class_docstr: Wide and deep model, implemented using TFLearn
docstr: Model - wide, i.e. normal linear model (for logistic regression)
docstr_tok: ["Model","-","wide",",","i.e",".","normal","linear","model","(","for","logistic","regression",")"]
code: def wide_model(self, inputs, n_inputs):
      """
      Model - wide, i.e. normal linear model (for logistic regression)
      """
      network = inputs
      # use fully_connected (instad of single_unit) because fc works properly with batches, whereas single_unit is 1D only
      network = tflearn.fully_connected(
          network,
          n_inputs,
          activation="linear",
          name="wide_linear",
          bias=False,
      )  # x*W (no bias)
      network = tf.reduce_sum(
          network, 1, name="reduce_sum"
      )  # batched sum, to produce logits
      network = tf.reshape(
          network, [-1, 1]
      )  # so that accuracy is binary_accuracy
      if self.verbose:
          print("Wide model network %s" % network)
      return network
  
imports: from __future__ import division, print_function
  import os
  import sys
  import argparse
  import tflearn
  import tempfile
  import urllib
  import numpy
  import pandas
  import tensorflow.compat.v1
label_desc: Use image node_id 5 for calling the TFLearnWideAndDeep obj's underlying member method code with example usage: obj.wide_model(inputs, n_inputs) and returns: network