提示：cnews数据集可在网上自行下载
#模块架构
'''
@author:/huangshaobo
1 构建计算图-LSTM模型embedding层LSTM层FC层train_op+loss
2 训练流程代码
3 数据集封装代码api: next_batch(batch_size)
4 词表封装代码api: sentence_to_id(text_sentence):句子->id
5 类别封装代码api: category_to_id(category)
'''import tensorflow as tf
import numpy as np
import math
import os
import sys#声明在notebook中打印日志
tf.logging.set_verbosity(tf.logging.INFO)def get_default_params():return tf.contrib.training.HParams(num_embedding_size = 16,#32 #each Vec's lengthnum_timesteps = 50,#600 #LSTM步长num_lstm_nodes = [32, 32], #[64,64] #每一层的size32 ，2层num_lstm_layers = 2,#层数num_fc_nodes = 32, #64 #全连接层神经单元数batch_size = 100,clip_lstm_grads = 1.0,#控制LSTM梯度大小learning_rate = 0.001,num_word_threshold = 10)
hps = get_default_params()train_file = 'cnews.train.seg.txt'
val_file = 'cnews.val.seg.txt'
test_file = 'cnews.test.seg.txt'
vocab_file = 'cnews.vocab.txt'
category_file = 'cnews.category.txt'
output_folder = 'run_text_rnn'if not os.path.exists(output_folder):os.mkdir(output_folder)#test
print(hps.num_word_threshold)
打印出结果：10
#词表封装模块
class Vocab:def __init__(self, filename, num_word_threshold):self._word_to_id = {}self._unk = -1self._num_word_threshold = num_word_thresholdself._read_dict(filename)#读取文件每一行，赋值一个iddef _read_dict(self, filename):with open(filename, 'r', encoding='UTF-8') as f:lines = f.readlines()for line in lines:word, frequency = line.strip('\r\n').split('\t')frequency = int(frequency)if frequency < self._num_word_threshold:continueidx = len(self._word_to_id)if word == '<UNK>':self._unk = idxself._word_to_id[word] = idxdef word_to_id(self, word):return self._word_to_id.get(word, self._unk)@propertydef unk(self):return self._unkdef size(self):return len(self._word_to_id)def sentence_to_id(self, sentence):word_ids = [self.word_to_id(cur_word) \for cur_word in sentence.split()]return word_ids
#test
vocab = Vocab(vocab_file, hps.num_word_threshold)
vocab_size = vocab.size()
tf.logging.info('vocab_size: %d' % vocab_size)test_str = '的 在 了 是'
print(vocab.sentence_to_id(test_str))
INFO:tensorflow:vocab_size: 77323
[2, 4, 6, 7]
#类别封装模块
class CategeoryDict:def __init__(self, filename):self._categeory_to_id = {}with open(filename, 'r', encoding='UTF-8') as f:lines = f.readlines()for line in lines:categeory = line.strip('\r\n')idx = len(self._categeory_to_id)self._categeory_to_id[categeory] = idxdef categeory_to_id(self, categeory):if not categeory in self._categeory_to_id:raise Exception("%s is not in our categeory list" % categeory_name)return self._categeory_to_id[categeory]def size(self):return len(self._categeory_to_id)
#test
categeory_vocab = CategeoryDict(category_file)
test_str= '娱乐'
num_classes = categeory_vocab.size()
tf.logging.info('label: %s,id: %d' % (test_str, categeory_vocab.categeory_to_id(test_str)))
tf.logging.info('num_classes: %d' % num_classes)
INFO:tensorflow? 娱乐,id: 1
INFO:tensorflow:num_classes: 10
#数据集封装模块
class TextDataSet:def __init__(self, filename, vocab, categeory_vocab, num_timesteps):self._vocab = vocabself._categeory_vocab = categeory_vocabself._num_timesteps =num_timestepsself._inputs = []#matrxiself._outputs = []#Vecself._indicator = 0self._parse_file(filename)def _parse_file(self, filename):tf.logging.info('Loading data from %s' , filename)with open(filename,'r',encoding='UTF-8') as f:lines = f.readlines()category_dict = {}for line in lines:label,content = line.strip('\r\n').split('\t') id_label = self._categeory_vocab.categeory_to_id(label)id_words = self._vocab.sentence_to_id(content)id_words = id_words[0:self._num_timesteps]padding_num = self._num_timesteps - len(id_words)id_words = id_words + [self._vocab.unk for i in range(padding_num)]self._inputs.append(id_words)self._outputs.append(id_label)#转换为numpy arrayself._inputs = np.asarray(self._inputs, dtype=np.int32)self._outputs = np.asarray(self._outputs, dtype=np.int32)self._random_shuffle()def _random_shuffle(self):p = np.random.permutation(len(self._inputs))self._inputs = self._inputs[p]self._outputs = self._outputs[p]def next_batch(self,batch_size):end_indicator = self._indicator + batch_sizeif end_indicator > len(self._inputs):self._random_shuffle()self._indicator = 0end_indicator = batch_sizeif end_indicator > len(self._inputs):raise Exception("batch_size: %d is too large" % batch_size)batch_inputs = self._inputs[self._indicator:end_indicator]batch_outputs = self._outputs[self._indicator:end_indicator]self._indicator = end_indicatorreturn batch_inputs, batch_outputs#test
train_dataset = TextDataSet(train_file, vocab, categeory_vocab, hps.num_timesteps)
val_dataset = TextDataSet(val_file, vocab, categeory_vocab, hps.num_timesteps)
test_dataset = TextDataSet(test_file, vocab, categeory_vocab, hps.num_timesteps)print(train_dataset.next_batch(2))
print(val_dataset.next_batch(2))
print(test_dataset.next_batch(2))
INFO:tensorflow:Loading data from cnews.train.seg.txt
INFO:tensorflow:Loading data from cnews.val.seg.txt
INFO:tensorflow:Loading data from cnews.test.seg.txt
(array([[ 128, 14848, 11, 37, 1575, 1755, 470, 75585, 14123,
1, 37, 2, 1755, 1575, 1071, 7958, 13470, 3,
261, 27, 1, 348, 1762, 255, 863, 293, 1575,
381, 6, 7160, 1, 32, 310, 11870, 1, 226,
27, 667, 539, 122, 6787, 2, 3332, 22879, 3,
268, 225, 5961, 9578, 1170],
[45395, 37, 8057, 5227, 87, 11, 13, 7105, 2,
4060, 17, 467, 16, 4, 5533, 4478, 7105, 2,
4060, 11, 25120, 2349, 11, 0, 5, 40814, 114,
0, 1079, 11, 191, 1382, 29, 8208, 2, 4060,
1, 25120, 2, 1505, 4, 37, 2451, 16530, 1,
7810, 4194, 40, 6958, 40]]), array([4, 4]))
(array([[ 1243, 623, 9001, 70, 27738, 74690, 11411, 1141, 23344,
61, 177, 4, 1243, 623, 90, 8, 7509, 9001,
9, 1, 2889, 9098, 8, 74690, 9, 3, 11564,
153, 11499, 1, 11207, 59713, 0, 2, 4957, 772,
29521, 282, 1, 55, 9098, 2, 9001, 70, 1760,
7, 17886, 2, 1, 457],
[ 467, 11, 12987, 104, 9659, 50830, 1039, 0, 24,
1378, 2320, 11, 22177, 3753, 1221, 235, 571, 483,
321, 50, 4, 324, 20038, 25, 254, 106, 638,
1047, 13465, 254, 53153, 2228, 1, 1140, 7, 26,
2786, 2228, 14316, 2, 12481, 1, 53, 25, 54,
3817, 87, 1042, 2, 638]]), array([2, 5]))
(array([[ 0, 4590, 1307, 1534, 172, 4780, 25640, 16712, 59,
4, 1005, 11305, 2, 37, 155, 1307, 217, 335,
396, 32, 1789, 9567, 29, 1, 0, 4590, 191,
1307, 1534, 17, 1607, 282, 8, 0, 9, 16,
512, 494, 1055, 4007, 3, 795, 44, 1, 0,
4590, 1613, 19384, 0, 173],
[ 261, 27, 130, 23, 1114, 10742, 1995, 8987, 2170,
2182, 17, 36349, 0, 978, 16, 0, 4438, 0,
0, 0, 0, 18606, 0, 10782, 12732, 17260, 0,
0, 1455, 0, 0, 0, 14071, 0, 0, 0,
61689, 16564, 65709, 0, 25097, 4129, 23181, 0, 1455,
1981, 0, 200, 2125, 0]]), array([2, 4]))
#计算图构建模块
def create_model(hps, vocab_size, num_classes):num_timesteps = hps.num_timestepsbatch_size = hps.batch_sizeinputs = tf.placeholder(tf.int32, (batch_size, num_timesteps))outputs = tf.placeholder(tf.int32, (batch_size,))#随机失活剩下的神经单元 keep_prob = 1-dropoutkeep_prob = tf.placeholder(tf.float32, name= 'keep_prob')#save training_stepglobal_step = tf.Variable(tf.zeros([], tf.int64), name='global_step',trainable=False)'''embedding层搭建'''#定义初始化函数[-1,1]embedding_initializer = tf.random_uniform_initializer(-1.0, 1.0)with tf.variable_scope('embedding', initializer=embedding_initializer):embeddings = tf.get_variable('embedding',[vocab_size, hps.num_embedding_size],tf.float32)embed_inputs = tf.nn.embedding_lookup(embeddings, inputs)#change inputs to embedding'''LSTM层搭建'''scale = (1.0 / math.sqrt(hps.num_embedding_size+hps.num_lstm_nodes[-1])) * 3.0lstm_init = tf.random_uniform_initializer(-scale, scale)def _generate_params_for_lstm_cell(x_size, h_size, bias_size):x_w = tf.get_variable('x_weights', x_size)h_w = tf.get_variable('h_weights', h_size)b = tf.get_variable('biases', bias_size, initializer=tf.constant_initializer(0.0))return x_w, h_w, bwith tf.variable_scope('lstm_nn', initializer=lstm_init):with tf.variable_scope('inputs'):ix, ih, ib = _generate_params_for_lstm_cell(x_size=[hps.num_embedding_size, hps.num_lstm_nodes[0]],h_size=[hps.num_lstm_nodes[0], hps.num_lstm_nodes[0]],bias_size=[1, hps.num_lstm_nodes[0]])with tf.variable_scope('outputs'):ox, oh, ob = _generate_params_for_lstm_cell(x_size=[hps.num_embedding_size, hps.num_lstm_nodes[0]],h_size=[hps.num_lstm_nodes[0], hps.num_lstm_nodes[0]],bias_size=[1, hps.num_lstm_nodes[0]])with tf.variable_scope('forget'):fx, fh, fb = _generate_params_for_lstm_cell(x_size=[hps.num_embedding_size, hps.num_lstm_nodes[0]],h_size=[hps.num_lstm_nodes[0], hps.num_lstm_nodes[0]],bias_size=[1, hps.num_lstm_nodes[0]])with tf.variable_scope('memory'):cx, ch, cb = _generate_params_for_lstm_cell(x_size=[hps.num_embedding_size, hps.num_lstm_nodes[0]],h_size=[hps.num_lstm_nodes[0], hps.num_lstm_nodes[0]],bias_size=[1, hps.num_lstm_nodes[0]])state = tf.Variable(tf.zeros([batch_size, hps.num_lstm_nodes[0]]),trainable=False)h = tf.Variable(tf.zeros([batch_size, hps.num_lstm_nodes[0]]),trainable=False)for i in range(num_timesteps):embed_input = embed_inputs[:, i, :]embed_input = tf.reshape(embed_input, [batch_size, hps.num_embedding_size])forget_gate = tf.sigmoid(tf.matmul(embed_input, fx) + tf.matmul(h, fh) + fb)input_gate = tf.sigmoid(tf.matmul(embed_input, ix) + tf.matmul(h, ih) + ib)output_gate = tf.sigmoid(tf.matmul(embed_input, ox) + tf.matmul(h, oh) + ob)mid_state = tf.tanh(tf.matmul(embed_input, cx) + tf.matmul(h, ch) + cb)state = mid_state * input_gate + state * forget_gateh = output_gate * tf.tanh(state)last = hcells = []for i in range(hps.num_lstm_layers):cell = tf.contrib.rnn.BasicLSTMCell(hps.num_lstm_nodes[i], state_is_tuple = True)cell = tf.contrib.rnn.DropoutWrapper(cell,output_keep_prob=keep_prob)cells.append(cell)cell = tf.contrib.rnn.MultiRNNCell(cells)#Cell is 多层LSTMinitial_state = cell.zero_state(batch_size,tf.float32) #初始化隐藏状态为0#RNNoutput:# 一维：batch_size# 二维：num_timesteps# 三维：lstm_outputs[-1]rnn_outputs, _ = tf.nn.dynamic_rnn(cell,embed_inputs,initial_state=initial_state)last = rnn_outputs[:, -1, : ]''''''FC层搭建'''fc_init = tf.uniform_unit_scaling_initializer(factor=1.0)with tf.variable_scope('fc', initializer=fc_init):fc1 = tf.layers.dense(last, hps.num_fc_nodes, activation=tf.nn.relu, name='fc1')fc1_dropout = tf.contrib.layers.dropout(fc1, keep_prob)logits = tf.layers.dense(fc1_dropout, num_classes, name='fc2')'''计算损失函数'''with tf.name_scope('metrics'):softmax_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=outputs)loss = tf.reduce_mean(softmax_loss)y_pred = tf.argmax(tf.nn.softmax(logits), 1, output_type=tf.int32)correct_pred = tf.equal(outputs, y_pred)accuary = tf.reduce_mean(tf.cast(correct_pred, tf.float32))'''计算图构建'''with tf.name_scope('train_op'):tvars = tf.trainable_variables()#获得所有训练变量for var in tvars:tf.logging.info('variable name: %s' % (var.name))grads, _ = tf.clip_by_global_norm(tf.gradients(loss, tvars), hps.clip_lstm_grads)#梯度截断optimizer = tf.train.AdamOptimizer(hps.learning_rate)train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=global_step)return ((inputs, outputs, keep_prob), (loss, accuary), (train_op, global_step))placeholders, metrics, others = create_model(hps, vocab_size, num_classes)
inputs, outputs, keep_prob = placeholders
loss, accuary = metrics
train_op, global_step = others
WARNING:tensorflow:From <ipython-input-6-d07947ed78f3>:88: UniformUnitScaling.__init__ (from tensorflow.python.ops.init_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.initializers.variance_scaling instead with distribution=uniform to get equivalent behavior.
