class_docstr: Wide and deep model, implemented using TFLearn
code: def output_confusion_matrix(self, y, y_pred):
      assert y.size == y_pred.size
      print("Actual IDV")
      print(y.value_counts())
      print("Predicted IDV")
      print(y_pred.value_counts())
      print()
      print("Confusion matrix:")
      cmat = pd.crosstab(
          y_pred, y, rownames=["predictions"], colnames=["actual"]
      )
      print(cmat)
      sys.stdout.flush()
      return cmat
  
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 9 for calling the TFLearnWideAndDeep obj's underlying member method code with example usage: obj.output_confusion_matrix(y, y_pred) and returns: cmat