctx = mx.cpu()  # 計算設備
# 輸出特徵數目 = 6, 卷積核 = (5,5)----------第一個卷積層
W1 = nd.random_normal(shape=(6, 1, 5, 5), scale=.1, ctx=ctx)
b1 = nd.zeros(W1.shape[0], ctx=ctx)

# 特徵數目 = 16, 卷積核 = (5,5)----------第二個卷積層
W2 = nd.random_normal(shape=(16, 6, 3, 3), scale=.1, ctx=ctx)
b2 = nd.zeros(W2.shape[0], ctx=ctx)

# 第一個全鏈接層
W3 = nd.random_normal(shape=(400, 120), scale=.1, ctx=ctx)
b3 = nd.zeros(W3.shape[1], ctx=ctx)

# 第二個全鏈接層
W4 = nd.random_normal(shape=(W3.shape[1], 84), scale=.1, ctx=ctx)
b4 = nd.zeros(W4.shape[1], ctx=ctx)

# 第三個全鏈接層
W5 = nd.random_normal(shape=(W4.shape[1], 10), scale=.1, ctx=ctx)
b5 = nd.zeros(W5.shape[1], ctx=ctx)

params = [W1, b1, W2, b2, W3, b3, W4, b4, W5, b5]

for param in params:
    param.attach_grad()


def net(X):
    X = X.as_in_context(W1.context)

    # 批量歸一化
    bn_X = nd.BatchNorm_v1(data=X, fix_gamma=True, eps=1e-5, output_mean_var=0.99, name='bn_X')

    # 第一層卷積
    h1_conv = nd.Convolution(data=bn_X, weight=W1, bias=b1, kernel=W1.shape[2:], num_filter=W1.shape[0], name='h1_conv')
    # 批量歸一化
    bn_h1_conv = mx.sym.BatchNorm(data=h1_conv, fix_gamma=False, eps=1e-5, momentum=0.99, name='bn_h1_conv')
    h1_activation = nd.relu(bn_h1_conv)

    # 第二層卷集
    # 批量歸一化
    bn_h1_conv2 = nd.BatchNorm_v1(data=h1_activation, fix_gamma=False, eps=1e-5, momentum=0.99, name='bn_h1_conv2')
    h1_conv2 = nd.Convolution(data=bn_h1_conv2, weight=W2, bias=b2, kernel=W1.shape[2:], num_filter=W1.shape[0],
                              name="h1_conv2")

    bn_h1_conv2 = nd.BatchNorm_v1(data=h1_conv2, fix_gamma=False, eps=1e-5, momentum=0.99, name='bn_h1_conv2')
    h2_activation = nd.relu(bn_h1_conv2)

    # 下采樣層
    # 下采樣層
    pooling_layer_2 = mx.symbol.Pooling(data=h2_activation, kernel=(5, 5), stride=(2, 2), pad=(2, 2), pool_type='max',
                                        name='pooling_layer_2')  # 16 *5 *5 flatten =

    # flatten
    fla = nd.flatten(data=pooling_layer_2, name='fla')
    # 全鏈接層---1
    fullcollect_layer = nd.dot(fla, W3) + b3
    bn_fullcollect_layer = mx.sym.BatchNorm(data=fullcollect_layer, fix_gamma=False, eps=1e-5, momentum=0.99,
                                            name='bn_fullcollect_layer')
    relu_bn_fullcollect_layer = nd.relu(data=bn_fullcollect_layer)

    # 全鏈接層-2
    fullcollect_layer_2 = nd.dot(relu_bn_fullcollect_layer, W4) + b4
    bn_fullcollect_layer_2 = mx.sym.BatchNorm(data=fullcollect_layer_2, fix_gamma=False, eps=1e-5, momentum=0.99,
                                            name='bn_fullcollect_layer_2')
    relu_bn_fullcollect_layer_2 = nd.relu(data=bn_fullcollect_layer_2)

    # 全鏈接層3
    fullcollect_layer_3 = nd.dot(relu_bn_fullcollect_layer_2, W5) + b5
    bn_fullcollect_layer_3 = mx.sym.BatchNorm(data=fullcollect_layer_3, fix_gamma=False, eps=1e-5, momentum=0.99,
                                              name='bn_fullcollect_layer_3')
    relu_bn_fullcollect_layer_3 = nd.relu(data=bn_fullcollect_layer_3)

    print('網絡結構:')
    print('第一個卷積層:', h1_activation.shape)
    print('第二個卷積層:', h2_activation.shape)
    print('下采樣層:', pooling_layer_2.shape)
    print('第一個全鏈接層:', relu_bn_fullcollect_layer.shape)
    print('第二個全鏈接層:', relu_bn_fullcollect_layer_2.shape)
    print('輸出層:', relu_bn_fullcollect_layer_3.shape)
    return relu_bn_fullcollect_layer_3

五、訓練網絡模型

        數據處理了,網絡模型構建好了,就可以將數據喂到網絡裏面去,訓練網絡模型了。

        trick:這裏需要說明一下,使用不同的組件構建的網絡模型,訓練的時候代碼可能有點差異。這裏分別針對不同組件構建的網絡該如何編制訓練程序進行說明。

        1、如果使用上面提到的symbol組件構建的網絡,那麼我們就可以編制下面的程序,訓練網絡。

           1)設置訓練日誌輸出格式:

# 檢查路徑
Util.check_all_path([config.saved_model_path, config.train_test_log_save_path.replace('/resnet_log.log', '')])
logger = logging.getLogger()
logging.basicConfig(level=logging.INFO,
                    format='%(message)s',
                    datefmt='%a, %d %b %Y %H:%M:%S',
                    filename=config.train_test_log_save_path,
                    filemode='w')

            2)獲取數據迭代器對象,代碼:

train_data, train_label, test_data, test_label = get_all_avaliable_data(config.train_data_path,
                                                                        config.train_label_path,
                                                                        config.test_data_path,
                                                                        config.test_label_path)
data_train = Inter(config.batch_size, train_data, train_label)  # 獲取訓練集的迭代器對象
_eval_data = Inter(config.batch_size*2, test_data, test_label)  # 獲取測試集的迭代器對象

            3)訓練:

        

_eval_data = mx.sym.Variable('eval_data:')
softmax_out = get_net(class_num=10, bn_mom=0.99, filter_list=[6, 16])
model = mx.mod.Module(symbol=softmax_out,
                      context=mx.cpu(),
                      data_names=['data'],
                      label_names=['softmax_label'])
model.fit(data_train,
          eval_data=_eval_data,
          optimizer='sgd',
          initializer=mx.init.Xavier(rnd_type='gaussian', factor_type='in', magnitude=2),
          eval_metric=['acc', 'ce'],
          optimizer_params={'learning_rate': config.learning_rate, 'momentum': config.momentum},
          batch_end_callback=mx.callback.Speedometer(config.batch_size, 1),
          epoch_end_callback=mx.callback.do_checkpoint(config.saved_model_path),
          num_epoch=config.num_epoch)

           4) 這部分的完整代碼:

import logging
import mxnet as mx
from net import get_net
from tool import Inter, Test
from util import Util
import config
from lodad_data import get_all_avaliable_data

# 檢查路徑
Util.check_all_path([config.saved_model_path, config.train_test_log_save_path.replace('/resnet_log.log', '')])
logger = logging.getLogger()
logging.basicConfig(level=logging.INFO,
                    format='%(message)s',
                    datefmt='%a, %d %b %Y %H:%M:%S',
                    filename=config.train_test_log_save_path,
                    filemode='w')

if __name__ == '__main__':
    """
      By nxg  read only  no copy and no broadcast......
    """
    _eval_data = mx.sym.Variable('eval_data:')
    softmax_out = get_net(class_num=10, bn_mom=0.99, filter_list=[6, 16])
    model = mx.mod.Module(symbol=softmax_out,
                          context=mx.cpu(),
                          data_names=['data'],
                          label_names=['softmax_label'])

    train_data, train_label, test_data, test_label = get_all_avaliable_data(config.train_data_path,
                                                                            config.train_label_path,
                                                                            config.test_data_path,
                                                                            config.test_label_path)
    data_train = Inter(config.batch_size, train_data, train_label)  # 獲取訓練集的迭代器對象
    _eval_data = Inter(config.batch_size*2, test_data, test_label)  # 獲取測試集的迭代器對象

    model.fit(data_train,
              eval_data=_eval_data,
              optimizer='sgd',
              initializer=mx.init.Xavier(rnd_type='gaussian', factor_type='in', magnitude=2),
              eval_metric=['acc', 'ce'],
              optimizer_params={'learning_rate': config.learning_rate, 'momentum': config.momentum},
              batch_end_callback=mx.callback.Speedometer(config.batch_size, 1),
              epoch_end_callback=mx.callback.do_checkpoint(config.saved_model_path),
              num_epoch=config.num_epoch)

            2、如果式樣上面提到的使用gluon組件構建的神經網絡,那麼訓練網絡時候的完整代碼如下:

def accuracy(output, label):
    return nd.mean(output.argmax(axis=1) == label).asscalar()


def evaluate_accuracy(_test_data, net):
    acc = 0.
    for test_data_label_data_names_label_names in _test_data:
        test_data = test_data_label_data_names_label_names.data
        test_label = test_data_label_data_names_label_names.label
        data = test_data[0].as_in_context(ctx)
        label = test_label[0].as_in_context(ctx)

        output = net(data)
        label = label.as_in_context(ctx)
        acc += accuracy(output, label)
    return acc / eval_data_batch_count


def main():

    train_data, train_label, test_data, test_label = get_all_avaliable_data(config.train_data_path,
                                                                            config.train_label_path,
                                                                            config.test_data_path,
                                                                            config.test_label_path)
    data_train = Inter(config.batch_size, train_data, train_label)
    _eval_data = Inter(config.batch_size, test_data, test_label)

    global train_data_batch_count
    global eval_data_batch_count
    global train_step
    train_step = 0
    train_data_batch_count = len(train_data) // config.batch_size  # 937
    eval_data_batch_count = len(test_data) // config.batch_size  # 156
    # 保存日誌
    log = open(file='train_test_log/resnet_log.log', mode='w')
    softmax_cross_entropy_loss = gluon.loss.SoftmaxCrossEntropyLoss()
    net = create_net()
    net.initialize(ctx=ctx)  # 初始化網絡參數
    trainer = gluon.Trainer(net.collect_params(), 'sgd', {"learning_rate": 0.5})

    for epoch in range(5):
        all_train_loss = 0.
        all_train_acc = 0.
        data_train.reset()  # 這句話如果不要,那麼整個數據集只會迭代一次
        _eval_data.reset()  # 這句話如果不要,那麼整個數據集只會迭代一次
        for data_label_data_names_label_names in data_train:
            train_step += 1
            data = data_label_data_names_label_names.data
            label = data_label_data_names_label_names.label
            data = data[0].as_in_context(ctx)  # 在何種計算設備上實施計算
            label = label[0].as_in_context(ctx)
            with autograd.record():
                output = net(data)
                loss = softmax_cross_entropy_loss(output, label)
            loss.backward()
            trainer.step(config.batch_size)

            train_loss = nd.mean(loss).asscalar()
            train_acc  = accuracy(output, label)
            all_train_loss += train_loss
            all_train_acc += train_acc

            log.writelines("Epoch:%d, train_step: %d, loss: %f, Train_acc: %f \n" %
                           (epoch, train_step, train_loss, train_acc))
        test_acc = evaluate_accuracy(_eval_data, net)
        log.writelines("\n\nEpoch:%d, avg_train_loss: %f, avg_train_acc: %f, Test_acc: %f \n" %
              (epoch, all_train_loss / train_data_batch_count, all_train_acc / train_data_batch_count, test_acc))


if __name__ == '__main__':
    main()
        可能上面貼出的代碼中的某些工具函數我並沒有給全,大家可以到我的github上去下載,也可以留言,我會把完整的代碼分享給大家。

 6、本地保存的訓練日誌:

