class_docstr: Usage for tf tensor output:
  o = CNN(x).tf_output()
class_docstr_tok: ["Usage","for","tf","tensor","output",":","o","=","CNN","(","x",")",".tf_output","(",")"]
docstr: https://github.com/bgshih/crnn/blob/master/model/crnn_demo/config.lua
  :return:
docstr_tok: ["https",":","\/\/github.com\/bgshih\/crnn\/blob\/master\/model\/crnn_demo\/config.lua",":","return",":"]
code: def _build_network(self, input_tensor, is_training):
      """
      https://github.com/bgshih/crnn/blob/master/model/crnn_demo/config.lua
      :return:
      """
      net = tf.add(input_tensor, (-128.0))
      net = tf.multiply(net, (1 / 128.0))
  
      net = ConvRelu(net, 64, (3, 3), "conv_conv1")
      net = max_2x2pool(net, "conv_pool1")
  
      net = ConvRelu(net, 128, (3, 3), "conv_conv2")
      net = max_2x2pool(net, "conv_pool2")
  
      net = ConvReluBN(net, 256, (3, 3), "conv_conv3", is_training)
      net = ConvRelu(net, 256, (3, 3), "conv_conv4")
      net = max_2x1pool(net, "conv_pool3")
  
      net = ConvReluBN(net, 512, (3, 3), "conv_conv5", is_training)
      net = ConvRelu(net, 512, (3, 3), "conv_conv6")
      net = max_2x1pool(net, "conv_pool4")
  
      net = ConvReluBN(net, 512, (2, 2), "conv_conv7", is_training)
      net = max_2x1pool(net, "conv_pool5")
      net = dropout(net, is_training)
  
      net = tf.squeeze(net, axis=1)
  
      self.model = net
  
file_path: attention-ocr/aocr/model/cnn.py
imports: from __future__ import absolute_import
  import numpy
  import tensorflow
label_desc: Use image node_id 2 for calling the CNN obj's underlying member method code with example usage: obj._build_network(input_tensor, is_training) without return types