Tutorials don't work.

Bug Description

Reproducing Steps

All the examples listed in https://autokeras.com/tutorial/ fail in version 1.0.0.

First example:

>>> import autokeras as ak
>>> from keras.datasets import mnist
Using TensorFlow backend.
>>> 
>>> # Prepare the data.
... (x_train, y_train), (x_test, y_test) = mnist.load_data()
>>> x_train = x_train.reshape(x_train.shape + (1,))
>>> x_test = x_test.reshape(x_test.shape + (1,))
>>> 
>>> # Search and train the classifier.
... clf = ak.ImageClassifier(max_trials=100)
>>> clf.fit(x_train, y_train)
2019-09-03 15:17:47.250598: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-09-03 15:17:47.271654: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1992000000 Hz
2019-09-03 15:17:47.272577: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4e7f8e0 executing computations on platform Host. Devices:
2019-09-03 15:17:47.272623: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/auto_model.py", line 101, in fit
    validation_split=validation_split)
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/auto_model.py", line 146, in prepare_data
    x_val, y_val = validation_data
TypeError: 'NoneType' object is not iterable

Second example:

>>> import numpy as np
>>> import autokeras as ak
>>> from keras.datasets import mnist
>>> 
>>> # Prepare the data.
... (x_train, y_classification), (x_test, y_test) = mnist.load_data()
>>> x_image = x_train.reshape(x_train.shape + (1,))
>>> x_test = x_test.reshape(x_test.shape + (1,))
>>> 
>>> x_structured = np.random.rand(x_train.shape[0], 100)
>>> y_regression = np.random.rand(x_train.shape[0], 1)
>>> 
>>> # Build model and train.
... automodel = ak.AutoModel(
...    inputs=[ak.ImageInput(),
...            ak.StructuredDataInput()],
...    outputs=[ak.RegressionHead(metrics=['mae']),
...             ak.ClassificationHead(loss='categorical_crossentropy',
...                                   metrics=['accuracy'])])
Traceback (most recent call last):
  File "<stdin>", line 4, in <module>
AttributeError: module 'autokeras' has no attribute 'StructuredDataInput'

This error can be fixed by replacing StructuredDataInput with StructuredInput, but this still fails:

>>> import numpy as np
>>> import autokeras as ak
>>> from keras.datasets import mnist
>>> 
>>> # Prepare the data.
... (x_train, y_classification), (x_test, y_test) = mnist.load_data()
>>> x_image = x_train.reshape(x_train.shape + (1,))
>>> x_test = x_test.reshape(x_test.shape + (1,))
>>> 
>>> x_structured = np.random.rand(x_train.shape[0], 100)
>>> y_regression = np.random.rand(x_train.shape[0], 1)
>>> 
>>> # Build model and train.
... automodel = ak.AutoModel(
...    inputs=[ak.ImageInput(),
...            ak.StructuredInput()],
...    outputs=[ak.RegressionHead(metrics=['mae']),
...             ak.ClassificationHead(loss='categorical_crossentropy',
...                                   metrics=['accuracy'])])
>>> automodel.fit([x_image, x_structured],
...               [y_regression, y_classification])
Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/auto_model.py", line 101, in fit
    validation_split=validation_split)
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/auto_model.py", line 131, in prepare_data
    y = self._label_encoding(y)
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/auto_model.py", line 182, in _label_encoding
    label_encoder.fit_with_labels(y)
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/utils.py", line 153, in fit_with_labels
    data = np.array(data).flatten()
ValueError: could not broadcast input array from shape (60000,1) into shape (60000)

Finally, the third example fails:

>>> import numpy as np
>>> import tensorflow as tf
>>> from keras.datasets import mnist
>>> 
>>> # Prepare the data.
... (x_train, y_classification), (x_test, y_test) = mnist.load_data()
>>> x_image = x_train.reshape(x_train.shape + (1,))
>>> x_test = x_test.reshape(x_test.shape + (1,))
>>> 
>>> x_structured = np.random.rand(x_train.shape[0], 100)
>>> y_regression = np.random.rand(x_train.shape[0], 1)
>>> 
>>> # Build model and train.
... inputs = ak.ImageInput(shape=(28, 28, 1))
>>> outputs1 = ak.ResNetBlock(version='next')(inputs)
>>> outputs2 = ak.XceptionBlock()(inputs)
>>> image_outputs = ak.Merge()((outputs1, outputs2))
>>> 
>>> structured_inputs = ak.StructuredInput()
>>> structured_outputs = ak.DenseBlock()(structured_inputs)
>>> merged_outputs = ak.Merge()((image_outputs, structured_outputs))
>>> 
>>> classification_outputs = ak.ClassificationHead()(merged_outputs)
>>> regression_outputs = ak.RegressionHead()(merged_outputs)
>>> automodel = ak.GraphAutoModel(inputs=inputs,
...                               outputs=[regression_outputs,
...                                        classification_outputs])
Traceback (most recent call last):
  File "<stdin>", line 3, in <module>
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/auto_model.py", line 243, in __init__
    self.hypermodel = graph.GraphHyperModel(self.inputs, self.outputs)
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/hypermodel/graph.py", line 33, in __init__
    self._build_network()
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/hypermodel/graph.py", line 135, in _build_network
    '{name}.'.format(name=block.name))
ValueError: A required input is missing for HyperModel merge_4.

In my own example, the choice of validation data does not appear to work:

>>> import pandas as pd
>>> import autokeras as ak
>>> import numpy as np
>>>
>>> automodel = ak.auto_model.AutoModel(
...         inputs=[ak.StructuredInput()],
...         outputs=[
...             ak.RegressionHead(metrics=['mae']),
...             ak.ClassificationHead(loss='categorical_crossentropy', metrics=['accuracy'])
...             ]
...         )
>>>
>>> df = pd.read_csv("samples.csv")
>>> X = np.array(df[df.columns[:-4]])
>>> yr = np.array(df[["residuary_resistance", "doubled"]])
>>> yc = np.array(df[["animal", "colour"]])
>>>
>>>
>>> automodel.fit(x=[X], y=[yr, yc])
2019-09-03 15:23:55.991949: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-09-03 15:23:56.015636: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1992000000 Hz
2019-09-03 15:23:56.016339: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4231040 executing computations on platform Host. Devices:
2019-09-03 15:23:56.016375: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/auto_model.py", line 101, in fit
    validation_split=validation_split)
  File "/home/michael/MonolithApp/dpu/venv/src/autokeras/autokeras/auto_model.py", line 146, in prepare_data
    x_val, y_val = validation_data
TypeError: 'NoneType' object is not iterable

Expected Behavior

Expected behaviour is that the examples are able to run to the end.

Setup Details

Include the details about the versions of:

• OS type and version: Ubuntu 18.04.3 LTS
• Python: 3.6.8
• autokeras: 1.0.0 (pip3 install git+git://github.com/keras-team/autokeras@master#egg=autokeras)
• scikit-learn: 0.20.2
• numpy: 1.16.1
• keras: 2.2.4
• scipy: 1.2.0
• tensorflow: 2.0.0rc0
• pytorch: 1.0.1post2

[DyassKhalid007]

same issues

[yokopi521]

I’m having the same issues, too