Epoch:0, train_step: 1, loss: 2.417782, Train_acc: 0.093750 
Epoch:0, train_step: 2, loss: 2.147448, Train_acc: 0.218750 
Epoch:0, train_step: 3, loss: 2.077140, Train_acc: 0.406250 
Epoch:0, train_step: 4, loss: 1.847961, Train_acc: 0.437500 
Epoch:0, train_step: 5, loss: 1.075216, Train_acc: 0.671875 
Epoch:0, train_step: 6, loss: 0.592741, Train_acc: 0.859375 
Epoch:0, train_step: 7, loss: 0.643913, Train_acc: 0.828125 
Epoch:0, train_step: 8, loss: 0.837896, Train_acc: 0.796875 
Epoch:0, train_step: 9, loss: 0.582398, Train_acc: 0.859375 
Epoch:0, train_step: 10, loss: 0.750824, Train_acc: 0.765625 
Epoch:0, train_step: 11, loss: 0.532329, Train_acc: 0.781250 
Epoch:0, train_step: 12, loss: 0.583528, Train_acc: 0.796875 
Epoch:0, train_step: 13, loss: 0.422033, Train_acc: 0.921875 
Epoch:0, train_step: 14, loss: 0.829014, Train_acc: 0.718750 
Epoch:0, train_step: 15, loss: 0.643326, Train_acc: 0.812500 
Epoch:0, train_step: 16, loss: 0.667152, Train_acc: 0.828125 
Epoch:0, train_step: 17, loss: 0.743936, Train_acc: 0.796875 
Epoch:0, train_step: 18, loss: 0.640609, Train_acc: 0.718750 
Epoch:0, train_step: 19, loss: 0.578947, Train_acc: 0.843750 
Epoch:0, train_step: 20, loss: 0.678622, Train_acc: 0.796875 
Epoch:0, train_step: 21, loss: 0.659916, Train_acc: 0.781250 
Epoch:0, train_step: 22, loss: 0.886372, Train_acc: 0.703125 
Epoch:0, train_step: 23, loss: 0.498017, Train_acc: 0.812500 
Epoch:0, train_step: 24, loss: 0.339886, Train_acc: 0.890625 
Epoch:0, train_step: 25, loss: 0.383869, Train_acc: 0.890625 
Epoch:0, train_step: 26, loss: 0.352800, Train_acc: 0.890625 
Epoch:0, train_step: 27, loss: 0.235351, Train_acc: 0.921875 
Epoch:0, train_step: 28, loss: 0.335911, Train_acc: 0.906250 
Epoch:0, train_step: 29, loss: 0.321678, Train_acc: 0.906250 
Epoch:0, train_step: 30, loss: 0.214269, Train_acc: 0.937500 
Epoch:0, train_step: 31, loss: 0.194405, Train_acc: 0.937500 
Epoch:0, train_step: 32, loss: 0.229423, Train_acc: 0.937500 
Epoch:0, train_step: 33, loss: 0.357825, Train_acc: 0.921875 
Epoch:0, train_step: 34, loss: 0.093697, Train_acc: 0.984375 
Epoch:0, train_step: 35, loss: 0.236372, Train_acc: 0.906250 
Epoch:0, train_step: 36, loss: 0.171640, Train_acc: 0.921875 
Epoch:0, train_step: 37, loss: 0.760929, Train_acc: 0.828125 
Epoch:0, train_step: 38, loss: 0.425227, Train_acc: 0.890625 
Epoch:0, train_step: 39, loss: 0.419191, Train_acc: 0.875000 
Epoch:0, train_step: 40, loss: 0.206767, Train_acc: 0.906250 
Epoch:0, train_step: 41, loss: 0.135619, Train_acc: 0.953125 
Epoch:0, train_step: 42, loss: 0.359003, Train_acc: 0.875000 
Epoch:0, train_step: 43, loss: 0.241495, Train_acc: 0.937500 
Epoch:0, train_step: 44, loss: 0.270616, Train_acc: 0.921875 
Epoch:0, train_step: 45, loss: 0.281466, Train_acc: 0.890625 
Epoch:0, train_step: 46, loss: 0.263769, Train_acc: 0.921875 
Epoch:0, train_step: 47, loss: 0.239509, Train_acc: 0.921875 
Epoch:0, train_step: 48, loss: 0.335962, Train_acc: 0.843750 
Epoch:0, train_step: 49, loss: 0.144546, Train_acc: 0.953125 
Epoch:0, train_step: 50, loss: 0.116990, Train_acc: 0.953125 
Epoch:0, train_step: 51, loss: 0.249545, Train_acc: 0.937500 
Epoch:0, train_step: 52, loss: 0.169997, Train_acc: 0.953125 
Epoch:0, train_step: 53, loss: 0.205849, Train_acc: 0.906250 
Epoch:0, train_step: 54, loss: 0.181003, Train_acc: 0.937500 
Epoch:0, train_step: 55, loss: 0.217988, Train_acc: 0.937500 
Epoch:0, train_step: 56, loss: 0.166839, Train_acc: 0.921875 
Epoch:0, train_step: 57, loss: 0.112745, Train_acc: 0.968750 
Epoch:0, train_step: 58, loss: 0.518607, Train_acc: 0.890625 
Epoch:0, train_step: 59, loss: 0.486200, Train_acc: 0.828125 
Epoch:0, train_step: 60, loss: 0.244532, Train_acc: 0.937500 
Epoch:0, train_step: 61, loss: 0.093446, Train_acc: 0.968750 
Epoch:0, train_step: 62, loss: 0.193257, Train_acc: 0.953125 
Epoch:0, train_step: 63, loss: 0.095059, Train_acc: 0.968750 
Epoch:0, train_step: 64, loss: 0.145965, Train_acc: 0.953125 
Epoch:0, train_step: 65, loss: 0.349815, Train_acc: 0.859375 
Epoch:0, train_step: 66, loss: 0.148771, Train_acc: 0.953125 
Epoch:0, train_step: 67, loss: 0.280851, Train_acc: 0.906250 
Epoch:0, train_step: 68, loss: 0.211508, Train_acc: 0.890625 
Epoch:0, train_step: 69, loss: 0.209474, Train_acc: 0.984375 
Epoch:0, train_step: 70, loss: 0.249752, Train_acc: 0.953125 
Epoch:0, train_step: 71, loss: 0.232584, Train_acc: 0.921875 
Epoch:0, train_step: 72, loss: 0.070046, Train_acc: 0.984375 
Epoch:0, train_step: 73, loss: 0.279436, Train_acc: 0.921875 
Epoch:0, train_step: 74, loss: 0.147189, Train_acc: 0.953125 
Epoch:0, train_step: 75, loss: 0.195968, Train_acc: 0.968750 
Epoch:0, train_step: 76, loss: 0.175260, Train_acc: 0.953125 
Epoch:0, train_step: 77, loss: 0.129287, Train_acc: 0.953125 
Epoch:0, train_step: 78, loss: 0.249973, Train_acc: 0.921875 
Epoch:0, train_step: 79, loss: 0.052008, Train_acc: 1.000000 
Epoch:0, train_step: 80, loss: 0.078089, Train_acc: 0.984375 
Epoch:0, train_step: 81, loss: 0.234961, Train_acc: 0.906250 
Epoch:0, train_step: 82, loss: 0.114855, Train_acc: 0.953125 
Epoch:0, train_step: 83, loss: 0.263273, Train_acc: 0.937500 
Epoch:0, train_step: 84, loss: 0.268444, Train_acc: 0.921875 
Epoch:0, train_step: 85, loss: 0.375152, Train_acc: 0.906250 
Epoch:0, train_step: 86, loss: 0.190591, Train_acc: 0.921875 
Epoch:0, train_step: 87, loss: 0.255328, Train_acc: 0.921875 
Epoch:0, train_step: 88, loss: 0.191680, Train_acc: 0.937500 
Epoch:0, train_step: 89, loss: 0.158745, Train_acc: 0.953125 
Epoch:0, train_step: 90, loss: 0.178674, Train_acc: 0.921875 
Epoch:0, train_step: 91, loss: 0.197587, Train_acc: 0.921875 
Epoch:0, train_step: 92, loss: 0.307597, Train_acc: 0.906250 
Epoch:0, train_step: 93, loss: 0.257246, Train_acc: 0.921875 
Epoch:0, train_step: 94, loss: 0.151752, Train_acc: 0.937500 
Epoch:0, train_step: 95, loss: 0.050518, Train_acc: 1.000000 
Epoch:0, train_step: 96, loss: 0.133955, Train_acc: 0.968750 
Epoch:0, train_step: 97, loss: 0.060623, Train_acc: 0.984375 
Epoch:0, train_step: 98, loss: 0.170524, Train_acc: 0.937500 
Epoch:0, train_step: 99, loss: 0.135782, Train_acc: 0.968750 
Epoch:0, train_step: 100, loss: 0.048621, Train_acc: 0.984375 
Epoch:0, train_step: 101, loss: 0.139361, Train_acc: 0.937500 
Epoch:0, train_step: 102, loss: 0.209602, Train_acc: 0.921875 
Epoch:0, train_step: 103, loss: 0.108424, Train_acc: 0.953125 
Epoch:0, train_step: 104, loss: 0.107708, Train_acc: 0.953125 
Epoch:0, train_step: 105, loss: 0.195755, Train_acc: 0.953125 
Epoch:0, train_step: 106, loss: 0.047515, Train_acc: 0.984375 
Epoch:0, train_step: 107, loss: 0.276701, Train_acc: 0.937500 
Epoch:0, train_step: 108, loss: 0.345514, Train_acc: 0.906250 
Epoch:0, train_step: 109, loss: 0.374395, Train_acc: 0.781250 
Epoch:0, train_step: 110, loss: 0.395531, Train_acc: 0.906250 
Epoch:0, train_step: 111, loss: 0.182749, Train_acc: 0.968750 
Epoch:0, train_step: 112, loss: 0.144250, Train_acc: 0.953125 
Epoch:0, train_step: 113, loss: 0.287096, Train_acc: 0.906250 
Epoch:0, train_step: 114, loss: 0.494812, Train_acc: 0.859375 
Epoch:0, train_step: 115, loss: 0.302390, Train_acc: 0.906250 
Epoch:0, train_step: 116, loss: 0.188245, Train_acc: 0.906250 
Epoch:0, train_step: 117, loss: 0.118966, Train_acc: 0.968750 
Epoch:0, train_step: 118, loss: 0.181855, Train_acc: 0.953125 
Epoch:0, train_step: 119, loss: 0.235975, Train_acc: 0.921875 
Epoch:0, train_step: 120, loss: 0.200544, Train_acc: 0.968750 
Epoch:0, train_step: 121, loss: 0.208240, Train_acc: 0.937500 
Epoch:0, train_step: 122, loss: 0.304978, Train_acc: 0.906250 
Epoch:0, train_step: 123, loss: 0.249742, Train_acc: 0.906250 
Epoch:0, train_step: 124, loss: 0.176322, Train_acc: 0.921875 
Epoch:0, train_step: 125, loss: 0.211738, Train_acc: 0.921875 
Epoch:0, train_step: 126, loss: 0.107232, Train_acc: 0.968750 
Epoch:0, train_step: 127, loss: 0.222557, Train_acc: 0.937500 
Epoch:0, train_step: 128, loss: 0.046454, Train_acc: 0.984375 
Epoch:0, train_step: 129, loss: 0.268393, Train_acc: 0.906250 
Epoch:0, train_step: 130, loss: 0.170945, Train_acc: 0.937500 
Epoch:0, train_step: 131, loss: 0.105138, Train_acc: 0.968750 
Epoch:0, train_step: 132, loss: 0.133199, Train_acc: 0.953125 
Epoch:0, train_step: 133, loss: 0.309434, Train_acc: 0.906250 
Epoch:0, train_step: 134, loss: 0.133829, Train_acc: 0.984375 
Epoch:0, train_step: 135, loss: 0.207350, Train_acc: 0.906250 
Epoch:0, train_step: 136, loss: 0.337783, Train_acc: 0.906250 
Epoch:0, train_step: 137, loss: 0.444962, Train_acc: 0.875000 
Epoch:0, train_step: 138, loss: 0.143963, Train_acc: 0.953125 
Epoch:0, train_step: 139, loss: 0.376271, Train_acc: 0.859375 
Epoch:0, train_step: 140, loss: 0.167714, Train_acc: 0.953125 
Epoch:0, train_step: 141, loss: 0.102254, Train_acc: 0.968750 
Epoch:0, train_step: 142, loss: 0.042115, Train_acc: 1.000000 
Epoch:0, train_step: 143, loss: 0.326979, Train_acc: 0.937500 
Epoch:0, train_step: 144, loss: 0.095411, Train_acc: 0.968750 
Epoch:0, train_step: 145, loss: 0.201675, Train_acc: 0.953125 
Epoch:0, train_step: 146, loss: 0.159263, Train_acc: 0.953125 
Epoch:0, train_step: 147, loss: 0.239955, Train_acc: 0.937500 
Epoch:0, train_step: 148, loss: 0.260774, Train_acc: 0.890625 
Epoch:0, train_step: 149, loss: 0.192994, Train_acc: 0.937500 
Epoch:0, train_step: 150, loss: 0.218349, Train_acc: 0.921875 
Epoch:0, train_step: 151, loss: 0.130956, Train_acc: 0.953125 
Epoch:0, train_step: 152, loss: 0.099249, Train_acc: 0.968750 
Epoch:0, train_step: 153, loss: 0.222351, Train_acc: 0.937500 
Epoch:0, train_step: 154, loss: 0.048579, Train_acc: 1.000000 
Epoch:0, train_step: 155, loss: 0.063588, Train_acc: 0.984375 
Epoch:0, train_step: 156, loss: 0.071235, Train_acc: 0.984375 
Epoch:0, train_step: 157, loss: 0.178361, Train_acc: 0.937500 
Epoch:0, train_step: 158, loss: 0.200687, Train_acc: 0.953125 
Epoch:0, train_step: 159, loss: 0.192534, Train_acc: 0.953125 
Epoch:0, train_step: 160, loss: 0.394295, Train_acc: 0.906250 
Epoch:0, train_step: 161, loss: 0.317833, Train_acc: 0.875000 
Epoch:0, train_step: 162, loss: 0.064191, Train_acc: 0.968750 
Epoch:0, train_step: 163, loss: 0.063043, Train_acc: 0.968750 
Epoch:0, train_step: 164, loss: 0.051433, Train_acc: 0.984375 
Epoch:0, train_step: 165, loss: 0.048169, Train_acc: 0.984375 
Epoch:0, train_step: 166, loss: 0.023904, Train_acc: 1.000000 
Epoch:0, train_step: 167, loss: 0.111142, Train_acc: 0.968750 
Epoch:0, train_step: 168, loss: 0.156100, Train_acc: 0.937500 
Epoch:0, train_step: 169, loss: 0.244502, Train_acc: 0.921875 
Epoch:0, train_step: 170, loss: 0.129055, Train_acc: 0.984375 
Epoch:0, train_step: 171, loss: 0.046237, Train_acc: 1.000000 
Epoch:0, train_step: 172, loss: 0.183251, Train_acc: 0.968750 
Epoch:0, train_step: 173, loss: 0.117667, Train_acc: 0.937500 
Epoch:0, train_step: 174, loss: 0.093352, Train_acc: 0.984375 
Epoch:0, train_step: 175, loss: 0.072180, Train_acc: 0.968750 
Epoch:0, train_step: 176, loss: 0.241708, Train_acc: 0.937500 
Epoch:0, train_step: 177, loss: 0.061940, Train_acc: 0.984375 
Epoch:0, train_step: 178, loss: 0.099466, Train_acc: 0.968750 
Epoch:0, train_step: 179, loss: 0.055113, Train_acc: 0.968750 
Epoch:0, train_step: 180, loss: 0.076228, Train_acc: 0.968750 
Epoch:0, train_step: 181, loss: 0.268029, Train_acc: 0.906250 
Epoch:0, train_step: 182, loss: 0.107267, Train_acc: 0.984375 
Epoch:0, train_step: 183, loss: 0.137517, Train_acc: 0.968750 
Epoch:0, train_step: 184, loss: 0.081613, Train_acc: 0.953125 
Epoch:0, train_step: 185, loss: 0.097723, Train_acc: 0.984375 
Epoch:0, train_step: 186, loss: 0.173998, Train_acc: 0.937500 
Epoch:0, train_step: 187, loss: 0.111674, Train_acc: 0.937500 
Epoch:0, train_step: 188, loss: 0.133634, Train_acc: 0.953125 
Epoch:0, train_step: 189, loss: 0.240766, Train_acc: 0.921875 
Epoch:0, train_step: 190, loss: 0.071469, Train_acc: 0.968750 
Epoch:0, train_step: 191, loss: 0.085162, Train_acc: 0.953125 
Epoch:0, train_step: 192, loss: 0.201763, Train_acc: 0.937500 
Epoch:0, train_step: 193, loss: 0.087841, Train_acc: 0.968750 
Epoch:0, train_step: 194, loss: 0.186591, Train_acc: 0.937500 
Epoch:0, train_step: 195, loss: 0.056906, Train_acc: 0.984375 
Epoch:0, train_step: 196, loss: 0.078472, Train_acc: 0.984375 
Epoch:0, train_step: 197, loss: 0.285432, Train_acc: 0.953125 
Epoch:0, train_step: 198, loss: 0.299019, Train_acc: 0.890625 
Epoch:0, train_step: 199, loss: 0.099036, Train_acc: 0.984375 
Epoch:0, train_step: 200, loss: 0.212280, Train_acc: 0.937500 
Epoch:0, train_step: 201, loss: 0.111627, Train_acc: 0.953125 
Epoch:0, train_step: 202, loss: 0.195275, Train_acc: 0.953125 
Epoch:0, train_step: 203, loss: 0.268554, Train_acc: 0.937500 
Epoch:0, train_step: 204, loss: 0.202567, Train_acc: 0.937500 
Epoch:0, train_step: 205, loss: 0.269112, Train_acc: 0.921875 
Epoch:0, train_step: 206, loss: 0.245677, Train_acc: 0.906250 
Epoch:0, train_step: 207, loss: 0.137429, Train_acc: 0.921875 
Epoch:0, train_step: 208, loss: 0.137760, Train_acc: 0.968750 
Epoch:0, train_step: 209, loss: 0.043124, Train_acc: 1.000000 
Epoch:0, train_step: 210, loss: 0.206140, Train_acc: 0.937500 
Epoch:0, train_step: 211, loss: 0.089152, Train_acc: 0.984375 
Epoch:0, train_step: 212, loss: 0.152733, Train_acc: 0.953125 
Epoch:0, train_step: 213, loss: 0.110357, Train_acc: 0.953125 
Epoch:0, train_step: 214, loss: 0.096450, Train_acc: 0.984375 
Epoch:0, train_step: 215, loss: 0.254843, Train_acc: 0.921875 
Epoch:0, train_step: 216, loss: 0.053830, Train_acc: 1.000000 
Epoch:0, train_step: 217, loss: 0.171203, Train_acc: 0.953125 
Epoch:0, train_step: 218, loss: 0.254211, Train_acc: 0.937500 
Epoch:0, train_step: 219, loss: 0.187960, Train_acc: 0.953125 
Epoch:0, train_step: 220, loss: 0.135573, Train_acc: 0.953125 
Epoch:0, train_step: 221, loss: 0.152126, Train_acc: 0.921875 
Epoch:0, train_step: 222, loss: 0.167882, Train_acc: 0.937500 
Epoch:0, train_step: 223, loss: 0.252212, Train_acc: 0.937500 
Epoch:0, train_step: 224, loss: 0.200252, Train_acc: 0.921875 
Epoch:0, train_step: 225, loss: 0.325268, Train_acc: 0.890625 
Epoch:0, train_step: 226, loss: 0.055470, Train_acc: 0.984375 
Epoch:0, train_step: 227, loss: 0.035767, Train_acc: 1.000000 
Epoch:0, train_step: 228, loss: 0.238885, Train_acc: 0.937500 
Epoch:0, train_step: 229, loss: 0.068784, Train_acc: 0.968750 
Epoch:0, train_step: 230, loss: 0.166429, Train_acc: 0.953125 
Epoch:0, train_step: 231, loss: 0.160921, Train_acc: 0.953125 
Epoch:0, train_step: 232, loss: 0.242600, Train_acc: 0.921875 
Epoch:0, train_step: 233, loss: 0.029633, Train_acc: 1.000000 
Epoch:0, train_step: 234, loss: 0.056294, Train_acc: 0.984375 
Epoch:0, train_step: 235, loss: 0.023245, Train_acc: 0.984375 
Epoch:0, train_step: 236, loss: 0.048721, Train_acc: 0.984375 
Epoch:0, train_step: 237, loss: 0.072512, Train_acc: 0.968750 
Epoch:0, train_step: 238, loss: 0.056075, Train_acc: 0.968750 
Epoch:0, train_step: 239, loss: 0.114910, Train_acc: 0.953125 
Epoch:0, train_step: 240, loss: 0.076293, Train_acc: 0.984375 
Epoch:0, train_step: 241, loss: 0.092031, Train_acc: 0.953125 
Epoch:0, train_step: 242, loss: 0.095848, Train_acc: 0.953125 
Epoch:0, train_step: 243, loss: 0.104798, Train_acc: 0.968750 
Epoch:0, train_step: 244, loss: 0.108040, Train_acc: 0.968750 
Epoch:0, train_step: 245, loss: 0.071164, Train_acc: 0.984375 
Epoch:0, train_step: 246, loss: 0.031898, Train_acc: 1.000000 
Epoch:0, train_step: 247, loss: 0.176472, Train_acc: 0.937500 
Epoch:0, train_step: 248, loss: 0.125027, Train_acc: 0.968750 
Epoch:0, train_step: 249, loss: 0.144656, Train_acc: 0.968750 
Epoch:0, train_step: 250, loss: 0.164103, Train_acc: 0.937500 
Epoch:0, train_step: 251, loss: 0.097101, Train_acc: 0.984375 
Epoch:0, train_step: 252, loss: 0.064158, Train_acc: 0.984375 
Epoch:0, train_step: 253, loss: 0.042128, Train_acc: 0.984375 
Epoch:0, train_step: 254, loss: 0.094065, Train_acc: 0.953125 
Epoch:0, train_step: 255, loss: 0.053626, Train_acc: 0.984375 
Epoch:0, train_step: 256, loss: 0.042115, Train_acc: 0.984375 
Epoch:0, train_step: 257, loss: 0.044252, Train_acc: 1.000000 
Epoch:0, train_step: 258, loss: 0.129825, Train_acc: 0.984375 
Epoch:0, train_step: 259, loss: 0.128804, Train_acc: 0.984375 
Epoch:0, train_step: 260, loss: 0.015984, Train_acc: 1.000000 
Epoch:0, train_step: 261, loss: 0.138158, Train_acc: 0.937500 
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Epoch:0, train_step: 387, loss: 0.060455, Train_acc: 0.984375 
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Epoch:0, train_step: 529, loss: 0.012281, Train_acc: 1.000000 
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Epoch:0, train_step: 531, loss: 0.007509, Train_acc: 1.000000 
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Epoch:0, train_step: 710, loss: 0.100359, Train_acc: 0.968750 
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Epoch:0, train_step: 813, loss: 0.115899, Train_acc: 0.953125 
Epoch:0, train_step: 814, loss: 0.032048, Train_acc: 0.984375 
Epoch:0, train_step: 815, loss: 0.159155, Train_acc: 0.953125 
Epoch:0, train_step: 816, loss: 0.092974, Train_acc: 0.953125 
Epoch:0, train_step: 817, loss: 0.031113, Train_acc: 1.000000 
Epoch:0, train_step: 818, loss: 0.085564, Train_acc: 0.984375 
Epoch:0, train_step: 819, loss: 0.017967, Train_acc: 0.984375 
Epoch:0, train_step: 820, loss: 0.025688, Train_acc: 0.984375 
Epoch:0, train_step: 821, loss: 0.034627, Train_acc: 1.000000 
Epoch:0, train_step: 822, loss: 0.014598, Train_acc: 1.000000 
Epoch:0, train_step: 823, loss: 0.034975, Train_acc: 0.984375 
Epoch:0, train_step: 824, loss: 0.069465, Train_acc: 0.968750 
Epoch:0, train_step: 825, loss: 0.077784, Train_acc: 0.984375 
Epoch:0, train_step: 826, loss: 0.197079, Train_acc: 0.953125 
Epoch:0, train_step: 827, loss: 0.185010, Train_acc: 0.937500 
Epoch:0, train_step: 828, loss: 0.369908, Train_acc: 0.953125 
Epoch:0, train_step: 829, loss: 0.011545, Train_acc: 1.000000 
Epoch:0, train_step: 830, loss: 0.030840, Train_acc: 0.984375 
Epoch:0, train_step: 831, loss: 0.074448, Train_acc: 0.968750 
Epoch:0, train_step: 832, loss: 0.094096, Train_acc: 0.984375 
Epoch:0, train_step: 833, loss: 0.016422, Train_acc: 1.000000 
Epoch:0, train_step: 834, loss: 0.027611, Train_acc: 0.984375 
Epoch:0, train_step: 835, loss: 0.061203, Train_acc: 0.984375 
Epoch:0, train_step: 836, loss: 0.107372, Train_acc: 0.968750 
Epoch:0, train_step: 837, loss: 0.053118, Train_acc: 0.953125 
Epoch:0, train_step: 838, loss: 0.040663, Train_acc: 0.984375 
Epoch:0, train_step: 839, loss: 0.234584, Train_acc: 0.937500 
Epoch:0, train_step: 840, loss: 0.025691, Train_acc: 1.000000 
Epoch:0, train_step: 841, loss: 0.083205, Train_acc: 0.984375 
Epoch:0, train_step: 842, loss: 0.087913, Train_acc: 0.968750 
Epoch:0, train_step: 843, loss: 0.015782, Train_acc: 1.000000 
Epoch:0, train_step: 844, loss: 0.067793, Train_acc: 0.968750 
Epoch:0, train_step: 845, loss: 0.175116, Train_acc: 0.937500 
Epoch:0, train_step: 846, loss: 0.047259, Train_acc: 0.984375 
Epoch:0, train_step: 847, loss: 0.097593, Train_acc: 0.968750 
Epoch:0, train_step: 848, loss: 0.020965, Train_acc: 1.000000 
Epoch:0, train_step: 849, loss: 0.052666, Train_acc: 0.968750 
Epoch:0, train_step: 850, loss: 0.026131, Train_acc: 1.000000 
Epoch:0, train_step: 851, loss: 0.027847, Train_acc: 0.984375 
Epoch:0, train_step: 852, loss: 0.045235, Train_acc: 0.984375 
Epoch:0, train_step: 853, loss: 0.036263, Train_acc: 0.984375 
Epoch:0, train_step: 854, loss: 0.011590, Train_acc: 1.000000 
Epoch:0, train_step: 855, loss: 0.013322, Train_acc: 1.000000 
Epoch:0, train_step: 856, loss: 0.122247, Train_acc: 0.968750 
Epoch:0, train_step: 857, loss: 0.117173, Train_acc: 0.968750 
Epoch:0, train_step: 858, loss: 0.168056, Train_acc: 0.968750 
Epoch:0, train_step: 859, loss: 0.035262, Train_acc: 0.984375 
Epoch:0, train_step: 860, loss: 0.043833, Train_acc: 0.984375 
Epoch:0, train_step: 861, loss: 0.030304, Train_acc: 1.000000 
Epoch:0, train_step: 862, loss: 0.072697, Train_acc: 0.984375 
Epoch:0, train_step: 863, loss: 0.028678, Train_acc: 1.000000 
Epoch:0, train_step: 864, loss: 0.138627, Train_acc: 0.953125 
Epoch:0, train_step: 865, loss: 0.129945, Train_acc: 0.968750 
Epoch:0, train_step: 866, loss: 0.113213, Train_acc: 0.937500 
Epoch:0, train_step: 867, loss: 0.098942, Train_acc: 0.968750 
Epoch:0, train_step: 868, loss: 0.064868, Train_acc: 0.968750 
Epoch:0, train_step: 869, loss: 0.165648, Train_acc: 0.921875 
Epoch:0, train_step: 870, loss: 0.023262, Train_acc: 1.000000 
Epoch:0, train_step: 871, loss: 0.080913, Train_acc: 0.968750 
Epoch:0, train_step: 872, loss: 0.083002, Train_acc: 0.984375 
Epoch:0, train_step: 873, loss: 0.030590, Train_acc: 1.000000 
Epoch:0, train_step: 874, loss: 0.078026, Train_acc: 0.984375 
Epoch:0, train_step: 875, loss: 0.024193, Train_acc: 1.000000 
Epoch:0, train_step: 876, loss: 0.083377, Train_acc: 0.968750 
Epoch:0, train_step: 877, loss: 0.055766, Train_acc: 0.984375 
Epoch:0, train_step: 878, loss: 0.100979, Train_acc: 0.968750 
Epoch:0, train_step: 879, loss: 0.067706, Train_acc: 0.968750 
Epoch:0, train_step: 880, loss: 0.083933, Train_acc: 0.953125 
Epoch:0, train_step: 881, loss: 0.039191, Train_acc: 0.984375 
Epoch:0, train_step: 882, loss: 0.097209, Train_acc: 0.984375 
Epoch:0, train_step: 883, loss: 0.123626, Train_acc: 0.953125 
Epoch:0, train_step: 884, loss: 0.019100, Train_acc: 0.984375 
Epoch:0, train_step: 885, loss: 0.041634, Train_acc: 0.984375 
Epoch:0, train_step: 886, loss: 0.027624, Train_acc: 0.984375 
Epoch:0, train_step: 887, loss: 0.039821, Train_acc: 0.984375 
Epoch:0, train_step: 888, loss: 0.093927, Train_acc: 0.968750 
Epoch:0, train_step: 889, loss: 0.049932, Train_acc: 0.984375 
Epoch:0, train_step: 890, loss: 0.035583, Train_acc: 0.984375 
Epoch:0, train_step: 891, loss: 0.010994, Train_acc: 1.000000 
Epoch:0, train_step: 892, loss: 0.117994, Train_acc: 0.937500 
Epoch:0, train_step: 893, loss: 0.008732, Train_acc: 1.000000 
Epoch:0, train_step: 894, loss: 0.010927, Train_acc: 1.000000 
Epoch:0, train_step: 895, loss: 0.066837, Train_acc: 0.968750 
Epoch:0, train_step: 896, loss: 0.070478, Train_acc: 0.953125 
Epoch:0, train_step: 897, loss: 0.030289, Train_acc: 0.984375 
Epoch:0, train_step: 898, loss: 0.012160, Train_acc: 1.000000 
Epoch:0, train_step: 899, loss: 0.056633, Train_acc: 0.968750 
Epoch:0, train_step: 900, loss: 0.045497, Train_acc: 0.984375 
Epoch:0, train_step: 901, loss: 0.146093, Train_acc: 0.953125 
Epoch:0, train_step: 902, loss: 0.062955, Train_acc: 0.968750 
Epoch:0, train_step: 903, loss: 0.196776, Train_acc: 0.937500 
Epoch:0, train_step: 904, loss: 0.095579, Train_acc: 0.937500 
Epoch:0, train_step: 905, loss: 0.022958, Train_acc: 1.000000 
Epoch:0, train_step: 906, loss: 0.105401, Train_acc: 0.953125 
Epoch:0, train_step: 907, loss: 0.012648, Train_acc: 1.000000 
Epoch:0, train_step: 908, loss: 0.247258, Train_acc: 0.937500 
Epoch:0, train_step: 909, loss: 0.034725, Train_acc: 0.984375 
Epoch:0, train_step: 910, loss: 0.002388, Train_acc: 1.000000 
Epoch:0, train_step: 911, loss: 0.008083, Train_acc: 1.000000 
Epoch:0, train_step: 912, loss: 0.025620, Train_acc: 0.984375 
Epoch:0, train_step: 913, loss: 0.065655, Train_acc: 0.968750 
Epoch:0, train_step: 914, loss: 0.019080, Train_acc: 1.000000 
Epoch:0, train_step: 915, loss: 0.007488, Train_acc: 1.000000 
Epoch:0, train_step: 916, loss: 0.003170, Train_acc: 1.000000 
Epoch:0, train_step: 917, loss: 0.034512, Train_acc: 0.968750 
Epoch:0, train_step: 918, loss: 0.000769, Train_acc: 1.000000 
Epoch:0, train_step: 919, loss: 0.098379, Train_acc: 0.984375 
Epoch:0, train_step: 920, loss: 0.100509, Train_acc: 0.984375 
Epoch:0, train_step: 921, loss: 0.002132, Train_acc: 1.000000 
Epoch:0, train_step: 922, loss: 0.000917, Train_acc: 1.000000 
Epoch:0, train_step: 923, loss: 0.004179, Train_acc: 1.000000 
Epoch:0, train_step: 924, loss: 0.001335, Train_acc: 1.000000 
Epoch:0, train_step: 925, loss: 0.001218, Train_acc: 1.000000 
Epoch:0, train_step: 926, loss: 0.001507, Train_acc: 1.000000 
Epoch:0, train_step: 927, loss: 0.036220, Train_acc: 0.984375 
Epoch:0, train_step: 928, loss: 0.031104, Train_acc: 0.984375 
Epoch:0, train_step: 929, loss: 0.007464, Train_acc: 1.000000 
Epoch:0, train_step: 930, loss: 0.002622, Train_acc: 1.000000 
Epoch:0, train_step: 931, loss: 0.001638, Train_acc: 1.000000 
Epoch:0, train_step: 932, loss: 0.003810, Train_acc: 1.000000 
Epoch:0, train_step: 933, loss: 0.142454, Train_acc: 0.968750 
Epoch:0, train_step: 934, loss: 0.360559, Train_acc: 0.921875 
Epoch:0, train_step: 935, loss: 0.035951, Train_acc: 0.984375 
Epoch:0, train_step: 936, loss: 0.002807, Train_acc: 1.000000 
Epoch:0, train_step: 937, loss: 0.222062, Train_acc: 0.984375 


