出于性能增强的原因，我试图将@ tf.function添加到Tensorflow 2.0中的自定义培训代码中。但是，运行代码会引发FailedPreconditionError：表已初始化。使用分类特征列时-我怀疑发生的事情是由于@ tf.function的工作方式而多次对分类特征列进行了初始化
作为一个例子，当未应用@ tf.function装饰器时，以下代码运行良好，但在添加@ tf.function装饰器时中断了。
import numpy as np
import pandas as pd
!pip install sklearn
!pip install tensorflow==2.0.0-alpha0
import tensorflow as tf
from tensorflow import feature_column
from tensorflow.keras import layers
from tensorflow.keras.models import Model
from sklearn.model_selection import train_test_split
URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
dataframe = pd.read_csv(URL)
train, test = train_test_split(dataframe, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
dataframe = dataframe.copy()
labels = dataframe.pop('target').values.reshape(-1,1)
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
if shuffle:
ds = ds.shuffle(buffer_size=len(dataframe))
ds = ds.batch(batch_size)
return ds
batch_size = 32
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)
feature_columns = []
# numeric cols
for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:
feature_columns.append(feature_column.numeric_column(header))
# bucketized cols
age = feature_column.numeric_column("age")
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
feature_columns.append(age_buckets)
# indicator cols
thal = feature_column.categorical_column_with_vocabulary_list(
'thal', ['fixed', 'normal', 'reversible'])
thal_one_hot = feature_column.indicator_column(thal)
feature_columns.append(thal_one_hot)
# embedding cols
thal_embedding = feature_column.embedding_column(thal, dimension=8)
feature_columns.append(thal_embedding)
#crossed cols
crossed_feature = feature_column.crossed_column([age_buckets, thal], hash_bucket_size=1000)
crossed_feature = feature_column.indicator_column(crossed_feature)
feature_columns.append(crossed_feature)
feature_layer = tf.keras.layers.DenseFeatures(feature_columns, trainable=False)
class MyModel(Model):
def __init__(self):
super(MyModel, self).__init__()
self.features = feature_layer#layers.DenseFeatures(feature_columns, trainable=False)
self.dense = layers.Dense(128, activation = 'relu')
self.dense2 = layers.Dense(128, activation = 'relu')
self.sigmoid = layers.Dense(1, activation = 'sigmoid')
def call(self, x):
x = self.features(x)
x = self.dense(x)
x = self.dense2(x)
return self.sigmoid(x)
model = MyModel()
loss_object = tf.keras.losses.BinaryCrossentropy()
optimizer = tf.keras.optimizers.Adam()
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.BinaryAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.BinaryAccuracy(name='test_accuracy')
@tf.function
def train_step(features, label):
with tf.GradientTape() as tape:
predictions = model(features)
loss = loss_object(label, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(loss)
train_accuracy(label, predictions)
@tf.function
def test_step(image, label):
predictions = model(image)
t_loss = loss_object(label, predictions)
test_loss(t_loss)
test_accuracy(label, predictions)
EPOCHS = 5
for epoch in range(EPOCHS):
for features, labels in train_ds:
train_step(features, labels)
for features, labels in val_ds:
test_step(features, labels)
template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
print (template.format(epoch+1,
train_loss.result(),
train_accuracy.result()*100,
test_loss.result(),
test_accuracy.result()*100))
实际结果：
Epoch 1, Loss: 1.2603175640106201, Accuracy: 61.65803146362305, Test Loss: 1.3003877401351929, Test Accuracy: 67.34693908691406
---------------------------------------------------------------------------
FailedPreconditionError Traceback (most recent call last)
<ipython-input-3-165a6f89be48> in <module>()
110 counter = 0
111 for features, labels in train_ds:
--> 112 train_step(features, labels, counter)
113 counter +=1
114
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
412 # In this case we have created variables on the first call, so we run the
413 # defunned version which is guaranteed to never create variables.
--> 414 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
415 elif self._stateful_fn is not None:
416 # In this case we have not created variables on the first call. So we can
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
1286 """Calls a graph function specialized to the inputs."""
1287 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 1288 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
1289
1290 @property
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _filtered_call(self, args, kwargs)
572 """
573 return self._call_flat(
--> 574 (t for t in nest.flatten((args, kwargs))
575 if isinstance(t, (ops.Tensor,
576 resource_variable_ops.ResourceVariable))))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _call_flat(self, args)
625 # Only need to override the gradient in graph mode and when we have outputs.
626 if context.executing_eagerly() or not self.outputs:
--> 627 outputs = self._inference_function.call(ctx, args)
628 else:
629 self._register_gradient()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in call(self, ctx, args)
413 attrs=("executor_type", executor_type,
414 "config_proto", config),
--> 415 ctx=ctx)
416 # Replace empty list with None
417 outputs = outputs or None
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
64 else:
65 message = e.message
---> 66 six.raise_from(core._status_to_exception(e.code, message), None)
67 except TypeError as e:
68 if any(ops._is_keras_symbolic_tensor(x) for x in inputs):
/usr/local/lib/python3.6/dist-packages/six.py in raise_from(value, from_value)
FailedPreconditionError: Table already initialized.
[[{{node my_model_2/dense_features_2/age_bucketized_X_thal_indicator/thal_lookup/hash_table/table_init/LookupTableImportV2}}]] [Op:__inference_train_step_34833]
预期结果：
Epoch 1, Loss: 0.8113343119621277, Accuracy: 63.21243667602539, Test Loss: 0.5340840816497803, Test Accuracy: 71.42857360839844
Epoch 2, Loss: 0.6469629406929016, Accuracy: 69.4300537109375, Test Loss: 0.5265070199966431, Test Accuracy: 72.44898223876953
Epoch 3, Loss: 0.5749971270561218, Accuracy: 71.84800720214844, Test Loss: 0.5283268094062805, Test Accuracy: 72.10884094238281
Epoch 4, Loss: 0.5360371470451355, Accuracy: 72.79792785644531, Test Loss: 0.5270806550979614, Test Accuracy: 72.44898223876953
Epoch 5, Loss: 0.5122867226600647, Accuracy: 73.57512664794922, Test Loss: 0.5229357481002808, Test Accuracy: 72.65306091308594