INFO:tensorflow:variable name: embedding/embedding:0
INFO:tensorflow:variable name: lstm_nn/inputs/x_weights:0
INFO:tensorflow:variable name: lstm_nn/inputs/h_weights:0
INFO:tensorflow:variable name: lstm_nn/inputs/biases:0
INFO:tensorflow:variable name: lstm_nn/outputs/x_weights:0
INFO:tensorflow:variable name: lstm_nn/outputs/h_weights:0
INFO:tensorflow:variable name: lstm_nn/outputs/biases:0
INFO:tensorflow:variable name: lstm_nn/forget/x_weights:0
INFO:tensorflow:variable name: lstm_nn/forget/h_weights:0
INFO:tensorflow:variable name: lstm_nn/forget/biases:0
INFO:tensorflow:variable name: lstm_nn/memory/x_weights:0
INFO:tensorflow:variable name: lstm_nn/memory/h_weights:0
INFO:tensorflow:variable name: lstm_nn/memory/biases:0
INFO:tensorflow:variable name: fc/fc1/kernel:0
INFO:tensorflow:variable name: fc/fc1/bias:0
INFO:tensorflow:variable name: fc/fc2/kernel:0
INFO:tensorflow:variable name: fc/fc2/bias:0
#训练流程模块
init_op = tf.global_variables_initializer()
train_keep_prob_value = 0.8
test_keep_prob_value = 1.0num_train_steps = 10000with tf.Session() as sess:sess.run(init_op)for i in range(num_train_steps):batch_inputs, batch_labels = train_dataset.next_batch(hps.batch_size)outputs_val = sess.run([loss, accuary, train_op, global_step], feed_dict = {inputs: batch_inputs,outputs: batch_labels,keep_prob: train_keep_prob_value})loss_val, accuary_val, _, global_step_val = outputs_valif global_step_val % 20 ==0:tf.logging.info("Step: %5d, loss: %3.3f, accuary: %3.3f" % (global_step_val, loss_val, accuary_val))
INFO:tensorflow:Step: 20, loss: 2.276, accuary: 0.140
INFO:tensorflow:Step: 40, loss: 2.312, accuary: 0.070
INFO:tensorflow:Step: 60, loss: 2.325, accuary: 0.120
INFO:tensorflow:Step: 80, loss: 2.244, accuary: 0.100
INFO:tensorflow:Step: 100, loss: 2.272, accuary: 0.160
INFO:tensorflow:Step: 120, loss: 2.219, accuary: 0.170
INFO:tensorflow:Step: 140, loss: 2.302, accuary: 0.100
INFO:tensorflow:Step: 160, loss: 2.237, accuary: 0.130
INFO:tensorflow:Step: 180, loss: 2.241, accuary: 0.170
INFO:tensorflow:Step: 200, loss: 2.300, accuary: 0.150
INFO:tensorflow:Step: 220, loss: 2.209, accuary: 0.210
INFO:tensorflow:Step: 240, loss: 2.141, accuary: 0.180
INFO:tensorflow:Step: 260, loss: 2.132, accuary: 0.230
INFO:tensorflow:Step: 280, loss: 2.153, accuary: 0.130
INFO:tensorflow:Step: 300, loss: 1.977, accuary: 0.290
INFO:tensorflow:Step: 320, loss: 1.952, accuary: 0.250
INFO:tensorflow:Step: 340, loss: 2.034, accuary: 0.250
INFO:tensorflow:Step: 360, loss: 1.948, accuary: 0.290
INFO:tensorflow:Step: 380, loss: 2.037, accuary: 0.210
INFO:tensorflow:Step: 400, loss: 1.909, accuary: 0.250
INFO:tensorflow:Step: 420, loss: 1.809, accuary: 0.320
INFO:tensorflow:Step: 440, loss: 1.919, accuary: 0.300
INFO:tensorflow:Step: 460, loss: 1.882, accuary: 0.280
INFO:tensorflow:Step: 480, loss: 1.773, accuary: 0.410
INFO:tensorflow:Step: 500, loss: 1.802, accuary: 0.380
INFO:tensorflow:Step: 520, loss: 1.919, accuary: 0.260
INFO:tensorflow:Step: 540, loss: 1.824, accuary: 0.270
INFO:tensorflow:Step: 560, loss: 1.583, accuary: 0.390
INFO:tensorflow:Step: 580, loss: 1.715, accuary: 0.290
INFO:tensorflow:Step: 600, loss: 1.600, accuary: 0.380
INFO:tensorflow:Step: 620, loss: 1.517, accuary: 0.410
INFO:tensorflow:Step: 640, loss: 1.569, accuary: 0.350
INFO:tensorflow:Step: 660, loss: 1.589, accuary: 0.390
INFO:tensorflow:Step: 680, loss: 1.557, accuary: 0.400
INFO:tensorflow:Step: 700, loss: 1.673, accuary: 0.300
INFO:tensorflow:Step: 720, loss: 1.300, accuary: 0.490
INFO:tensorflow:Step: 740, loss: 1.345, accuary: 0.470
INFO:tensorflow:Step: 760, loss: 1.535, accuary: 0.430
INFO:tensorflow:Step: 780, loss: 1.232, accuary: 0.500
INFO:tensorflow:Step: 800, loss: 1.469, accuary: 0.410
INFO:tensorflow:Step: 820, loss: 1.383, accuary: 0.450
INFO:tensorflow:Step: 840, loss: 1.274, accuary: 0.550
INFO:tensorflow:Step: 860, loss: 1.250, accuary: 0.540
INFO:tensorflow:Step: 880, loss: 1.278, accuary: 0.530
INFO:tensorflow:Step: 900, loss: 1.152, accuary: 0.570
INFO:tensorflow:Step: 920, loss: 1.135, accuary: 0.550
INFO:tensorflow:Step: 940, loss: 1.288, accuary: 0.500