Epoch:0, avg_train_loss: 0.132467, avg_train_acc: 0.959495, Test_acc: 0.981370 
Epoch:1, train_step: 938, loss: 0.024458, Train_acc: 0.984375 
Epoch:1, train_step: 939, loss: 0.090091, Train_acc: 0.968750 
Epoch:1, train_step: 940, loss: 0.335877, Train_acc: 0.921875 
Epoch:1, train_step: 941, loss: 0.056512, Train_acc: 0.984375 
Epoch:1, train_step: 942, loss: 0.026927, Train_acc: 0.984375 
Epoch:1, train_step: 943, loss: 0.006757, Train_acc: 1.000000 
Epoch:1, train_step: 944, loss: 0.049244, Train_acc: 0.968750 
Epoch:1, train_step: 945, loss: 0.197179, Train_acc: 0.968750 
Epoch:1, train_step: 946, loss: 0.015287, Train_acc: 1.000000 
Epoch:1, train_step: 947, loss: 0.203276, Train_acc: 0.953125 
Epoch:1, train_step: 948, loss: 0.028431, Train_acc: 0.984375 
Epoch:1, train_step: 949, loss: 0.080308, Train_acc: 0.984375 
Epoch:1, train_step: 950, loss: 0.076476, Train_acc: 0.968750 
Epoch:1, train_step: 951, loss: 0.107651, Train_acc: 0.968750 
Epoch:1, train_step: 952, loss: 0.184043, Train_acc: 0.968750 
Epoch:1, train_step: 953, loss: 0.076401, Train_acc: 0.968750 
Epoch:1, train_step: 954, loss: 0.115396, Train_acc: 0.953125 
Epoch:1, train_step: 955, loss: 0.157918, Train_acc: 0.921875 
Epoch:1, train_step: 956, loss: 0.039221, Train_acc: 0.984375 
Epoch:1, train_step: 957, loss: 0.085195, Train_acc: 0.968750 
Epoch:1, train_step: 958, loss: 0.084867, Train_acc: 0.968750 
Epoch:1, train_step: 959, loss: 0.134926, Train_acc: 0.953125 
Epoch:1, train_step: 960, loss: 0.005242, Train_acc: 1.000000 
Epoch:1, train_step: 961, loss: 0.068374, Train_acc: 0.984375 
Epoch:1, train_step: 962, loss: 0.060103, Train_acc: 0.984375 
Epoch:1, train_step: 963, loss: 0.201356, Train_acc: 0.953125 
Epoch:1, train_step: 964, loss: 0.053252, Train_acc: 0.984375 
Epoch:1, train_step: 965, loss: 0.079910, Train_acc: 0.968750 
Epoch:1, train_step: 966, loss: 0.016053, Train_acc: 1.000000 
Epoch:1, train_step: 967, loss: 0.016077, Train_acc: 1.000000 
Epoch:1, train_step: 968, loss: 0.110585, Train_acc: 0.968750 
Epoch:1, train_step: 969, loss: 0.046955, Train_acc: 0.968750 
Epoch:1, train_step: 970, loss: 0.041192, Train_acc: 0.968750 
Epoch:1, train_step: 971, loss: 0.077525, Train_acc: 0.984375 
Epoch:1, train_step: 972, loss: 0.041719, Train_acc: 0.984375 
Epoch:1, train_step: 973, loss: 0.034905, Train_acc: 0.984375 
Epoch:1, train_step: 974, loss: 0.005043, Train_acc: 1.000000 
Epoch:1, train_step: 975, loss: 0.077613, Train_acc: 0.984375 
Epoch:1, train_step: 976, loss: 0.036764, Train_acc: 0.968750 
Epoch:1, train_step: 977, loss: 0.050239, Train_acc: 0.968750 
Epoch:1, train_step: 978, loss: 0.085424, Train_acc: 0.984375 
Epoch:1, train_step: 979, loss: 0.127186, Train_acc: 0.968750 
Epoch:1, train_step: 980, loss: 0.162237, Train_acc: 0.953125 
Epoch:1, train_step: 981, loss: 0.009085, Train_acc: 1.000000 
Epoch:1, train_step: 982, loss: 0.030395, Train_acc: 1.000000 
Epoch:1, train_step: 983, loss: 0.018612, Train_acc: 1.000000 
Epoch:1, train_step: 984, loss: 0.011074, Train_acc: 1.000000 
Epoch:1, train_step: 985, loss: 0.050869, Train_acc: 0.984375 
Epoch:1, train_step: 986, loss: 0.018823, Train_acc: 1.000000 
Epoch:1, train_step: 987, loss: 0.002219, Train_acc: 1.000000 
Epoch:1, train_step: 988, loss: 0.085333, Train_acc: 0.984375 
Epoch:1, train_step: 989, loss: 0.048177, Train_acc: 0.984375 
Epoch:1, train_step: 990, loss: 0.010411, Train_acc: 1.000000 
Epoch:1, train_step: 991, loss: 0.078645, Train_acc: 0.968750 
Epoch:1, train_step: 992, loss: 0.050817, Train_acc: 0.984375 
Epoch:1, train_step: 993, loss: 0.012464, Train_acc: 1.000000 
Epoch:1, train_step: 994, loss: 0.025207, Train_acc: 0.984375 
Epoch:1, train_step: 995, loss: 0.386211, Train_acc: 0.937500 
Epoch:1, train_step: 996, loss: 0.044157, Train_acc: 1.000000 
Epoch:1, train_step: 997, loss: 0.029231, Train_acc: 0.984375 
Epoch:1, train_step: 998, loss: 0.008043, Train_acc: 1.000000 
Epoch:1, train_step: 999, loss: 0.036379, Train_acc: 0.984375 
Epoch:1, train_step: 1000, loss: 0.135790, Train_acc: 0.953125 
Epoch:1, train_step: 1001, loss: 0.026449, Train_acc: 1.000000 
Epoch:1, train_step: 1002, loss: 0.065872, Train_acc: 0.984375 
Epoch:1, train_step: 1003, loss: 0.018775, Train_acc: 0.984375 
Epoch:1, train_step: 1004, loss: 0.034122, Train_acc: 0.984375 
Epoch:1, train_step: 1005, loss: 0.032323, Train_acc: 0.984375 
Epoch:1, train_step: 1006, loss: 0.020981, Train_acc: 1.000000 
Epoch:1, train_step: 1007, loss: 0.164091, Train_acc: 0.953125 
Epoch:1, train_step: 1008, loss: 0.016277, Train_acc: 0.984375 
Epoch:1, train_step: 1009, loss: 0.022845, Train_acc: 0.984375 
Epoch:1, train_step: 1010, loss: 0.082793, Train_acc: 0.984375 
Epoch:1, train_step: 1011, loss: 0.032599, Train_acc: 0.984375 
Epoch:1, train_step: 1012, loss: 0.036063, Train_acc: 0.984375 
Epoch:1, train_step: 1013, loss: 0.015374, Train_acc: 1.000000 
Epoch:1, train_step: 1014, loss: 0.008429, Train_acc: 1.000000 
Epoch:1, train_step: 1015, loss: 0.067247, Train_acc: 0.968750 
Epoch:1, train_step: 1016, loss: 0.041650, Train_acc: 0.984375 
Epoch:1, train_step: 1017, loss: 0.041630, Train_acc: 0.984375 
Epoch:1, train_step: 1018, loss: 0.023889, Train_acc: 1.000000 
Epoch:1, train_step: 1019, loss: 0.018447, Train_acc: 1.000000 
Epoch:1, train_step: 1020, loss: 0.169477, Train_acc: 0.968750 
Epoch:1, train_step: 1021, loss: 0.071444, Train_acc: 0.968750 
Epoch:1, train_step: 1022, loss: 0.034456, Train_acc: 0.984375 
Epoch:1, train_step: 1023, loss: 0.072218, Train_acc: 0.984375 
Epoch:1, train_step: 1024, loss: 0.073448, Train_acc: 0.968750 
Epoch:1, train_step: 1025, loss: 0.014264, Train_acc: 1.000000 
Epoch:1, train_step: 1026, loss: 0.046867, Train_acc: 1.000000 
Epoch:1, train_step: 1027, loss: 0.095335, Train_acc: 0.937500 
Epoch:1, train_step: 1028, loss: 0.104384, Train_acc: 0.968750 
Epoch:1, train_step: 1029, loss: 0.038103, Train_acc: 0.984375 
Epoch:1, train_step: 1030, loss: 0.137027, Train_acc: 0.984375 
Epoch:1, train_step: 1031, loss: 0.056230, Train_acc: 0.984375 
Epoch:1, train_step: 1032, loss: 0.026024, Train_acc: 1.000000 
Epoch:1, train_step: 1033, loss: 0.127700, Train_acc: 0.984375 
Epoch:1, train_step: 1034, loss: 0.014893, Train_acc: 1.000000 
Epoch:1, train_step: 1035, loss: 0.145359, Train_acc: 0.968750 
Epoch:1, train_step: 1036, loss: 0.015837, Train_acc: 1.000000 
Epoch:1, train_step: 1037, loss: 0.013972, Train_acc: 1.000000 
Epoch:1, train_step: 1038, loss: 0.131495, Train_acc: 0.968750 
Epoch:1, train_step: 1039, loss: 0.033242, Train_acc: 0.984375 
Epoch:1, train_step: 1040, loss: 0.005205, Train_acc: 1.000000 
Epoch:1, train_step: 1041, loss: 0.018534, Train_acc: 0.984375 
Epoch:1, train_step: 1042, loss: 0.115117, Train_acc: 0.937500 
Epoch:1, train_step: 1043, loss: 0.007965, Train_acc: 1.000000 
Epoch:1, train_step: 1044, loss: 0.142098, Train_acc: 0.968750 
Epoch:1, train_step: 1045, loss: 0.231231, Train_acc: 0.953125 
Epoch:1, train_step: 1046, loss: 0.151597, Train_acc: 0.921875 
Epoch:1, train_step: 1047, loss: 0.185358, Train_acc: 0.953125 
Epoch:1, train_step: 1048, loss: 0.041049, Train_acc: 0.984375 
Epoch:1, train_step: 1049, loss: 0.005625, Train_acc: 1.000000 
Epoch:1, train_step: 1050, loss: 0.054028, Train_acc: 0.968750 
Epoch:1, train_step: 1051, loss: 0.349905, Train_acc: 0.953125 
Epoch:1, train_step: 1052, loss: 0.092818, Train_acc: 0.953125 
Epoch:1, train_step: 1053, loss: 0.023645, Train_acc: 1.000000 
Epoch:1, train_step: 1054, loss: 0.008072, Train_acc: 1.000000 
Epoch:1, train_step: 1055, loss: 0.065541, Train_acc: 0.968750 
Epoch:1, train_step: 1056, loss: 0.219798, Train_acc: 0.937500 
Epoch:1, train_step: 1057, loss: 0.113375, Train_acc: 0.984375 
Epoch:1, train_step: 1058, loss: 0.059414, Train_acc: 0.984375 
Epoch:1, train_step: 1059, loss: 0.070043, Train_acc: 0.968750 
Epoch:1, train_step: 1060, loss: 0.063710, Train_acc: 0.968750 
Epoch:1, train_step: 1061, loss: 0.066200, Train_acc: 0.968750 
Epoch:1, train_step: 1062, loss: 0.094509, Train_acc: 0.968750 
Epoch:1, train_step: 1063, loss: 0.074021, Train_acc: 0.968750 
Epoch:1, train_step: 1064, loss: 0.072358, Train_acc: 0.968750 
Epoch:1, train_step: 1065, loss: 0.015203, Train_acc: 1.000000 
Epoch:1, train_step: 1066, loss: 0.112173, Train_acc: 0.968750 
Epoch:1, train_step: 1067, loss: 0.027910, Train_acc: 1.000000 
Epoch:1, train_step: 1068, loss: 0.023895, Train_acc: 0.984375 
Epoch:1, train_step: 1069, loss: 0.041531, Train_acc: 0.984375 
Epoch:1, train_step: 1070, loss: 0.024782, Train_acc: 1.000000 
Epoch:1, train_step: 1071, loss: 0.006455, Train_acc: 1.000000 
Epoch:1, train_step: 1072, loss: 0.045976, Train_acc: 0.984375 
Epoch:1, train_step: 1073, loss: 0.091791, Train_acc: 0.968750 
Epoch:1, train_step: 1074, loss: 0.160312, Train_acc: 0.953125 
Epoch:1, train_step: 1075, loss: 0.042302, Train_acc: 1.000000 
Epoch:1, train_step: 1076, loss: 0.140800, Train_acc: 0.921875 
Epoch:1, train_step: 1077, loss: 0.034969, Train_acc: 1.000000 
Epoch:1, train_step: 1078, loss: 0.040444, Train_acc: 0.984375 
Epoch:1, train_step: 1079, loss: 0.014679, Train_acc: 1.000000 
Epoch:1, train_step: 1080, loss: 0.188078, Train_acc: 0.953125 
Epoch:1, train_step: 1081, loss: 0.039897, Train_acc: 0.968750 
Epoch:1, train_step: 1082, loss: 0.076407, Train_acc: 0.968750 
Epoch:1, train_step: 1083, loss: 0.082423, Train_acc: 0.984375 
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Epoch:1, train_step: 1643, loss: 0.079113, Train_acc: 0.984375 
Epoch:1, train_step: 1644, loss: 0.004725, Train_acc: 1.000000 
Epoch:1, train_step: 1645, loss: 0.013309, Train_acc: 1.000000 
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Epoch:1, train_step: 1647, loss: 0.022233, Train_acc: 1.000000 
Epoch:1, train_step: 1648, loss: 0.014732, Train_acc: 1.000000 
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Epoch:1, train_step: 1651, loss: 0.023913, Train_acc: 1.000000 
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Epoch:1, train_step: 1654, loss: 0.107786, Train_acc: 0.984375 
Epoch:1, train_step: 1655, loss: 0.044408, Train_acc: 0.968750 
Epoch:1, train_step: 1656, loss: 0.141514, Train_acc: 0.953125 
Epoch:1, train_step: 1657, loss: 0.080544, Train_acc: 0.968750 
Epoch:1, train_step: 1658, loss: 0.086222, Train_acc: 0.953125 
Epoch:1, train_step: 1659, loss: 0.014233, Train_acc: 1.000000 
Epoch:1, train_step: 1660, loss: 0.076184, Train_acc: 0.968750 
Epoch:1, train_step: 1661, loss: 0.060892, Train_acc: 0.984375 
Epoch:1, train_step: 1662, loss: 0.032306, Train_acc: 0.984375 
Epoch:1, train_step: 1663, loss: 0.040733, Train_acc: 1.000000 
Epoch:1, train_step: 1664, loss: 0.009471, Train_acc: 1.000000 
Epoch:1, train_step: 1665, loss: 0.005519, Train_acc: 1.000000 
Epoch:1, train_step: 1666, loss: 0.023039, Train_acc: 0.984375 
Epoch:1, train_step: 1667, loss: 0.089492, Train_acc: 0.984375 
Epoch:1, train_step: 1668, loss: 0.069719, Train_acc: 0.984375 
Epoch:1, train_step: 1669, loss: 0.034170, Train_acc: 0.984375 
Epoch:1, train_step: 1670, loss: 0.092907, Train_acc: 0.984375 
Epoch:1, train_step: 1671, loss: 0.011993, Train_acc: 1.000000 
Epoch:1, train_step: 1672, loss: 0.082502, Train_acc: 0.984375 
Epoch:1, train_step: 1673, loss: 0.029105, Train_acc: 0.984375 
Epoch:1, train_step: 1674, loss: 0.092391, Train_acc: 0.953125 
Epoch:1, train_step: 1675, loss: 0.039839, Train_acc: 0.984375 
Epoch:1, train_step: 1676, loss: 0.119613, Train_acc: 0.953125 
Epoch:1, train_step: 1677, loss: 0.029298, Train_acc: 0.984375 
Epoch:1, train_step: 1678, loss: 0.049658, Train_acc: 0.984375 
Epoch:1, train_step: 1679, loss: 0.097120, Train_acc: 0.968750 
Epoch:1, train_step: 1680, loss: 0.032238, Train_acc: 0.984375 
Epoch:1, train_step: 1681, loss: 0.173200, Train_acc: 0.953125 
Epoch:1, train_step: 1682, loss: 0.020796, Train_acc: 1.000000 
Epoch:1, train_step: 1683, loss: 0.159190, Train_acc: 0.953125 
Epoch:1, train_step: 1684, loss: 0.080437, Train_acc: 0.968750 
Epoch:1, train_step: 1685, loss: 0.022113, Train_acc: 1.000000 
Epoch:1, train_step: 1686, loss: 0.063797, Train_acc: 0.968750 
Epoch:1, train_step: 1687, loss: 0.022494, Train_acc: 1.000000 
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Epoch:1, train_step: 1689, loss: 0.085822, Train_acc: 0.953125 
Epoch:1, train_step: 1690, loss: 0.005409, Train_acc: 1.000000 
Epoch:1, train_step: 1691, loss: 0.028855, Train_acc: 0.984375 
Epoch:1, train_step: 1692, loss: 0.028847, Train_acc: 0.984375 
Epoch:1, train_step: 1693, loss: 0.081878, Train_acc: 0.984375 
Epoch:1, train_step: 1694, loss: 0.007568, Train_acc: 1.000000 
Epoch:1, train_step: 1695, loss: 0.019306, Train_acc: 1.000000 
Epoch:1, train_step: 1696, loss: 0.035546, Train_acc: 0.984375 
Epoch:1, train_step: 1697, loss: 0.017789, Train_acc: 1.000000 
Epoch:1, train_step: 1698, loss: 0.029162, Train_acc: 1.000000 
Epoch:1, train_step: 1699, loss: 0.008407, Train_acc: 1.000000 
Epoch:1, train_step: 1700, loss: 0.026606, Train_acc: 0.984375 
Epoch:1, train_step: 1701, loss: 0.041978, Train_acc: 0.984375 
Epoch:1, train_step: 1702, loss: 0.044890, Train_acc: 1.000000 
Epoch:1, train_step: 1703, loss: 0.244183, Train_acc: 0.921875 
Epoch:1, train_step: 1704, loss: 0.158519, Train_acc: 0.968750 
Epoch:1, train_step: 1705, loss: 0.010329, Train_acc: 1.000000 
Epoch:1, train_step: 1706, loss: 0.167046, Train_acc: 0.968750 
Epoch:1, train_step: 1707, loss: 0.027354, Train_acc: 0.984375 
Epoch:1, train_step: 1708, loss: 0.005591, Train_acc: 1.000000 
Epoch:1, train_step: 1709, loss: 0.037072, Train_acc: 0.984375 
Epoch:1, train_step: 1710, loss: 0.147422, Train_acc: 0.953125 
Epoch:1, train_step: 1711, loss: 0.109685, Train_acc: 0.968750 
Epoch:1, train_step: 1712, loss: 0.054842, Train_acc: 0.968750 
Epoch:1, train_step: 1713, loss: 0.023478, Train_acc: 0.984375 
Epoch:1, train_step: 1714, loss: 0.021057, Train_acc: 1.000000 
Epoch:1, train_step: 1715, loss: 0.055469, Train_acc: 0.984375 
Epoch:1, train_step: 1716, loss: 0.025409, Train_acc: 0.984375 
Epoch:1, train_step: 1717, loss: 0.069352, Train_acc: 0.953125 
Epoch:1, train_step: 1718, loss: 0.015434, Train_acc: 1.000000 
Epoch:1, train_step: 1719, loss: 0.009202, Train_acc: 1.000000 
Epoch:1, train_step: 1720, loss: 0.013941, Train_acc: 1.000000 
Epoch:1, train_step: 1721, loss: 0.007692, Train_acc: 1.000000 
Epoch:1, train_step: 1722, loss: 0.036477, Train_acc: 0.984375 
Epoch:1, train_step: 1723, loss: 0.010282, Train_acc: 1.000000 
Epoch:1, train_step: 1724, loss: 0.053968, Train_acc: 0.984375 
Epoch:1, train_step: 1725, loss: 0.048720, Train_acc: 0.984375 
Epoch:1, train_step: 1726, loss: 0.016889, Train_acc: 1.000000 
Epoch:1, train_step: 1727, loss: 0.072172, Train_acc: 0.984375 
Epoch:1, train_step: 1728, loss: 0.048062, Train_acc: 0.968750 
Epoch:1, train_step: 1729, loss: 0.001926, Train_acc: 1.000000 
Epoch:1, train_step: 1730, loss: 0.025171, Train_acc: 1.000000 
Epoch:1, train_step: 1731, loss: 0.007335, Train_acc: 1.000000 
Epoch:1, train_step: 1732, loss: 0.009793, Train_acc: 1.000000 
Epoch:1, train_step: 1733, loss: 0.055896, Train_acc: 0.968750 
Epoch:1, train_step: 1734, loss: 0.089053, Train_acc: 0.968750 
Epoch:1, train_step: 1735, loss: 0.004545, Train_acc: 1.000000 
Epoch:1, train_step: 1736, loss: 0.002600, Train_acc: 1.000000 
Epoch:1, train_step: 1737, loss: 0.015825, Train_acc: 1.000000 
Epoch:1, train_step: 1738, loss: 0.092358, Train_acc: 0.968750 
Epoch:1, train_step: 1739, loss: 0.037471, Train_acc: 0.984375 
Epoch:1, train_step: 1740, loss: 0.049103, Train_acc: 0.984375 
Epoch:1, train_step: 1741, loss: 0.041438, Train_acc: 0.968750 
Epoch:1, train_step: 1742, loss: 0.029007, Train_acc: 0.984375 
Epoch:1, train_step: 1743, loss: 0.011783, Train_acc: 1.000000 
Epoch:1, train_step: 1744, loss: 0.011957, Train_acc: 1.000000 
Epoch:1, train_step: 1745, loss: 0.060932, Train_acc: 0.984375 
Epoch:1, train_step: 1746, loss: 0.062116, Train_acc: 0.984375 
Epoch:1, train_step: 1747, loss: 0.003493, Train_acc: 1.000000 
Epoch:1, train_step: 1748, loss: 0.004304, Train_acc: 1.000000 
Epoch:1, train_step: 1749, loss: 0.124298, Train_acc: 0.984375 
Epoch:1, train_step: 1750, loss: 0.056901, Train_acc: 0.984375 
Epoch:1, train_step: 1751, loss: 0.008309, Train_acc: 1.000000 
Epoch:1, train_step: 1752, loss: 0.057649, Train_acc: 0.968750 
Epoch:1, train_step: 1753, loss: 0.077888, Train_acc: 0.968750 
Epoch:1, train_step: 1754, loss: 0.011772, Train_acc: 1.000000 
Epoch:1, train_step: 1755, loss: 0.039106, Train_acc: 0.984375 
Epoch:1, train_step: 1756, loss: 0.013586, Train_acc: 1.000000 
Epoch:1, train_step: 1757, loss: 0.036578, Train_acc: 0.984375 
Epoch:1, train_step: 1758, loss: 0.015817, Train_acc: 1.000000 
Epoch:1, train_step: 1759, loss: 0.008866, Train_acc: 1.000000 
Epoch:1, train_step: 1760, loss: 0.004995, Train_acc: 1.000000 
Epoch:1, train_step: 1761, loss: 0.012170, Train_acc: 1.000000 
Epoch:1, train_step: 1762, loss: 0.024324, Train_acc: 1.000000 
Epoch:1, train_step: 1763, loss: 0.123443, Train_acc: 0.968750 
Epoch:1, train_step: 1764, loss: 0.056428, Train_acc: 0.984375 
Epoch:1, train_step: 1765, loss: 0.346876, Train_acc: 0.953125 
Epoch:1, train_step: 1766, loss: 0.005921, Train_acc: 1.000000 
Epoch:1, train_step: 1767, loss: 0.005686, Train_acc: 1.000000 
Epoch:1, train_step: 1768, loss: 0.030143, Train_acc: 1.000000 
Epoch:1, train_step: 1769, loss: 0.058174, Train_acc: 0.984375 
Epoch:1, train_step: 1770, loss: 0.012629, Train_acc: 1.000000 
Epoch:1, train_step: 1771, loss: 0.008110, Train_acc: 1.000000 
Epoch:1, train_step: 1772, loss: 0.071616, Train_acc: 0.984375 
Epoch:1, train_step: 1773, loss: 0.022374, Train_acc: 1.000000 
Epoch:1, train_step: 1774, loss: 0.026213, Train_acc: 1.000000 
Epoch:1, train_step: 1775, loss: 0.007081, Train_acc: 1.000000 
Epoch:1, train_step: 1776, loss: 0.112083, Train_acc: 0.953125 
Epoch:1, train_step: 1777, loss: 0.014607, Train_acc: 1.000000 
Epoch:1, train_step: 1778, loss: 0.036364, Train_acc: 0.984375 
Epoch:1, train_step: 1779, loss: 0.032702, Train_acc: 0.984375 
Epoch:1, train_step: 1780, loss: 0.011502, Train_acc: 1.000000 
Epoch:1, train_step: 1781, loss: 0.050995, Train_acc: 0.984375 
Epoch:1, train_step: 1782, loss: 0.068551, Train_acc: 0.968750 
Epoch:1, train_step: 1783, loss: 0.022688, Train_acc: 1.000000 
Epoch:1, train_step: 1784, loss: 0.030481, Train_acc: 0.984375 
Epoch:1, train_step: 1785, loss: 0.032350, Train_acc: 0.984375 
Epoch:1, train_step: 1786, loss: 0.069258, Train_acc: 0.984375 
Epoch:1, train_step: 1787, loss: 0.013405, Train_acc: 1.000000 
Epoch:1, train_step: 1788, loss: 0.012626, Train_acc: 1.000000 
Epoch:1, train_step: 1789, loss: 0.011884, Train_acc: 1.000000 
Epoch:1, train_step: 1790, loss: 0.020724, Train_acc: 1.000000 
Epoch:1, train_step: 1791, loss: 0.026241, Train_acc: 0.984375 
Epoch:1, train_step: 1792, loss: 0.009173, Train_acc: 1.000000 
Epoch:1, train_step: 1793, loss: 0.021020, Train_acc: 1.000000 
Epoch:1, train_step: 1794, loss: 0.044317, Train_acc: 0.984375 
Epoch:1, train_step: 1795, loss: 0.093904, Train_acc: 0.984375 
Epoch:1, train_step: 1796, loss: 0.012059, Train_acc: 1.000000 
Epoch:1, train_step: 1797, loss: 0.007944, Train_acc: 1.000000 
Epoch:1, train_step: 1798, loss: 0.015315, Train_acc: 1.000000 
Epoch:1, train_step: 1799, loss: 0.009105, Train_acc: 1.000000 
Epoch:1, train_step: 1800, loss: 0.007966, Train_acc: 1.000000 
Epoch:1, train_step: 1801, loss: 0.038789, Train_acc: 0.968750 
Epoch:1, train_step: 1802, loss: 0.069789, Train_acc: 0.984375 
Epoch:1, train_step: 1803, loss: 0.074268, Train_acc: 0.984375 
Epoch:1, train_step: 1804, loss: 0.052971, Train_acc: 0.968750 
Epoch:1, train_step: 1805, loss: 0.021473, Train_acc: 1.000000 
Epoch:1, train_step: 1806, loss: 0.187349, Train_acc: 0.906250 
Epoch:1, train_step: 1807, loss: 0.009108, Train_acc: 1.000000 
Epoch:1, train_step: 1808, loss: 0.056710, Train_acc: 0.984375 
Epoch:1, train_step: 1809, loss: 0.013982, Train_acc: 1.000000 
Epoch:1, train_step: 1810, loss: 0.008763, Train_acc: 1.000000 
Epoch:1, train_step: 1811, loss: 0.018516, Train_acc: 0.984375 
Epoch:1, train_step: 1812, loss: 0.007913, Train_acc: 1.000000 
Epoch:1, train_step: 1813, loss: 0.035344, Train_acc: 0.984375 
Epoch:1, train_step: 1814, loss: 0.025418, Train_acc: 1.000000 
Epoch:1, train_step: 1815, loss: 0.089421, Train_acc: 0.984375 
Epoch:1, train_step: 1816, loss: 0.072652, Train_acc: 0.968750 
Epoch:1, train_step: 1817, loss: 0.021188, Train_acc: 1.000000 
Epoch:1, train_step: 1818, loss: 0.029620, Train_acc: 0.984375 
Epoch:1, train_step: 1819, loss: 0.075182, Train_acc: 0.968750 
Epoch:1, train_step: 1820, loss: 0.112076, Train_acc: 0.968750 
Epoch:1, train_step: 1821, loss: 0.001839, Train_acc: 1.000000 
Epoch:1, train_step: 1822, loss: 0.032965, Train_acc: 0.984375 
Epoch:1, train_step: 1823, loss: 0.020017, Train_acc: 0.984375 
Epoch:1, train_step: 1824, loss: 0.012038, Train_acc: 1.000000 
Epoch:1, train_step: 1825, loss: 0.125250, Train_acc: 0.968750 
Epoch:1, train_step: 1826, loss: 0.031676, Train_acc: 0.984375 
Epoch:1, train_step: 1827, loss: 0.017276, Train_acc: 0.984375 
Epoch:1, train_step: 1828, loss: 0.015320, Train_acc: 1.000000 
Epoch:1, train_step: 1829, loss: 0.021616, Train_acc: 1.000000 
Epoch:1, train_step: 1830, loss: 0.001809, Train_acc: 1.000000 
Epoch:1, train_step: 1831, loss: 0.003316, Train_acc: 1.000000 
Epoch:1, train_step: 1832, loss: 0.057030, Train_acc: 0.984375 
Epoch:1, train_step: 1833, loss: 0.027074, Train_acc: 1.000000 
Epoch:1, train_step: 1834, loss: 0.005355, Train_acc: 1.000000 
Epoch:1, train_step: 1835, loss: 0.029019, Train_acc: 0.984375 
Epoch:1, train_step: 1836, loss: 0.042131, Train_acc: 0.984375 
Epoch:1, train_step: 1837, loss: 0.005670, Train_acc: 1.000000 
Epoch:1, train_step: 1838, loss: 0.071209, Train_acc: 0.968750 
Epoch:1, train_step: 1839, loss: 0.030052, Train_acc: 1.000000 
Epoch:1, train_step: 1840, loss: 0.209030, Train_acc: 0.937500 
Epoch:1, train_step: 1841, loss: 0.108574, Train_acc: 0.937500 
Epoch:1, train_step: 1842, loss: 0.007739, Train_acc: 1.000000 
Epoch:1, train_step: 1843, loss: 0.050842, Train_acc: 0.968750 
Epoch:1, train_step: 1844, loss: 0.007601, Train_acc: 1.000000 
Epoch:1, train_step: 1845, loss: 0.121161, Train_acc: 0.953125 
Epoch:1, train_step: 1846, loss: 0.007328, Train_acc: 1.000000 
Epoch:1, train_step: 1847, loss: 0.011932, Train_acc: 0.984375 
Epoch:1, train_step: 1848, loss: 0.001456, Train_acc: 1.000000 
Epoch:1, train_step: 1849, loss: 0.017428, Train_acc: 0.984375 
Epoch:1, train_step: 1850, loss: 0.027469, Train_acc: 0.984375 
Epoch:1, train_step: 1851, loss: 0.070726, Train_acc: 0.968750 
Epoch:1, train_step: 1852, loss: 0.004458, Train_acc: 1.000000 
Epoch:1, train_step: 1853, loss: 0.003107, Train_acc: 1.000000 
Epoch:1, train_step: 1854, loss: 0.015542, Train_acc: 1.000000 
Epoch:1, train_step: 1855, loss: 0.000285, Train_acc: 1.000000 
Epoch:1, train_step: 1856, loss: 0.061825, Train_acc: 0.984375 
Epoch:1, train_step: 1857, loss: 0.044111, Train_acc: 0.984375 
Epoch:1, train_step: 1858, loss: 0.001302, Train_acc: 1.000000 
Epoch:1, train_step: 1859, loss: 0.000769, Train_acc: 1.000000 
Epoch:1, train_step: 1860, loss: 0.000962, Train_acc: 1.000000 
Epoch:1, train_step: 1861, loss: 0.000630, Train_acc: 1.000000 
Epoch:1, train_step: 1862, loss: 0.000315, Train_acc: 1.000000 
Epoch:1, train_step: 1863, loss: 0.000948, Train_acc: 1.000000 
Epoch:1, train_step: 1864, loss: 0.010800, Train_acc: 1.000000 
Epoch:1, train_step: 1865, loss: 0.041330, Train_acc: 0.984375 
Epoch:1, train_step: 1866, loss: 0.002359, Train_acc: 1.000000 
Epoch:1, train_step: 1867, loss: 0.005621, Train_acc: 1.000000 
Epoch:1, train_step: 1868, loss: 0.000917, Train_acc: 1.000000 
Epoch:1, train_step: 1869, loss: 0.018762, Train_acc: 0.984375 
Epoch:1, train_step: 1870, loss: 0.109288, Train_acc: 0.953125 
Epoch:1, train_step: 1871, loss: 0.288927, Train_acc: 0.937500 
Epoch:1, train_step: 1872, loss: 0.017636, Train_acc: 0.984375 
Epoch:1, train_step: 1873, loss: 0.001580, Train_acc: 1.000000 
Epoch:1, train_step: 1874, loss: 0.194396, Train_acc: 0.984375 


