带有 TFX 的模型中的 Tensorflow TextVectorization 层
Tensorflow TextVectorization layer in model with TFX
我目前正在尝试使用 TFX 实现管道（我已经关注了这个笔记本：TFX - Chicago Taxi) in order to serve it with Tensorflow Serving. As I'm trying to implement my own pipeline to classify texts (comming from this dataset: Kaggle - BBC News Classification）
所以，现在我能够实现每个组件，直到培训师。例如，这是我的 Transform 组件：
import tensorflow as tf
import tensorflow_transform as tft
from utils import documents_constants
_TEXT_FEATURE_KEYS = documents_constants.TEXT_FEATURE_KEYS
_VOCAB_SIZE = documents_constants.VOCAB_SIZE
_OOV_SIZE = documents_constants.OOV_SIZE
_LABEL_KEY = documents_constants.LABEL_KEY
_transformed_name = documents_constants.transformed_name
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
# Pre-process the text
for key in _TEXT_FEATURE_KEYS:
outputs[_transformed_name(key)] = inputs[key]
# Make a dictionary out of output label
outputs[_transformed_name(_LABEL_KEY)] = tft.compute_and_apply_vocabulary(
_fill_in_missing(inputs[_LABEL_KEY]),
top_k=_VOCAB_SIZE,
num_oov_buckets=_OOV_SIZE)
return outputs
def _fill_in_missing(x):
"""Replace missing values in a SparseTensor.
Fills in missing values of `x` with '' or 0, and converts to a dense tensor.
Args:
x: A `SparseTensor` of rank 2. Its dense shape should have size at most 1
in the second dimension.
Returns:
A rank 1 tensor where missing values of `x` have been filled in.
"""
default_value = '' if x.dtype == tf.string else 0
return tf.squeeze(
tf.sparse.to_dense(
tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),
default_value),
axis=1)
这个只是旨在获取原始 'Text' 列并简单地计算输出类别的词汇表。
我的问题是，当我尝试构建一个包含
的模型时
tensorflow.keras.layers.experimental.preprocessing.TextVectorization
在我的模型层中。我的意思是，我可以很容易地将它包含在这样的模型中：
def _build_keras_model(vectorize_layer: TextVectorization) -> tf.keras.Model:
"""Creates a DNN Keras model for classifying documents.
Args:
vectorize_layer: TextVectorization, the layer sizes of the DNN (input layer first).
Returns:
A keras Model.
"""
# The first layer in our model is the vectorization layer. After this layer,
# we have a tensor of shape (batch_size, features) containing TF-IDF features.
input_layer = tf.keras.layers.Input(name="Text_xf", shape=(), dtype=tf.string)
deep = vectorize_layer(input_layer)
deep = layers.Embedding(_max_features + 1, _embedding_dim)(deep)
deep = layers.Dropout(0.2)(deep)
deep = layers.GlobalAveragePooling1D()(deep)
deep = layers.Dropout(0.2)(deep)
output = layers.Dense(5, activation='sigmoid', name='predictions')(deep)
# Compile the model with binary crossentropy loss and an adam optimizer.
model = tf.keras.Model(input_layer, output)
model.compile(
loss=losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer='adam',
metrics=['accuracy'])
return model
还有这项工作。但是当我尝试将它与我的数据集相匹配时，我得到了这个：
TypeError: in user code:
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:747 train_step
y_pred = self(x, training=True)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
outputs = call_fn(inputs, *args, **kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/functional.py:386 call
inputs, training=training, mask=mask)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph
outputs = node.layer(*args, **kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
outputs = call_fn(inputs, *args, **kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization.py:571 call
inputs = self._preprocess(inputs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization.py:527 _preprocess
lowercase_inputs = gen_string_ops.string_lower(inputs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/ops/gen_string_ops.py:1028 string_lower
"StringLower", input=input, encoding=encoding, name=name)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:479 _apply_op_helper
repr(values), type(values).__name__, err))
TypeError: Expected string passed to parameter 'input' of op 'StringLower', got of type 'SparseTensor' instead. Error: Expected string, got of type 'SparseTensor' instead.
我是 Tensorflow 的新手，我正在尝试了解使用 TFX 编写管道的整个过程我不明白为什么矢量化层似乎不期望 SparseTensor 而期望一个字符串。我确实知道使用实验性功能的含义，但如果有人有想法，或者可以指出我正在犯的一个明显错误，那就太好了！！
我 运行 没有办法完成这项工作。
注意：我认为它会来自于我检索数据集的方式：
def _input_fn(file_pattern: List[Text],
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) -> tf.data.Dataset:
"""Generates features and label for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
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.