INFO:tensorflow:Step: 960, loss: 1.273, accuary: 0.510
INFO:tensorflow:Step: 980, loss: 1.185, accuary: 0.540
INFO:tensorflow:Step: 1000, loss: 1.297, accuary: 0.580
INFO:tensorflow:Step: 1020, loss: 1.129, accuary: 0.590
INFO:tensorflow:Step: 1040, loss: 0.938, accuary: 0.710
INFO:tensorflow:Step: 1060, loss: 0.975, accuary: 0.640
INFO:tensorflow:Step: 1080, loss: 1.121, accuary: 0.540
INFO:tensorflow:Step: 1100, loss: 0.997, accuary: 0.650
INFO:tensorflow:Step: 1120, loss: 1.076, accuary: 0.570
INFO:tensorflow:Step: 1140, loss: 1.173, accuary: 0.560
INFO:tensorflow:Step: 1160, loss: 0.974, accuary: 0.670
INFO:tensorflow:Step: 1180, loss: 1.051, accuary: 0.630
INFO:tensorflow:Step: 1200, loss: 0.961, accuary: 0.710
INFO:tensorflow:Step: 1220, loss: 1.055, accuary: 0.630
INFO:tensorflow:Step: 1240, loss: 0.930, accuary: 0.650
INFO:tensorflow:Step: 1260, loss: 1.244, accuary: 0.540
INFO:tensorflow:Step: 1280, loss: 1.019, accuary: 0.610
INFO:tensorflow:Step: 1300, loss: 0.796, accuary: 0.750
INFO:tensorflow:Step: 1320, loss: 1.065, accuary: 0.580
INFO:tensorflow:Step: 1340, loss: 0.926, accuary: 0.700
INFO:tensorflow:Step: 1360, loss: 0.931, accuary: 0.670
INFO:tensorflow:Step: 1380, loss: 0.883, accuary: 0.710
INFO:tensorflow:Step: 1400, loss: 0.747, accuary: 0.720
INFO:tensorflow:Step: 1420, loss: 0.811, accuary: 0.670
INFO:tensorflow:Step: 1440, loss: 0.859, accuary: 0.680
INFO:tensorflow:Step: 1460, loss: 0.738, accuary: 0.740
INFO:tensorflow:Step: 1480, loss: 0.781, accuary: 0.730
INFO:tensorflow:Step: 1500, loss: 0.827, accuary: 0.720
INFO:tensorflow:Step: 1520, loss: 0.688, accuary: 0.810
INFO:tensorflow:Step: 1540, loss: 0.795, accuary: 0.670
INFO:tensorflow:Step: 1560, loss: 0.747, accuary: 0.770
INFO:tensorflow:Step: 1580, loss: 0.690, accuary: 0.710
INFO:tensorflow:Step: 1600, loss: 0.922, accuary: 0.740
INFO:tensorflow:Step: 1620, loss: 0.843, accuary: 0.700
INFO:tensorflow:Step: 1640, loss: 0.618, accuary: 0.790
INFO:tensorflow:Step: 1660, loss: 0.777, accuary: 0.760
INFO:tensorflow:Step: 1680, loss: 0.737, accuary: 0.740
INFO:tensorflow:Step: 1700, loss: 0.750, accuary: 0.750
INFO:tensorflow:Step: 1720, loss: 0.602, accuary: 0.780
INFO:tensorflow:Step: 1740, loss: 0.834, accuary: 0.720
INFO:tensorflow:Step: 1760, loss: 0.757, accuary: 0.750
INFO:tensorflow:Step: 1780, loss: 0.713, accuary: 0.750
INFO:tensorflow:Step: 1800, loss: 0.849, accuary: 0.730
INFO:tensorflow:Step: 1820, loss: 0.929, accuary: 0.680
INFO:tensorflow:Step: 1840, loss: 0.625, accuary: 0.770
INFO:tensorflow:Step: 1860, loss: 0.627, accuary: 0.800
INFO:tensorflow:Step: 1880, loss: 0.753, accuary: 0.740
INFO:tensorflow:Step: 1900, loss: 0.747, accuary: 0.770
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INFO:tensorflow:Step: 1940, loss: 0.811, accuary: 0.720
INFO:tensorflow:Step: 1960, loss: 0.593, accuary: 0.840
INFO:tensorflow:Step: 1980, loss: 0.661, accuary: 0.730
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INFO:tensorflow:Step: 2020, loss: 0.803, accuary: 0.770
INFO:tensorflow:Step: 2040, loss: 0.530, accuary: 0.820
INFO:tensorflow:Step: 2060, loss: 0.602, accuary: 0.790
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INFO:tensorflow:Step: 2100, loss: 0.539, accuary: 0.840
INFO:tensorflow:Step: 2120, loss: 0.682, accuary: 0.790
INFO:tensorflow:Step: 2140, loss: 0.396, accuary: 0.860
INFO:tensorflow:Step: 2160, loss: 0.554, accuary: 0.860
INFO:tensorflow:Step: 2180, loss: 0.702, accuary: 0.770
INFO:tensorflow:Step: 2200, loss: 0.630, accuary: 0.760
INFO:tensorflow:Step: 2220, loss: 0.452, accuary: 0.860
INFO:tensorflow:Step: 2240, loss: 0.497, accuary: 0.840
INFO:tensorflow:Step: 2260, loss: 0.381, accuary: 0.870
INFO:tensorflow:Step: 2280, loss: 0.406, accuary: 0.870
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INFO:tensorflow:Step: 2500, loss: 0.386, accuary: 0.860
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INFO:tensorflow:Step: 2680, loss: 0.445, accuary: 0.880
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INFO:tensorflow:Step: 2940, loss: 0.421, accuary: 0.810
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INFO:tensorflow:Step: 3000, loss: 0.480, accuary: 0.830
