Tensorflow: Unsupported callable

I tried following the tutorial on the tensorflow website https://www.tensorflow.org/tutorials/estimator/linear

It is a code to use linear regression but I'm not able to do so due to specified errors which I'm not able to understand

But I'm getting a unsupported callable error

Here is the code

import os
import sys

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import clear_output
from six.moves import urllib

import tensorflow.compat.v2.feature_column as fc

import tensorflow as tf

dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')
dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')
y_train = dftrain.pop('survived')
y_eval = dfeval.pop('survived')

CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck', 'embark_town', 'alone']
NUMERIC_COLUMNS = ['age', 'fare']

feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
    vocabulary = dftrain[feature_name].unique()
    feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))

for feature_name in NUMERIC_COLUMNS:
    feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))


def make_input_fn(data_df, label_df, num_epochs=20, shuffle=True, batch_size=32):
    def input_function():
        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))
        if shuffle:
            ds = ds.shuffle(1000)
        ds = ds.batch(batch_size).repeat(num_epochs)
        return ds

   return input_function()


train_input_fn = make_input_fn(dftrain, y_train)
eval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)

linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)
linear_est.train(train_input_fn)
result = linear_est.evaluate(eval_input_fn)

clear_output()
print(result)

And this is the error which I'm getting

Traceback (most recent call last):
  File "C:/Users/gotru/PycharmProjects/tensor/Linear_Regression.py", line 46, in <module>
    linear_est.train(train_input_fn)
  File "C:\Users\gotru\anaconda3\envs\tensorflow\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 374, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "C:\Users\gotru\anaconda3\envs\tensorflow\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1164, in _train_model
    return self._train_model_default(input_fn, hooks, saving_listeners)
  File "C:\Users\gotru\anaconda3\envs\tensorflow\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1191, in _train_model_default
    input_fn, ModeKeys.TRAIN))
  File "C:\Users\gotru\anaconda3\envs\tensorflow\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1028, in _get_features_and_labels_from_input_fn
    self._call_input_fn(input_fn, mode))
  File "C:\Users\gotru\anaconda3\envs\tensorflow\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1106, in _call_input_fn
    input_fn_args = function_utils.fn_args(input_fn)
  File "C:\Users\gotru\anaconda3\envs\tensorflow\lib\site-packages\tensorflow_core\python\util\function_utils.py", line 57, in fn_args
    args = tf_inspect.getfullargspec(fn).args
  File "C:\Users\gotru\anaconda3\envs\tensorflow\lib\site-packages\tensorflow_core\python\util\tf_inspect.py", line 257, in getfullargspec
    return _getfullargspec(target)
  File "C:\Users\gotru\anaconda3\envs\tensorflow\lib\inspect.py", line 1132, in getfullargspec
    raise TypeError('unsupported callable') from ex
TypeError: unsupported callable

Thanks in advance

The below code is working without any error

import os
import sys

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import clear_output
from six.moves import urllib

import tensorflow.compat.v2.feature_column as fc

import tensorflow as tf

# Load dataset.
dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')
dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')
y_train = dftrain.pop('survived')
y_eval = dfeval.pop('survived')

CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',
                       'embark_town', 'alone']
NUMERIC_COLUMNS = ['age', 'fare']

feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
  vocabulary = dftrain[feature_name].unique()
  feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))

for feature_name in NUMERIC_COLUMNS:
  feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))

def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):
  def input_function():
    ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))
    if shuffle:
      ds = ds.shuffle(1000)
    ds = ds.batch(batch_size).repeat(num_epochs)
    return ds
  return input_function

train_input_fn = make_input_fn(dftrain, y_train)
eval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)

#ds = make_input_fn(dftrain, y_train, batch_size=10)()
#for feature_batch, label_batch in ds.take(1):
#  print('Some feature keys:', list(feature_batch.keys()))
#  print()
#  print('A batch of class:', feature_batch['class'].numpy())
#  print()
#  print('A batch of Labels:', label_batch.numpy())

linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)
linear_est.train(train_input_fn)
result = linear_est.evaluate(eval_input_fn)

clear_output()
print(result)

Output

{'accuracy': 0.75, 'accuracy_baseline': 0.625, 'auc': 0.83060914, 'auc_precision_recall': 0.78848886, 'average_loss': 0.5077252, 'label/mean': 0.375, 'loss': 0.5028537, 'precision': 0.6386555, 'prediction/mean': 0.46588355, 'recall': 0.7676768, 'global_step': 200}

I know this is a year old but I figured I'd answer anyways. Your error is in the make_input_fn. At the end you call the input_function in the return statement. Remove the parentheses so you return the function itself. I made the same mistake:

change return input_function() to return input_function