As the title suggests I have been trying to create a pipeline for training an Autoencoder model using TFX. The problem I'm having is fitting the tf.Dataset returned by the DataAccessor.tf_dataset_factory object to the Autoencoder.
Below I summarise the steps I've taken through this project, and have some Questions at the bottom if you wish to skip the background information.
The TFX components I have used so far have been:
The model that I am attempting to train is based off of the example laid out here https://www.tensorflow.org/tutorials/generative/autoencoder. However, my model is being trained on tabular data, searching for anomalous results, as opposed to image data.
As I have tried a couple of solutions I have tried using both the Keras.layers and Keras.model format for defining the model and I outline both below:
class Autoencoder(keras.models.Model):
def __init__(self, features):
super(Autoencoder, self).__init__()
self.encoder = tf.keras.Sequential([
keras.layers.Dense(82, activation = 'relu'),
keras.layers.Dense(32, activation = 'relu'),
keras.layers.Dense(16, activation = 'relu'),
keras.layers.Dense(8, activation = 'relu')
])
self.decoder = tf.keras.Sequential([
keras.layers.Dense(16, activation = 'relu'),
keras.layers.Dense(32, activation = 'relu'),
keras.layers.Dense(len(features), activation = 'sigmoid')
])
def call(self, x):
inputs = [keras.layers.Input(shape = (1,), name = f) for f in features]
dense = keras.layers.concatenate(inputs)
encoded = self.encoder(dense)
decoded = self.decoder(encoded)
return decoded
def _build_keras_model(features: List[str]) -> tf.keras.Model:
inputs = [keras.layers.Input(shape = (1,), name = f) for f in features]
dense = keras.layers.concatenate(inputs)
dense = keras.layers.Dense(32, activation = 'relu')(dense)
dense = keras.layers.Dense(16, activation = 'relu')(dense)
dense = keras.layers.Dense(8, activation = 'relu')(dense)
dense = keras.layers.Dense(16, activation = 'relu')(dense)
dense = keras.layers.Dense(32, activation = 'relu')(dense)
outputs = keras.layers.Dense(len(features), activation = 'sigmoid')(dense)
model = keras.Model(inputs = inputs, outputs = outputs)
model.compile(
optimizer = 'adam',
loss = 'mae'
)
return model
For creating the Trainer Component I have been mainly following the implementation details laid out here: https://www.tensorflow.org/tfx/guide/trainer
As well as following the default penguins example: https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple#write_model_training_code
def run_fn(fn_args: tfx.components.FnArgs) -> None:
tft_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(
file_pattern = fn_args.train_files,
data_accessor = fn_args.data_accessor,
tf_transform_output = tft_output,
batch_size = fn_args.train_steps
)
eval_dataset = _input_fn(
file_pattern = fn_args.eval_files,
data_accessor = fn_args.data_accessor,
tf_transform_output = tft_output,
batch_size = fn_args.custom_config['eval_batch_size']
)
# model = Autoencoder(
# features = fn_args.custom_config['features']
# )
model = _build_keras_model(features = fn_args.custom_config['features'])
model.compile(optimizer = 'adam', loss = 'mse')
model.fit(
train_dataset,
steps_per_epoch = fn_args.train_steps,
validation_data = eval_dataset,
validation_steps = fn_args.eval_steps
)
...
def _apply_preprocessing(raw_features, tft_layer):
transformed_features = tft_layer(raw_features)
return transformed_features
def _input_fn(
file_pattern,
data_accessor: tfx.components.DataAccessor,
tf_transform_output: tft.TFTransformOutput,
batch_size: int) -> tf.data.Dataset:
"""
Generates features and label for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
tf_transform_output: A TFTransformOutput.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains features where features is a
dictionary of Tensors.
"""
dataset = data_accessor.tf_dataset_factory(
file_pattern,
tfxio.TensorFlowDatasetOptions(batch_size = batch_size),
tf_transform_output.transformed_metadata.schema
)
transform_layer = tf_transform_output.transform_features_layer()
def apply_transform(raw_features):
return _apply_preprocessing(raw_features, transform_layer)
return dataset.map(apply_transform).repeat()
This differs from the _input_fn example given above as I was following the example in the next tfx tutorial found here: https://www.tensorflow.org/tfx/tutorials/tfx/penguin_tft#run_fn
Also for reference, there is no Target within the example data so there is no label_key to be passed to the tfxio.TensorFlowDatasetOptions object.
When trying to run the Trainer component using a TFX InteractiveContext object I receive the following error.