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INFO:tensorflow:Step: 3080, loss: 0.409, accuary: 0.870
INFO:tensorflow:Step: 3100, loss: 0.278, accuary: 0.940
INFO:tensorflow:Step: 3120, loss: 0.429, accuary: 0.910
INFO:tensorflow:Step: 3140, loss: 0.340, accuary: 0.900
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INFO:tensorflow:Step: 3960, loss: 0.231, accuary: 0.940
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INFO:tensorflow:Step: 4000, loss: 0.158, accuary: 0.980
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INFO:tensorflow:Step: 4400, loss: 0.183, accuary: 0.940
INFO:tensorflow:Step: 4420, loss: 0.263, accuary: 0.950
INFO:tensorflow:Step: 4440, loss: 0.170, accuary: 0.940
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INFO:tensorflow:Step: 4480, loss: 0.124, accuary: 0.980
INFO:tensorflow:Step: 4500, loss: 0.146, accuary: 0.940
INFO:tensorflow:Step: 4520, loss: 0.139, accuary: 0.970
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INFO:tensorflow:Step: 4600, loss: 0.188, accuary: 0.960
INFO:tensorflow:Step: 4620, loss: 0.072, accuary: 0.980
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INFO:tensorflow:Step: 4700, loss: 0.103, accuary: 0.970
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INFO:tensorflow:Step: 4780, loss: 0.170, accuary: 0.960
INFO:tensorflow:Step: 4800, loss: 0.138, accuary: 0.950
INFO:tensorflow:Step: 4820, loss: 0.149, accuary: 0.970
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INFO:tensorflow:Step: 4860, loss: 0.127, accuary: 0.960
INFO:tensorflow:Step: 4880, loss: 0.154, accuary: 0.920
INFO:tensorflow:Step: 4900, loss: 0.077, accuary: 0.970
INFO:tensorflow:Step: 4920, loss: 0.086, accuary: 0.980
INFO:tensorflow:Step: 4940, loss: 0.178, accuary: 0.960
INFO:tensorflow:Step: 4960, loss: 0.123, accuary: 0.960
INFO:tensorflow:Step: 4980, loss: 0.155, accuary: 0.970
INFO:tensorflow:Step: 5000, loss: 0.126, accuary: 0.950
INFO:tensorflow:Step: 5020, loss: 0.163, accuary: 0.960
INFO:tensorflow:Step: 5040, loss: 0.170, accuary: 0.950
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INFO:tensorflow:Step: 5080, loss: 0.142, accuary: 0.970
INFO:tensorflow:Step: 5100, loss: 0.194, accuary: 0.970
INFO:tensorflow:Step: 5120, loss: 0.140, accuary: 0.960
INFO:tensorflow:Step: 5140, loss: 0.079, accuary: 0.990
INFO:tensorflow:Step: 5160, loss: 0.175, accuary: 0.960
INFO:tensorflow:Step: 5180, loss: 0.073, accuary: 0.990
INFO:tensorflow:Step: 5200, loss: 0.232, accuary: 0.950
INFO:tensorflow:Step: 5220, loss: 0.151, accuary: 0.960
INFO:tensorflow:Step: 5240, loss: 0.131, accuary: 0.960
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INFO:tensorflow:Step: 5280, loss: 0.079, accuary: 0.980
INFO:tensorflow:Step: 5300, loss: 0.198, accuary: 0.960
INFO:tensorflow:Step: 5320, loss: 0.219, accuary: 0.980
INFO:tensorflow:Step: 5340, loss: 0.150, accuary: 0.970
INFO:tensorflow:Step: 5360, loss: 0.085, accuary: 0.970
INFO:tensorflow:Step: 5380, loss: 0.334, accuary: 0.960
INFO:tensorflow:Step: 5400, loss: 0.155, accuary: 0.980
INFO:tensorflow:Step: 5420, loss: 0.173, accuary: 0.970
INFO:tensorflow:Step: 5440, loss: 0.276, accuary: 0.890
INFO:tensorflow:Step: 5460, loss: 0.212, accuary: 0.950
INFO:tensorflow:Step: 5480, loss: 0.060, accuary: 0.970
INFO:tensorflow:Step: 5500, loss: 0.036, accuary: 0.990
INFO:tensorflow:Step: 5520, loss: 0.151, accuary: 0.970
INFO:tensorflow:Step: 5540, loss: 0.073, accuary: 0.990
INFO:tensorflow:Step: 5560, loss: 0.201, accuary: 0.950
INFO:tensorflow:Step: 5580, loss: 0.115, accuary: 0.960
INFO:tensorflow:Step: 5600, loss: 0.156, accuary: 0.930
INFO:tensorflow:Step: 5620, loss: 0.115, accuary: 0.950
INFO:tensorflow:Step: 5640, loss: 0.181, accuary: 0.950
INFO:tensorflow:Step: 5660, loss: 0.055, accuary: 0.990
INFO:tensorflow:Step: 5680, loss: 0.163, accuary: 0.950
INFO:tensorflow:Step: 5700, loss: 0.055, accuary: 0.990
INFO:tensorflow:Step: 5720, loss: 0.051, accuary: 0.980
INFO:tensorflow:Step: 5740, loss: 0.116, accuary: 0.990
INFO:tensorflow:Step: 5760, loss: 0.058, accuary: 0.990