Epoch:1, avg_train_loss: 0.052591, avg_train_acc: 0.984125, Test_acc: 0.979768 
Epoch:2, train_step: 1875, loss: 0.023960, Train_acc: 0.984375 
Epoch:2, train_step: 1876, loss: 0.063411, Train_acc: 0.984375 
Epoch:2, train_step: 1877, loss: 0.240169, Train_acc: 0.968750 
Epoch:2, train_step: 1878, loss: 0.065802, Train_acc: 0.968750 
Epoch:2, train_step: 1879, loss: 0.042118, Train_acc: 0.984375 
Epoch:2, train_step: 1880, loss: 0.009022, Train_acc: 1.000000 
Epoch:2, train_step: 1881, loss: 0.030457, Train_acc: 0.984375 
Epoch:2, train_step: 1882, loss: 0.174054, Train_acc: 0.968750 
Epoch:2, train_step: 1883, loss: 0.009489, Train_acc: 1.000000 
Epoch:2, train_step: 1884, loss: 0.160137, Train_acc: 0.984375 
Epoch:2, train_step: 1885, loss: 0.005727, Train_acc: 1.000000 
Epoch:2, train_step: 1886, loss: 0.033890, Train_acc: 0.984375 
Epoch:2, train_step: 1887, loss: 0.058954, Train_acc: 0.984375 
Epoch:2, train_step: 1888, loss: 0.065088, Train_acc: 0.968750 
Epoch:2, train_step: 1889, loss: 0.110371, Train_acc: 0.968750 
Epoch:2, train_step: 1890, loss: 0.080671, Train_acc: 0.984375 
Epoch:2, train_step: 1891, loss: 0.039509, Train_acc: 1.000000 
Epoch:2, train_step: 1892, loss: 0.080851, Train_acc: 0.968750 
Epoch:2, train_step: 1893, loss: 0.027604, Train_acc: 1.000000 
Epoch:2, train_step: 1894, loss: 0.019986, Train_acc: 1.000000 
Epoch:2, train_step: 1895, loss: 0.044996, Train_acc: 0.984375 
Epoch:2, train_step: 1896, loss: 0.094933, Train_acc: 0.953125 
Epoch:2, train_step: 1897, loss: 0.004534, Train_acc: 1.000000 
Epoch:2, train_step: 1898, loss: 0.031015, Train_acc: 0.984375 
Epoch:2, train_step: 1899, loss: 0.012059, Train_acc: 1.000000 
Epoch:2, train_step: 1900, loss: 0.147273, Train_acc: 0.968750 
Epoch:2, train_step: 1901, loss: 0.017002, Train_acc: 1.000000 
Epoch:2, train_step: 1902, loss: 0.120555, Train_acc: 0.968750 
Epoch:2, train_step: 1903, loss: 0.023446, Train_acc: 1.000000 
Epoch:2, train_step: 1904, loss: 0.005364, Train_acc: 1.000000 
Epoch:2, train_step: 1905, loss: 0.046077, Train_acc: 0.984375 
Epoch:2, train_step: 1906, loss: 0.008177, Train_acc: 1.000000 
Epoch:2, train_step: 1907, loss: 0.015399, Train_acc: 1.000000 
Epoch:2, train_step: 1908, loss: 0.054910, Train_acc: 0.984375 
Epoch:2, train_step: 1909, loss: 0.034569, Train_acc: 0.984375 
Epoch:2, train_step: 1910, loss: 0.018546, Train_acc: 1.000000 
Epoch:2, train_step: 1911, loss: 0.007902, Train_acc: 1.000000 
Epoch:2, train_step: 1912, loss: 0.069716, Train_acc: 0.984375 
Epoch:2, train_step: 1913, loss: 0.004114, Train_acc: 1.000000 
Epoch:2, train_step: 1914, loss: 0.030129, Train_acc: 0.984375 
Epoch:2, train_step: 1915, loss: 0.031083, Train_acc: 0.984375 
Epoch:2, train_step: 1916, loss: 0.073787, Train_acc: 0.984375 
Epoch:2, train_step: 1917, loss: 0.137437, Train_acc: 0.953125 
Epoch:2, train_step: 1918, loss: 0.012591, Train_acc: 1.000000 
Epoch:2, train_step: 1919, loss: 0.020201, Train_acc: 0.984375 
Epoch:2, train_step: 1920, loss: 0.010786, Train_acc: 1.000000 
Epoch:2, train_step: 1921, loss: 0.006952, Train_acc: 1.000000 
Epoch:2, train_step: 1922, loss: 0.020520, Train_acc: 1.000000 
Epoch:2, train_step: 1923, loss: 0.006916, Train_acc: 1.000000 
Epoch:2, train_step: 1924, loss: 0.001710, Train_acc: 1.000000 
Epoch:2, train_step: 1925, loss: 0.086597, Train_acc: 0.984375 
Epoch:2, train_step: 1926, loss: 0.016185, Train_acc: 1.000000 
Epoch:2, train_step: 1927, loss: 0.006305, Train_acc: 1.000000 
Epoch:2, train_step: 1928, loss: 0.024834, Train_acc: 0.984375 
Epoch:2, train_step: 1929, loss: 0.066062, Train_acc: 0.968750 
Epoch:2, train_step: 1930, loss: 0.009396, Train_acc: 1.000000 
Epoch:2, train_step: 1931, loss: 0.012777, Train_acc: 1.000000 
Epoch:2, train_step: 1932, loss: 0.398243, Train_acc: 0.921875 
Epoch:2, train_step: 1933, loss: 0.040733, Train_acc: 0.984375 
Epoch:2, train_step: 1934, loss: 0.008559, Train_acc: 1.000000 
Epoch:2, train_step: 1935, loss: 0.003255, Train_acc: 1.000000 
Epoch:2, train_step: 1936, loss: 0.046137, Train_acc: 0.984375 
Epoch:2, train_step: 1937, loss: 0.030870, Train_acc: 0.984375 
Epoch:2, train_step: 1938, loss: 0.011372, Train_acc: 1.000000 
Epoch:2, train_step: 1939, loss: 0.038151, Train_acc: 1.000000 
Epoch:2, train_step: 1940, loss: 0.007599, Train_acc: 1.000000 
Epoch:2, train_step: 1941, loss: 0.014950, Train_acc: 1.000000 
Epoch:2, train_step: 1942, loss: 0.034491, Train_acc: 0.984375 
Epoch:2, train_step: 1943, loss: 0.017359, Train_acc: 1.000000 
Epoch:2, train_step: 1944, loss: 0.115730, Train_acc: 0.953125 
Epoch:2, train_step: 1945, loss: 0.003030, Train_acc: 1.000000 
Epoch:2, train_step: 1946, loss: 0.007972, Train_acc: 1.000000 
Epoch:2, train_step: 1947, loss: 0.060925, Train_acc: 0.984375 
Epoch:2, train_step: 1948, loss: 0.014182, Train_acc: 1.000000 
Epoch:2, train_step: 1949, loss: 0.007987, Train_acc: 1.000000 
Epoch:2, train_step: 1950, loss: 0.005862, Train_acc: 1.000000 
Epoch:2, train_step: 1951, loss: 0.006742, Train_acc: 1.000000 
Epoch:2, train_step: 1952, loss: 0.029709, Train_acc: 1.000000 
Epoch:2, train_step: 1953, loss: 0.035035, Train_acc: 0.984375 
Epoch:2, train_step: 1954, loss: 0.034334, Train_acc: 0.984375 
Epoch:2, train_step: 1955, loss: 0.020659, Train_acc: 0.984375 
Epoch:2, train_step: 1956, loss: 0.020259, Train_acc: 1.000000 
Epoch:2, train_step: 1957, loss: 0.075307, Train_acc: 0.968750 
Epoch:2, train_step: 1958, loss: 0.071030, Train_acc: 0.968750 
Epoch:2, train_step: 1959, loss: 0.042257, Train_acc: 0.984375 
Epoch:2, train_step: 1960, loss: 0.041502, Train_acc: 0.984375 
Epoch:2, train_step: 1961, loss: 0.032381, Train_acc: 1.000000 
Epoch:2, train_step: 1962, loss: 0.003026, Train_acc: 1.000000 
Epoch:2, train_step: 1963, loss: 0.033610, Train_acc: 0.984375 
Epoch:2, train_step: 1964, loss: 0.094408, Train_acc: 0.953125 
Epoch:2, train_step: 1965, loss: 0.068125, Train_acc: 0.984375 
Epoch:2, train_step: 1966, loss: 0.016874, Train_acc: 1.000000 
Epoch:2, train_step: 1967, loss: 0.107194, Train_acc: 0.984375 
Epoch:2, train_step: 1968, loss: 0.024507, Train_acc: 0.984375 
Epoch:2, train_step: 1969, loss: 0.011205, Train_acc: 1.000000 
Epoch:2, train_step: 1970, loss: 0.067888, Train_acc: 0.968750 
Epoch:2, train_step: 1971, loss: 0.008421, Train_acc: 1.000000 
Epoch:2, train_step: 1972, loss: 0.109069, Train_acc: 0.953125 
Epoch:2, train_step: 1973, loss: 0.012134, Train_acc: 1.000000 
Epoch:2, train_step: 1974, loss: 0.011197, Train_acc: 1.000000 
Epoch:2, train_step: 1975, loss: 0.056652, Train_acc: 0.968750 
Epoch:2, train_step: 1976, loss: 0.015081, Train_acc: 1.000000 
Epoch:2, train_step: 1977, loss: 0.004361, Train_acc: 1.000000 
Epoch:2, train_step: 1978, loss: 0.006617, Train_acc: 1.000000 
Epoch:2, train_step: 1979, loss: 0.035826, Train_acc: 0.984375 
Epoch:2, train_step: 1980, loss: 0.001655, Train_acc: 1.000000 
Epoch:2, train_step: 1981, loss: 0.062475, Train_acc: 0.984375 
Epoch:2, train_step: 1982, loss: 0.123699, Train_acc: 0.968750 
Epoch:2, train_step: 1983, loss: 0.040849, Train_acc: 0.984375 
Epoch:2, train_step: 1984, loss: 0.091525, Train_acc: 0.953125 
Epoch:2, train_step: 1985, loss: 0.092456, Train_acc: 0.984375 
Epoch:2, train_step: 1986, loss: 0.011818, Train_acc: 1.000000 
Epoch:2, train_step: 1987, loss: 0.031945, Train_acc: 1.000000 
Epoch:2, train_step: 1988, loss: 0.350314, Train_acc: 0.953125 
Epoch:2, train_step: 1989, loss: 0.054617, Train_acc: 0.984375 
Epoch:2, train_step: 1990, loss: 0.003764, Train_acc: 1.000000 
Epoch:2, train_step: 1991, loss: 0.013595, Train_acc: 1.000000 
Epoch:2, train_step: 1992, loss: 0.033049, Train_acc: 0.968750 
Epoch:2, train_step: 1993, loss: 0.126700, Train_acc: 0.937500 
Epoch:2, train_step: 1994, loss: 0.078696, Train_acc: 0.984375 
Epoch:2, train_step: 1995, loss: 0.051058, Train_acc: 0.984375 
Epoch:2, train_step: 1996, loss: 0.078381, Train_acc: 0.968750 
Epoch:2, train_step: 1997, loss: 0.063603, Train_acc: 0.953125 
Epoch:2, train_step: 1998, loss: 0.039955, Train_acc: 0.984375 
Epoch:2, train_step: 1999, loss: 0.061709, Train_acc: 0.968750 
Epoch:2, train_step: 2000, loss: 0.045900, Train_acc: 0.984375 
Epoch:2, train_step: 2001, loss: 0.049694, Train_acc: 0.984375 
Epoch:2, train_step: 2002, loss: 0.010235, Train_acc: 1.000000 
Epoch:2, train_step: 2003, loss: 0.097039, Train_acc: 0.968750 
Epoch:2, train_step: 2004, loss: 0.006470, Train_acc: 1.000000 
Epoch:2, train_step: 2005, loss: 0.011139, Train_acc: 1.000000 
Epoch:2, train_step: 2006, loss: 0.033205, Train_acc: 0.984375 
Epoch:2, train_step: 2007, loss: 0.030921, Train_acc: 0.984375 
Epoch:2, train_step: 2008, loss: 0.014707, Train_acc: 1.000000 
Epoch:2, train_step: 2009, loss: 0.031981, Train_acc: 0.968750 
Epoch:2, train_step: 2010, loss: 0.076742, Train_acc: 0.953125 
Epoch:2, train_step: 2011, loss: 0.118447, Train_acc: 0.984375 
Epoch:2, train_step: 2012, loss: 0.017620, Train_acc: 1.000000 
Epoch:2, train_step: 2013, loss: 0.069114, Train_acc: 0.984375 
Epoch:2, train_step: 2014, loss: 0.070084, Train_acc: 0.984375 
Epoch:2, train_step: 2015, loss: 0.045349, Train_acc: 0.984375 
Epoch:2, train_step: 2016, loss: 0.003880, Train_acc: 1.000000 
Epoch:2, train_step: 2017, loss: 0.128304, Train_acc: 0.968750 
Epoch:2, train_step: 2018, loss: 0.007553, Train_acc: 1.000000 
Epoch:2, train_step: 2019, loss: 0.032588, Train_acc: 0.984375 
Epoch:2, train_step: 2020, loss: 0.021483, Train_acc: 0.984375 
Epoch:2, train_step: 2021, loss: 0.057080, Train_acc: 0.968750 
Epoch:2, train_step: 2022, loss: 0.101699, Train_acc: 0.984375 
Epoch:2, train_step: 2023, loss: 0.020939, Train_acc: 1.000000 
Epoch:2, train_step: 2024, loss: 0.017526, Train_acc: 1.000000 
Epoch:2, train_step: 2025, loss: 0.072281, Train_acc: 0.937500 
Epoch:2, train_step: 2026, loss: 0.036673, Train_acc: 0.984375 
Epoch:2, train_step: 2027, loss: 0.098097, Train_acc: 0.984375 
Epoch:2, train_step: 2028, loss: 0.003325, Train_acc: 1.000000 
Epoch:2, train_step: 2029, loss: 0.004074, Train_acc: 1.000000 
Epoch:2, train_step: 2030, loss: 0.022157, Train_acc: 1.000000 
Epoch:2, train_step: 2031, loss: 0.027402, Train_acc: 0.984375 
Epoch:2, train_step: 2032, loss: 0.016414, Train_acc: 1.000000 
Epoch:2, train_step: 2033, loss: 0.012525, Train_acc: 1.000000 
Epoch:2, train_step: 2034, loss: 0.060635, Train_acc: 0.953125 
Epoch:2, train_step: 2035, loss: 0.073965, Train_acc: 0.984375 
Epoch:2, train_step: 2036, loss: 0.012491, Train_acc: 1.000000 
Epoch:2, train_step: 2037, loss: 0.046705, Train_acc: 0.984375 
Epoch:2, train_step: 2038, loss: 0.012270, Train_acc: 1.000000 
Epoch:2, train_step: 2039, loss: 0.002187, Train_acc: 1.000000 
Epoch:2, train_step: 2040, loss: 0.002655, Train_acc: 1.000000 
Epoch:2, train_step: 2041, loss: 0.003142, Train_acc: 1.000000 
Epoch:2, train_step: 2042, loss: 0.013128, Train_acc: 1.000000 
Epoch:2, train_step: 2043, loss: 0.067231, Train_acc: 0.984375 
Epoch:2, train_step: 2044, loss: 0.015661, Train_acc: 1.000000 
Epoch:2, train_step: 2045, loss: 0.003068, Train_acc: 1.000000 
Epoch:2, train_step: 2046, loss: 0.161746, Train_acc: 0.984375 
Epoch:2, train_step: 2047, loss: 0.018186, Train_acc: 1.000000 
Epoch:2, train_step: 2048, loss: 0.002479, Train_acc: 1.000000 
Epoch:2, train_step: 2049, loss: 0.004785, Train_acc: 1.000000 
Epoch:2, train_step: 2050, loss: 0.115502, Train_acc: 0.984375 
Epoch:2, train_step: 2051, loss: 0.006856, Train_acc: 1.000000 
Epoch:2, train_step: 2052, loss: 0.082288, Train_acc: 0.968750 
Epoch:2, train_step: 2053, loss: 0.008120, Train_acc: 1.000000 
Epoch:2, train_step: 2054, loss: 0.003927, Train_acc: 1.000000 
Epoch:2, train_step: 2055, loss: 0.042386, Train_acc: 0.984375 
Epoch:2, train_step: 2056, loss: 0.032038, Train_acc: 0.984375 
Epoch:2, train_step: 2057, loss: 0.017274, Train_acc: 1.000000 
Epoch:2, train_step: 2058, loss: 0.021630, Train_acc: 1.000000 
Epoch:2, train_step: 2059, loss: 0.023256, Train_acc: 1.000000 
Epoch:2, train_step: 2060, loss: 0.048114, Train_acc: 0.984375 
Epoch:2, train_step: 2061, loss: 0.017068, Train_acc: 1.000000 
Epoch:2, train_step: 2062, loss: 0.017057, Train_acc: 1.000000 
Epoch:2, train_step: 2063, loss: 0.052848, Train_acc: 0.968750 
Epoch:2, train_step: 2064, loss: 0.013523, Train_acc: 1.000000 
Epoch:2, train_step: 2065, loss: 0.019472, Train_acc: 1.000000 
Epoch:2, train_step: 2066, loss: 0.021377, Train_acc: 1.000000 
Epoch:2, train_step: 2067, loss: 0.035684, Train_acc: 0.968750 
Epoch:2, train_step: 2068, loss: 0.020607, Train_acc: 1.000000 
Epoch:2, train_step: 2069, loss: 0.007959, Train_acc: 1.000000 
Epoch:2, train_step: 2070, loss: 0.001045, Train_acc: 1.000000 
Epoch:2, train_step: 2071, loss: 0.107369, Train_acc: 0.984375 
Epoch:2, train_step: 2072, loss: 0.082577, Train_acc: 0.968750 
Epoch:2, train_step: 2073, loss: 0.004552, Train_acc: 1.000000 
Epoch:2, train_step: 2074, loss: 0.008570, Train_acc: 1.000000 
Epoch:2, train_step: 2075, loss: 0.076581, Train_acc: 0.953125 
Epoch:2, train_step: 2076, loss: 0.023094, Train_acc: 0.984375 
Epoch:2, train_step: 2077, loss: 0.096560, Train_acc: 0.984375 
Epoch:2, train_step: 2078, loss: 0.029286, Train_acc: 0.984375 
Epoch:2, train_step: 2079, loss: 0.024043, Train_acc: 1.000000 
Epoch:2, train_step: 2080, loss: 0.017620, Train_acc: 1.000000 
Epoch:2, train_step: 2081, loss: 0.005837, Train_acc: 1.000000 
Epoch:2, train_step: 2082, loss: 0.039588, Train_acc: 0.984375 
Epoch:2, train_step: 2083, loss: 0.009814, Train_acc: 1.000000 
Epoch:2, train_step: 2084, loss: 0.097833, Train_acc: 0.953125 
Epoch:2, train_step: 2085, loss: 0.005705, Train_acc: 1.000000 
Epoch:2, train_step: 2086, loss: 0.024912, Train_acc: 0.984375 
Epoch:2, train_step: 2087, loss: 0.016925, Train_acc: 0.984375 
Epoch:2, train_step: 2088, loss: 0.026775, Train_acc: 1.000000 
Epoch:2, train_step: 2089, loss: 0.024954, Train_acc: 0.984375 
Epoch:2, train_step: 2090, loss: 0.003312, Train_acc: 1.000000 
Epoch:2, train_step: 2091, loss: 0.095640, Train_acc: 0.984375 
Epoch:2, train_step: 2092, loss: 0.028967, Train_acc: 0.984375 
Epoch:2, train_step: 2093, loss: 0.053197, Train_acc: 0.968750 
Epoch:2, train_step: 2094, loss: 0.015483, Train_acc: 1.000000 
Epoch:2, train_step: 2095, loss: 0.019107, Train_acc: 0.984375 
Epoch:2, train_step: 2096, loss: 0.037446, Train_acc: 0.984375 
Epoch:2, train_step: 2097, loss: 0.045168, Train_acc: 0.984375 
Epoch:2, train_step: 2098, loss: 0.050584, Train_acc: 0.984375 
Epoch:2, train_step: 2099, loss: 0.031693, Train_acc: 1.000000 
Epoch:2, train_step: 2100, loss: 0.026199, Train_acc: 0.984375 
Epoch:2, train_step: 2101, loss: 0.011252, Train_acc: 1.000000 
Epoch:2, train_step: 2102, loss: 0.105662, Train_acc: 0.984375 
Epoch:2, train_step: 2103, loss: 0.030527, Train_acc: 0.984375 
Epoch:2, train_step: 2104, loss: 0.014048, Train_acc: 1.000000 
Epoch:2, train_step: 2105, loss: 0.025645, Train_acc: 0.984375 
Epoch:2, train_step: 2106, loss: 0.035837, Train_acc: 0.968750 
Epoch:2, train_step: 2107, loss: 0.006304, Train_acc: 1.000000 
Epoch:2, train_step: 2108, loss: 0.012658, Train_acc: 1.000000 
Epoch:2, train_step: 2109, loss: 0.001421, Train_acc: 1.000000 
Epoch:2, train_step: 2110, loss: 0.061716, Train_acc: 0.968750 
Epoch:2, train_step: 2111, loss: 0.007471, Train_acc: 1.000000 
Epoch:2, train_step: 2112, loss: 0.011171, Train_acc: 1.000000 
Epoch:2, train_step: 2113, loss: 0.003866, Train_acc: 1.000000 
Epoch:2, train_step: 2114, loss: 0.068676, Train_acc: 0.984375 
Epoch:2, train_step: 2115, loss: 0.010875, Train_acc: 1.000000 
Epoch:2, train_step: 2116, loss: 0.015376, Train_acc: 1.000000 
Epoch:2, train_step: 2117, loss: 0.003531, Train_acc: 1.000000 
Epoch:2, train_step: 2118, loss: 0.010646, Train_acc: 1.000000 
Epoch:2, train_step: 2119, loss: 0.004322, Train_acc: 1.000000 
Epoch:2, train_step: 2120, loss: 0.022460, Train_acc: 0.984375 
Epoch:2, train_step: 2121, loss: 0.063857, Train_acc: 0.984375 
Epoch:2, train_step: 2122, loss: 0.010664, Train_acc: 1.000000 
Epoch:2, train_step: 2123, loss: 0.040211, Train_acc: 0.984375 
Epoch:2, train_step: 2124, loss: 0.081792, Train_acc: 0.968750 
Epoch:2, train_step: 2125, loss: 0.049593, Train_acc: 0.984375 
Epoch:2, train_step: 2126, loss: 0.046912, Train_acc: 0.968750 
Epoch:2, train_step: 2127, loss: 0.074782, Train_acc: 0.953125 
Epoch:2, train_step: 2128, loss: 0.009954, Train_acc: 1.000000 
Epoch:2, train_step: 2129, loss: 0.006214, Train_acc: 1.000000 
Epoch:2, train_step: 2130, loss: 0.008588, Train_acc: 1.000000 
Epoch:2, train_step: 2131, loss: 0.009964, Train_acc: 1.000000 
Epoch:2, train_step: 2132, loss: 0.019154, Train_acc: 1.000000 
Epoch:2, train_step: 2133, loss: 0.036303, Train_acc: 0.984375 
Epoch:2, train_step: 2134, loss: 0.001744, Train_acc: 1.000000 
Epoch:2, train_step: 2135, loss: 0.075836, Train_acc: 0.968750 
Epoch:2, train_step: 2136, loss: 0.048116, Train_acc: 0.984375 
Epoch:2, train_step: 2137, loss: 0.019638, Train_acc: 1.000000 
Epoch:2, train_step: 2138, loss: 0.025167, Train_acc: 1.000000 
Epoch:2, train_step: 2139, loss: 0.026914, Train_acc: 1.000000 
Epoch:2, train_step: 2140, loss: 0.053209, Train_acc: 0.984375 
Epoch:2, train_step: 2141, loss: 0.011643, Train_acc: 1.000000 
Epoch:2, train_step: 2142, loss: 0.048237, Train_acc: 0.984375 
Epoch:2, train_step: 2143, loss: 0.066174, Train_acc: 0.984375 
Epoch:2, train_step: 2144, loss: 0.060244, Train_acc: 0.968750 
Epoch:2, train_step: 2145, loss: 0.005860, Train_acc: 1.000000 
Epoch:2, train_step: 2146, loss: 0.008588, Train_acc: 1.000000 
Epoch:2, train_step: 2147, loss: 0.025841, Train_acc: 0.984375 
Epoch:2, train_step: 2148, loss: 0.012693, Train_acc: 1.000000 