"""
transformed_feature_spec = (
tf_transform_output.transformed_feature_spec().copy())
dataset = tf.data.experimental.make_batched_features_dataset(
file_pattern=file_pattern,
batch_size=batch_size,
features=transformed_feature_spec,
reader=_gzip_reader_fn,
label_key=_transformed_name(_LABEL_KEY))
return dataset
我也是这样用的:
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.
"""
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(fn_args.train_files, tf_transform_output, 40)
eval_dataset = _input_fn(fn_args.eval_files, tf_transform_output, 40)
# TODO: Make better method to adapt vectorizer layer
text_feature_spec = {_transformed_name('Text'): tf.io.FixedLenFeature([], dtype=tf.string)}
text_dataset = _input_text_fn(fn_args.train_files, text_feature_spec, 978)
text_dataset = text_dataset.map(lambda d: d[_transformed_name('Text')]).take(1)
vectorize_layer = get_vectorize_layer()
vectorize_layer.adapt(text_dataset)
model = _build_keras_model(vectorize_layer)
log_dir = os.path.join(os.path.dirname(fn_args.serving_model_dir), 'logs')
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=log_dir, update_freq='batch')
print(train_dataset)
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)
我已经设法让它工作了，但不是以最干净的方式。
我收到此消息的原因是模型中的 TextVectorization 层将只接受张量（看起来很密集）、numpy 数组、列表或数据集。所以我通过像这样调整我的代码来给他他想要的东西（这是更新的完整功能）：
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.
"""
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(fn_args.train_files, tf_transform_output, 40)
eval_dataset = _input_fn(fn_args.eval_files, tf_transform_output, 40)
vectorize_dataset = train_dataset.map(lambda f, l: tf.sparse.to_dense(f[_transformed_name('Text')])).unbatch()
vectorize_layer = TextVectorization(
max_tokens=_max_features,
output_mode='int',
output_sequence_length=500
)
vectorize_layer.adapt(vectorize_dataset.take(900))
model = _build_keras_model(vectorize_layer)
log_dir = os.path.join(os.path.dirname(fn_args.serving_model_dir), 'logs')
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=log_dir, update_freq='batch')
model.fit(
train_dataset.map(lambda f, l: (tf.sparse.to_dense(f[_transformed_name('Text')]), l)),
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset.map(lambda f, l: (tf.sparse.to_dense(f[_transformed_name('Text')]), l)),
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)
注意 fit 函数参数中的 map 函数。其余保持不变（差不多，我只是调整了输入层的形状并调整了模型以获得更好的结果）。
我想知道是否有更简单的方法来实现这一点并仍然保持 SparseTensor 的优势。
我目前正在尝试使用 TFX 实现管道（我已经关注了这个笔记本：TFX - Chicago Taxi) in order to serve it with Tensorflow Serving. As I'm trying to implement my own pipeline to classify texts (comming from this dataset: Kaggle - BBC News Classification）
所以，现在我能够实现每个组件，直到培训师。例如，这是我的 Transform 组件：
import tensorflow as tf
import tensorflow_transform as tft
from utils import documents_constants
_TEXT_FEATURE_KEYS = documents_constants.TEXT_FEATURE_KEYS
_VOCAB_SIZE = documents_constants.VOCAB_SIZE
_OOV_SIZE = documents_constants.OOV_SIZE
_LABEL_KEY = documents_constants.LABEL_KEY
_transformed_name = documents_constants.transformed_name
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
# Pre-process the text
for key in _TEXT_FEATURE_KEYS:
outputs[_transformed_name(key)] = inputs[key]
# Make a dictionary out of output label
outputs[_transformed_name(_LABEL_KEY)] = tft.compute_and_apply_vocabulary(
_fill_in_missing(inputs[_LABEL_KEY]),
top_k=_VOCAB_SIZE,
num_oov_buckets=_OOV_SIZE)
return outputs
def _fill_in_missing(x):
"""Replace missing values in a SparseTensor.
Fills in missing values of `x` with '' or 0, and converts to a dense tensor.
Args:
x: A `SparseTensor` of rank 2. Its dense shape should have size at most 1
in the second dimension.
Returns:
A rank 1 tensor where missing values of `x` have been filled in.
"""
default_value = '' if x.dtype == tf.string else 0
return tf.squeeze(
tf.sparse.to_dense(
tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),
default_value),
axis=1)
这个只是旨在获取原始 'Text' 列并简单地计算输出类别的词汇表。
我的问题是，当我尝试构建一个包含
的模型时tensorflow.keras.layers.experimental.preprocessing.TextVectorization
在我的模型层中。我的意思是，我可以很容易地将它包含在这样的模型中：
def _build_keras_model(vectorize_layer: TextVectorization) -> tf.keras.Model:
"""Creates a DNN Keras model for classifying documents.
Args:
vectorize_layer: TextVectorization, the layer sizes of the DNN (input layer first).
Returns:
A keras Model.