INFO:tensorflow:Step: 5780, loss: 0.147, accuary: 0.970
INFO:tensorflow:Step: 5800, loss: 0.205, accuary: 0.940
INFO:tensorflow:Step: 5820, loss: 0.096, accuary: 0.980
INFO:tensorflow:Step: 5840, loss: 0.109, accuary: 0.960
INFO:tensorflow:Step: 5860, loss: 0.181, accuary: 0.940
INFO:tensorflow:Step: 5880, loss: 0.245, accuary: 0.960
INFO:tensorflow:Step: 5900, loss: 0.045, accuary: 0.990
INFO:tensorflow:Step: 5920, loss: 0.087, accuary: 0.970
INFO:tensorflow:Step: 5940, loss: 0.234, accuary: 0.950
INFO:tensorflow:Step: 5960, loss: 0.208, accuary: 0.950
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INFO:tensorflow:Step: 6000, loss: 0.031, accuary: 0.990
INFO:tensorflow:Step: 6020, loss: 0.043, accuary: 0.980
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INFO:tensorflow:Step: 6120, loss: 0.063, accuary: 0.990
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INFO:tensorflow:Step: 6180, loss: 0.029, accuary: 1.000
INFO:tensorflow:Step: 6200, loss: 0.025, accuary: 1.000
INFO:tensorflow:Step: 6220, loss: 0.031, accuary: 1.000
INFO:tensorflow:Step: 6240, loss: 0.109, accuary: 0.980
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INFO:tensorflow:Step: 6280, loss: 0.066, accuary: 0.980
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INFO:tensorflow:Step: 6320, loss: 0.087, accuary: 0.980
INFO:tensorflow:Step: 6340, loss: 0.073, accuary: 0.990
INFO:tensorflow:Step: 6360, loss: 0.038, accuary: 1.000
INFO:tensorflow:Step: 6380, loss: 0.259, accuary: 0.970
INFO:tensorflow:Step: 6400, loss: 0.057, accuary: 0.980
INFO:tensorflow:Step: 6420, loss: 0.152, accuary: 0.950
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INFO:tensorflow:Step: 6460, loss: 0.161, accuary: 0.960
INFO:tensorflow:Step: 6480, loss: 0.051, accuary: 0.990
INFO:tensorflow:Step: 6500, loss: 0.078, accuary: 0.980
INFO:tensorflow:Step: 6520, loss: 0.082, accuary: 0.980
INFO:tensorflow:Step: 6540, loss: 0.045, accuary: 0.990
INFO:tensorflow:Step: 6560, loss: 0.042, accuary: 0.990
INFO:tensorflow:Step: 6580, loss: 0.066, accuary: 0.980
INFO:tensorflow:Step: 6600, loss: 0.051, accuary: 0.990
INFO:tensorflow:Step: 6620, loss: 0.121, accuary: 0.980
INFO:tensorflow:Step: 6640, loss: 0.069, accuary: 0.970
INFO:tensorflow:Step: 6660, loss: 0.077, accuary: 0.980
INFO:tensorflow:Step: 6680, loss: 0.135, accuary: 0.970
INFO:tensorflow:Step: 6700, loss: 0.072, accuary: 0.970
INFO:tensorflow:Step: 6720, loss: 0.049, accuary: 0.980
INFO:tensorflow:Step: 6740, loss: 0.134, accuary: 0.980
INFO:tensorflow:Step: 6760, loss: 0.035, accuary: 0.990
INFO:tensorflow:Step: 6780, loss: 0.040, accuary: 0.980
INFO:tensorflow:Step: 6800, loss: 0.138, accuary: 0.960
INFO:tensorflow:Step: 6820, loss: 0.059, accuary: 0.980
INFO:tensorflow:Step: 6840, loss: 0.064, accuary: 0.990
INFO:tensorflow:Step: 6860, loss: 0.196, accuary: 0.960
INFO:tensorflow:Step: 6880, loss: 0.178, accuary: 0.980
INFO:tensorflow:Step: 6900, loss: 0.137, accuary: 0.980
INFO:tensorflow:Step: 6920, loss: 0.051, accuary: 0.980
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INFO:tensorflow:Step: 6960, loss: 0.031, accuary: 1.000
INFO:tensorflow:Step: 6980, loss: 0.066, accuary: 0.980
INFO:tensorflow:Step: 7000, loss: 0.013, accuary: 1.000
INFO:tensorflow:Step: 7020, loss: 0.035, accuary: 0.990
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INFO:tensorflow:Step: 7140, loss: 0.107, accuary: 0.980
INFO:tensorflow:Step: 7160, loss: 0.023, accuary: 1.000
INFO:tensorflow:Step: 7180, loss: 0.019, accuary: 1.000
INFO:tensorflow:Step: 7200, loss: 0.091, accuary: 0.970
INFO:tensorflow:Step: 7220, loss: 0.096, accuary: 0.970
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INFO:tensorflow:Step: 7260, loss: 0.032, accuary: 0.980
INFO:tensorflow:Step: 7280, loss: 0.071, accuary: 0.990
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INFO:tensorflow:Step: 7620, loss: 0.035, accuary: 0.990
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INFO:tensorflow:Step: 7800, loss: 0.030, accuary: 0.990
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INFO:tensorflow:Step: 7900, loss: 0.174, accuary: 0.970