Epoch:2, train_step: 2149, loss: 0.165367, Train_acc: 0.968750 
Epoch:2, train_step: 2150, loss: 0.006508, Train_acc: 1.000000 
Epoch:2, train_step: 2151, loss: 0.061622, Train_acc: 0.968750 
Epoch:2, train_step: 2152, loss: 0.025944, Train_acc: 0.984375 
Epoch:2, train_step: 2153, loss: 0.082919, Train_acc: 0.953125 
Epoch:2, train_step: 2154, loss: 0.015023, Train_acc: 1.000000 
Epoch:2, train_step: 2155, loss: 0.024200, Train_acc: 0.984375 
Epoch:2, train_step: 2156, loss: 0.011153, Train_acc: 1.000000 
Epoch:2, train_step: 2157, loss: 0.010630, Train_acc: 1.000000 
Epoch:2, train_step: 2158, loss: 0.075184, Train_acc: 0.984375 
Epoch:2, train_step: 2159, loss: 0.018815, Train_acc: 1.000000 
Epoch:2, train_step: 2160, loss: 0.030952, Train_acc: 0.984375 
Epoch:2, train_step: 2161, loss: 0.056841, Train_acc: 0.968750 
Epoch:2, train_step: 2162, loss: 0.056688, Train_acc: 0.968750 
Epoch:2, train_step: 2163, loss: 0.013655, Train_acc: 1.000000 
Epoch:2, train_step: 2164, loss: 0.025092, Train_acc: 0.984375 
Epoch:2, train_step: 2165, loss: 0.001752, Train_acc: 1.000000 
Epoch:2, train_step: 2166, loss: 0.013465, Train_acc: 1.000000 
Epoch:2, train_step: 2167, loss: 0.018498, Train_acc: 1.000000 
Epoch:2, train_step: 2168, loss: 0.005948, Train_acc: 1.000000 
Epoch:2, train_step: 2169, loss: 0.023577, Train_acc: 1.000000 
Epoch:2, train_step: 2170, loss: 0.002748, Train_acc: 1.000000 
Epoch:2, train_step: 2171, loss: 0.001610, Train_acc: 1.000000 
Epoch:2, train_step: 2172, loss: 0.008632, Train_acc: 1.000000 
Epoch:2, train_step: 2173, loss: 0.019065, Train_acc: 1.000000 
Epoch:2, train_step: 2174, loss: 0.012110, Train_acc: 1.000000 
Epoch:2, train_step: 2175, loss: 0.062887, Train_acc: 0.984375 
Epoch:2, train_step: 2176, loss: 0.011544, Train_acc: 1.000000 
Epoch:2, train_step: 2177, loss: 0.082400, Train_acc: 0.968750 
Epoch:2, train_step: 2178, loss: 0.004700, Train_acc: 1.000000 
Epoch:2, train_step: 2179, loss: 0.024026, Train_acc: 0.984375 
Epoch:2, train_step: 2180, loss: 0.037146, Train_acc: 0.984375 
Epoch:2, train_step: 2181, loss: 0.030049, Train_acc: 0.984375 
Epoch:2, train_step: 2182, loss: 0.002861, Train_acc: 1.000000 
Epoch:2, train_step: 2183, loss: 0.006392, Train_acc: 1.000000 
Epoch:2, train_step: 2184, loss: 0.001876, Train_acc: 1.000000 
Epoch:2, train_step: 2185, loss: 0.062553, Train_acc: 0.968750 
Epoch:2, train_step: 2186, loss: 0.020015, Train_acc: 0.984375 
Epoch:2, train_step: 2187, loss: 0.019786, Train_acc: 0.984375 
Epoch:2, train_step: 2188, loss: 0.049918, Train_acc: 0.984375 
Epoch:2, train_step: 2189, loss: 0.020677, Train_acc: 1.000000 
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Epoch:2, train_step: 2213, loss: 0.031019, Train_acc: 0.984375 
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Epoch:2, train_step: 2236, loss: 0.009694, Train_acc: 1.000000 
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Epoch:2, train_step: 2247, loss: 0.095488, Train_acc: 0.984375 
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Epoch:2, train_step: 2252, loss: 0.005040, Train_acc: 1.000000 
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Epoch:2, train_step: 2255, loss: 0.000859, Train_acc: 1.000000 
Epoch:2, train_step: 2256, loss: 0.005498, Train_acc: 1.000000 
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Epoch:2, train_step: 2258, loss: 0.023416, Train_acc: 0.984375 
Epoch:2, train_step: 2259, loss: 0.027356, Train_acc: 0.984375 
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Epoch:2, train_step: 2262, loss: 0.070538, Train_acc: 0.984375 
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Epoch:2, train_step: 2264, loss: 0.006489, Train_acc: 1.000000 
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Epoch:2, train_step: 2266, loss: 0.003209, Train_acc: 1.000000 
Epoch:2, train_step: 2267, loss: 0.035253, Train_acc: 0.984375 
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Epoch:2, train_step: 2271, loss: 0.002803, Train_acc: 1.000000 
Epoch:2, train_step: 2272, loss: 0.000933, Train_acc: 1.000000 
Epoch:2, train_step: 2273, loss: 0.025096, Train_acc: 1.000000 
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Epoch:2, train_step: 2275, loss: 0.014435, Train_acc: 0.984375 
Epoch:2, train_step: 2276, loss: 0.068393, Train_acc: 0.984375 
Epoch:2, train_step: 2277, loss: 0.049259, Train_acc: 0.984375 
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Epoch:2, train_step: 2279, loss: 0.026285, Train_acc: 0.984375 
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Epoch:2, train_step: 2281, loss: 0.083986, Train_acc: 0.984375 
Epoch:2, train_step: 2282, loss: 0.018672, Train_acc: 0.984375 
Epoch:2, train_step: 2283, loss: 0.007401, Train_acc: 1.000000 
Epoch:2, train_step: 2284, loss: 0.071019, Train_acc: 0.984375 
Epoch:2, train_step: 2285, loss: 0.012770, Train_acc: 1.000000 
Epoch:2, train_step: 2286, loss: 0.019663, Train_acc: 0.984375 
Epoch:2, train_step: 2287, loss: 0.070968, Train_acc: 0.968750 
Epoch:2, train_step: 2288, loss: 0.041648, Train_acc: 0.968750 
Epoch:2, train_step: 2289, loss: 0.119142, Train_acc: 0.953125 
Epoch:2, train_step: 2290, loss: 0.168403, Train_acc: 0.968750 
Epoch:2, train_step: 2291, loss: 0.168947, Train_acc: 0.968750 
Epoch:2, train_step: 2292, loss: 0.097753, Train_acc: 0.968750 
Epoch:2, train_step: 2293, loss: 0.042911, Train_acc: 0.968750 
Epoch:2, train_step: 2294, loss: 0.097552, Train_acc: 0.953125 
Epoch:2, train_step: 2295, loss: 0.117873, Train_acc: 0.968750 
Epoch:2, train_step: 2296, loss: 0.014414, Train_acc: 0.984375 
Epoch:2, train_step: 2297, loss: 0.006974, Train_acc: 1.000000 
Epoch:2, train_step: 2298, loss: 0.011496, Train_acc: 1.000000 
Epoch:2, train_step: 2299, loss: 0.079264, Train_acc: 0.968750 
Epoch:2, train_step: 2300, loss: 0.072446, Train_acc: 0.968750 
Epoch:2, train_step: 2301, loss: 0.010106, Train_acc: 1.000000 
Epoch:2, train_step: 2302, loss: 0.002055, Train_acc: 1.000000 
Epoch:2, train_step: 2303, loss: 0.015216, Train_acc: 1.000000 
Epoch:2, train_step: 2304, loss: 0.078255, Train_acc: 0.984375 
Epoch:2, train_step: 2305, loss: 0.012415, Train_acc: 1.000000 
Epoch:2, train_step: 2306, loss: 0.078667, Train_acc: 0.968750 
Epoch:2, train_step: 2307, loss: 0.028669, Train_acc: 1.000000 
Epoch:2, train_step: 2308, loss: 0.006591, Train_acc: 1.000000 
Epoch:2, train_step: 2309, loss: 0.011578, Train_acc: 1.000000 
Epoch:2, train_step: 2310, loss: 0.075074, Train_acc: 0.984375 
Epoch:2, train_step: 2311, loss: 0.003030, Train_acc: 1.000000 
Epoch:2, train_step: 2312, loss: 0.088226, Train_acc: 0.984375 
Epoch:2, train_step: 2313, loss: 0.000650, Train_acc: 1.000000 
Epoch:2, train_step: 2314, loss: 0.006414, Train_acc: 1.000000 
Epoch:2, train_step: 2315, loss: 0.009596, Train_acc: 1.000000 
Epoch:2, train_step: 2316, loss: 0.013230, Train_acc: 1.000000 
Epoch:2, train_step: 2317, loss: 0.013724, Train_acc: 0.984375 
Epoch:2, train_step: 2318, loss: 0.047431, Train_acc: 0.984375 
Epoch:2, train_step: 2319, loss: 0.011152, Train_acc: 1.000000 
Epoch:2, train_step: 2320, loss: 0.074791, Train_acc: 0.984375 
Epoch:2, train_step: 2321, loss: 0.005094, Train_acc: 1.000000 
Epoch:2, train_step: 2322, loss: 0.097863, Train_acc: 0.968750 
Epoch:2, train_step: 2323, loss: 0.154822, Train_acc: 0.968750 
Epoch:2, train_step: 2324, loss: 0.011731, Train_acc: 1.000000 
Epoch:2, train_step: 2325, loss: 0.013526, Train_acc: 1.000000 
Epoch:2, train_step: 2326, loss: 0.005037, Train_acc: 1.000000 
Epoch:2, train_step: 2327, loss: 0.006387, Train_acc: 1.000000 
Epoch:2, train_step: 2328, loss: 0.032149, Train_acc: 0.968750 
Epoch:2, train_step: 2329, loss: 0.020645, Train_acc: 0.984375 
Epoch:2, train_step: 2330, loss: 0.029897, Train_acc: 1.000000 
Epoch:2, train_step: 2331, loss: 0.031228, Train_acc: 0.984375 
Epoch:2, train_step: 2332, loss: 0.035745, Train_acc: 0.984375 
Epoch:2, train_step: 2333, loss: 0.004695, Train_acc: 1.000000 
Epoch:2, train_step: 2334, loss: 0.008098, Train_acc: 1.000000 
Epoch:2, train_step: 2335, loss: 0.035862, Train_acc: 0.984375 
Epoch:2, train_step: 2336, loss: 0.003242, Train_acc: 1.000000 
Epoch:2, train_step: 2337, loss: 0.048965, Train_acc: 0.984375 
Epoch:2, train_step: 2338, loss: 0.002589, Train_acc: 1.000000 
Epoch:2, train_step: 2339, loss: 0.045769, Train_acc: 0.984375 
Epoch:2, train_step: 2340, loss: 0.005803, Train_acc: 1.000000 
Epoch:2, train_step: 2341, loss: 0.012493, Train_acc: 1.000000 
Epoch:2, train_step: 2342, loss: 0.011857, Train_acc: 1.000000 
Epoch:2, train_step: 2343, loss: 0.005584, Train_acc: 1.000000 
Epoch:2, train_step: 2344, loss: 0.021927, Train_acc: 0.984375 
Epoch:2, train_step: 2345, loss: 0.049026, Train_acc: 0.984375 
Epoch:2, train_step: 2346, loss: 0.017504, Train_acc: 1.000000 
Epoch:2, train_step: 2347, loss: 0.006584, Train_acc: 1.000000 
Epoch:2, train_step: 2348, loss: 0.019719, Train_acc: 0.984375 
Epoch:2, train_step: 2349, loss: 0.026558, Train_acc: 0.984375 
Epoch:2, train_step: 2350, loss: 0.004691, Train_acc: 1.000000 
Epoch:2, train_step: 2351, loss: 0.023035, Train_acc: 1.000000 
Epoch:2, train_step: 2352, loss: 0.004232, Train_acc: 1.000000 
Epoch:2, train_step: 2353, loss: 0.109883, Train_acc: 0.968750 
Epoch:2, train_step: 2354, loss: 0.011228, Train_acc: 1.000000 
Epoch:2, train_step: 2355, loss: 0.013183, Train_acc: 1.000000 
Epoch:2, train_step: 2356, loss: 0.058727, Train_acc: 0.968750 
Epoch:2, train_step: 2357, loss: 0.011838, Train_acc: 1.000000 
Epoch:2, train_step: 2358, loss: 0.035774, Train_acc: 0.984375 
Epoch:2, train_step: 2359, loss: 0.003005, Train_acc: 1.000000 
Epoch:2, train_step: 2360, loss: 0.016232, Train_acc: 1.000000 
Epoch:2, train_step: 2361, loss: 0.006395, Train_acc: 1.000000 
Epoch:2, train_step: 2362, loss: 0.021697, Train_acc: 1.000000 
Epoch:2, train_step: 2363, loss: 0.013275, Train_acc: 1.000000 
Epoch:2, train_step: 2364, loss: 0.011093, Train_acc: 1.000000 
Epoch:2, train_step: 2365, loss: 0.020400, Train_acc: 1.000000 
Epoch:2, train_step: 2366, loss: 0.003776, Train_acc: 1.000000 
Epoch:2, train_step: 2367, loss: 0.054985, Train_acc: 0.968750 
Epoch:2, train_step: 2368, loss: 0.052541, Train_acc: 0.984375 
Epoch:2, train_step: 2369, loss: 0.113277, Train_acc: 0.953125 
Epoch:2, train_step: 2370, loss: 0.067322, Train_acc: 0.984375 
Epoch:2, train_step: 2371, loss: 0.014938, Train_acc: 0.984375 
Epoch:2, train_step: 2372, loss: 0.085828, Train_acc: 0.953125 
Epoch:2, train_step: 2373, loss: 0.002327, Train_acc: 1.000000 
Epoch:2, train_step: 2374, loss: 0.041338, Train_acc: 0.984375 
Epoch:2, train_step: 2375, loss: 0.028908, Train_acc: 0.984375 
Epoch:2, train_step: 2376, loss: 0.113914, Train_acc: 0.984375 
Epoch:2, train_step: 2377, loss: 0.018130, Train_acc: 1.000000 
Epoch:2, train_step: 2378, loss: 0.007751, Train_acc: 1.000000 
Epoch:2, train_step: 2379, loss: 0.006112, Train_acc: 1.000000 
Epoch:2, train_step: 2380, loss: 0.301816, Train_acc: 0.953125 
Epoch:2, train_step: 2381, loss: 0.029056, Train_acc: 0.984375 
Epoch:2, train_step: 2382, loss: 0.041877, Train_acc: 0.984375 
Epoch:2, train_step: 2383, loss: 0.032831, Train_acc: 0.984375 
Epoch:2, train_step: 2384, loss: 0.002520, Train_acc: 1.000000 
Epoch:2, train_step: 2385, loss: 0.029623, Train_acc: 0.984375 
Epoch:2, train_step: 2386, loss: 0.052974, Train_acc: 0.984375 
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Epoch:2, train_step: 2388, loss: 0.024635, Train_acc: 0.984375 
Epoch:2, train_step: 2389, loss: 0.013678, Train_acc: 1.000000 
Epoch:2, train_step: 2390, loss: 0.003298, Train_acc: 1.000000 
Epoch:2, train_step: 2391, loss: 0.017502, Train_acc: 1.000000 
Epoch:2, train_step: 2392, loss: 0.007683, Train_acc: 1.000000 
Epoch:2, train_step: 2393, loss: 0.009726, Train_acc: 1.000000 
Epoch:2, train_step: 2394, loss: 0.041403, Train_acc: 0.968750 
Epoch:2, train_step: 2395, loss: 0.023218, Train_acc: 1.000000 
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Epoch:2, train_step: 2397, loss: 0.027748, Train_acc: 0.984375 
Epoch:2, train_step: 2398, loss: 0.013901, Train_acc: 1.000000 
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Epoch:2, train_step: 2403, loss: 0.008875, Train_acc: 1.000000 
Epoch:2, train_step: 2404, loss: 0.001540, Train_acc: 1.000000 
Epoch:2, train_step: 2405, loss: 0.000935, Train_acc: 1.000000 
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Epoch:2, train_step: 2408, loss: 0.037071, Train_acc: 0.984375 
Epoch:2, train_step: 2409, loss: 0.005398, Train_acc: 1.000000 
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Epoch:2, train_step: 2412, loss: 0.161845, Train_acc: 0.984375 
Epoch:2, train_step: 2413, loss: 0.011679, Train_acc: 1.000000 
Epoch:2, train_step: 2414, loss: 0.005977, Train_acc: 1.000000 
Epoch:2, train_step: 2415, loss: 0.006437, Train_acc: 1.000000 
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Epoch:2, train_step: 2417, loss: 0.158484, Train_acc: 0.968750 
Epoch:2, train_step: 2418, loss: 0.030655, Train_acc: 0.984375 
Epoch:2, train_step: 2419, loss: 0.128647, Train_acc: 0.937500 
Epoch:2, train_step: 2420, loss: 0.098189, Train_acc: 0.984375 
Epoch:2, train_step: 2421, loss: 0.005325, Train_acc: 1.000000 
Epoch:2, train_step: 2422, loss: 0.068156, Train_acc: 0.953125 
Epoch:2, train_step: 2423, loss: 0.003033, Train_acc: 1.000000 
Epoch:2, train_step: 2424, loss: 0.088872, Train_acc: 0.984375 
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Epoch:2, train_step: 2426, loss: 0.064256, Train_acc: 0.968750 
Epoch:2, train_step: 2427, loss: 0.022470, Train_acc: 1.000000 
Epoch:2, train_step: 2428, loss: 0.031693, Train_acc: 0.984375 
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Epoch:2, train_step: 2430, loss: 0.026094, Train_acc: 0.984375 
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Epoch:2, train_step: 2433, loss: 0.047309, Train_acc: 0.984375 
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Epoch:2, train_step: 2438, loss: 0.005449, Train_acc: 1.000000 
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Epoch:2, train_step: 2442, loss: 0.001854, Train_acc: 1.000000 
Epoch:2, train_step: 2443, loss: 0.008697, Train_acc: 1.000000 
Epoch:2, train_step: 2444, loss: 0.123273, Train_acc: 0.984375 
Epoch:2, train_step: 2445, loss: 0.006557, Train_acc: 1.000000 
Epoch:2, train_step: 2446, loss: 0.002132, Train_acc: 1.000000 
Epoch:2, train_step: 2447, loss: 0.023955, Train_acc: 0.984375 
Epoch:2, train_step: 2448, loss: 0.107971, Train_acc: 0.953125 
Epoch:2, train_step: 2449, loss: 0.055454, Train_acc: 0.984375 
Epoch:2, train_step: 2450, loss: 0.027797, Train_acc: 0.984375 
Epoch:2, train_step: 2451, loss: 0.003706, Train_acc: 1.000000 
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Epoch:2, train_step: 2453, loss: 0.132449, Train_acc: 0.984375 
Epoch:2, train_step: 2454, loss: 0.030769, Train_acc: 0.984375 
Epoch:2, train_step: 2455, loss: 0.061824, Train_acc: 0.968750 
Epoch:2, train_step: 2456, loss: 0.009722, Train_acc: 1.000000 
Epoch:2, train_step: 2457, loss: 0.013868, Train_acc: 0.984375 
Epoch:2, train_step: 2458, loss: 0.005046, Train_acc: 1.000000 
Epoch:2, train_step: 2459, loss: 0.095501, Train_acc: 0.968750 
Epoch:2, train_step: 2460, loss: 0.022557, Train_acc: 0.984375 
Epoch:2, train_step: 2461, loss: 0.008726, Train_acc: 1.000000 
Epoch:2, train_step: 2462, loss: 0.008164, Train_acc: 1.000000 
Epoch:2, train_step: 2463, loss: 0.036202, Train_acc: 0.984375 
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Epoch:2, train_step: 2466, loss: 0.098885, Train_acc: 0.968750 
Epoch:2, train_step: 2467, loss: 0.007771, Train_acc: 1.000000 
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Epoch:2, train_step: 2473, loss: 0.012597, Train_acc: 1.000000 
Epoch:2, train_step: 2474, loss: 0.035380, Train_acc: 0.968750 
Epoch:2, train_step: 2475, loss: 0.064066, Train_acc: 0.984375 
Epoch:2, train_step: 2476, loss: 0.051450, Train_acc: 0.984375 
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Epoch:2, train_step: 2485, loss: 0.001327, Train_acc: 1.000000 
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Epoch:2, train_step: 2487, loss: 0.029291, Train_acc: 0.984375 
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Epoch:2, train_step: 2490, loss: 0.082134, Train_acc: 0.968750 
Epoch:2, train_step: 2491, loss: 0.025822, Train_acc: 0.984375 
Epoch:2, train_step: 2492, loss: 0.017694, Train_acc: 1.000000 
Epoch:2, train_step: 2493, loss: 0.013272, Train_acc: 1.000000 
Epoch:2, train_step: 2494, loss: 0.050431, Train_acc: 0.984375 
Epoch:2, train_step: 2495, loss: 0.028769, Train_acc: 0.984375 
Epoch:2, train_step: 2496, loss: 0.041791, Train_acc: 0.984375 
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Epoch:2, train_step: 2505, loss: 0.042002, Train_acc: 0.984375 
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Epoch:2, train_step: 2509, loss: 0.029568, Train_acc: 0.984375 
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Epoch:2, train_step: 2512, loss: 0.005074, Train_acc: 1.000000 
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Epoch:2, train_step: 2524, loss: 0.034784, Train_acc: 0.984375 
Epoch:2, train_step: 2525, loss: 0.082828, Train_acc: 0.984375 
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Epoch:2, train_step: 2528, loss: 0.012740, Train_acc: 1.000000 
Epoch:2, train_step: 2529, loss: 0.088761, Train_acc: 0.953125 
Epoch:2, train_step: 2530, loss: 0.003791, Train_acc: 1.000000 
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Epoch:2, train_step: 2532, loss: 0.015619, Train_acc: 0.984375 
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Epoch:2, train_step: 2534, loss: 0.005370, Train_acc: 1.000000 