"""
# The first layer in our model is the vectorization layer. After this layer,
# we have a tensor of shape (batch_size, features) containing TF-IDF features.
input_layer = tf.keras.layers.Input(name="Text_xf", shape=(), dtype=tf.string)
deep = vectorize_layer(input_layer)
deep = layers.Embedding(_max_features + 1, _embedding_dim)(deep)
deep = layers.Dropout(0.2)(deep)
deep = layers.GlobalAveragePooling1D()(deep)
deep = layers.Dropout(0.2)(deep)
output = layers.Dense(5, activation='sigmoid', name='predictions')(deep)
# Compile the model with binary crossentropy loss and an adam optimizer.
model = tf.keras.Model(input_layer, output)
model.compile(
loss=losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer='adam',
metrics=['accuracy'])
return model
还有这项工作。但是当我尝试将它与我的数据集相匹配时，我得到了这个：
TypeError: in user code:
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:747 train_step
y_pred = self(x, training=True)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
outputs = call_fn(inputs, *args, **kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/functional.py:386 call
inputs, training=training, mask=mask)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph
outputs = node.layer(*args, **kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
outputs = call_fn(inputs, *args, **kwargs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization.py:571 call
inputs = self._preprocess(inputs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization.py:527 _preprocess
lowercase_inputs = gen_string_ops.string_lower(inputs)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/ops/gen_string_ops.py:1028 string_lower
"StringLower", input=input, encoding=encoding, name=name)
/opt/miniconda3/envs/archiving/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:479 _apply_op_helper
repr(values), type(values).__name__, err))
TypeError: Expected string passed to parameter 'input' of op 'StringLower', got of type 'SparseTensor' instead. Error: Expected string, got of type 'SparseTensor' instead.
我是 Tensorflow 的新手，我正在尝试了解使用 TFX 编写管道的整个过程我不明白为什么矢量化层似乎不期望 SparseTensor 而期望一个字符串。我确实知道使用实验性功能的含义，但如果有人有想法，或者可以指出我正在犯的一个明显错误，那就太好了！！
我 运行 没有办法完成这项工作。
注意：我认为它会来自于我检索数据集的方式：
def _input_fn(file_pattern: List[Text],
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) -> tf.data.Dataset:
"""Generates features and label for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
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.
"""
transformed_feature_spec = (
tf_transform_output.transformed_feature_spec().copy())
dataset = tf.data.experimental.make_batched_features_dataset(
file_pattern=file_pattern,
batch_size=batch_size,
features=transformed_feature_spec,
reader=_gzip_reader_fn,
label_key=_transformed_name(_LABEL_KEY))
return dataset
我也是这样用的:
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.
"""
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(fn_args.train_files, tf_transform_output, 40)
eval_dataset = _input_fn(fn_args.eval_files, tf_transform_output, 40)
# TODO: Make better method to adapt vectorizer layer
text_feature_spec = {_transformed_name('Text'): tf.io.FixedLenFeature([], dtype=tf.string)}
text_dataset = _input_text_fn(fn_args.train_files, text_feature_spec, 978)
text_dataset = text_dataset.map(lambda d: d[_transformed_name('Text')]).take(1)
vectorize_layer = get_vectorize_layer()
vectorize_layer.adapt(text_dataset)
model = _build_keras_model(vectorize_layer)
log_dir = os.path.join(os.path.dirname(fn_args.serving_model_dir), 'logs')
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=log_dir, update_freq='batch')
print(train_dataset)
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)
我已经设法让它工作了，但不是以最干净的方式。
我收到此消息的原因是模型中的 TextVectorization 层将只接受张量（看起来很密集）、numpy 数组、列表或数据集。所以我通过像这样调整我的代码来给他他想要的东西（这是更新的完整功能）：
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.
"""
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(fn_args.train_files, tf_transform_output, 40)
eval_dataset = _input_fn(fn_args.eval_files, tf_transform_output, 40)
vectorize_dataset = train_dataset.map(lambda f, l: tf.sparse.to_dense(f[_transformed_name('Text')])).unbatch()
vectorize_layer = TextVectorization(
max_tokens=_max_features,
output_mode='int',
output_sequence_length=500
)
vectorize_layer.adapt(vectorize_dataset.take(900))
model = _build_keras_model(vectorize_layer)
log_dir = os.path.join(os.path.dirname(fn_args.serving_model_dir), 'logs')
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=log_dir, update_freq='batch')
model.fit(
train_dataset.map(lambda f, l: (tf.sparse.to_dense(f[_transformed_name('Text')]), l)),
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset.map(lambda f, l: (tf.sparse.to_dense(f[_transformed_name('Text')]), l)),
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)
注意 fit 函数参数中的 map 函数。其余保持不变（差不多，我只是调整了输入层的形状并调整了模型以获得更好的结果）。
我想知道是否有更简单的方法来实现这一点并仍然保持 SparseTensor 的优势。