tf.data.Dataset:不得为给定的输入类型指定`batch_size`参数 [英] tf.data.Dataset: The `batch_size` argument must not be specified for the given input type

问题描述

我正在使用 Talos 和Google colab TPU 来运行 Keras 模型的超参数调整.请注意，我正在使用Tensorflow 1.15.0和Keras 2.2.4-tf.

I'm using Talos and Google colab TPU to run hyperparameter tuning of a Keras model. Note that I'm using Tensorflow 1.15.0 and 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将火车集转换为数据集后，使用out = model.fit拟合模型时出现以下错误:

After converting the train set to a Dataset using tf.data.Dataset, I get the following error when fitting the model with out = model.fit:

---------------------------------------------------------------------------
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，如果我遵循这些说明，并且未将batch-size参数设置为model.fit.我又遇到了另一个错误:

Then, if I follow those instructions and don't set the batch-size argument to model.fit. I get another error in:

---------------------------------------------------------------------------
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'

推荐答案

能否从代码中删除这些行并尝试:

Could you remove, these lines from your code and try:

    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，您在tf.data.Dataset.from_tensor_slices中编写的内容与错误有关.

Otherwise what you wrote in tf.data.Dataset.from_tensor_slices has something to do with the error.

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