从 tensorflow 和 keras 中的 mnist 训练 model 的问题

Question

这是我的代码：

import tensorflow as tf
import tensorflow_datasets as tfds
import math
import numpy as np
import matplotlib.pyplot as plt
dataset = tfds.load('fashion_mnist', as_supervised=True)
train = dataset['train'];
test = dataset['test'];

def normalize(image, label):
  image = tf.cast(image, tf.float32);
  image = image/255;
  return image, label


train = train.map(normalize);
test = test.map(normalize);

train = train.cache();
test = test.cache();

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(500, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy,
              metrics=['accuracy'])

model.fit(train, epochs=10, batch_size=100);

告诉我这个错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-57-07d41cbbc0f1> in <module>()
----> 1 model.fit(train, epochs=10, batch_size=100);

1 frames
/usr/local/lib/python3.7/dist-packages/keras/engine/data_adapter.py in _validate_args(self, y, sample_weights, steps)
    759       if size == tf.data.experimental.INFINITE_CARDINALITY and steps is None:
    760         raise ValueError(
--> 761             "When providing an infinite dataset, you must specify "
    762             "the number of steps to run (if you did not intend to "
    763             "create an infinite dataset, make sure to not call "

ValueError: When providing an infinite dataset, you must specify the number of steps to run (if you did not intend to create an infinite dataset, make sure to not call `repeat()` on the dataset).

Answer 1

可能这就足够了：

train.repeat().batch(batch_size)