class_docstr: Wide and deep model, implemented using TFLearn
docstr: Prepare input data dicts
docstr_tok: ["Prepare","input","data","dicts"]
code: def prepare_input_data(self, input_data, name="", category_map=None):
      """
      Prepare input data dicts
      """
      print("-" * 40 + " Preparing %s" % name)
      X = input_data[self.continuous_columns].values.astype(np.float32)
      Y = input_data[self.label_column].values.astype(np.float32)
      Y = Y.reshape([-1, 1])
      if self.verbose:
          print("  Y shape=%s, X shape=%s" % (Y.shape, X.shape))
  
      X_dict = {"wide_X": X}
  
      if "deep" in self.model_type:
          # map categorical value strings to integers
          td = input_data
          if category_map is None:
              category_map = {}
              for cc in self.categorical_columns:
                  if not cc in td.columns:
                      continue
                  cc_values = sorted(td[cc].unique())
                  cc_max = 1 + len(cc_values)
                  cc_map = dict(
                      zip(cc_values, range(1, cc_max))
                  )  # start from 1 to avoid 0:0 mapping (save 0 for missing)
                  if self.verbose:
                      print(
                          "  category %s max=%s,  map=%s"
                          % (cc, cc_max, cc_map)
                      )
                  category_map[cc] = cc_map
  
          td = td.replace(category_map)
  
          # bin ages (cuts off extreme values)
          age_bins = [
              0,
              12,
              18,
              25,
              30,
              35,
              40,
              45,
              50,
              55,
              60,
              65,
              80,
              65535,
          ]
          td["age_binned"] = pd.cut(td["age"], age_bins, labels=False)
          td = td.replace({"age_binned": {np.nan: 0}})
          print(
              "  %d age bins: age bins = %s" % (len(age_bins), age_bins)
          )
  
          X_dict.update(
              {
                  ("%s_in" % cc): td[cc]
                  .values.astype(np.int32)
                  .reshape([-1, 1])
                  for cc in self.categorical_columns
              }
          )
  
      Y_dict = {"Y": Y}
      if self.verbose:
          print("-" * 40)
      return X_dict, Y_dict, category_map
  
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 6 for calling the TFLearnWideAndDeep obj's underlying member method code with example usage: obj.prepare_input_data(input_data, name, category_map) and returns: X_dict,  Y_dict,  category_map