tf.data.Dataset：不得为给定的输入类型指定“batch_size”参数

Question

我正在使用 Talos 和 Google colab TPU 来运行超参数调整 Keras 模型。请注意，我使用的是 Tensorflow 1.15.0 和 Keras 2.2.4-tf.

import os
import tensorflow as tf
import talos as ta
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import train_test_split

def iris_model(x_train, y_train, x_val, y_val, params):

    # Specify a distributed strategy to use TPU
    resolver = tf.contrib.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])
    tf.contrib.distribute.initialize_tpu_system(resolver)
    strategy = tf.contrib.distribute.TPUStrategy(resolver)

    # Use the strategy to create and compile a Keras model
    with strategy.scope():
      model = Sequential()
      model.add(Dense(32, input_shape=(4,), activation=tf.nn.relu, name="relu"))
      model.add(Dense(3, activation=tf.nn.softmax, name="softmax"))
      model.compile(optimizer=Adam(learning_rate=0.1), loss=params['losses'])

    # Convert data type to use TPU
    x_train = x_train.astype('float32')
    x_val = x_val.astype('float32')

    dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    dataset = dataset.cache()
    dataset = dataset.shuffle(1000, reshuffle_each_iteration=True).repeat()
    dataset = dataset.batch(params['batch_size'], drop_remainder=True)

    # Fit the Keras model on the dataset
    out = model.fit(dataset, batch_size=params['batch_size'], epochs=params['epochs'], validation_data=[x_val, y_val], verbose=0, steps_per_epoch=2)

    return out, model

# Load dataset
X, y = ta.templates.datasets.iris()

# Train and test set
x_train, x_val, y_train, y_val = train_test_split(X, y, test_size=0.30, shuffle=False)

# Create a hyperparameter distributions 
p = {'losses': ['logcosh'], 'batch_size': [128, 256, 384, 512, 1024], 'epochs': [10, 20]}

# Use Talos to scan the best hyperparameters of the Keras model
scan_object = ta.Scan(x_train, y_train, params=p, model=iris_model, experiment_name='test', x_val=x_val, y_val=y_val, fraction_limit=0.1)

使用 tf.data.Dataset 将训练集转换为数据集后，使用 out = model.fit 拟合模型时出现以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-c812209b95d0> in <module>()
      8 
      9 # Use Talos to scan the best hyperparameters of the Keras model
---> 10 scan_object = ta.Scan(x_train, y_train, params=p, model=iris_model, experiment_name='test', x_val=x_val, y_val=y_val, fraction_limit=0.1)

8 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py in _validate_or_infer_batch_size(self, batch_size, steps, x)
   1813             'The `batch_size` argument must not be specified for the given '
   1814             'input type. Received input: {}, batch_size: {}'.format(
-> 1815                 x, batch_size))
   1816       return
   1817 

ValueError: The `batch_size` argument must not be specified for the given input type. Received input: <DatasetV1Adapter shapes: ((512, 4), (512, 3)), types: (tf.float32, tf.float32)>, batch_size: 512

然后，如果我按照这些说明操作并且不将批量大小参数设置为 model.fit。我收到另一个错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-c812209b95d0> in <module>()
      8 
      9 # Use Talos to scan the best hyperparameters of the Keras model
---> 10 scan_object = ta.Scan(x_train, y_train, params=p, model=iris_model, experiment_name='test', x_val=x_val, y_val=y_val, fraction_limit=0.1)

8 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py in _distribution_standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, validation_split, shuffle, epochs, allow_partial_batch)
   2307             strategy) and not drop_remainder:
   2308           dataset_size = first_x_value.shape[0]
-> 2309           if dataset_size % batch_size == 0:
   2310             drop_remainder = True
   2311 

TypeError: unsupported operand type(s) for %: 'int' and 'NoneType'

Answer 1

来自 github code :

ValueError will be raised if x is a generator or Sequence instance and batch_size is specified as we expect users to provide batched datasets.

