I am building a TFX pipeline for timeseries with a uci open-source data https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction. As timeseries training and evaluation requires window sequence of data, i used the tf.keras.preprocessing.timeseries_dataset_from_array API within the input_fn function in the Trainer.py module file to create the window for training and evaluation but i got a TypeError: dataset length is infinite.
Trainer.py module file contains below code:
from typing import List, Text
import os
import absl
import datetime
import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.executor import TrainerFnArgs
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import dataset_options
LABEL_KEY = 'Appliances'
_DENSE_FLOAT_FEATURE_KEYS = ['lights', 'T1', 'RH_1', 'T2', 'RH_2', 'T3',
'RH_3', 'T4', 'RH_4', 'T5', 'RH_5', 'T6', 'RH_6', 'T7', 'RH_7', 'T8',
'RH_8', 'T9', 'RH_9', 'T_out', 'Press_mm_hg', 'RH_out', 'Windspeed',
'Visibility', 'Tdewpoint', 'rv1', 'rv2']
def _transformed_name(key):
return key + '_xf'
def _transformed_names(keys):
return [_transformed_name(key) for key in keys]
def _get_serve_tf_examples_fn(model, tf_transform_output):
"""Returns a function that parses a serialized tf.Example and applies TFT."""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function
def serve_tf_examples_fn(serialized_tf_examples):
"""Returns the output to be used in the serving signature."""
feature_spec = tf_transform_output.raw_feature_spec()
feature_spec.pop(LABEL_KEY)
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return model(transformed_features)
return serve_tf_examples_fn
**def _input_fn(file_pattern: List[Text],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) -> tf.data.Dataset:
import numpy as np
"""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, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_transformed_name(LABEL_KEY)),
tf_transform_output.transformed_metadata.schema)
sequence_generator = tf.keras.preprocessing.timeseries_dataset_from_array(
dataset, dataset, sequence_length=144, batch_size=1)
return sequence_generator**
def _build_keras_model(hidden_units: List[int] = None) -> tf.keras.Model:
"""Creates a DNN Keras model for classifying taxi data.
Args:
hidden_units: [int], the layer sizes of the DNN (input layer first).
Returns:
A keras Model.
"""
real_valued_columns = [
tf.feature_column.numeric_column(key, shape=())
for key in _transformed_names(_DENSE_FLOAT_FEATURE_KEYS)
]
model = regressor_model(
deep_columns=real_valued_columns,
dnn_hidden_units=hidden_units or [100, 70, 50, 25])
return model
def regressor_model(deep_columns, dnn_hidden_units):
"""Build a simple keras regressor model.
Args:
deep_columns: Feature columns for deep part of the model.
dnn_hidden_units: [int], the layer sizes of the hidden DNN.
Returns:
A Deep Keras model
"""
# Following values are hard coded for simplicity in this example,
# However prefarably they should be passsed in as hparams.
# Keras needs the feature definitions at compile time.
input_layers = {
colname: tf.keras.layers.Input(name=colname, shape=(), dtype=tf.float32)
for colname in _transformed_names(_DENSE_FLOAT_FEATURE_KEYS)
}
deep = tf.keras.layers.DenseFeatures(deep_columns)(input_layers)
for numnodes in dnn_hidden_units:
deep = tf.keras.layers.Dense(numnodes)(deep)
output = tf.keras.layers.Dense(1)(deep)
model = tf.keras.Model(input_layers, output)
model.compile(
loss='mean_absolute_error',
optimizer=tf.keras.optimizers.Adam(lr=0.001),
metrics=[tf.keras.metrics.MeanAbsoluteError()])
model.summary(print_fn=absl.logging.info)
return model
TFX Trainer will call this function.
