class_docstr: Wide and deep model, implemented using TFLearn
code: def evaluate(self):
      logits = np.array(self.model.predict(self.testX_dict)).reshape(
          [-1]
      )
      print("=" * 60 + "  Evaluation")
      print(
          "  logits: %s, min=%s, max=%s"
          % (logits.shape, logits.min(), logits.max())
      )
      probs = 1.0 / (1.0 + np.exp(-logits))
      y_pred = pd.Series((probs > 0.5).astype(np.int32))
      Y = pd.Series(self.testY_dict["Y"].astype(np.int32).reshape([-1]))
      self.confusion_matrix = self.output_confusion_matrix(Y, y_pred)
      print("=" * 60)
  
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 8 for calling the TFLearnWideAndDeep obj's underlying member method code with example usage: obj.evaluate() without return types