class_docstr: Wide and deep model, implemented using TFLearn
docstr: Model - deep, i.e. two-layer fully connected network model
docstr_tok: ["Model","-","deep",",","i.e",".","two-layer","fully","connected","network","model"]
code: def deep_model(
      self, wide_inputs, n_inputs, n_nodes=[100, 50], use_dropout=False
  ):
      """
      Model - deep, i.e. two-layer fully connected network model
      """
      cc_input_var = {}
      cc_embed_var = {}
      flat_vars = []
      if self.verbose:
          print(
              "--> deep model: %s categories, %d continuous"
              % (len(self.categorical_columns), n_inputs)
          )
      for cc, cc_size in self.categorical_columns.items():
          cc_input_var[cc] = tflearn.input_data(
              shape=[None, 1], name="%s_in" % cc, dtype=tf.int32
          )
          # embedding layers only work on CPU!  No GPU implementation in tensorflow, yet!
          cc_embed_var[cc] = tflearn.layers.embedding_ops.embedding(
              cc_input_var[cc], cc_size, 8, name="deep_%s_embed" % cc
          )
          if self.verbose:
              print("    %s_embed = %s" % (cc, cc_embed_var[cc]))
          flat_vars.append(
              tf.squeeze(
                  cc_embed_var[cc],
                  squeeze_dims=[1],
                  name="%s_squeeze" % cc,
              )
          )
  
      network = tf.concat(
          [wide_inputs] + flat_vars, 1, name="deep_concat"
      )
      for k in range(len(n_nodes)):
          network = tflearn.fully_connected(
              network,
              n_nodes[k],
              activation="relu",
              name="deep_fc%d" % (k + 1),
          )
          if use_dropout:
              network = tflearn.dropout(
                  network, 0.5, name="deep_dropout%d" % (k + 1)
              )
      if self.verbose:
          print("Deep model network before output %s" % network)
      network = tflearn.fully_connected(
          network,
          1,
          activation="linear",
          name="deep_fc_output",
          bias=False,
      )
      network = tf.reshape(
          network, [-1, 1]
      )  # so that accuracy is binary_accuracy
      if self.verbose:
          print("Deep 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 4 for calling the TFLearnWideAndDeep obj's underlying member method code with example usage: obj.deep_model(wide_inputs, n_inputs, n_nodes, use_dropout) and returns: network