def run_fn(fn_args: TrainerFnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
# Number of nodes in the first layer of the DNN
first_dnn_layer_size = 100
num_dnn_layers = 4
dnn_decay_factor = 0.7
sequence_length = 144
batch_size = 1
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(fn_args.train_files, fn_args.data_accessor,
tf_transform_output, 40)
print('fn_args.train_files:', fn_args.train_files)
print('fn_args.data_accessor:', fn_args.data_accessor)
print('tf_transform_output:', tf_transform_output)
print('train_dataset:')
#for example in train_dataset.take(1):
# print(example)
eval_dataset = _input_fn(fn_args.eval_files, fn_args.data_accessor,
tf_transform_output, 40)
print('fn_args.eval_files:', fn_args.eval_files)
print('fn_args.data_accessor:', fn_args.data_accessor)
print('tf_transform_output:', tf_transform_output)
print('eval_dataset:', eval_dataset)
model = _build_keras_model(
# Construct layers sizes with exponetial decay
hidden_units=[
max(2, int(first_dnn_layer_size * dnn_decay_factor**i))
for i in range(num_dnn_layers)
])
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=fn_args.model_run_dir, update_freq='batch')
model.fit(
train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps,
callbacks=[tensorboard_callback])
signatures = {
'serving_default':
_get_serve_tf_examples_fn(model,
tf_transform_output).get_concrete_function(
tf.TensorSpec(
shape=[None],
dtype=tf.string,
name='examples')),
}
model.save(fn_args.serving_model_dir, save_format='tf', signatures=signatures)
Trainer configuration:
trainer = Trainer(
module_file=os.path.abspath(trainer_module_file),
custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
context.run(trainer)
Error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last) <ipython-input-53-cc5cfdc341eb> in <module>()
7 train_args=trainer_pb2.TrainArgs(num_steps=10000),
8 eval_args=trainer_pb2.EvalArgs(num_steps=5000))
----> 9 context.run(trainer)
/usr/local/lib/python3.6/dist-packages/tfx/orchestration/experimental/interactive/interactive_context.py in run_if_ipython(*args, **kwargs)
65 # __IPYTHON__ variable is set by IPython, see
66 # https://ipython.org/ipython-doc/rel-0.10.2/html/interactive/reference.html#embedding-ipython. ---> 67 return fn(*args, **kwargs)
68 else:
69 absl.logging.warning(
/usr/local/lib/python3.6/dist-packages/tfx/orchestration/experimental/interactive/interactive_context.py in run(self, component, enable_cache, beam_pipeline_args)
180 telemetry_utils.LABEL_TFX_RUNNER: runner_label,
181 }):
--> 182 execution_id = launcher.launch().execution_id
183
184 return execution_result.ExecutionResult(
/usr/local/lib/python3.6/dist-packages/tfx/orchestration/launcher/base_component_launcher.py in launch(self)
203 execution_decision.input_dict,
204 execution_decision.output_dict,
--> 205 execution_decision.exec_properties)
206
207 absl.logging.info('Running publisher for %s', /usr/local/lib/python3.6/dist-packages/tfx/orchestration/launcher/in_process_component_launcher.py in _run_executor(self, execution_id, input_dict, output_dict, exec_properties)
65 executor_context) # type: ignore
66
---> 67 executor.Do(input_dict, output_dict, exec_properties)
/usr/local/lib/python3.6/dist-packages/tfx/components/trainer/executor.py in Do(self, input_dict, output_dict, exec_properties)
217 # Train the model
218 absl.logging.info('Training model.')
--> 219 run_fn(fn_args)
220
221 # Note: If trained with multi-node distribution workers, it is the user /content/trainer.py in run_fn(fn_args)
160
161 train_dataset = _input_fn(fn_args.train_files, fn_args.data_accessor,
--> 162 tf_transform_output, 40)
163
164 print('fn_args.train_files:', fn_args.train_files) /content/trainer.py in _input_fn(file_pattern, data_accessor, tf_transform_output, batch_size)
70
71 dataset = tf.keras.preprocessing.timeseries_dataset_from_array(
---> 72 dataset, dataset, sequence_length=144, batch_size=1)
73
74 #label_key=_transformed_name(LABEL_KEY)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/preprocessing/timeseries.py in timeseries_dataset_from_array(data, targets, sequence_length, sequence_stride, sampling_rate, batch_size, shuffle, seed, start_index, end_index)
117 """
118 # Validate the shape of data and targets -->
119 if targets is not None and len(targets) != len(data):
120 raise ValueError('Expected data and targets to have the same number of '
121 'time steps (axis 0) but got '
/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py in len(self)
443 length = self.cardinality()
444 if length.numpy() == INFINITE: -->
445 raise TypeError("dataset length is infinite.")
446 if length.numpy() == UNKNOWN:
447 raise TypeError("dataset length is unknown.")
TypeError: dataset length is infinite.
