Hi !
I’m having troubles making the Preprocessing layers and the Keras Tuner cooperate.
I am referring to this tutorial Load CSV data | TensorFlow Core for the Preprocessing part, and to the Getting started with KerasTuner documentation for the Keras Tuner part.
Briefly, here’s the code.
He loads the data:
titanic = pd.read_csv("https://storage.googleapis.com/tf-datasets/titanic/train.csv")
titanic_features = titanic.copy()
titanic_labels = titanic_features.pop('survived')
He creates the symbolic tensors of the features in a dictionary
inputs = {}
for name, column in titanic_features.items():
dtype = column.dtype
if dtype == object:
dtype = tf.string
else:
dtype = tf.float32
inputs[name] = tf.keras.Input(shape=(1,), name=name, dtype=dtype)
inputs
Then he applies normalization on the numerical features:
numeric_inputs = {name:input for name,input in inputs.items()
if input.dtype==tf.float32}
x = layers.Concatenate()(list(numeric_inputs.values()))
norm = layers.Normalization()
norm.adapt(np.array(titanic[numeric_inputs.keys()]))
all_numeric_inputs = norm(x)
all_numeric_inputs
He creates a list
preprocessed_inputs = [all_numeric_inputs]
He one hot encodes the categorical features:
for name, input in inputs.items():
if input.dtype == tf.float32:
continue
lookup = layers.StringLookup(vocabulary=np.unique(titanic_features[name]))
one_hot = layers.CategoryEncoding(num_tokens=lookup.vocabulary_size())
x = lookup(input)
x = one_hot(x)
preprocessed_inputs.append(x)
and then He concatenates it:
preprocessed_inputs_cat = layers.Concatenate()(preprocessed_inputs)
titanic_preprocessing = tf.keras.Model(inputs, preprocessed_inputs_cat)
Now what I want to do is insert this preprocessing part in KerasTuner. I tried this:
def titanic_model(units, activation):
model_inputs = tf.keras.Input(shape=28)
dense_1 = layers.Dense(units=units, activation=activation)(model_inputs)
dense_output = layers.Dense(1)(dense_1)
body = tf.keras.Model(inputs = model_inputs, outputs = dense_output)
return body
def build_model(hp,preprocessing_head, inputs):
units = hp.Int("units", min_value=32, max_value=512, step=32)
activation = hp.Choice("activation", ["relu", "tanh"])
preprocessed_inputs = preprocessing_head(inputs)
result = titanic_model(units,
activation)(preprocessed_inputs)
model = tf.keras.Model(inputs, result)
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(),
metrics = ['accuracy'])
return model
titanic_model = build_model(keras_tuner.HyperParameters(),titanic_preprocessing, inputs)
but it gives me the following error:
Inputs to a layer should be tensors. Got: <keras_tuner.engine.hyperparameters.HyperParameters object at 0x7ff52844da30>
I cannot understand if I am close to the solution, or this is not the right way to proceed.
However, the workaround i found was to insert directly in the build_model function the preprocessing layer (titanic_preprocessing) and the inputs dictionary, without passing it as an argument of the function.
Hence:
def titanic_model(units, activation):
model_inputs = tf.keras.Input(shape=28)
dense_1 = layers.Dense(units=units, activation=activation)(model_inputs)
dense_output = layers.Dense(1)(dense_1)
body = tf.keras.Model(inputs = model_inputs, outputs = dense_output)
return body
def build_model(hp):
units = hp.Int("units", min_value=32, max_value=512, step=32)
activation = hp.Choice("activation", ["relu", "tanh"])
preprocessed_inputs = titanic_preprocessing(inputs)
result = titanic_model(units,
activation)(preprocessed_inputs)
model = tf.keras.Model(inputs, result)
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(),
metrics = ['accuracy'])
return model
titanic_model = build_model()
In this case it seems to work, and by setting the tuner
tuner = keras_tuner.RandomSearch(
hypermodel = build_model,
objective=keras_tuner.Objective("accuracy", direction="max"),
max_trials = 1,
overwrite = True,
directory = "tuner_dir",
project_name = "regression_tuner")
and searching it works:
tuner.search(x=titanic_features_dict, y=titanic_labels, epochs=10)
However, I am doubting this solution and would appreciate your feedback on this.
Thank you!