尝试使用 batch_size = None

Answer 2

不确定以下内容是否符合您的要求，但可以尝试一下。我所做的只是从数据集中删除 repeat() 并从 model.fit

中删除 batch_size=params['batch_size']

如果以上不是你准备牺牲的，那么请忽略post。

import os
import tensorflow as tf
import talos as ta
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

def iris_model(x_train, y_train, x_val, y_val, params):

    # Specify a distributed strategy to use TPU
    resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])
    tf.config.experimental_connect_to_host(resolver.master())
    tf.tpu.experimental.initialize_tpu_system(resolver)
    strategy = tf.distribute.experimental.TPUStrategy(resolver)

    with strategy.scope():
        model = Sequential()
        model.add(Dense(32, input_dim=4, activation=params['activation']))
        model.add(Dense(3, activation='softmax'))
        model.compile(optimizer=params['optimizer'], loss=params['losses'])

    # Convert the train set to a Dataset to use TPU
    dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    dataset = dataset.cache().shuffle(1000, reshuffle_each_iteration=True).batch(params['batch_size'], drop_remainder=True)

    out = model.fit(dataset, epochs=params['epochs'], validation_data=[x_val, y_val], verbose=0)

    return out, model

x, y = ta.templates.datasets.iris()

p = {'activation': ['relu', 'elu'],
       'optimizer': ['Nadam', 'Adam'],
       'losses': ['logcosh'],
       'batch_size': (20, 50, 5),
       'epochs': [10, 20]}

scan_object = ta.Scan(x, y, model=iris_model, params=p, fraction_limit=0.1, experiment_name='first_test')

Answer 3

您在 _distribution_standardize_user_data 中遇到的第二个错误是当您没有将 batch_size 传递给 fit 时。

您为该函数运行编写的代码在这里：

https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/keras/engine/training.py#L2192

您没有 post 追溯，但我敢打赌它在 line 2294 上失败了，因为那是唯一 batch_size 乘以某些东西的地方。

if shuffle:
          # We want a buffer size that is larger than the batch size provided by
          # the user and provides sufficient randomness. Note that larger
          # numbers introduce more memory usage based on the size of each
          # sample.
          ds = ds.shuffle(max(1024, batch_size * 8))

看来你可以通过设置shuffle=False来关闭它。

fit(ds, shuffle=False,...)

行得通吗？

Answer 4

你能从你的代码中删除这些行并尝试：

    dataset = dataset.cache()
    dataset = dataset.shuffle(1000, reshuffle_each_iteration=True).repeat()
    dataset = dataset.batch(params['batch_size'], drop_remainder=True)
WITH THESE:
    dataset = dataset.repeat()
    dataset = dataset.batch(128, drop_remainder=True)
    dataset = dataset.prefetch(1)

否则你在tf.data.Dataset.from_tensor_slices中所写的内容与错误有关。

Answer 5

在我看来，您的代码的问题在于训练和验证数据的格式不同。你正在批处理训练数据而不是验证示例。

您可以通过替换 iris_model 函数的下半部分：

def fix_data(x, y):
    x = x.astype('float32')
    ds = Dataset.from_tensor_slices((x, y))
    ds = ds.cache()
    ds = ds.shuffle(1000, reshuffle_each_iteration = True)
    ds = ds.repeat()
    ds = ds.batch(params['batch_size'], drop_remainder = True)
    return ds
train = fix_data(x_train, y_train)
val = fix_data(x_val, y_val)

# Fit the Keras model on the dataset
out = model.fit(x = train, epochs = params['epochs'],
                steps_per_epoch = 2,
                validation_data = val,
                validation_steps = 2)

至少这对我有用，而且你的代码运行没有错误。

tf.data.Dataset：不得为给定的输入类型指定“batch_size”参数

tf.data.Dataset: The `batch_size` argument must not be specified for the given input type

keras

tensorflow

google-colaboratory

google-cloud-tpu

talos