Issue With Keras Functional API and Keras Tuner

[PotnisAkshay]

**Hi,

I am facing an issue with my code which uses a Keras functional API and a Keras Tuner. I didn't know where else to post this. Below is my code**

import numpy as np
import pandas as pd
import tensorflow as tf

from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing

import os
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
import seaborn as sns
import numpy as np

pip install -q -U keras-tuner

import keras_tuner as kt

df = pd.read_csv("/content/credit-approval_csv.csv", delimiter=',')

train, test = train_test_split(df, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
print(len(train), 'train examples')
print(len(val), 'validation examples')
print(len(test), 'test examples')

def df_to_dataset(dataframe, shuffle=True, batch_size=32):
  dataframe = df.copy()
  labels = dataframe.pop('class')
  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
  if shuffle:
    ds = ds.shuffle(buffer_size=len(dataframe))
  ds = ds.batch(batch_size)
  ds = ds.prefetch(batch_size)
  return ds

def get_normalization_layer(name, dataset):
  # Create a Normalization layer for our feature.
  normalizer = preprocessing.Normalization(axis=None)

  # Prepare a Dataset that only yields our feature.
  feature_ds = dataset.map(lambda x, y: x[name])

  # Learn the statistics of the data.
  normalizer.adapt(feature_ds)

  return normalizer

def get_category_encoding_layer(name, dataset, dtype, max_tokens=None):
  # Create a StringLookup layer which will turn strings into integer indices
  if dtype == 'string':
    index = preprocessing.StringLookup(max_tokens=max_tokens)
  else:
    index = preprocessing.IntegerLookup(max_tokens=max_tokens)

  # Prepare a Dataset that only yields our feature
  feature_ds = dataset.map(lambda x, y: x[name])

  # Learn the set of possible values and assign them a fixed integer index.
  index.adapt(feature_ds)

  # Create a Discretization for our integer indices.
  encoder = preprocessing.CategoryEncoding(num_tokens=index.vocabulary_size())

  # Apply one-hot encoding to our indices. The lambda function captures the
  # layer so we can use them, or include them in the functional model later.
  return lambda feature: encoder(index(feature))

batch_size = 256
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)

all_inputs = []
encoded_features = []

# Numeric features.
for header in ['A2', 'A3', 'A8', 'A11', 'A14', 'A15']:
  numeric_col = tf.keras.Input(shape=(15), name=header)
  normalization_layer = get_normalization_layer(header, train_ds)
  encoded_numeric_col = normalization_layer(numeric_col)
  all_inputs.append(numeric_col)
  encoded_features.append(encoded_numeric_col)

# Categorical features encoded as string.
categorical_cols = ['A13', 'A12', 'A10', 'A9',
                    'A7', 'A6', 'A5', 'A4', 'A1']
for header in categorical_cols:
  categorical_col = tf.keras.Input(shape=(15), name=header, dtype='string')
  encoding_layer = get_category_encoding_layer(header, train_ds, dtype='string',
                                               max_tokens=5)
  encoded_categorical_col = encoding_layer(categorical_col)
  all_inputs.append(categorical_col)
  encoded_features.append(encoded_categorical_col)

def build_model(hp):
  hp_units = hp.Int('units', min_value=1, max_value=1512, step=32)
  all_features = tf.keras.layers.concatenate(encoded_features)
  dense = layers.Dense(units=hp_units, activation="relu")
  x = dense(all_features)
  x = layers.Dense(units=hp_units, activation="relu")(x)
  x = layers.Dense(units=hp_units, activation="relu")(x)
  x = layers.Dense(units=hp_units, activation="relu")(x)
  x = layers.Dropout(rate=0.5)(x)
  outputs = layers.Dense(units=hp_units)(x)

  model = tf.keras.Model(all_inputs, outputs)

  hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])

  optimizer = hp.Choice("optimizer", ["adam", "sgd", "RMSprop"])
  loss = hp.Choice("loss", ["BinaryCrossentropy", "CategoricalCrossentropy", "SparseCategoricalCrossentropy"])

  model.compile(optimizer,
                loss,
                metrics=['accuracy'])

  return model

tuner = kt.Hyperband(build_model,
                     objective='val_accuracy',
                     max_epochs=10,
                     factor=3,
                     hyperband_iterations=1,
                     directory='my_dir',
                     project_name='intro_to_kt',
                     overwrite=True)

tuner.search(train_ds, epochs=50, validation_data=val_ds)`

After this I run into the below error

`Epoch 1/2
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.float32, name='A2'), name='A2', description="created by layer 'A2'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.float32, name='A3'), name='A3', description="created by layer 'A3'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.float32, name='A8'), name='A8', description="created by layer 'A8'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.float32, name='A11'), name='A11', description="created by layer 'A11'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.float32, name='A14'), name='A14', description="created by layer 'A14'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.float32, name='A15'), name='A15', description="created by layer 'A15'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.string, name='A13'), name='A13', description="created by layer 'A13'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.string, name='A12'), name='A12', description="created by layer 'A12'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.string, name='A10'), name='A10', description="created by layer 'A10'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.string, name='A9'), name='A9', description="created by layer 'A9'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.string, name='A7'), name='A7', description="created by layer 'A7'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.string, name='A6'), name='A6', description="created by layer 'A6'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.string, name='A5'), name='A5', description="created by layer 'A5'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.string, name='A4'), name='A4', description="created by layer 'A4'"), but it was called on an input with incompatible shape (None, 1).
WARNING:tensorflow:Model was constructed with shape (None, 15) for input KerasTensor(type_spec=TensorSpec(shape=(None, 15), dtype=tf.string, name='A1'), name='A1', description="created by layer 'A1'"), but it was called on an input with incompatible shape (None, 1).
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-82-848b8addbd7e> in <module>()
----> 1 tuner.search(train_ds, epochs=50, validation_data=val_ds)

13 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    984           except Exception as e:  # pylint:disable=broad-except
    985             if hasattr(e, "ag_error_metadata"):
--> 986               raise e.ag_error_metadata.to_exception(e)
    987             else:
    988               raise

ValueError: in user code:

    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:855 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:845 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1285 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:838 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 train_step
        y_pred = self(x, training=True)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1030 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:421 call
        inputs, training=training, mask=mask)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:556 _run_internal_graph
        outputs = node.layer(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1013 __call__
        input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_spec.py:255 assert_input_compatibility
        ' but received input with shape ' + display_shape(x.shape))

    ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 131 but received input with shape (None, 47)`

[haifeng-jin]

I think this is an Keras issue. You can first try to implement the model without KerasTuner but only with Keras.