Epoch:2, train_step: 2535, loss: 0.003399, Train_acc: 1.000000 
Epoch:2, train_step: 2536, loss: 0.041327, Train_acc: 0.984375 
Epoch:2, train_step: 2537, loss: 0.014516, Train_acc: 1.000000 
Epoch:2, train_step: 2538, loss: 0.075961, Train_acc: 0.984375 
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Epoch:2, train_step: 2542, loss: 0.015332, Train_acc: 1.000000 
Epoch:2, train_step: 2543, loss: 0.010219, Train_acc: 1.000000 
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Epoch:2, train_step: 2545, loss: 0.014978, Train_acc: 1.000000 
Epoch:2, train_step: 2546, loss: 0.048495, Train_acc: 0.984375 
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Epoch:2, train_step: 2548, loss: 0.186944, Train_acc: 0.984375 
Epoch:2, train_step: 2549, loss: 0.015164, Train_acc: 1.000000 
Epoch:2, train_step: 2550, loss: 0.006798, Train_acc: 1.000000 
Epoch:2, train_step: 2551, loss: 0.003291, Train_acc: 1.000000 
Epoch:2, train_step: 2552, loss: 0.004289, Train_acc: 1.000000 
Epoch:2, train_step: 2553, loss: 0.154319, Train_acc: 0.984375 
Epoch:2, train_step: 2554, loss: 0.018973, Train_acc: 0.984375 
Epoch:2, train_step: 2555, loss: 0.048847, Train_acc: 0.984375 
Epoch:2, train_step: 2556, loss: 0.019871, Train_acc: 0.984375 
Epoch:2, train_step: 2557, loss: 0.141337, Train_acc: 0.968750 
Epoch:2, train_step: 2558, loss: 0.005861, Train_acc: 1.000000 
Epoch:2, train_step: 2559, loss: 0.003575, Train_acc: 1.000000 
Epoch:2, train_step: 2560, loss: 0.019191, Train_acc: 1.000000 
Epoch:2, train_step: 2561, loss: 0.015047, Train_acc: 1.000000 
Epoch:2, train_step: 2562, loss: 0.009504, Train_acc: 1.000000 
Epoch:2, train_step: 2563, loss: 0.038401, Train_acc: 0.984375 
Epoch:2, train_step: 2564, loss: 0.007301, Train_acc: 1.000000 
Epoch:2, train_step: 2565, loss: 0.007927, Train_acc: 1.000000 
Epoch:2, train_step: 2566, loss: 0.053148, Train_acc: 0.984375 
Epoch:2, train_step: 2567, loss: 0.008893, Train_acc: 1.000000 
Epoch:2, train_step: 2568, loss: 0.006031, Train_acc: 1.000000 
Epoch:2, train_step: 2569, loss: 0.069459, Train_acc: 0.953125 
Epoch:2, train_step: 2570, loss: 0.015586, Train_acc: 1.000000 
Epoch:2, train_step: 2571, loss: 0.002054, Train_acc: 1.000000 
Epoch:2, train_step: 2572, loss: 0.002913, Train_acc: 1.000000 
Epoch:2, train_step: 2573, loss: 0.003656, Train_acc: 1.000000 
Epoch:2, train_step: 2574, loss: 0.009942, Train_acc: 1.000000 
Epoch:2, train_step: 2575, loss: 0.027948, Train_acc: 1.000000 
Epoch:2, train_step: 2576, loss: 0.007179, Train_acc: 1.000000 
Epoch:2, train_step: 2577, loss: 0.007202, Train_acc: 1.000000 
Epoch:2, train_step: 2578, loss: 0.012028, Train_acc: 1.000000 
Epoch:2, train_step: 2579, loss: 0.011747, Train_acc: 1.000000 
Epoch:2, train_step: 2580, loss: 0.066926, Train_acc: 0.984375 
Epoch:2, train_step: 2581, loss: 0.006218, Train_acc: 1.000000 
Epoch:2, train_step: 2582, loss: 0.008392, Train_acc: 1.000000 
Epoch:2, train_step: 2583, loss: 0.113521, Train_acc: 0.984375 
Epoch:2, train_step: 2584, loss: 0.011058, Train_acc: 1.000000 
Epoch:2, train_step: 2585, loss: 0.005119, Train_acc: 1.000000 
Epoch:2, train_step: 2586, loss: 0.053153, Train_acc: 0.968750 
Epoch:2, train_step: 2587, loss: 0.066001, Train_acc: 0.968750 
Epoch:2, train_step: 2588, loss: 0.015233, Train_acc: 1.000000 
Epoch:2, train_step: 2589, loss: 0.012516, Train_acc: 1.000000 
Epoch:2, train_step: 2590, loss: 0.041632, Train_acc: 0.984375 
Epoch:2, train_step: 2591, loss: 0.134357, Train_acc: 0.968750 
Epoch:2, train_step: 2592, loss: 0.026310, Train_acc: 1.000000 
Epoch:2, train_step: 2593, loss: 0.090399, Train_acc: 0.984375 
Epoch:2, train_step: 2594, loss: 0.082489, Train_acc: 0.968750 
Epoch:2, train_step: 2595, loss: 0.041190, Train_acc: 0.984375 
Epoch:2, train_step: 2596, loss: 0.007427, Train_acc: 1.000000 
Epoch:2, train_step: 2597, loss: 0.042975, Train_acc: 0.984375 
Epoch:2, train_step: 2598, loss: 0.035108, Train_acc: 0.984375 
Epoch:2, train_step: 2599, loss: 0.015500, Train_acc: 1.000000 
Epoch:2, train_step: 2600, loss: 0.016642, Train_acc: 1.000000 
Epoch:2, train_step: 2601, loss: 0.014136, Train_acc: 0.984375 
Epoch:2, train_step: 2602, loss: 0.004021, Train_acc: 1.000000 
Epoch:2, train_step: 2603, loss: 0.004100, Train_acc: 1.000000 
Epoch:2, train_step: 2604, loss: 0.021153, Train_acc: 0.984375 
Epoch:2, train_step: 2605, loss: 0.034331, Train_acc: 0.984375 
Epoch:2, train_step: 2606, loss: 0.019574, Train_acc: 0.984375 
Epoch:2, train_step: 2607, loss: 0.101693, Train_acc: 0.984375 
Epoch:2, train_step: 2608, loss: 0.008180, Train_acc: 1.000000 
Epoch:2, train_step: 2609, loss: 0.019838, Train_acc: 1.000000 
Epoch:2, train_step: 2610, loss: 0.007837, Train_acc: 1.000000 
Epoch:2, train_step: 2611, loss: 0.017758, Train_acc: 0.984375 
Epoch:2, train_step: 2612, loss: 0.016019, Train_acc: 1.000000 
Epoch:2, train_step: 2613, loss: 0.057512, Train_acc: 0.984375 
Epoch:2, train_step: 2614, loss: 0.031913, Train_acc: 0.984375 
Epoch:2, train_step: 2615, loss: 0.044700, Train_acc: 0.984375 
Epoch:2, train_step: 2616, loss: 0.029113, Train_acc: 1.000000 
Epoch:2, train_step: 2617, loss: 0.039991, Train_acc: 0.968750 
Epoch:2, train_step: 2618, loss: 0.100180, Train_acc: 0.984375 
Epoch:2, train_step: 2619, loss: 0.026093, Train_acc: 0.984375 
Epoch:2, train_step: 2620, loss: 0.109765, Train_acc: 0.953125 
Epoch:2, train_step: 2621, loss: 0.030644, Train_acc: 0.984375 
Epoch:2, train_step: 2622, loss: 0.004227, Train_acc: 1.000000 
Epoch:2, train_step: 2623, loss: 0.020477, Train_acc: 1.000000 
Epoch:2, train_step: 2624, loss: 0.015211, Train_acc: 1.000000 
Epoch:2, train_step: 2625, loss: 0.016833, Train_acc: 1.000000 
Epoch:2, train_step: 2626, loss: 0.067963, Train_acc: 0.968750 
Epoch:2, train_step: 2627, loss: 0.007405, Train_acc: 1.000000 
Epoch:2, train_step: 2628, loss: 0.013119, Train_acc: 1.000000 
Epoch:2, train_step: 2629, loss: 0.011524, Train_acc: 1.000000 
Epoch:2, train_step: 2630, loss: 0.054366, Train_acc: 0.984375 
Epoch:2, train_step: 2631, loss: 0.007820, Train_acc: 1.000000 
Epoch:2, train_step: 2632, loss: 0.027102, Train_acc: 0.984375 
Epoch:2, train_step: 2633, loss: 0.010454, Train_acc: 1.000000 
Epoch:2, train_step: 2634, loss: 0.022293, Train_acc: 0.984375 
Epoch:2, train_step: 2635, loss: 0.020583, Train_acc: 1.000000 
Epoch:2, train_step: 2636, loss: 0.002701, Train_acc: 1.000000 
Epoch:2, train_step: 2637, loss: 0.008000, Train_acc: 1.000000 
Epoch:2, train_step: 2638, loss: 0.016207, Train_acc: 0.984375 
Epoch:2, train_step: 2639, loss: 0.010145, Train_acc: 1.000000 
Epoch:2, train_step: 2640, loss: 0.076861, Train_acc: 0.984375 
Epoch:2, train_step: 2641, loss: 0.095045, Train_acc: 0.984375 
Epoch:2, train_step: 2642, loss: 0.017411, Train_acc: 0.984375 
Epoch:2, train_step: 2643, loss: 0.135827, Train_acc: 0.968750 
Epoch:2, train_step: 2644, loss: 0.005649, Train_acc: 1.000000 
Epoch:2, train_step: 2645, loss: 0.005691, Train_acc: 1.000000 
Epoch:2, train_step: 2646, loss: 0.035656, Train_acc: 0.984375 
Epoch:2, train_step: 2647, loss: 0.125232, Train_acc: 0.953125 
Epoch:2, train_step: 2648, loss: 0.070117, Train_acc: 0.968750 
Epoch:2, train_step: 2649, loss: 0.013218, Train_acc: 1.000000 
Epoch:2, train_step: 2650, loss: 0.034272, Train_acc: 1.000000 
Epoch:2, train_step: 2651, loss: 0.005502, Train_acc: 1.000000 
Epoch:2, train_step: 2652, loss: 0.029067, Train_acc: 1.000000 
Epoch:2, train_step: 2653, loss: 0.023883, Train_acc: 0.984375 
Epoch:2, train_step: 2654, loss: 0.051257, Train_acc: 0.968750 
Epoch:2, train_step: 2655, loss: 0.004695, Train_acc: 1.000000 
Epoch:2, train_step: 2656, loss: 0.007673, Train_acc: 1.000000 
Epoch:2, train_step: 2657, loss: 0.022869, Train_acc: 0.984375 
Epoch:2, train_step: 2658, loss: 0.004524, Train_acc: 1.000000 
Epoch:2, train_step: 2659, loss: 0.050737, Train_acc: 0.984375 
Epoch:2, train_step: 2660, loss: 0.004761, Train_acc: 1.000000 
Epoch:2, train_step: 2661, loss: 0.049928, Train_acc: 0.968750 
Epoch:2, train_step: 2662, loss: 0.025653, Train_acc: 1.000000 
Epoch:2, train_step: 2663, loss: 0.015289, Train_acc: 1.000000 
Epoch:2, train_step: 2664, loss: 0.012736, Train_acc: 1.000000 
Epoch:2, train_step: 2665, loss: 0.036004, Train_acc: 0.984375 
Epoch:2, train_step: 2666, loss: 0.000878, Train_acc: 1.000000 
Epoch:2, train_step: 2667, loss: 0.023201, Train_acc: 1.000000 
Epoch:2, train_step: 2668, loss: 0.002072, Train_acc: 1.000000 
Epoch:2, train_step: 2669, loss: 0.002288, Train_acc: 1.000000 
Epoch:2, train_step: 2670, loss: 0.022783, Train_acc: 1.000000 
Epoch:2, train_step: 2671, loss: 0.051699, Train_acc: 0.968750 
Epoch:2, train_step: 2672, loss: 0.011792, Train_acc: 1.000000 
Epoch:2, train_step: 2673, loss: 0.002227, Train_acc: 1.000000 
Epoch:2, train_step: 2674, loss: 0.008540, Train_acc: 1.000000 
Epoch:2, train_step: 2675, loss: 0.051961, Train_acc: 0.984375 
Epoch:2, train_step: 2676, loss: 0.037255, Train_acc: 0.968750 
Epoch:2, train_step: 2677, loss: 0.003549, Train_acc: 1.000000 
Epoch:2, train_step: 2678, loss: 0.008085, Train_acc: 1.000000 
Epoch:2, train_step: 2679, loss: 0.011163, Train_acc: 1.000000 
Epoch:2, train_step: 2680, loss: 0.016875, Train_acc: 0.984375 
Epoch:2, train_step: 2681, loss: 0.006138, Train_acc: 1.000000 
Epoch:2, train_step: 2682, loss: 0.013119, Train_acc: 1.000000 
Epoch:2, train_step: 2683, loss: 0.033295, Train_acc: 0.984375 
Epoch:2, train_step: 2684, loss: 0.004424, Train_acc: 1.000000 
Epoch:2, train_step: 2685, loss: 0.001662, Train_acc: 1.000000 
Epoch:2, train_step: 2686, loss: 0.137422, Train_acc: 0.984375 
Epoch:2, train_step: 2687, loss: 0.014645, Train_acc: 1.000000 
Epoch:2, train_step: 2688, loss: 0.007458, Train_acc: 1.000000 
Epoch:2, train_step: 2689, loss: 0.026886, Train_acc: 1.000000 
Epoch:2, train_step: 2690, loss: 0.025419, Train_acc: 0.984375 
Epoch:2, train_step: 2691, loss: 0.006252, Train_acc: 1.000000 
Epoch:2, train_step: 2692, loss: 0.015688, Train_acc: 1.000000 
Epoch:2, train_step: 2693, loss: 0.012163, Train_acc: 1.000000 
Epoch:2, train_step: 2694, loss: 0.016498, Train_acc: 0.984375 
Epoch:2, train_step: 2695, loss: 0.046069, Train_acc: 0.984375 
Epoch:2, train_step: 2696, loss: 0.003085, Train_acc: 1.000000 
Epoch:2, train_step: 2697, loss: 0.004019, Train_acc: 1.000000 
Epoch:2, train_step: 2698, loss: 0.008540, Train_acc: 1.000000 
Epoch:2, train_step: 2699, loss: 0.016149, Train_acc: 1.000000 
Epoch:2, train_step: 2700, loss: 0.056539, Train_acc: 0.968750 
Epoch:2, train_step: 2701, loss: 0.033542, Train_acc: 0.968750 
Epoch:2, train_step: 2702, loss: 0.286972, Train_acc: 0.937500 
Epoch:2, train_step: 2703, loss: 0.008970, Train_acc: 1.000000 
Epoch:2, train_step: 2704, loss: 0.004348, Train_acc: 1.000000 
Epoch:2, train_step: 2705, loss: 0.004340, Train_acc: 1.000000 
Epoch:2, train_step: 2706, loss: 0.008803, Train_acc: 1.000000 
Epoch:2, train_step: 2707, loss: 0.001208, Train_acc: 1.000000 
Epoch:2, train_step: 2708, loss: 0.011929, Train_acc: 0.984375 
Epoch:2, train_step: 2709, loss: 0.076533, Train_acc: 0.984375 
Epoch:2, train_step: 2710, loss: 0.023993, Train_acc: 1.000000 
Epoch:2, train_step: 2711, loss: 0.028627, Train_acc: 0.984375 
Epoch:2, train_step: 2712, loss: 0.005494, Train_acc: 1.000000 
Epoch:2, train_step: 2713, loss: 0.084669, Train_acc: 0.953125 
Epoch:2, train_step: 2714, loss: 0.006466, Train_acc: 1.000000 
Epoch:2, train_step: 2715, loss: 0.038221, Train_acc: 0.984375 
Epoch:2, train_step: 2716, loss: 0.020220, Train_acc: 1.000000 
Epoch:2, train_step: 2717, loss: 0.008175, Train_acc: 1.000000 
Epoch:2, train_step: 2718, loss: 0.030952, Train_acc: 0.984375 
Epoch:2, train_step: 2719, loss: 0.053168, Train_acc: 0.968750 
Epoch:2, train_step: 2720, loss: 0.012546, Train_acc: 1.000000 
Epoch:2, train_step: 2721, loss: 0.021632, Train_acc: 0.984375 
Epoch:2, train_step: 2722, loss: 0.021109, Train_acc: 0.984375 
Epoch:2, train_step: 2723, loss: 0.064107, Train_acc: 0.984375 
Epoch:2, train_step: 2724, loss: 0.018561, Train_acc: 0.984375 
Epoch:2, train_step: 2725, loss: 0.026320, Train_acc: 0.984375 
Epoch:2, train_step: 2726, loss: 0.017864, Train_acc: 0.984375 
Epoch:2, train_step: 2727, loss: 0.017428, Train_acc: 1.000000 
Epoch:2, train_step: 2728, loss: 0.012447, Train_acc: 0.984375 
Epoch:2, train_step: 2729, loss: 0.013946, Train_acc: 1.000000 
Epoch:2, train_step: 2730, loss: 0.008061, Train_acc: 1.000000 
Epoch:2, train_step: 2731, loss: 0.047390, Train_acc: 0.984375 
Epoch:2, train_step: 2732, loss: 0.069826, Train_acc: 0.984375 
Epoch:2, train_step: 2733, loss: 0.004813, Train_acc: 1.000000 
Epoch:2, train_step: 2734, loss: 0.003198, Train_acc: 1.000000 
Epoch:2, train_step: 2735, loss: 0.011092, Train_acc: 1.000000 
Epoch:2, train_step: 2736, loss: 0.001573, Train_acc: 1.000000 
Epoch:2, train_step: 2737, loss: 0.002629, Train_acc: 1.000000 
Epoch:2, train_step: 2738, loss: 0.027831, Train_acc: 0.984375 
Epoch:2, train_step: 2739, loss: 0.041327, Train_acc: 0.984375 
Epoch:2, train_step: 2740, loss: 0.086305, Train_acc: 0.968750 
Epoch:2, train_step: 2741, loss: 0.032188, Train_acc: 1.000000 
Epoch:2, train_step: 2742, loss: 0.018757, Train_acc: 1.000000 
Epoch:2, train_step: 2743, loss: 0.071980, Train_acc: 0.968750 
Epoch:2, train_step: 2744, loss: 0.003346, Train_acc: 1.000000 
Epoch:2, train_step: 2745, loss: 0.063884, Train_acc: 0.968750 
Epoch:2, train_step: 2746, loss: 0.018546, Train_acc: 0.984375 
Epoch:2, train_step: 2747, loss: 0.010466, Train_acc: 1.000000 
Epoch:2, train_step: 2748, loss: 0.014039, Train_acc: 1.000000 
Epoch:2, train_step: 2749, loss: 0.026797, Train_acc: 0.984375 
Epoch:2, train_step: 2750, loss: 0.043638, Train_acc: 0.984375 
Epoch:2, train_step: 2751, loss: 0.024728, Train_acc: 0.984375 
Epoch:2, train_step: 2752, loss: 0.069161, Train_acc: 0.984375 
Epoch:2, train_step: 2753, loss: 0.049728, Train_acc: 0.984375 
Epoch:2, train_step: 2754, loss: 0.023865, Train_acc: 1.000000 
Epoch:2, train_step: 2755, loss: 0.035389, Train_acc: 0.984375 
Epoch:2, train_step: 2756, loss: 0.056049, Train_acc: 0.984375 
Epoch:2, train_step: 2757, loss: 0.092270, Train_acc: 0.968750 
Epoch:2, train_step: 2758, loss: 0.001120, Train_acc: 1.000000 
Epoch:2, train_step: 2759, loss: 0.022169, Train_acc: 0.984375 
Epoch:2, train_step: 2760, loss: 0.010187, Train_acc: 1.000000 
Epoch:2, train_step: 2761, loss: 0.010109, Train_acc: 1.000000 
Epoch:2, train_step: 2762, loss: 0.069673, Train_acc: 0.984375 
Epoch:2, train_step: 2763, loss: 0.021443, Train_acc: 1.000000 
Epoch:2, train_step: 2764, loss: 0.005311, Train_acc: 1.000000 
Epoch:2, train_step: 2765, loss: 0.005075, Train_acc: 1.000000 
Epoch:2, train_step: 2766, loss: 0.009806, Train_acc: 1.000000 
Epoch:2, train_step: 2767, loss: 0.000322, Train_acc: 1.000000 
Epoch:2, train_step: 2768, loss: 0.001866, Train_acc: 1.000000 
Epoch:2, train_step: 2769, loss: 0.018497, Train_acc: 1.000000 
Epoch:2, train_step: 2770, loss: 0.010115, Train_acc: 1.000000 
Epoch:2, train_step: 2771, loss: 0.005547, Train_acc: 1.000000 
Epoch:2, train_step: 2772, loss: 0.003562, Train_acc: 1.000000 
Epoch:2, train_step: 2773, loss: 0.022191, Train_acc: 1.000000 
Epoch:2, train_step: 2774, loss: 0.005488, Train_acc: 1.000000 
Epoch:2, train_step: 2775, loss: 0.030948, Train_acc: 1.000000 
Epoch:2, train_step: 2776, loss: 0.005884, Train_acc: 1.000000 
Epoch:2, train_step: 2777, loss: 0.109762, Train_acc: 0.984375 
Epoch:2, train_step: 2778, loss: 0.054122, Train_acc: 0.953125 
Epoch:2, train_step: 2779, loss: 0.014606, Train_acc: 1.000000 
Epoch:2, train_step: 2780, loss: 0.028236, Train_acc: 0.984375 
Epoch:2, train_step: 2781, loss: 0.013556, Train_acc: 0.984375 
Epoch:2, train_step: 2782, loss: 0.097502, Train_acc: 0.984375 
Epoch:2, train_step: 2783, loss: 0.001039, Train_acc: 1.000000 
Epoch:2, train_step: 2784, loss: 0.006006, Train_acc: 1.000000 
Epoch:2, train_step: 2785, loss: 0.001564, Train_acc: 1.000000 
Epoch:2, train_step: 2786, loss: 0.004349, Train_acc: 1.000000 
Epoch:2, train_step: 2787, loss: 0.008669, Train_acc: 1.000000 
Epoch:2, train_step: 2788, loss: 0.007857, Train_acc: 1.000000 
Epoch:2, train_step: 2789, loss: 0.007225, Train_acc: 1.000000 
Epoch:2, train_step: 2790, loss: 0.001098, Train_acc: 1.000000 
Epoch:2, train_step: 2791, loss: 0.014453, Train_acc: 1.000000 
Epoch:2, train_step: 2792, loss: 0.000137, Train_acc: 1.000000 
Epoch:2, train_step: 2793, loss: 0.012418, Train_acc: 1.000000 
Epoch:2, train_step: 2794, loss: 0.016799, Train_acc: 0.984375 
Epoch:2, train_step: 2795, loss: 0.002365, Train_acc: 1.000000 
Epoch:2, train_step: 2796, loss: 0.000227, Train_acc: 1.000000 
Epoch:2, train_step: 2797, loss: 0.001221, Train_acc: 1.000000 
Epoch:2, train_step: 2798, loss: 0.001608, Train_acc: 1.000000 
Epoch:2, train_step: 2799, loss: 0.000195, Train_acc: 1.000000 
Epoch:2, train_step: 2800, loss: 0.000415, Train_acc: 1.000000 
Epoch:2, train_step: 2801, loss: 0.014176, Train_acc: 1.000000 
Epoch:2, train_step: 2802, loss: 0.020000, Train_acc: 0.984375 
Epoch:2, train_step: 2803, loss: 0.002377, Train_acc: 1.000000 
Epoch:2, train_step: 2804, loss: 0.009519, Train_acc: 1.000000 
Epoch:2, train_step: 2805, loss: 0.000452, Train_acc: 1.000000 
Epoch:2, train_step: 2806, loss: 0.001504, Train_acc: 1.000000 
Epoch:2, train_step: 2807, loss: 0.073860, Train_acc: 0.968750 
Epoch:2, train_step: 2808, loss: 0.177141, Train_acc: 0.968750 
Epoch:2, train_step: 2809, loss: 0.002303, Train_acc: 1.000000 
Epoch:2, train_step: 2810, loss: 0.000907, Train_acc: 1.000000 
Epoch:2, train_step: 2811, loss: 0.187105, Train_acc: 0.984375 


