class_docstr: Wide and deep model, implemented using TFLearn
docstr: model_type = `str`: wide or deep or wide+deep
  verbose = `bool`
  name = `str` used for run_id (defaults to model_type)
  tensorboard_verbose = `int`: logging level for tensorboard (0, 1, 2, or 3)
  wide_learning_rate = `float`: defaults to 0.001
  deep_learning_rate = `float`: defaults to 0.001
  checkpoints_dir = `str`: where checkpoint files will be stored (defaults to "CHECKPOINTS")
docstr_tok: ["model_type","=","`","str","`",":","wide","or","deep","or","wide+deep","verbose","=","`","bool","`","name","=","`","str","`","used","for","run_id","(","defaults","to","model_type",")","tensorboard_verbose","=","`","int","`",":","logging","level","for","tensorboard","(","0",",","1",",","2",",","or","3",")","wide_learning_rate","=","`","float","`",":","defaults","to","0.001","deep_learning_rate","=","`","float","`",":","defaults","to","0.001","checkpoints_dir","=","`","str","`",":","where","checkpoint","files","will","be","stored","(","defaults","to","``","CHECKPOINTS","''",")"]
code: def __init__(
      self,
      model_type="wide+deep",
      verbose=None,
      name=None,
      tensorboard_verbose=3,
      wide_learning_rate=0.001,
      deep_learning_rate=0.001,
      checkpoints_dir=None,
  ):
      """
      model_type = `str`: wide or deep or wide+deep
      verbose = `bool`
      name = `str` used for run_id (defaults to model_type)
      tensorboard_verbose = `int`: logging level for tensorboard (0, 1, 2, or 3)
      wide_learning_rate = `float`: defaults to 0.001
      deep_learning_rate = `float`: defaults to 0.001
      checkpoints_dir = `str`: where checkpoint files will be stored (defaults to "CHECKPOINTS")
      """
      self.model_type = model_type or "wide+deep"
      assert self.model_type in self.AVAILABLE_MODELS
      self.verbose = verbose or 0
      self.tensorboard_verbose = tensorboard_verbose
      self.name = name or self.model_type  # name is used for the run_id
      self.data_columns = COLUMNS
      self.continuous_columns = CONTINUOUS_COLUMNS
      self.categorical_columns = (
          CATEGORICAL_COLUMNS  # dict with category_name: category_size
      )
      self.label_column = LABEL_COLUMN
      self.checkpoints_dir = checkpoints_dir or "CHECKPOINTS"
      if not os.path.exists(self.checkpoints_dir):
          os.mkdir(self.checkpoints_dir)
          print(
              "Created checkpoints directory %s" % self.checkpoints_dir
          )
      self.build_model([wide_learning_rate, deep_learning_rate])
  
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 1 to create a new TFLearnWideAndDeep object from inherited base classes: object with example: obj = TFLearnWideAndDeep(model_type, verbose, name, tensorboard_verbose, wide_learning_rate, deep_learning_rate, checkpoints_dir)