我是张量流 keras 和数据集的新手。谁能帮我理解为什么下面的代码不起作用？
import tensorflow as tf
import tensorflow.keras as keras
import numpy as np
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.ops import iterator_ops
from tensorflow.python.keras.utils import multi_gpu_model
from tensorflow.python.keras import backend as K
data = np.random.random((1000,32))
labels = np.random.random((1000,10))
dataset = tf.data.Dataset.from_tensor_slices((data,labels))
print( dataset)
print( dataset.output_types)
print( dataset.output_shapes)
dataset.batch(10)
dataset.repeat(100)
inputs = keras.Input(shape=(32,)) # Returns a placeholder tensor
# A layer instance is callable on a tensor, and returns a tensor.
x = keras.layers.Dense(64, activation='relu')(inputs)
x = keras.layers.Dense(64, activation='relu')(x)
predictions = keras.layers.Dense(10, activation='softmax')(x)
# Instantiate the model given inputs and outputs.
model = keras.Model(inputs=inputs, outputs=predictions)
# The compile step specifies the training configuration.
model.compile(optimizer=tf.train.RMSPropOptimizer(0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Trains for 5 epochs
model.fit(dataset, epochs=5, steps_per_epoch=100)
它失败并出现以下错误：
model.fit(x=dataset, y=None, epochs=5, steps_per_epoch=100)
File "/home/wuxinyu/pyEnv/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py", line 1510, in fit
validation_split=validation_split)
File "/home/wuxinyu/pyEnv/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py", line 994, in _standardize_user_data
class_weight, batch_size)
File "/home/wuxinyu/pyEnv/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py", line 1113, in _standardize_weights
exception_prefix='input')
File "/home/wuxinyu/pyEnv/lib/python3.5/site-packages/tensorflow/python/keras/engine/training_utils.py", line 325, in standardize_input_data
'with shape ' + str(data_shape))
ValueError: Error when checking input: expected input_1 to have 2 dimensions, but got array with shape (32,)
根据 tf.keras 指南，我应该能够直接将数据集传递给 model.fit，如本例所示：
输入 tf.data 数据集
使用数据集 API 扩展到大型数据集或多设备训练。将 tf.data.Dataset 实例传递给 fit 方法：
# Instantiates a toy dataset instance:
dataset = tf.data.Dataset.from_tensor_slices((data, labels))
dataset = dataset.batch(32)
dataset = dataset.repeat()
不要忘记指定steps_per_epoch打电话时fit在数据集上。
model.fit（数据集，epochs=10，steps_per_epoch=30）
这里，fit方法使用steps_per_epoch参数——这是模型在进入下一个纪元之前运行的训练步骤数。由于数据集产生批量数据，因此此代码片段不需要batch_size。
数据集也可用于验证：
dataset = tf.data.Dataset.from_tensor_slices((data, labels))
dataset = dataset.batch(32).repeat()
val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_labels))
val_dataset = val_dataset.batch(32).repeat()
model.fit(dataset, epochs=10, steps_per_epoch=30,
validation_data=val_dataset,
validation_steps=3)
我的代码有什么问题，正确的方法是什么？
