fix

tasks/zhihu/captcha/ocrmodel.py → tasks/zhihu/captcha/orcmodel.py

 import tensorflow as tf
-from zhihu_captcha import utils
+from captcha import utils
 from tensorflow.python.training import moving_averages
 
 
@@ -225,4 +225,4 @@ class LSTMOCR(object):
                               ksize=[1, ksize, ksize, 1],
                               strides=[1, strides, strides, 1],
                               padding='SAME',
-                              name='max_pool')
\ No newline at end of file
+                              name='max_pool')

tasks/zhihu/captcha/utils.py

@@ -9,38 +9,38 @@ num_classes = 38#3 + 2 + 10 + 1 + 1
 
 maxPrintLen = 100
 
-tf.compat.v1.flags.DEFINE_boolean('restore', True, 'whether to restore from the latest checkpoint')
-tf.compat.v1.flags.DEFINE_string('checkpoint_dir', './checkpoint/', 'the checkpoint dir')
-tf.compat.v1.flags.DEFINE_float('initial_learning_rate', 1e-3, 'inital lr')
+tf.app.flags.DEFINE_boolean('restore', True, 'whether to restore from the latest checkpoint')
+tf.app.flags.DEFINE_string('checkpoint_dir', './checkpoint/', 'the checkpoint dir')
+tf.app.flags.DEFINE_float('initial_learning_rate', 1e-3, 'inital lr')
 
-tf.compat.v1.flags.DEFINE_integer('image_height', 60, 'image height')
-tf.compat.v1.flags.DEFINE_integer('image_width', 150, 'image width')
-tf.compat.v1.flags.DEFINE_integer('image_channel', 1, 'image channels as input')
+tf.app.flags.DEFINE_integer('image_height', 60, 'image height')
+tf.app.flags.DEFINE_integer('image_width', 150, 'image width')
+tf.app.flags.DEFINE_integer('image_channel', 1, 'image channels as input')
 
-tf.compat.v1.flags.DEFINE_integer('max_stepsize', 64, 'max stepsize in lstm, as well as '
+tf.app.flags.DEFINE_integer('max_stepsize', 64, 'max stepsize in lstm, as well as '
                                                 'the output channels of last layer in CNN')
-tf.compat.v1.flags.DEFINE_integer('num_hidden', 128, 'number of hidden units in lstm')
-tf.compat.v1.flags.DEFINE_integer('num_epochs', 1000, 'maximum epochs')
-tf.compat.v1.flags.DEFINE_integer('batch_size', 128, 'the batch_size')
-tf.compat.v1.flags.DEFINE_integer('save_steps', 500, 'the step to save checkpoint')
-tf.compat.v1.flags.DEFINE_integer('validation_steps', 500, 'the step to validation')
+tf.app.flags.DEFINE_integer('num_hidden', 128, 'number of hidden units in lstm')
+tf.app.flags.DEFINE_integer('num_epochs', 1000, 'maximum epochs')
+tf.app.flags.DEFINE_integer('batch_size', 128, 'the batch_size')
+tf.app.flags.DEFINE_integer('save_steps', 500, 'the step to save checkpoint')
+tf.app.flags.DEFINE_integer('validation_steps', 500, 'the step to validation')
 
-tf.compat.v1.flags.DEFINE_float('decay_rate', 0.98, 'the lr decay rate')
-tf.compat.v1.flags.DEFINE_float('beta1', 0.9, 'parameter of adam optimizer beta1')
-tf.compat.v1.flags.DEFINE_float('beta2', 0.999, 'adam parameter beta2')
+tf.app.flags.DEFINE_float('decay_rate', 0.98, 'the lr decay rate')
+tf.app.flags.DEFINE_float('beta1', 0.9, 'parameter of adam optimizer beta1')
+tf.app.flags.DEFINE_float('beta2', 0.999, 'adam parameter beta2')
 
-tf.compat.v1.flags.DEFINE_integer('decay_steps', 1000, 'the lr decay_step for optimizer')
-tf.compat.v1.flags.DEFINE_float('momentum', 0.9, 'the momentum')
+tf.app.flags.DEFINE_integer('decay_steps', 1000, 'the lr decay_step for optimizer')
+tf.app.flags.DEFINE_float('momentum', 0.9, 'the momentum')
 
-tf.compat.v1.flags.DEFINE_string('train_dir', './imgs/train/', 'the train data dir')
-tf.compat.v1.flags.DEFINE_string('val_dir', './imgs/val/', 'the val data dir')
-tf.compat.v1.flags.DEFINE_string('infer_dir', './imgs/infer/', 'the infer data dir')
-tf.compat.v1.flags.DEFINE_string('logs_dir', './log', 'the logging dir')
-tf.compat.v1.flags.DEFINE_string('mode', 'train', 'train, val or infer')
-tf.compat.v1.flags.DEFINE_integer('num_gpus', 1, 'num of gpus')
+tf.app.flags.DEFINE_string('train_dir', './imgs/train/', 'the train data dir')
+tf.app.flags.DEFINE_string('val_dir', './imgs/val/', 'the val data dir')
+tf.app.flags.DEFINE_string('infer_dir', './imgs/infer/', 'the infer data dir')
+tf.app.flags.DEFINE_string('logs_dir', './log', 'the logging dir')
+tf.app.flags.DEFINE_string('mode', 'train', 'train, val or infer')
+tf.app.flags.DEFINE_integer('num_gpus', 1, 'num of gpus')
 
 
-FLAGS = tf.compat.v1.flags.FLAGS
+FLAGS = tf.app.flags.FLAGS
 
 # num_batches_per_epoch = int(num_train_samples/FLAGS.batch_size)
 
@@ -171,4 +171,4 @@ def sparse_tuple_from_label(sequences, dtype=np.int32):
     values = np.asarray(values, dtype=dtype)
     shape = np.asarray([len(sequences), np.asarray(indices).max(0)[1] + 1], dtype=np.int64)
 
-    return indices, values, shape
\ No newline at end of file
+    return indices, values, shape

tasks/zhihu/captcha/zhihu_captcha.py

@@ -10,7 +10,7 @@ import webbrowser
 from io import BytesIO
 from captcha import utils
 from captcha import orcmodel
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
 from PIL import Image
 import numpy as np
 
@@ -70,4 +70,4 @@ class ZhihuCaptcha():
                 expression += ''
             else:
                 expression += utils.decode_maps[i]
-        return expression
\ No newline at end of file
+        return expression

tasks/zhihu/spider.py

@@ -27,7 +27,6 @@ class Spider(object):
         '''
             初始化数据库，调整js规则
         '''
-        self.login_req = self._login()
         self.conn = pymysql.connect(host=HOST, port=PORT, user=USER,
                                     passwd=PASSWD,
                                     db=DB, charset='utf8')
@@ -53,6 +52,8 @@ class Spider(object):
         self.session = requests.session()
         self.HTMLSession = HTMLSession()
 
+        self.login_req = self._login()
+
     def _login(self):
         url = 'https://www.zhihu.com'
         loginUrl = 'https://www.zhihu.com/login/email'