Autokeras: Following the tutorial for text classification returns an error in Colab

[max1563]

Bug Description

Simply copy-pasting the code from the example usage for TextClassification gives the following error in Colab:

`/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(args, *kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise

AttributeError: in user code:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:505 train_function  *
    outputs = self.distribute_strategy.run(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
    return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:477 train_step  **
    self.compiled_metrics.update_state(y, y_pred, sample_weight)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:386 update_state
    self._build(y_pred, y_true)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:317 _build
    self._metrics, y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py:1118 map_structure_up_to
    **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py:1214 map_structure_with_tuple_paths_up_to
    *flat_value_lists)]
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py:1213 <listcomp>
    results = [func(*args, **kwargs) for args in zip(flat_path_list,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py:1116 <lambda>
    lambda _, *values: func(*values),  # Discards the path arg.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:416 _get_metric_objects
    return [self._get_metric_object(m, y_t, y_p) for m in metrics]
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:416 <listcomp>
    return [self._get_metric_object(m, y_t, y_p) for m in metrics]
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:437 _get_metric_object
    y_t_rank = len(y_t.shape.as_list())

AttributeError: 'tuple' object has no attribute 'shape'

`

Bug Reproduction

Code for reproducing the bug:

`
import numpy as np
from tensorflow.keras.datasets import imdb
index_offset = 3 # word index offset
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=1000,
index_from=index_offset)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
word_to_id = imdb.get_word_index()
word_to_id = {k: (v + index_offset) for k, v in word_to_id.items()}
word_to_id[""] = 0
word_to_id[""] = 1
word_to_id[""] = 2
id_to_word = {value: key for key, value in word_to_id.items()}
x_train = list(map(lambda sentence: ' '.join(
id_to_word[i] for i in sentence), x_train))
x_test = list(map(lambda sentence: ' '.join(
id_to_word[i] for i in sentence), x_test))
x_train = np.array(x_train, dtype=np.str)
x_test = np.array(x_test, dtype=np.str)
print(x_train.shape) # (25000,)
print(y_train.shape) # (25000, 1)
print(x_train[0][:50]) # this film was just brilliant casting

import autokeras as ak
clf = ak.TextClassifier(max_trials=10) # It tries 10 different models.
clf.fit(x_train, y_train)
predicted_y = clf.predict(x_test)
print(clf.evaluate(x_test, y_test))
`

Expected Behavior

Setup Details

Include the details about the versions of:

• OS type and version:
• Python: 3.6.9
• autokeras: 1.0.2
• keras-tuner: 1.0.1
• scikit-learn:
• numpy: 1.18.2
• pandas: 1.0.3
• tensorflow: 2.2.0-rc2

Additional context

[marcelogp]

I'm also having this issue and seems a duplicate of #1095.

It works with tensorflow 2.1.0 but I need to use a newer version to be able to export the model.

[WaseemRaza844]

I am also facing the same issue on the titanic survival problem I got from the following link. https://autokeras.com/examples/titanic/

I have also done some tweaking.

import autokeras as ak

Initialize the classifier.

clf = ak.StructuredDataClassifier(max_trials=30)

x is the path to the csv file. y is the column name of the column to predict.

clf.fit(x=train, y='survived') # @ instead of using name of the column, I have used the its original values

clf.fit(x=train, y=train[['survived']])

Evaluate the accuracy of the found model.

print('Accuracy: {accuracy}'.format(accuracy=clf.evaluate(x=eval, y=eval[['survived']])))

[Agnelgiles]

Change the imports to

from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
from keras.layers import Dense
from keras.layers import Dropout
from keras import models

[haifeng-jin]

This is fixed in 1.0.3 release. You can also export the model with 1.0.3. Thanks.