class_docstr: dnn MNIST
class_docstr_tok: ["dnn","MNIST"]
code: def __init__(self):
      # Building deep neural network
      network = tflearn.input_data(shape=[None, 784], name="input")
      network = self.make_core_network(network)
  
      # Regression using SGD with learning rate decay and Top-3 accuracy
      sgd = tflearn.SGD(
          learning_rate=0.1, lr_decay=0.96, decay_step=1000
      )
      top_k = tflearn.metrics.Top_k(3)
  
      network = tflearn.regression(
          network,
          optimizer=sgd,
          metric=top_k,
          loss="categorical_crossentropy",
          name="target",
      )
      model = tflearn.DNN(network, tensorboard_verbose=0)
      self.model = model
  
imports: from __future__ import division, print_function, absolute_import
  import re
  import tflearn
  import tensorflow.compat.v1
  import tflearn.datasets.mnist
  from tflearn.layers.core import input_data, dropout, fully_connected
  from tflearn.layers.conv import conv_2d, max_pool_2d
  from tflearn.layers.normalization import local_response_normalization
  from tflearn.layers.estimator import regression
label_desc: Use image node_id 1 to create a new Model2 object from inherited base classes: object with example: obj = Model2()