Epoch:2, avg_train_loss: 0.034751, avg_train_acc: 0.989511, Test_acc: 0.985477 
Epoch:3, train_step: 2812, loss: 0.018478, Train_acc: 0.984375 
Epoch:3, train_step: 2813, loss: 0.024846, Train_acc: 1.000000 
Epoch:3, train_step: 2814, loss: 0.184328, Train_acc: 0.968750 
Epoch:3, train_step: 2815, loss: 0.033348, Train_acc: 0.984375 
Epoch:3, train_step: 2816, loss: 0.011308, Train_acc: 1.000000 
Epoch:3, train_step: 2817, loss: 0.004363, Train_acc: 1.000000 
Epoch:3, train_step: 2818, loss: 0.033815, Train_acc: 0.984375 
Epoch:3, train_step: 2819, loss: 0.161287, Train_acc: 0.953125 
Epoch:3, train_step: 2820, loss: 0.003903, Train_acc: 1.000000 
Epoch:3, train_step: 2821, loss: 0.139119, Train_acc: 0.968750 
Epoch:3, train_step: 2822, loss: 0.008131, Train_acc: 1.000000 
Epoch:3, train_step: 2823, loss: 0.015766, Train_acc: 1.000000 
Epoch:3, train_step: 2824, loss: 0.022582, Train_acc: 0.984375 
Epoch:3, train_step: 2825, loss: 0.029572, Train_acc: 0.984375 
Epoch:3, train_step: 2826, loss: 0.083495, Train_acc: 0.984375 
Epoch:3, train_step: 2827, loss: 0.035984, Train_acc: 0.984375 
Epoch:3, train_step: 2828, loss: 0.015625, Train_acc: 1.000000 
Epoch:3, train_step: 2829, loss: 0.042761, Train_acc: 0.984375 
Epoch:3, train_step: 2830, loss: 0.022046, Train_acc: 0.984375 
Epoch:3, train_step: 2831, loss: 0.015674, Train_acc: 1.000000 
Epoch:3, train_step: 2832, loss: 0.069076, Train_acc: 0.968750 
Epoch:3, train_step: 2833, loss: 0.081501, Train_acc: 0.968750 
Epoch:3, train_step: 2834, loss: 0.001563, Train_acc: 1.000000 
Epoch:3, train_step: 2835, loss: 0.003347, Train_acc: 1.000000 
Epoch:3, train_step: 2836, loss: 0.002182, Train_acc: 1.000000 
Epoch:3, train_step: 2837, loss: 0.166720, Train_acc: 0.968750 
Epoch:3, train_step: 2838, loss: 0.004326, Train_acc: 1.000000 
Epoch:3, train_step: 2839, loss: 0.102479, Train_acc: 0.968750 
Epoch:3, train_step: 2840, loss: 0.009722, Train_acc: 1.000000 
Epoch:3, train_step: 2841, loss: 0.004085, Train_acc: 1.000000 
Epoch:3, train_step: 2842, loss: 0.012796, Train_acc: 1.000000 
Epoch:3, train_step: 2843, loss: 0.044372, Train_acc: 0.984375 
Epoch:3, train_step: 2844, loss: 0.007292, Train_acc: 1.000000 
Epoch:3, train_step: 2845, loss: 0.008401, Train_acc: 1.000000 
Epoch:3, train_step: 2846, loss: 0.065598, Train_acc: 0.984375 
Epoch:3, train_step: 2847, loss: 0.017590, Train_acc: 1.000000 
Epoch:3, train_step: 2848, loss: 0.001287, Train_acc: 1.000000 
Epoch:3, train_step: 2849, loss: 0.039398, Train_acc: 0.968750 
Epoch:3, train_step: 2850, loss: 0.002469, Train_acc: 1.000000 
Epoch:3, train_step: 2851, loss: 0.019431, Train_acc: 0.984375 
Epoch:3, train_step: 2852, loss: 0.009353, Train_acc: 1.000000 
Epoch:3, train_step: 2853, loss: 0.039959, Train_acc: 0.984375 
Epoch:3, train_step: 2854, loss: 0.134177, Train_acc: 0.968750 
Epoch:3, train_step: 2855, loss: 0.019985, Train_acc: 1.000000 
Epoch:3, train_step: 2856, loss: 0.005892, Train_acc: 1.000000 
Epoch:3, train_step: 2857, loss: 0.007810, Train_acc: 1.000000 
Epoch:3, train_step: 2858, loss: 0.002313, Train_acc: 1.000000 
Epoch:3, train_step: 2859, loss: 0.011195, Train_acc: 1.000000 
Epoch:3, train_step: 2860, loss: 0.002003, Train_acc: 1.000000 
Epoch:3, train_step: 2861, loss: 0.000619, Train_acc: 1.000000 
Epoch:3, train_step: 2862, loss: 0.095354, Train_acc: 0.984375 
Epoch:3, train_step: 2863, loss: 0.006953, Train_acc: 1.000000 
Epoch:3, train_step: 2864, loss: 0.003236, Train_acc: 1.000000 
Epoch:3, train_step: 2865, loss: 0.022052, Train_acc: 1.000000 
Epoch:3, train_step: 2866, loss: 0.009364, Train_acc: 1.000000 
Epoch:3, train_step: 2867, loss: 0.015427, Train_acc: 1.000000 
Epoch:3, train_step: 2868, loss: 0.011596, Train_acc: 1.000000 
Epoch:3, train_step: 2869, loss: 0.221677, Train_acc: 0.953125 
Epoch:3, train_step: 2870, loss: 0.035883, Train_acc: 0.984375 
Epoch:3, train_step: 2871, loss: 0.007574, Train_acc: 1.000000 
Epoch:3, train_step: 2872, loss: 0.017079, Train_acc: 0.984375 
Epoch:3, train_step: 2873, loss: 0.035072, Train_acc: 0.984375 
Epoch:3, train_step: 2874, loss: 0.012018, Train_acc: 1.000000 
Epoch:3, train_step: 2875, loss: 0.014400, Train_acc: 1.000000 
Epoch:3, train_step: 2876, loss: 0.018167, Train_acc: 1.000000 
Epoch:3, train_step: 2877, loss: 0.002819, Train_acc: 1.000000 
Epoch:3, train_step: 2878, loss: 0.016021, Train_acc: 1.000000 
Epoch:3, train_step: 2879, loss: 0.010202, Train_acc: 1.000000 
Epoch:3, train_step: 2880, loss: 0.018895, Train_acc: 0.984375 
Epoch:3, train_step: 2881, loss: 0.098812, Train_acc: 0.968750 
Epoch:3, train_step: 2882, loss: 0.003493, Train_acc: 1.000000 
Epoch:3, train_step: 2883, loss: 0.006514, Train_acc: 1.000000 
Epoch:3, train_step: 2884, loss: 0.020370, Train_acc: 0.984375 
Epoch:3, train_step: 2885, loss: 0.009642, Train_acc: 1.000000 
Epoch:3, train_step: 2886, loss: 0.013280, Train_acc: 1.000000 
Epoch:3, train_step: 2887, loss: 0.027275, Train_acc: 0.984375 
Epoch:3, train_step: 2888, loss: 0.001996, Train_acc: 1.000000 
Epoch:3, train_step: 2889, loss: 0.005509, Train_acc: 1.000000 
Epoch:3, train_step: 2890, loss: 0.033209, Train_acc: 0.984375 
Epoch:3, train_step: 2891, loss: 0.006740, Train_acc: 1.000000 
Epoch:3, train_step: 2892, loss: 0.011388, Train_acc: 1.000000 
Epoch:3, train_step: 2893, loss: 0.026206, Train_acc: 1.000000 
Epoch:3, train_step: 2894, loss: 0.012467, Train_acc: 1.000000 
Epoch:3, train_step: 2895, loss: 0.018371, Train_acc: 1.000000 
Epoch:3, train_step: 2896, loss: 0.007832, Train_acc: 1.000000 
Epoch:3, train_step: 2897, loss: 0.014931, Train_acc: 1.000000 
Epoch:3, train_step: 2898, loss: 0.020492, Train_acc: 0.984375 
Epoch:3, train_step: 2899, loss: 0.013735, Train_acc: 1.000000 
Epoch:3, train_step: 2900, loss: 0.014492, Train_acc: 1.000000 
Epoch:3, train_step: 2901, loss: 0.096533, Train_acc: 0.968750 
Epoch:3, train_step: 2902, loss: 0.064216, Train_acc: 0.984375 
Epoch:3, train_step: 2903, loss: 0.019109, Train_acc: 1.000000 
Epoch:3, train_step: 2904, loss: 0.040814, Train_acc: 0.984375 
Epoch:3, train_step: 2905, loss: 0.021987, Train_acc: 0.984375 
Epoch:3, train_step: 2906, loss: 0.010553, Train_acc: 1.000000 
Epoch:3, train_step: 2907, loss: 0.021950, Train_acc: 0.984375 
Epoch:3, train_step: 2908, loss: 0.001581, Train_acc: 1.000000 
Epoch:3, train_step: 2909, loss: 0.112620, Train_acc: 0.953125 
Epoch:3, train_step: 2910, loss: 0.011887, Train_acc: 1.000000 
Epoch:3, train_step: 2911, loss: 0.006214, Train_acc: 1.000000 
Epoch:3, train_step: 2912, loss: 0.016298, Train_acc: 1.000000 
Epoch:3, train_step: 2913, loss: 0.008139, Train_acc: 1.000000 
Epoch:3, train_step: 2914, loss: 0.012698, Train_acc: 1.000000 
Epoch:3, train_step: 2915, loss: 0.002561, Train_acc: 1.000000 
Epoch:3, train_step: 2916, loss: 0.055852, Train_acc: 0.968750 
Epoch:3, train_step: 2917, loss: 0.001253, Train_acc: 1.000000 
Epoch:3, train_step: 2918, loss: 0.042977, Train_acc: 0.984375 
Epoch:3, train_step: 2919, loss: 0.108379, Train_acc: 0.968750 
Epoch:3, train_step: 2920, loss: 0.013535, Train_acc: 1.000000 
Epoch:3, train_step: 2921, loss: 0.051259, Train_acc: 0.984375 
Epoch:3, train_step: 2922, loss: 0.091883, Train_acc: 0.984375 
Epoch:3, train_step: 2923, loss: 0.004381, Train_acc: 1.000000 
Epoch:3, train_step: 2924, loss: 0.025768, Train_acc: 1.000000 
Epoch:3, train_step: 2925, loss: 0.282928, Train_acc: 0.953125 
Epoch:3, train_step: 2926, loss: 0.027051, Train_acc: 0.984375 
Epoch:3, train_step: 2927, loss: 0.007485, Train_acc: 1.000000 
Epoch:3, train_step: 2928, loss: 0.007569, Train_acc: 1.000000 
Epoch:3, train_step: 2929, loss: 0.010365, Train_acc: 1.000000 
Epoch:3, train_step: 2930, loss: 0.111325, Train_acc: 0.968750 
Epoch:3, train_step: 2931, loss: 0.052265, Train_acc: 0.984375 
Epoch:3, train_step: 2932, loss: 0.019962, Train_acc: 1.000000 
Epoch:3, train_step: 2933, loss: 0.016847, Train_acc: 1.000000 
Epoch:3, train_step: 2934, loss: 0.055196, Train_acc: 0.968750 
Epoch:3, train_step: 2935, loss: 0.007526, Train_acc: 1.000000 
Epoch:3, train_step: 2936, loss: 0.054061, Train_acc: 0.968750 
Epoch:3, train_step: 2937, loss: 0.026619, Train_acc: 0.984375 
Epoch:3, train_step: 2938, loss: 0.063170, Train_acc: 0.968750 
Epoch:3, train_step: 2939, loss: 0.002014, Train_acc: 1.000000 
Epoch:3, train_step: 2940, loss: 0.032270, Train_acc: 0.984375 
Epoch:3, train_step: 2941, loss: 0.010334, Train_acc: 1.000000 
Epoch:3, train_step: 2942, loss: 0.009514, Train_acc: 1.000000 
Epoch:3, train_step: 2943, loss: 0.005567, Train_acc: 1.000000 
Epoch:3, train_step: 2944, loss: 0.007321, Train_acc: 1.000000 
Epoch:3, train_step: 2945, loss: 0.004127, Train_acc: 1.000000 
Epoch:3, train_step: 2946, loss: 0.035106, Train_acc: 0.984375 
Epoch:3, train_step: 2947, loss: 0.020013, Train_acc: 1.000000 
Epoch:3, train_step: 2948, loss: 0.031143, Train_acc: 0.968750 
Epoch:3, train_step: 2949, loss: 0.007757, Train_acc: 1.000000 
Epoch:3, train_step: 2950, loss: 0.061492, Train_acc: 0.968750 
Epoch:3, train_step: 2951, loss: 0.034325, Train_acc: 0.984375 
Epoch:3, train_step: 2952, loss: 0.005615, Train_acc: 1.000000 
Epoch:3, train_step: 2953, loss: 0.009089, Train_acc: 1.000000 
Epoch:3, train_step: 2954, loss: 0.020094, Train_acc: 1.000000 
Epoch:3, train_step: 2955, loss: 0.004228, Train_acc: 1.000000 
Epoch:3, train_step: 2956, loss: 0.026380, Train_acc: 0.984375 
Epoch:3, train_step: 2957, loss: 0.007315, Train_acc: 1.000000 
Epoch:3, train_step: 2958, loss: 0.038816, Train_acc: 0.984375 
Epoch:3, train_step: 2959, loss: 0.052798, Train_acc: 0.984375 
Epoch:3, train_step: 2960, loss: 0.017936, Train_acc: 0.984375 
Epoch:3, train_step: 2961, loss: 0.026307, Train_acc: 0.984375 
Epoch:3, train_step: 2962, loss: 0.009688, Train_acc: 1.000000 
Epoch:3, train_step: 2963, loss: 0.012990, Train_acc: 1.000000 
Epoch:3, train_step: 2964, loss: 0.079947, Train_acc: 0.984375 
Epoch:3, train_step: 2965, loss: 0.000637, Train_acc: 1.000000 
Epoch:3, train_step: 2966, loss: 0.011974, Train_acc: 1.000000 
Epoch:3, train_step: 2967, loss: 0.011543, Train_acc: 1.000000 
Epoch:3, train_step: 2968, loss: 0.003959, Train_acc: 1.000000 
Epoch:3, train_step: 2969, loss: 0.034370, Train_acc: 0.984375 
Epoch:3, train_step: 2970, loss: 0.016170, Train_acc: 1.000000 
Epoch:3, train_step: 2971, loss: 0.048070, Train_acc: 0.984375 
Epoch:3, train_step: 2972, loss: 0.024483, Train_acc: 1.000000 
Epoch:3, train_step: 2973, loss: 0.013590, Train_acc: 0.984375 
Epoch:3, train_step: 2974, loss: 0.020742, Train_acc: 0.984375 
Epoch:3, train_step: 2975, loss: 0.002284, Train_acc: 1.000000 
Epoch:3, train_step: 2976, loss: 0.003862, Train_acc: 1.000000 
Epoch:3, train_step: 2977, loss: 0.001290, Train_acc: 1.000000 
Epoch:3, train_step: 2978, loss: 0.009031, Train_acc: 1.000000 
Epoch:3, train_step: 2979, loss: 0.003311, Train_acc: 1.000000 
Epoch:3, train_step: 2980, loss: 0.049019, Train_acc: 0.984375 
Epoch:3, train_step: 2981, loss: 0.008466, Train_acc: 1.000000 
Epoch:3, train_step: 2982, loss: 0.002528, Train_acc: 1.000000 
Epoch:3, train_step: 2983, loss: 0.124536, Train_acc: 0.984375 
Epoch:3, train_step: 2984, loss: 0.047097, Train_acc: 0.984375 
Epoch:3, train_step: 2985, loss: 0.001771, Train_acc: 1.000000 
Epoch:3, train_step: 2986, loss: 0.002878, Train_acc: 1.000000 
Epoch:3, train_step: 2987, loss: 0.104581, Train_acc: 0.984375 
Epoch:3, train_step: 2988, loss: 0.010045, Train_acc: 1.000000 
Epoch:3, train_step: 2989, loss: 0.041965, Train_acc: 0.968750 
Epoch:3, train_step: 2990, loss: 0.011937, Train_acc: 1.000000 
Epoch:3, train_step: 2991, loss: 0.002281, Train_acc: 1.000000 
Epoch:3, train_step: 2992, loss: 0.032480, Train_acc: 0.984375 
Epoch:3, train_step: 2993, loss: 0.013275, Train_acc: 1.000000 
Epoch:3, train_step: 2994, loss: 0.005213, Train_acc: 1.000000 
Epoch:3, train_step: 2995, loss: 0.006203, Train_acc: 1.000000 
Epoch:3, train_step: 2996, loss: 0.029444, Train_acc: 1.000000 
Epoch:3, train_step: 2997, loss: 0.027548, Train_acc: 0.984375 
Epoch:3, train_step: 2998, loss: 0.026309, Train_acc: 0.984375 
Epoch:3, train_step: 2999, loss: 0.007133, Train_acc: 1.000000 
Epoch:3, train_step: 3000, loss: 0.018180, Train_acc: 0.984375 
Epoch:3, train_step: 3001, loss: 0.001969, Train_acc: 1.000000 
Epoch:3, train_step: 3002, loss: 0.008014, Train_acc: 1.000000 
Epoch:3, train_step: 3003, loss: 0.007898, Train_acc: 1.000000 
Epoch:3, train_step: 3004, loss: 0.011543, Train_acc: 1.000000 
Epoch:3, train_step: 3005, loss: 0.008032, Train_acc: 1.000000 
Epoch:3, train_step: 3006, loss: 0.004326, Train_acc: 1.000000 
Epoch:3, train_step: 3007, loss: 0.000618, Train_acc: 1.000000 
Epoch:3, train_step: 3008, loss: 0.084039, Train_acc: 0.984375 
Epoch:3, train_step: 3009, loss: 0.049622, Train_acc: 0.984375 
Epoch:3, train_step: 3010, loss: 0.001637, Train_acc: 1.000000 
Epoch:3, train_step: 3011, loss: 0.007230, Train_acc: 1.000000 
Epoch:3, train_step: 3012, loss: 0.034168, Train_acc: 0.984375 
Epoch:3, train_step: 3013, loss: 0.031800, Train_acc: 0.984375 
Epoch:3, train_step: 3014, loss: 0.057846, Train_acc: 0.984375 
Epoch:3, train_step: 3015, loss: 0.020843, Train_acc: 1.000000 
Epoch:3, train_step: 3016, loss: 0.008282, Train_acc: 1.000000 
Epoch:3, train_step: 3017, loss: 0.005373, Train_acc: 1.000000 
Epoch:3, train_step: 3018, loss: 0.004988, Train_acc: 1.000000 
Epoch:3, train_step: 3019, loss: 0.001971, Train_acc: 1.000000 
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Epoch:3, train_step: 3409, loss: 0.004491, Train_acc: 1.000000 
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Epoch:3, train_step: 3415, loss: 0.029460, Train_acc: 0.984375 
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Epoch:3, train_step: 3423, loss: 0.002469, Train_acc: 1.000000 
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Epoch:3, train_step: 3426, loss: 0.017006, Train_acc: 1.000000 
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Epoch:3, train_step: 3433, loss: 0.125240, Train_acc: 0.953125 
Epoch:3, train_step: 3434, loss: 0.065404, Train_acc: 0.984375 
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Epoch:3, train_step: 3438, loss: 0.014462, Train_acc: 1.000000 
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Epoch:3, train_step: 3478, loss: 0.035029, Train_acc: 0.984375 
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Epoch:3, train_step: 3567, loss: 0.013181, Train_acc: 1.000000 
Epoch:3, train_step: 3568, loss: 0.004782, Train_acc: 1.000000 
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Epoch:3, train_step: 3571, loss: 0.007957, Train_acc: 1.000000 
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Epoch:3, train_step: 3573, loss: 0.007152, Train_acc: 1.000000 
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Epoch:3, train_step: 3576, loss: 0.014206, Train_acc: 1.000000 
Epoch:3, train_step: 3577, loss: 0.035397, Train_acc: 0.984375 
Epoch:3, train_step: 3578, loss: 0.016952, Train_acc: 1.000000 
Epoch:3, train_step: 3579, loss: 0.015390, Train_acc: 0.984375 
Epoch:3, train_step: 3580, loss: 0.052079, Train_acc: 0.984375 
Epoch:3, train_step: 3581, loss: 0.002746, Train_acc: 1.000000 
Epoch:3, train_step: 3582, loss: 0.002101, Train_acc: 1.000000 
Epoch:3, train_step: 3583, loss: 0.003872, Train_acc: 1.000000 
Epoch:3, train_step: 3584, loss: 0.052345, Train_acc: 0.968750 
Epoch:3, train_step: 3585, loss: 0.026411, Train_acc: 1.000000 
Epoch:3, train_step: 3586, loss: 0.019477, Train_acc: 1.000000 
Epoch:3, train_step: 3587, loss: 0.004786, Train_acc: 1.000000 
Epoch:3, train_step: 3588, loss: 0.009234, Train_acc: 1.000000 
Epoch:3, train_step: 3589, loss: 0.028939, Train_acc: 1.000000 
Epoch:3, train_step: 3590, loss: 0.015030, Train_acc: 0.984375 
Epoch:3, train_step: 3591, loss: 0.039156, Train_acc: 0.984375 
Epoch:3, train_step: 3592, loss: 0.004045, Train_acc: 1.000000 
Epoch:3, train_step: 3593, loss: 0.011009, Train_acc: 1.000000 
Epoch:3, train_step: 3594, loss: 0.002532, Train_acc: 1.000000 
Epoch:3, train_step: 3595, loss: 0.023843, Train_acc: 0.984375 
Epoch:3, train_step: 3596, loss: 0.013181, Train_acc: 1.000000 
Epoch:3, train_step: 3597, loss: 0.002159, Train_acc: 1.000000 
Epoch:3, train_step: 3598, loss: 0.025913, Train_acc: 0.984375 
Epoch:3, train_step: 3599, loss: 0.013717, Train_acc: 1.000000 
Epoch:3, train_step: 3600, loss: 0.008331, Train_acc: 1.000000 
Epoch:3, train_step: 3601, loss: 0.003211, Train_acc: 1.000000 
Epoch:3, train_step: 3602, loss: 0.016378, Train_acc: 1.000000 
Epoch:3, train_step: 3603, loss: 0.000323, Train_acc: 1.000000 
Epoch:3, train_step: 3604, loss: 0.005673, Train_acc: 1.000000 
Epoch:3, train_step: 3605, loss: 0.002683, Train_acc: 1.000000 
Epoch:3, train_step: 3606, loss: 0.005088, Train_acc: 1.000000 
Epoch:3, train_step: 3607, loss: 0.056767, Train_acc: 0.984375 
Epoch:3, train_step: 3608, loss: 0.038235, Train_acc: 0.984375 
Epoch:3, train_step: 3609, loss: 0.001950, Train_acc: 1.000000 
Epoch:3, train_step: 3610, loss: 0.005472, Train_acc: 1.000000 
Epoch:3, train_step: 3611, loss: 0.007332, Train_acc: 1.000000 
Epoch:3, train_step: 3612, loss: 0.038391, Train_acc: 0.968750 
Epoch:3, train_step: 3613, loss: 0.016793, Train_acc: 1.000000 
Epoch:3, train_step: 3614, loss: 0.006871, Train_acc: 1.000000 
Epoch:3, train_step: 3615, loss: 0.009694, Train_acc: 1.000000 
Epoch:3, train_step: 3616, loss: 0.014079, Train_acc: 1.000000 
Epoch:3, train_step: 3617, loss: 0.005746, Train_acc: 1.000000 
Epoch:3, train_step: 3618, loss: 0.005189, Train_acc: 1.000000 
Epoch:3, train_step: 3619, loss: 0.008879, Train_acc: 1.000000 
Epoch:3, train_step: 3620, loss: 0.017097, Train_acc: 0.984375 
Epoch:3, train_step: 3621, loss: 0.001015, Train_acc: 1.000000 
Epoch:3, train_step: 3622, loss: 0.000416, Train_acc: 1.000000 
Epoch:3, train_step: 3623, loss: 0.126812, Train_acc: 0.984375 
Epoch:3, train_step: 3624, loss: 0.016254, Train_acc: 1.000000 
Epoch:3, train_step: 3625, loss: 0.006099, Train_acc: 1.000000 
Epoch:3, train_step: 3626, loss: 0.029149, Train_acc: 0.984375 
Epoch:3, train_step: 3627, loss: 0.025082, Train_acc: 0.984375 
Epoch:3, train_step: 3628, loss: 0.027112, Train_acc: 0.984375 
Epoch:3, train_step: 3629, loss: 0.005303, Train_acc: 1.000000 
Epoch:3, train_step: 3630, loss: 0.012355, Train_acc: 1.000000 
Epoch:3, train_step: 3631, loss: 0.007410, Train_acc: 1.000000 
Epoch:3, train_step: 3632, loss: 0.018076, Train_acc: 0.984375 
Epoch:3, train_step: 3633, loss: 0.001694, Train_acc: 1.000000 
Epoch:3, train_step: 3634, loss: 0.007466, Train_acc: 1.000000 
Epoch:3, train_step: 3635, loss: 0.005870, Train_acc: 1.000000 
Epoch:3, train_step: 3636, loss: 0.005734, Train_acc: 1.000000 
Epoch:3, train_step: 3637, loss: 0.051995, Train_acc: 0.984375 
Epoch:3, train_step: 3638, loss: 0.027550, Train_acc: 0.984375 
Epoch:3, train_step: 3639, loss: 0.138714, Train_acc: 0.953125 
Epoch:3, train_step: 3640, loss: 0.026317, Train_acc: 0.984375 
Epoch:3, train_step: 3641, loss: 0.001063, Train_acc: 1.000000 
Epoch:3, train_step: 3642, loss: 0.013421, Train_acc: 1.000000 
Epoch:3, train_step: 3643, loss: 0.004253, Train_acc: 1.000000 
Epoch:3, train_step: 3644, loss: 0.005556, Train_acc: 1.000000 
Epoch:3, train_step: 3645, loss: 0.007289, Train_acc: 1.000000 
Epoch:3, train_step: 3646, loss: 0.048016, Train_acc: 0.984375 
Epoch:3, train_step: 3647, loss: 0.011754, Train_acc: 1.000000 
Epoch:3, train_step: 3648, loss: 0.012285, Train_acc: 1.000000 
Epoch:3, train_step: 3649, loss: 0.001486, Train_acc: 1.000000 
Epoch:3, train_step: 3650, loss: 0.055091, Train_acc: 0.953125 
Epoch:3, train_step: 3651, loss: 0.002899, Train_acc: 1.000000 
Epoch:3, train_step: 3652, loss: 0.010292, Train_acc: 1.000000 
Epoch:3, train_step: 3653, loss: 0.004939, Train_acc: 1.000000 
Epoch:3, train_step: 3654, loss: 0.013641, Train_acc: 1.000000 
Epoch:3, train_step: 3655, loss: 0.009982, Train_acc: 1.000000 
Epoch:3, train_step: 3656, loss: 0.027182, Train_acc: 0.984375 
Epoch:3, train_step: 3657, loss: 0.035782, Train_acc: 0.984375 
Epoch:3, train_step: 3658, loss: 0.002601, Train_acc: 1.000000 
Epoch:3, train_step: 3659, loss: 0.027456, Train_acc: 0.984375 
Epoch:3, train_step: 3660, loss: 0.060613, Train_acc: 0.984375 
Epoch:3, train_step: 3661, loss: 0.001391, Train_acc: 1.000000 
Epoch:3, train_step: 3662, loss: 0.005673, Train_acc: 1.000000 
Epoch:3, train_step: 3663, loss: 0.003122, Train_acc: 1.000000 
Epoch:3, train_step: 3664, loss: 0.032174, Train_acc: 0.984375 
Epoch:3, train_step: 3665, loss: 0.008053, Train_acc: 1.000000 
Epoch:3, train_step: 3666, loss: 0.017573, Train_acc: 0.984375 
Epoch:3, train_step: 3667, loss: 0.010104, Train_acc: 1.000000 
Epoch:3, train_step: 3668, loss: 0.010776, Train_acc: 1.000000 
Epoch:3, train_step: 3669, loss: 0.046462, Train_acc: 0.968750 
Epoch:3, train_step: 3670, loss: 0.005224, Train_acc: 1.000000 
Epoch:3, train_step: 3671, loss: 0.001027, Train_acc: 1.000000 
Epoch:3, train_step: 3672, loss: 0.011425, Train_acc: 1.000000 
Epoch:3, train_step: 3673, loss: 0.000882, Train_acc: 1.000000 
Epoch:3, train_step: 3674, loss: 0.005384, Train_acc: 1.000000 
Epoch:3, train_step: 3675, loss: 0.011595, Train_acc: 1.000000 
Epoch:3, train_step: 3676, loss: 0.010826, Train_acc: 1.000000 
Epoch:3, train_step: 3677, loss: 0.049137, Train_acc: 0.984375 
Epoch:3, train_step: 3678, loss: 0.040564, Train_acc: 0.953125 
Epoch:3, train_step: 3679, loss: 0.006912, Train_acc: 1.000000 
Epoch:3, train_step: 3680, loss: 0.047906, Train_acc: 0.984375 
Epoch:3, train_step: 3681, loss: 0.002082, Train_acc: 1.000000 
Epoch:3, train_step: 3682, loss: 0.017243, Train_acc: 0.984375 
Epoch:3, train_step: 3683, loss: 0.006311, Train_acc: 1.000000 
Epoch:3, train_step: 3684, loss: 0.006881, Train_acc: 1.000000 
Epoch:3, train_step: 3685, loss: 0.004866, Train_acc: 1.000000 
Epoch:3, train_step: 3686, loss: 0.006194, Train_acc: 1.000000 
Epoch:3, train_step: 3687, loss: 0.013436, Train_acc: 0.984375 
Epoch:3, train_step: 3688, loss: 0.002076, Train_acc: 1.000000 
Epoch:3, train_step: 3689, loss: 0.061712, Train_acc: 0.984375 
Epoch:3, train_step: 3690, loss: 0.049164, Train_acc: 0.984375 
Epoch:3, train_step: 3691, loss: 0.006980, Train_acc: 1.000000 
Epoch:3, train_step: 3692, loss: 0.005720, Train_acc: 1.000000 
Epoch:3, train_step: 3693, loss: 0.028363, Train_acc: 0.984375 
Epoch:3, train_step: 3694, loss: 0.030432, Train_acc: 0.984375 
Epoch:3, train_step: 3695, loss: 0.000868, Train_acc: 1.000000 
Epoch:3, train_step: 3696, loss: 0.019956, Train_acc: 1.000000 
Epoch:3, train_step: 3697, loss: 0.012755, Train_acc: 0.984375 
Epoch:3, train_step: 3698, loss: 0.006585, Train_acc: 1.000000 
Epoch:3, train_step: 3699, loss: 0.029570, Train_acc: 1.000000 
Epoch:3, train_step: 3700, loss: 0.003910, Train_acc: 1.000000 
Epoch:3, train_step: 3701, loss: 0.026518, Train_acc: 0.984375 
Epoch:3, train_step: 3702, loss: 0.002875, Train_acc: 1.000000 
Epoch:3, train_step: 3703, loss: 0.012902, Train_acc: 1.000000 
Epoch:3, train_step: 3704, loss: 0.000283, Train_acc: 1.000000 
Epoch:3, train_step: 3705, loss: 0.002296, Train_acc: 1.000000 
Epoch:3, train_step: 3706, loss: 0.007121, Train_acc: 1.000000 
Epoch:3, train_step: 3707, loss: 0.006880, Train_acc: 1.000000 
Epoch:3, train_step: 3708, loss: 0.005419, Train_acc: 1.000000 
Epoch:3, train_step: 3709, loss: 0.008283, Train_acc: 1.000000 
Epoch:3, train_step: 3710, loss: 0.032851, Train_acc: 0.984375 
Epoch:3, train_step: 3711, loss: 0.002699, Train_acc: 1.000000 
Epoch:3, train_step: 3712, loss: 0.022338, Train_acc: 0.984375 
Epoch:3, train_step: 3713, loss: 0.015073, Train_acc: 0.984375 
Epoch:3, train_step: 3714, loss: 0.037019, Train_acc: 0.984375 
Epoch:3, train_step: 3715, loss: 0.019223, Train_acc: 1.000000 
Epoch:3, train_step: 3716, loss: 0.005243, Train_acc: 1.000000 
Epoch:3, train_step: 3717, loss: 0.016573, Train_acc: 1.000000 
Epoch:3, train_step: 3718, loss: 0.025970, Train_acc: 0.984375 
Epoch:3, train_step: 3719, loss: 0.044893, Train_acc: 0.984375 
Epoch:3, train_step: 3720, loss: 0.000279, Train_acc: 1.000000 
Epoch:3, train_step: 3721, loss: 0.005862, Train_acc: 1.000000 
Epoch:3, train_step: 3722, loss: 0.000349, Train_acc: 1.000000 
Epoch:3, train_step: 3723, loss: 0.002115, Train_acc: 1.000000 
Epoch:3, train_step: 3724, loss: 0.004473, Train_acc: 1.000000 
Epoch:3, train_step: 3725, loss: 0.001319, Train_acc: 1.000000 
Epoch:3, train_step: 3726, loss: 0.001473, Train_acc: 1.000000 
Epoch:3, train_step: 3727, loss: 0.000576, Train_acc: 1.000000 
Epoch:3, train_step: 3728, loss: 0.013294, Train_acc: 0.984375 
Epoch:3, train_step: 3729, loss: 0.000040, Train_acc: 1.000000 
Epoch:3, train_step: 3730, loss: 0.003680, Train_acc: 1.000000 
Epoch:3, train_step: 3731, loss: 0.004520, Train_acc: 1.000000 
Epoch:3, train_step: 3732, loss: 0.006867, Train_acc: 1.000000 
Epoch:3, train_step: 3733, loss: 0.000084, Train_acc: 1.000000 
Epoch:3, train_step: 3734, loss: 0.001099, Train_acc: 1.000000 
Epoch:3, train_step: 3735, loss: 0.000183, Train_acc: 1.000000 
Epoch:3, train_step: 3736, loss: 0.000252, Train_acc: 1.000000 
Epoch:3, train_step: 3737, loss: 0.000121, Train_acc: 1.000000 
Epoch:3, train_step: 3738, loss: 0.003407, Train_acc: 1.000000 
Epoch:3, train_step: 3739, loss: 0.005592, Train_acc: 1.000000 
Epoch:3, train_step: 3740, loss: 0.006940, Train_acc: 1.000000 
Epoch:3, train_step: 3741, loss: 0.004402, Train_acc: 1.000000 
Epoch:3, train_step: 3742, loss: 0.000218, Train_acc: 1.000000 
Epoch:3, train_step: 3743, loss: 0.001558, Train_acc: 1.000000 
Epoch:3, train_step: 3744, loss: 0.031732, Train_acc: 0.984375 
Epoch:3, train_step: 3745, loss: 0.130916, Train_acc: 0.953125 
Epoch:3, train_step: 3746, loss: 0.001182, Train_acc: 1.000000 
Epoch:3, train_step: 3747, loss: 0.000468, Train_acc: 1.000000 
Epoch:3, train_step: 3748, loss: 0.179660, Train_acc: 0.984375 


