import tensorflow as tf
import numpy as np
train_data = tf.constant([
[[0, 1, 2, 3, 4],
[10, 11, 12, 13, 14],
[5, 6, 7, 8, 9]],
[[10, 11, 12, 13, 14],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]],
[[20, 21, 22, 23, 24],
[10, 11, 12, 13, 14],
[25, 26, 27, 28, 29]], ])
print(train_data)
train_labels = tf.constant([[[0]],
[[0]],
[[1]], ])
print(train_labels)
train_data = np.array(train_data)
train_labels = np.array(train_data)
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(1)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(train_data, train_labels, epochs=10)
我是机器学习的新手，需要一些帮助。尝试将列车内数据设置为3种情况，并将列车内标签设置为3个标签。
它应该类似于https://www.tensorflow.org/api_docs/python/tf/keras/datasets/fashion_mnist/load_data
但是当我尝试运行它时，我得到了这个错误：
节点：'sparse_categorical_crossementory/SparseSoftmaxCrossEntropyWithLogits/SparseSoftwaremaxCrossEntrCopyWithLogits'logits和labels必须具有相同的第一维度，得到logits形状[9,1]和labels形状[45][[{节点稀疏分类交叉熵/SparseSoftmaxCrossEntropyWithLogits/SparseSoftwaremaxCrossEntrCopyWithLogits}}]][操作：__explorence_train_function_718]
我想你可能打字错了？在哪里
train_data=np.array(train_data(
train_labels=np.alley(train_data(
第二行应该是
train_labels=np.array(train_labels(