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INFO:tensorflow:Step: 8000, loss: 0.014, accuary: 0.990
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INFO:tensorflow:Step: 8160, loss: 0.051, accuary: 0.980
INFO:tensorflow:Step: 8180, loss: 0.110, accuary: 0.990
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INFO:tensorflow:Step: 8240, loss: 0.090, accuary: 0.980
INFO:tensorflow:Step: 8260, loss: 0.072, accuary: 0.990
INFO:tensorflow:Step: 8280, loss: 0.087, accuary: 0.960
INFO:tensorflow:Step: 8300, loss: 0.088, accuary: 0.990
INFO:tensorflow:Step: 8320, loss: 0.057, accuary: 0.970
INFO:tensorflow:Step: 8340, loss: 0.073, accuary: 0.990
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INFO:tensorflow:Step: 8380, loss: 0.017, accuary: 0.990
INFO:tensorflow:Step: 8400, loss: 0.074, accuary: 0.980
INFO:tensorflow:Step: 8420, loss: 0.009, accuary: 1.000
INFO:tensorflow:Step: 8440, loss: 0.041, accuary: 0.980
INFO:tensorflow:Step: 8460, loss: 0.074, accuary: 0.980
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INFO:tensorflow:Step: 8680, loss: 0.069, accuary: 0.980
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INFO:tensorflow:Step: 8740, loss: 0.018, accuary: 1.000
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INFO:tensorflow:Step: 8920, loss: 0.011, accuary: 0.990
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INFO:tensorflow:Step: 9040, loss: 0.038, accuary: 0.990
INFO:tensorflow:Step: 9060, loss: 0.054, accuary: 0.990
INFO:tensorflow:Step: 9080, loss: 0.008, accuary: 1.000
INFO:tensorflow:Step: 9100, loss: 0.006, accuary: 1.000
INFO:tensorflow:Step: 9120, loss: 0.007, accuary: 1.000
INFO:tensorflow:Step: 9140, loss: 0.066, accuary: 0.980
INFO:tensorflow:Step: 9160, loss: 0.019, accuary: 0.990
INFO:tensorflow:Step: 9180, loss: 0.017, accuary: 1.000
INFO:tensorflow:Step: 9200, loss: 0.036, accuary: 0.990
INFO:tensorflow:Step: 9220, loss: 0.136, accuary: 0.980
INFO:tensorflow:Step: 9240, loss: 0.085, accuary: 0.970
INFO:tensorflow:Step: 9260, loss: 0.029, accuary: 0.990
INFO:tensorflow:Step: 9280, loss: 0.016, accuary: 0.990
INFO:tensorflow:Step: 9300, loss: 0.024, accuary: 0.990
INFO:tensorflow:Step: 9320, loss: 0.025, accuary: 0.990
INFO:tensorflow:Step: 9340, loss: 0.013, accuary: 1.000
INFO:tensorflow:Step: 9360, loss: 0.011, accuary: 1.000
INFO:tensorflow:Step: 9380, loss: 0.029, accuary: 0.990
INFO:tensorflow:Step: 9400, loss: 0.017, accuary: 1.000
INFO:tensorflow:Step: 9420, loss: 0.009, accuary: 1.000
INFO:tensorflow:Step: 9440, loss: 0.012, accuary: 1.000
INFO:tensorflow:Step: 9460, loss: 0.024, accuary: 0.990
INFO:tensorflow:Step: 9480, loss: 0.018, accuary: 0.990
INFO:tensorflow:Step: 9500, loss: 0.011, accuary: 1.000
INFO:tensorflow:Step: 9520, loss: 0.005, accuary: 1.000
INFO:tensorflow:Step: 9540, loss: 0.015, accuary: 1.000
INFO:tensorflow:Step: 9560, loss: 0.038, accuary: 0.990
INFO:tensorflow:Step: 9580, loss: 0.125, accuary: 0.980
INFO:tensorflow:Step: 9600, loss: 0.033, accuary: 0.990
INFO:tensorflow:Step: 9620, loss: 0.005, accuary: 1.000
INFO:tensorflow:Step: 9640, loss: 0.026, accuary: 0.990
INFO:tensorflow:Step: 9660, loss: 0.084, accuary: 0.980
INFO:tensorflow:Step: 9680, loss: 0.061, accuary: 0.980
INFO:tensorflow:Step: 9700, loss: 0.072, accuary: 0.990
INFO:tensorflow:Step: 9720, loss: 0.038, accuary: 0.990
INFO:tensorflow:Step: 9740, loss: 0.014, accuary: 1.000
INFO:tensorflow:Step: 9760, loss: 0.020, accuary: 1.000
INFO:tensorflow:Step: 9780, loss: 0.006, accuary: 1.000
INFO:tensorflow:Step: 9800, loss: 0.020, accuary: 0.990
INFO:tensorflow:Step: 9820, loss: 0.103, accuary: 0.970
INFO:tensorflow:Step: 9840, loss: 0.065, accuary: 0.980
INFO:tensorflow:Step: 9860, loss: 0.018, accuary: 1.000
INFO:tensorflow:Step: 9880, loss: 0.009, accuary: 1.000
INFO:tensorflow:Step: 9900, loss: 0.031, accuary: 0.980
INFO:tensorflow:Step: 9920, loss: 0.023, accuary: 0.990
INFO:tensorflow:Step: 9940, loss: 0.003, accuary: 1.000
INFO:tensorflow:Step: 9960, loss: 0.058, accuary: 0.990
INFO:tensorflow:Step: 9980, loss: 0.110, accuary: 0.980
INFO:tensorflow:Step: 10000, loss: 0.009, accuary: 1.000
迭代到10000准确率基本达到要求