Epoch:3, avg_train_loss: 0.022916, avg_train_acc: 0.993013, Test_acc: 0.985877 
Epoch:4, train_step: 3749, loss: 0.009126, Train_acc: 1.000000 
Epoch:4, train_step: 3750, loss: 0.024680, Train_acc: 0.984375 
Epoch:4, train_step: 3751, loss: 0.147683, Train_acc: 0.968750 
Epoch:4, train_step: 3752, loss: 0.023776, Train_acc: 1.000000 
Epoch:4, train_step: 3753, loss: 0.003802, Train_acc: 1.000000 
Epoch:4, train_step: 3754, loss: 0.007601, Train_acc: 1.000000 
Epoch:4, train_step: 3755, loss: 0.042103, Train_acc: 0.984375 
Epoch:4, train_step: 3756, loss: 0.125880, Train_acc: 0.968750 
Epoch:4, train_step: 3757, loss: 0.012667, Train_acc: 1.000000 
Epoch:4, train_step: 3758, loss: 0.052398, Train_acc: 0.968750 
Epoch:4, train_step: 3759, loss: 0.003783, Train_acc: 1.000000 
Epoch:4, train_step: 3760, loss: 0.021817, Train_acc: 0.984375 
Epoch:4, train_step: 3761, loss: 0.058172, Train_acc: 0.968750 
Epoch:4, train_step: 3762, loss: 0.014380, Train_acc: 1.000000 
Epoch:4, train_step: 3763, loss: 0.053778, Train_acc: 0.984375 
Epoch:4, train_step: 3764, loss: 0.053831, Train_acc: 0.968750 
Epoch:4, train_step: 3765, loss: 0.017609, Train_acc: 1.000000 
Epoch:4, train_step: 3766, loss: 0.021377, Train_acc: 1.000000 
Epoch:4, train_step: 3767, loss: 0.025528, Train_acc: 0.984375 
Epoch:4, train_step: 3768, loss: 0.002300, Train_acc: 1.000000 
Epoch:4, train_step: 3769, loss: 0.015291, Train_acc: 1.000000 
Epoch:4, train_step: 3770, loss: 0.067967, Train_acc: 0.968750 
Epoch:4, train_step: 3771, loss: 0.000269, Train_acc: 1.000000 
Epoch:4, train_step: 3772, loss: 0.002990, Train_acc: 1.000000 
Epoch:4, train_step: 3773, loss: 0.004560, Train_acc: 1.000000 
Epoch:4, train_step: 3774, loss: 0.127328, Train_acc: 0.968750 
Epoch:4, train_step: 3775, loss: 0.004151, Train_acc: 1.000000 
Epoch:4, train_step: 3776, loss: 0.021585, Train_acc: 0.984375 
Epoch:4, train_step: 3777, loss: 0.032701, Train_acc: 0.984375 
Epoch:4, train_step: 3778, loss: 0.001470, Train_acc: 1.000000 
Epoch:4, train_step: 3779, loss: 0.015922, Train_acc: 0.984375 
Epoch:4, train_step: 3780, loss: 0.007235, Train_acc: 1.000000 
Epoch:4, train_step: 3781, loss: 0.003630, Train_acc: 1.000000 
Epoch:4, train_step: 3782, loss: 0.003871, Train_acc: 1.000000 
Epoch:4, train_step: 3783, loss: 0.017872, Train_acc: 0.984375 
Epoch:4, train_step: 3784, loss: 0.002064, Train_acc: 1.000000 
Epoch:4, train_step: 3785, loss: 0.001159, Train_acc: 1.000000 
Epoch:4, train_step: 3786, loss: 0.037710, Train_acc: 0.984375 
Epoch:4, train_step: 3787, loss: 0.000869, Train_acc: 1.000000 
Epoch:4, train_step: 3788, loss: 0.018919, Train_acc: 1.000000 
Epoch:4, train_step: 3789, loss: 0.018038, Train_acc: 0.984375 
Epoch:4, train_step: 3790, loss: 0.034950, Train_acc: 0.984375 
Epoch:4, train_step: 3791, loss: 0.121048, Train_acc: 0.953125 
Epoch:4, train_step: 3792, loss: 0.005853, Train_acc: 1.000000 
Epoch:4, train_step: 3793, loss: 0.007758, Train_acc: 1.000000 
Epoch:4, train_step: 3794, loss: 0.009444, Train_acc: 1.000000 
Epoch:4, train_step: 3795, loss: 0.004717, Train_acc: 1.000000 
Epoch:4, train_step: 3796, loss: 0.011254, Train_acc: 1.000000 
Epoch:4, train_step: 3797, loss: 0.004167, Train_acc: 1.000000 
Epoch:4, train_step: 3798, loss: 0.000526, Train_acc: 1.000000 
Epoch:4, train_step: 3799, loss: 0.074317, Train_acc: 0.984375 
Epoch:4, train_step: 3800, loss: 0.016611, Train_acc: 0.984375 
Epoch:4, train_step: 3801, loss: 0.000600, Train_acc: 1.000000 
Epoch:4, train_step: 3802, loss: 0.006287, Train_acc: 1.000000 
Epoch:4, train_step: 3803, loss: 0.010314, Train_acc: 1.000000 
Epoch:4, train_step: 3804, loss: 0.008826, Train_acc: 1.000000 
Epoch:4, train_step: 3805, loss: 0.002595, Train_acc: 1.000000 
Epoch:4, train_step: 3806, loss: 0.202718, Train_acc: 0.953125 
Epoch:4, train_step: 3807, loss: 0.033574, Train_acc: 0.984375 
Epoch:4, train_step: 3808, loss: 0.005158, Train_acc: 1.000000 
Epoch:4, train_step: 3809, loss: 0.003276, Train_acc: 1.000000 
Epoch:4, train_step: 3810, loss: 0.020452, Train_acc: 0.984375 
Epoch:4, train_step: 3811, loss: 0.005761, Train_acc: 1.000000 
Epoch:4, train_step: 3812, loss: 0.003476, Train_acc: 1.000000 
Epoch:4, train_step: 3813, loss: 0.027199, Train_acc: 0.984375 
Epoch:4, train_step: 3814, loss: 0.002377, Train_acc: 1.000000 
Epoch:4, train_step: 3815, loss: 0.009345, Train_acc: 1.000000 
Epoch:4, train_step: 3816, loss: 0.019845, Train_acc: 0.984375 
Epoch:4, train_step: 3817, loss: 0.018391, Train_acc: 0.984375 
Epoch:4, train_step: 3818, loss: 0.068507, Train_acc: 0.984375 
Epoch:4, train_step: 3819, loss: 0.001424, Train_acc: 1.000000 
Epoch:4, train_step: 3820, loss: 0.003551, Train_acc: 1.000000 
Epoch:4, train_step: 3821, loss: 0.011377, Train_acc: 1.000000 
Epoch:4, train_step: 3822, loss: 0.005842, Train_acc: 1.000000 
Epoch:4, train_step: 3823, loss: 0.002848, Train_acc: 1.000000 
Epoch:4, train_step: 3824, loss: 0.008482, Train_acc: 1.000000 
Epoch:4, train_step: 3825, loss: 0.000652, Train_acc: 1.000000 
Epoch:4, train_step: 3826, loss: 0.004725, Train_acc: 1.000000 
Epoch:4, train_step: 3827, loss: 0.020289, Train_acc: 1.000000 
Epoch:4, train_step: 3828, loss: 0.008885, Train_acc: 1.000000 
Epoch:4, train_step: 3829, loss: 0.048011, Train_acc: 0.984375 
Epoch:4, train_step: 3830, loss: 0.001330, Train_acc: 1.000000 
Epoch:4, train_step: 3831, loss: 0.009058, Train_acc: 1.000000 
Epoch:4, train_step: 3832, loss: 0.017301, Train_acc: 1.000000 
Epoch:4, train_step: 3833, loss: 0.013082, Train_acc: 0.984375 
Epoch:4, train_step: 3834, loss: 0.010526, Train_acc: 1.000000 
Epoch:4, train_step: 3835, loss: 0.004610, Train_acc: 1.000000 
Epoch:4, train_step: 3836, loss: 0.002725, Train_acc: 1.000000 
Epoch:4, train_step: 3837, loss: 0.016851, Train_acc: 1.000000 
Epoch:4, train_step: 3838, loss: 0.033385, Train_acc: 0.984375 
Epoch:4, train_step: 3839, loss: 0.068854, Train_acc: 0.984375 
Epoch:4, train_step: 3840, loss: 0.013426, Train_acc: 1.000000 
Epoch:4, train_step: 3841, loss: 0.012987, Train_acc: 1.000000 
Epoch:4, train_step: 3842, loss: 0.002608, Train_acc: 1.000000 
Epoch:4, train_step: 3843, loss: 0.001999, Train_acc: 1.000000 
Epoch:4, train_step: 3844, loss: 0.004619, Train_acc: 1.000000 
Epoch:4, train_step: 3845, loss: 0.001190, Train_acc: 1.000000 
Epoch:4, train_step: 3846, loss: 0.023257, Train_acc: 0.984375 
Epoch:4, train_step: 3847, loss: 0.010008, Train_acc: 1.000000 
Epoch:4, train_step: 3848, loss: 0.031431, Train_acc: 0.984375 
Epoch:4, train_step: 3849, loss: 0.018297, Train_acc: 0.984375 
Epoch:4, train_step: 3850, loss: 0.002126, Train_acc: 1.000000 
Epoch:4, train_step: 3851, loss: 0.004379, Train_acc: 1.000000 
Epoch:4, train_step: 3852, loss: 0.003585, Train_acc: 1.000000 
Epoch:4, train_step: 3853, loss: 0.019178, Train_acc: 0.984375 
Epoch:4, train_step: 3854, loss: 0.000451, Train_acc: 1.000000 
Epoch:4, train_step: 3855, loss: 0.006900, Train_acc: 1.000000 
Epoch:4, train_step: 3856, loss: 0.025544, Train_acc: 1.000000 
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Epoch:4, train_step: 4419, loss: 0.022650, Train_acc: 0.984375 
Epoch:4, train_step: 4420, loss: 0.015971, Train_acc: 0.984375 
Epoch:4, train_step: 4421, loss: 0.018231, Train_acc: 1.000000 
Epoch:4, train_step: 4422, loss: 0.142656, Train_acc: 0.984375 
Epoch:4, train_step: 4423, loss: 0.010590, Train_acc: 1.000000 
Epoch:4, train_step: 4424, loss: 0.003193, Train_acc: 1.000000 
Epoch:4, train_step: 4425, loss: 0.006119, Train_acc: 1.000000 
Epoch:4, train_step: 4426, loss: 0.004708, Train_acc: 1.000000 
Epoch:4, train_step: 4427, loss: 0.140235, Train_acc: 0.984375 
Epoch:4, train_step: 4428, loss: 0.019421, Train_acc: 0.984375 
Epoch:4, train_step: 4429, loss: 0.013262, Train_acc: 1.000000 
Epoch:4, train_step: 4430, loss: 0.006697, Train_acc: 1.000000 
Epoch:4, train_step: 4431, loss: 0.055443, Train_acc: 0.984375 
Epoch:4, train_step: 4432, loss: 0.001742, Train_acc: 1.000000 
Epoch:4, train_step: 4433, loss: 0.002174, Train_acc: 1.000000 
Epoch:4, train_step: 4434, loss: 0.034062, Train_acc: 0.984375 
Epoch:4, train_step: 4435, loss: 0.036806, Train_acc: 0.984375 
Epoch:4, train_step: 4436, loss: 0.004285, Train_acc: 1.000000 
Epoch:4, train_step: 4437, loss: 0.054347, Train_acc: 0.968750 
Epoch:4, train_step: 4438, loss: 0.004131, Train_acc: 1.000000 
Epoch:4, train_step: 4439, loss: 0.001479, Train_acc: 1.000000 
Epoch:4, train_step: 4440, loss: 0.009222, Train_acc: 1.000000 
Epoch:4, train_step: 4441, loss: 0.003496, Train_acc: 1.000000 
Epoch:4, train_step: 4442, loss: 0.002545, Train_acc: 1.000000 
Epoch:4, train_step: 4443, loss: 0.007368, Train_acc: 1.000000 
Epoch:4, train_step: 4444, loss: 0.004074, Train_acc: 1.000000 
Epoch:4, train_step: 4445, loss: 0.000452, Train_acc: 1.000000 
Epoch:4, train_step: 4446, loss: 0.005274, Train_acc: 1.000000 
Epoch:4, train_step: 4447, loss: 0.006994, Train_acc: 1.000000 
Epoch:4, train_step: 4448, loss: 0.012617, Train_acc: 1.000000 
Epoch:4, train_step: 4449, loss: 0.010407, Train_acc: 1.000000 
Epoch:4, train_step: 4450, loss: 0.002444, Train_acc: 1.000000 
Epoch:4, train_step: 4451, loss: 0.001839, Train_acc: 1.000000 
Epoch:4, train_step: 4452, loss: 0.008537, Train_acc: 1.000000 
Epoch:4, train_step: 4453, loss: 0.001095, Train_acc: 1.000000 
Epoch:4, train_step: 4454, loss: 0.037107, Train_acc: 0.984375 
Epoch:4, train_step: 4455, loss: 0.001882, Train_acc: 1.000000 
Epoch:4, train_step: 4456, loss: 0.004659, Train_acc: 1.000000 
Epoch:4, train_step: 4457, loss: 0.093999, Train_acc: 0.984375 
Epoch:4, train_step: 4458, loss: 0.002812, Train_acc: 1.000000 
Epoch:4, train_step: 4459, loss: 0.002653, Train_acc: 1.000000 
Epoch:4, train_step: 4460, loss: 0.019578, Train_acc: 0.984375 
Epoch:4, train_step: 4461, loss: 0.032920, Train_acc: 0.984375 
Epoch:4, train_step: 4462, loss: 0.011780, Train_acc: 1.000000 
Epoch:4, train_step: 4463, loss: 0.000692, Train_acc: 1.000000 
Epoch:4, train_step: 4464, loss: 0.011267, Train_acc: 1.000000 
Epoch:4, train_step: 4465, loss: 0.037887, Train_acc: 0.984375 
Epoch:4, train_step: 4466, loss: 0.005328, Train_acc: 1.000000 
Epoch:4, train_step: 4467, loss: 0.017671, Train_acc: 0.984375 
Epoch:4, train_step: 4468, loss: 0.066780, Train_acc: 0.968750 
Epoch:4, train_step: 4469, loss: 0.004378, Train_acc: 1.000000 
Epoch:4, train_step: 4470, loss: 0.006495, Train_acc: 1.000000 
Epoch:4, train_step: 4471, loss: 0.025913, Train_acc: 1.000000 
Epoch:4, train_step: 4472, loss: 0.008711, Train_acc: 1.000000 
Epoch:4, train_step: 4473, loss: 0.007264, Train_acc: 1.000000 
Epoch:4, train_step: 4474, loss: 0.006384, Train_acc: 1.000000 
Epoch:4, train_step: 4475, loss: 0.001973, Train_acc: 1.000000 
Epoch:4, train_step: 4476, loss: 0.004236, Train_acc: 1.000000 
Epoch:4, train_step: 4477, loss: 0.006345, Train_acc: 1.000000 
Epoch:4, train_step: 4478, loss: 0.004569, Train_acc: 1.000000 
Epoch:4, train_step: 4479, loss: 0.015853, Train_acc: 0.984375 
Epoch:4, train_step: 4480, loss: 0.005192, Train_acc: 1.000000 
Epoch:4, train_step: 4481, loss: 0.063347, Train_acc: 0.984375 
Epoch:4, train_step: 4482, loss: 0.001911, Train_acc: 1.000000 
Epoch:4, train_step: 4483, loss: 0.006620, Train_acc: 1.000000 
Epoch:4, train_step: 4484, loss: 0.002039, Train_acc: 1.000000 
Epoch:4, train_step: 4485, loss: 0.042262, Train_acc: 0.984375 
Epoch:4, train_step: 4486, loss: 0.005348, Train_acc: 1.000000 
Epoch:4, train_step: 4487, loss: 0.026577, Train_acc: 1.000000 
Epoch:4, train_step: 4488, loss: 0.013207, Train_acc: 1.000000 
Epoch:4, train_step: 4489, loss: 0.018635, Train_acc: 0.984375 
Epoch:4, train_step: 4490, loss: 0.026928, Train_acc: 0.984375 
Epoch:4, train_step: 4491, loss: 0.001754, Train_acc: 1.000000 
Epoch:4, train_step: 4492, loss: 0.040337, Train_acc: 0.984375 
Epoch:4, train_step: 4493, loss: 0.002657, Train_acc: 1.000000 
Epoch:4, train_step: 4494, loss: 0.027132, Train_acc: 0.984375 
Epoch:4, train_step: 4495, loss: 0.013280, Train_acc: 1.000000 
Epoch:4, train_step: 4496, loss: 0.002108, Train_acc: 1.000000 
Epoch:4, train_step: 4497, loss: 0.013477, Train_acc: 1.000000 
Epoch:4, train_step: 4498, loss: 0.014099, Train_acc: 1.000000 
Epoch:4, train_step: 4499, loss: 0.021984, Train_acc: 0.984375 
Epoch:4, train_step: 4500, loss: 0.043356, Train_acc: 0.968750 
Epoch:4, train_step: 4501, loss: 0.000847, Train_acc: 1.000000 
Epoch:4, train_step: 4502, loss: 0.007909, Train_acc: 1.000000 
Epoch:4, train_step: 4503, loss: 0.009316, Train_acc: 1.000000 
Epoch:4, train_step: 4504, loss: 0.020021, Train_acc: 0.984375 
Epoch:4, train_step: 4505, loss: 0.001537, Train_acc: 1.000000 
Epoch:4, train_step: 4506, loss: 0.031299, Train_acc: 0.984375 
Epoch:4, train_step: 4507, loss: 0.004188, Train_acc: 1.000000 
Epoch:4, train_step: 4508, loss: 0.015510, Train_acc: 1.000000 
Epoch:4, train_step: 4509, loss: 0.032228, Train_acc: 0.984375 
Epoch:4, train_step: 4510, loss: 0.038235, Train_acc: 0.984375 
Epoch:4, train_step: 4511, loss: 0.006475, Train_acc: 1.000000 
Epoch:4, train_step: 4512, loss: 0.008101, Train_acc: 1.000000 
Epoch:4, train_step: 4513, loss: 0.172209, Train_acc: 0.984375 
Epoch:4, train_step: 4514, loss: 0.053055, Train_acc: 0.968750 
Epoch:4, train_step: 4515, loss: 0.098359, Train_acc: 0.968750 
Epoch:4, train_step: 4516, loss: 0.000864, Train_acc: 1.000000 
Epoch:4, train_step: 4517, loss: 0.099131, Train_acc: 0.968750 
Epoch:4, train_step: 4518, loss: 0.002585, Train_acc: 1.000000 
Epoch:4, train_step: 4519, loss: 0.002596, Train_acc: 1.000000 
Epoch:4, train_step: 4520, loss: 0.007240, Train_acc: 1.000000 
Epoch:4, train_step: 4521, loss: 0.032530, Train_acc: 0.968750 
Epoch:4, train_step: 4522, loss: 0.032581, Train_acc: 0.984375 
Epoch:4, train_step: 4523, loss: 0.005516, Train_acc: 1.000000 
Epoch:4, train_step: 4524, loss: 0.004626, Train_acc: 1.000000 
Epoch:4, train_step: 4525, loss: 0.002067, Train_acc: 1.000000 
Epoch:4, train_step: 4526, loss: 0.007212, Train_acc: 1.000000 
Epoch:4, train_step: 4527, loss: 0.004827, Train_acc: 1.000000 
Epoch:4, train_step: 4528, loss: 0.025258, Train_acc: 0.984375 
Epoch:4, train_step: 4529, loss: 0.003282, Train_acc: 1.000000 
Epoch:4, train_step: 4530, loss: 0.003226, Train_acc: 1.000000 
Epoch:4, train_step: 4531, loss: 0.004611, Train_acc: 1.000000 
Epoch:4, train_step: 4532, loss: 0.002960, Train_acc: 1.000000 
Epoch:4, train_step: 4533, loss: 0.001629, Train_acc: 1.000000 
Epoch:4, train_step: 4534, loss: 0.000260, Train_acc: 1.000000 
Epoch:4, train_step: 4535, loss: 0.003174, Train_acc: 1.000000 
Epoch:4, train_step: 4536, loss: 0.010497, Train_acc: 1.000000 
Epoch:4, train_step: 4537, loss: 0.053527, Train_acc: 0.984375 
Epoch:4, train_step: 4538, loss: 0.004257, Train_acc: 1.000000 
Epoch:4, train_step: 4539, loss: 0.017497, Train_acc: 1.000000 
Epoch:4, train_step: 4540, loss: 0.000202, Train_acc: 1.000000 
Epoch:4, train_step: 4541, loss: 0.004066, Train_acc: 1.000000 
Epoch:4, train_step: 4542, loss: 0.000568, Train_acc: 1.000000 
Epoch:4, train_step: 4543, loss: 0.001151, Train_acc: 1.000000 
Epoch:4, train_step: 4544, loss: 0.016735, Train_acc: 1.000000 
Epoch:4, train_step: 4545, loss: 0.016120, Train_acc: 1.000000 
Epoch:4, train_step: 4546, loss: 0.002930, Train_acc: 1.000000 
Epoch:4, train_step: 4547, loss: 0.010299, Train_acc: 1.000000 
Epoch:4, train_step: 4548, loss: 0.001160, Train_acc: 1.000000 
Epoch:4, train_step: 4549, loss: 0.019655, Train_acc: 1.000000 
Epoch:4, train_step: 4550, loss: 0.006685, Train_acc: 1.000000 
Epoch:4, train_step: 4551, loss: 0.002230, Train_acc: 1.000000 
Epoch:4, train_step: 4552, loss: 0.004235, Train_acc: 1.000000 
Epoch:4, train_step: 4553, loss: 0.005311, Train_acc: 1.000000 
Epoch:4, train_step: 4554, loss: 0.011384, Train_acc: 1.000000 
Epoch:4, train_step: 4555, loss: 0.013399, Train_acc: 1.000000 
Epoch:4, train_step: 4556, loss: 0.018525, Train_acc: 1.000000 
Epoch:4, train_step: 4557, loss: 0.005084, Train_acc: 1.000000 
Epoch:4, train_step: 4558, loss: 0.004482, Train_acc: 1.000000 
Epoch:4, train_step: 4559, loss: 0.000238, Train_acc: 1.000000 
Epoch:4, train_step: 4560, loss: 0.125593, Train_acc: 0.984375 
Epoch:4, train_step: 4561, loss: 0.030978, Train_acc: 1.000000 
Epoch:4, train_step: 4562, loss: 0.002856, Train_acc: 1.000000 
Epoch:4, train_step: 4563, loss: 0.015316, Train_acc: 1.000000 
Epoch:4, train_step: 4564, loss: 0.003299, Train_acc: 1.000000 
Epoch:4, train_step: 4565, loss: 0.005323, Train_acc: 1.000000 
Epoch:4, train_step: 4566, loss: 0.004487, Train_acc: 1.000000 
Epoch:4, train_step: 4567, loss: 0.002876, Train_acc: 1.000000 
Epoch:4, train_step: 4568, loss: 0.003537, Train_acc: 1.000000 
Epoch:4, train_step: 4569, loss: 0.003676, Train_acc: 1.000000 
Epoch:4, train_step: 4570, loss: 0.001119, Train_acc: 1.000000 
Epoch:4, train_step: 4571, loss: 0.000599, Train_acc: 1.000000 
Epoch:4, train_step: 4572, loss: 0.001054, Train_acc: 1.000000 
Epoch:4, train_step: 4573, loss: 0.005624, Train_acc: 1.000000 
Epoch:4, train_step: 4574, loss: 0.017939, Train_acc: 1.000000 
Epoch:4, train_step: 4575, loss: 0.028901, Train_acc: 0.984375 
Epoch:4, train_step: 4576, loss: 0.113929, Train_acc: 0.984375 
Epoch:4, train_step: 4577, loss: 0.001556, Train_acc: 1.000000 
Epoch:4, train_step: 4578, loss: 0.021931, Train_acc: 0.984375 
Epoch:4, train_step: 4579, loss: 0.005304, Train_acc: 1.000000 
Epoch:4, train_step: 4580, loss: 0.001043, Train_acc: 1.000000 
Epoch:4, train_step: 4581, loss: 0.000678, Train_acc: 1.000000 
Epoch:4, train_step: 4582, loss: 0.000517, Train_acc: 1.000000 
Epoch:4, train_step: 4583, loss: 0.062325, Train_acc: 0.984375 
Epoch:4, train_step: 4584, loss: 0.005431, Train_acc: 1.000000 
Epoch:4, train_step: 4585, loss: 0.001240, Train_acc: 1.000000 
Epoch:4, train_step: 4586, loss: 0.000731, Train_acc: 1.000000 
Epoch:4, train_step: 4587, loss: 0.042454, Train_acc: 0.968750 
Epoch:4, train_step: 4588, loss: 0.013803, Train_acc: 0.984375 
Epoch:4, train_step: 4589, loss: 0.005095, Train_acc: 1.000000 
Epoch:4, train_step: 4590, loss: 0.009731, Train_acc: 1.000000 
Epoch:4, train_step: 4591, loss: 0.003930, Train_acc: 1.000000 
Epoch:4, train_step: 4592, loss: 0.006254, Train_acc: 1.000000 
Epoch:4, train_step: 4593, loss: 0.019605, Train_acc: 0.984375 
Epoch:4, train_step: 4594, loss: 0.006940, Train_acc: 1.000000 
Epoch:4, train_step: 4595, loss: 0.001667, Train_acc: 1.000000 
Epoch:4, train_step: 4596, loss: 0.010604, Train_acc: 1.000000 
Epoch:4, train_step: 4597, loss: 0.003742, Train_acc: 1.000000 
Epoch:4, train_step: 4598, loss: 0.002476, Train_acc: 1.000000 
Epoch:4, train_step: 4599, loss: 0.061072, Train_acc: 0.984375 
Epoch:4, train_step: 4600, loss: 0.006573, Train_acc: 1.000000 
Epoch:4, train_step: 4601, loss: 0.028051, Train_acc: 0.984375 
Epoch:4, train_step: 4602, loss: 0.010519, Train_acc: 1.000000 
Epoch:4, train_step: 4603, loss: 0.001819, Train_acc: 1.000000 
Epoch:4, train_step: 4604, loss: 0.011131, Train_acc: 1.000000 
Epoch:4, train_step: 4605, loss: 0.015042, Train_acc: 1.000000 
Epoch:4, train_step: 4606, loss: 0.025956, Train_acc: 1.000000 
Epoch:4, train_step: 4607, loss: 0.005424, Train_acc: 1.000000 
Epoch:4, train_step: 4608, loss: 0.000552, Train_acc: 1.000000 
Epoch:4, train_step: 4609, loss: 0.001574, Train_acc: 1.000000 
Epoch:4, train_step: 4610, loss: 0.002414, Train_acc: 1.000000 
Epoch:4, train_step: 4611, loss: 0.058362, Train_acc: 0.984375 
Epoch:4, train_step: 4612, loss: 0.015419, Train_acc: 1.000000 
Epoch:4, train_step: 4613, loss: 0.045299, Train_acc: 0.968750 
Epoch:4, train_step: 4614, loss: 0.036246, Train_acc: 0.984375 
Epoch:4, train_step: 4615, loss: 0.014974, Train_acc: 0.984375 
Epoch:4, train_step: 4616, loss: 0.005563, Train_acc: 1.000000 
Epoch:4, train_step: 4617, loss: 0.063160, Train_acc: 0.984375 
Epoch:4, train_step: 4618, loss: 0.003169, Train_acc: 1.000000 
Epoch:4, train_step: 4619, loss: 0.013708, Train_acc: 1.000000 
Epoch:4, train_step: 4620, loss: 0.004433, Train_acc: 1.000000 
Epoch:4, train_step: 4621, loss: 0.018232, Train_acc: 1.000000 
Epoch:4, train_step: 4622, loss: 0.004088, Train_acc: 1.000000 
Epoch:4, train_step: 4623, loss: 0.004904, Train_acc: 1.000000 
Epoch:4, train_step: 4624, loss: 0.014987, Train_acc: 1.000000 
Epoch:4, train_step: 4625, loss: 0.003224, Train_acc: 1.000000 
Epoch:4, train_step: 4626, loss: 0.065013, Train_acc: 0.984375 
Epoch:4, train_step: 4627, loss: 0.015311, Train_acc: 0.984375 
Epoch:4, train_step: 4628, loss: 0.008445, Train_acc: 1.000000 
Epoch:4, train_step: 4629, loss: 0.006273, Train_acc: 1.000000 
Epoch:4, train_step: 4630, loss: 0.015948, Train_acc: 1.000000 
Epoch:4, train_step: 4631, loss: 0.013378, Train_acc: 1.000000 
Epoch:4, train_step: 4632, loss: 0.003214, Train_acc: 1.000000 
Epoch:4, train_step: 4633, loss: 0.005450, Train_acc: 1.000000 
Epoch:4, train_step: 4634, loss: 0.006493, Train_acc: 1.000000 
Epoch:4, train_step: 4635, loss: 0.001834, Train_acc: 1.000000 
Epoch:4, train_step: 4636, loss: 0.024179, Train_acc: 0.984375 
Epoch:4, train_step: 4637, loss: 0.022755, Train_acc: 0.984375 
Epoch:4, train_step: 4638, loss: 0.000265, Train_acc: 1.000000 
Epoch:4, train_step: 4639, loss: 0.002019, Train_acc: 1.000000 
Epoch:4, train_step: 4640, loss: 0.008307, Train_acc: 1.000000 
Epoch:4, train_step: 4641, loss: 0.000571, Train_acc: 1.000000 
Epoch:4, train_step: 4642, loss: 0.002582, Train_acc: 1.000000 
Epoch:4, train_step: 4643, loss: 0.043802, Train_acc: 0.984375 
Epoch:4, train_step: 4644, loss: 0.005830, Train_acc: 1.000000 
Epoch:4, train_step: 4645, loss: 0.032212, Train_acc: 0.984375 
Epoch:4, train_step: 4646, loss: 0.009485, Train_acc: 1.000000 
Epoch:4, train_step: 4647, loss: 0.024277, Train_acc: 0.984375 
Epoch:4, train_step: 4648, loss: 0.001220, Train_acc: 1.000000 
Epoch:4, train_step: 4649, loss: 0.009742, Train_acc: 1.000000 
Epoch:4, train_step: 4650, loss: 0.003131, Train_acc: 1.000000 
Epoch:4, train_step: 4651, loss: 0.076755, Train_acc: 0.984375 
Epoch:4, train_step: 4652, loss: 0.041819, Train_acc: 0.968750 
Epoch:4, train_step: 4653, loss: 0.007329, Train_acc: 1.000000 
Epoch:4, train_step: 4654, loss: 0.024101, Train_acc: 1.000000 
Epoch:4, train_step: 4655, loss: 0.029153, Train_acc: 0.984375 
Epoch:4, train_step: 4656, loss: 0.065994, Train_acc: 0.984375 
Epoch:4, train_step: 4657, loss: 0.000176, Train_acc: 1.000000 
Epoch:4, train_step: 4658, loss: 0.001461, Train_acc: 1.000000 
Epoch:4, train_step: 4659, loss: 0.000234, Train_acc: 1.000000 
Epoch:4, train_step: 4660, loss: 0.002864, Train_acc: 1.000000 
Epoch:4, train_step: 4661, loss: 0.001337, Train_acc: 1.000000 
Epoch:4, train_step: 4662, loss: 0.000416, Train_acc: 1.000000 
Epoch:4, train_step: 4663, loss: 0.000935, Train_acc: 1.000000 
Epoch:4, train_step: 4664, loss: 0.001171, Train_acc: 1.000000 
Epoch:4, train_step: 4665, loss: 0.024274, Train_acc: 0.984375 
Epoch:4, train_step: 4666, loss: 0.000032, Train_acc: 1.000000 
Epoch:4, train_step: 4667, loss: 0.002931, Train_acc: 1.000000 
Epoch:4, train_step: 4668, loss: 0.011314, Train_acc: 1.000000 
Epoch:4, train_step: 4669, loss: 0.001236, Train_acc: 1.000000 
Epoch:4, train_step: 4670, loss: 0.000085, Train_acc: 1.000000 
Epoch:4, train_step: 4671, loss: 0.000431, Train_acc: 1.000000 
Epoch:4, train_step: 4672, loss: 0.000504, Train_acc: 1.000000 
Epoch:4, train_step: 4673, loss: 0.000061, Train_acc: 1.000000 
Epoch:4, train_step: 4674, loss: 0.000257, Train_acc: 1.000000 
Epoch:4, train_step: 4675, loss: 0.006405, Train_acc: 1.000000 
Epoch:4, train_step: 4676, loss: 0.008080, Train_acc: 1.000000 
Epoch:4, train_step: 4677, loss: 0.000531, Train_acc: 1.000000 
Epoch:4, train_step: 4678, loss: 0.020647, Train_acc: 0.984375 
Epoch:4, train_step: 4679, loss: 0.000083, Train_acc: 1.000000 
Epoch:4, train_step: 4680, loss: 0.001731, Train_acc: 1.000000 
Epoch:4, train_step: 4681, loss: 0.003079, Train_acc: 1.000000 
Epoch:4, train_step: 4682, loss: 0.095505, Train_acc: 0.968750 
Epoch:4, train_step: 4683, loss: 0.002659, Train_acc: 1.000000 
Epoch:4, train_step: 4684, loss: 0.000122, Train_acc: 1.000000 
Epoch:4, train_step: 4685, loss: 0.185106, Train_acc: 0.984375 


Epoch:4, avg_train_loss: 0.017215, avg_train_acc: 0.994731, Test_acc: 0.985677 


        

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