ValueError: No gradients provided for any variable: ['dense_460/kernel:0', 'dense_460/bias:0', 'dense_461/kernel:0', 'dense_461/bias:0', 'dense_462/kernel:0', 'dense_462/bias:0', 'dense_463/kernel:0', 'dense_463/bias:0', 'dense_464/kernel:0', 'dense_464/bias:0', 'dense_465/kernel:0', 'dense_465/bias:0'].
From my own attempts to solve this I believe the problem lies in the way that an Autoencoder is trained. From the Autoencoder example linked here https://www.tensorflow.org/tutorials/generative/autoencoder the data is fitted like so:
autoencoder.fit(x_train, x_train,
epochs=10,
shuffle=True,
validation_data=(x_test, x_test))
therefore it stands to reason that the tf.Dataset should also mimic this behaviour and when testing with plain Tensor objects I have been able to recreate the error above and then solve it when adding the target to be the same as the training data in the .fit() function.
model.fit(
train_dataset,
train_dataset,
steps_per_epoch = fn_args.train_steps,
validation_data = eval_dataset,
validation_steps = fn_args.eval_steps
)
Raises error due to Keras not accepting a 'y' value when a dataset is passed.
ValueError: `y` argument is not supported when using dataset as input.
def _input_fn(...
dataset = data_accessor.tf_dataset_factory(
file_pattern,
tfxio.TensorFlowDatasetOptions(batch_size = batch_size),
tf_transform_output.transformed_metadata.schema
)
transform_layer = tf_transform_output.transform_features_layer()
def apply_transform(raw_features):
return _apply_preprocessing(raw_features, transform_layer)
dataset = dataset.map(apply_transform)
return dataset.map(lambda x: (x, x))
This raises an error where the keys from the features dictionary don't match the output of the model.
ValueError: Found unexpected keys that do not correspond to any Model output: dict_keys(['feature_string', ...]). Expected: ['dense_477']
At this point I switched to using the keras.model Autoencoder subclass and tried to add output keys to the Model using an output which I tried to create dynamically in the same way as the inputs.
def call(self, x):
inputs = [keras.layers.Input(shape = (1,), name = f) for f in x]
dense = keras.layers.concatenate(inputs)
encoded = self.encoder(dense)
decoded = self.decoder(encoded)
outputs = {}
for feature_name in x:
outputs[feature_name] = keras.layers.Dense(1, activation = 'sigmoid')(decoded)
return outputs
This raises the following error:
TypeError: Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.
I've been looking into solving this issue but am no longer sure if the data is being passed correctly and am beginning to think I'm getting side-tracked from the actual problem.
So I managed to find an answer to this and wanted to leave what I found here in case anyone else stumbles onto a similar problem.
It turns out my feelings around the error were correct and the solution did indeed lie in how the tf.Dataset object was presented.
This can be demonstrated when I ran some code which simulated the incoming data using randomly generated tensors.
tensors = [tf.random.uniform(shape = (1, 82)) for i in range(739)]
# This gives us a list of 739 tensors which hold 1 value for 82 'features' simulating the dataset I had
dataset = tf.data.Dataset.from_tensor_slices(tensors)
dataset = dataset.map(lambda x : (x, x))
# This returns a dataset which marks the training set and target as the same
# which is what the Autoecnoder model is looking for
model.fit(dataset ...)
Following this I proceeded to do the same thing with the dataset returned by the _input_fn. Given that the tfx DataAccessor object returns a features_dict however I needed to combine the tensors in that dict together to create a single tensor.
This is how my _input_fn looks now:
def create_target_values(features_dict: Dict[str, tf.Tensor]) -> tuple:
value_tensor = tf.concat(list(features_dict.values()), axis = 1)
return (features_dict, value_tensor)
def _input_fn(
file_pattern,
data_accessor: tfx.components.DataAccessor,
tf_transform_output: tft.TFTransformOutput,
batch_size: int) -> tf.data.Dataset:
"""
Generates features and label for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
tf_transform_output: A TFTransformOutput.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, target_tensor) tuple where features is a
dictionary of Tensors, and target_tensor is a single Tensor that is a concatenated tensor of all the
feature values.
"""
dataset = data_accessor.tf_dataset_factory(
file_pattern,
tfxio.TensorFlowDatasetOptions(batch_size = batch_size),
tf_transform_output.transformed_metadata.schema
)
dataset = dataset.map(lambda x: create_target_values(features_dict = x))
return dataset.repeat()