Python 分类形状中的keras值误差

这是我的密码：

# data consists of 1 dimensional data (3277 elements). Number of data is 439  
train_data = .... # numpy.ndarray
# I would like to classify data into 5 classes.
train_labels = .... # numpy.ndarray

print(train_data.shape) # -> Shape of train_data: (439, 3277)
print('Shape of train_labels:', train_labels.shape) # -> Shape of train_labels: (439,)
# prepare 5 one hot encoding array
categorical_labels = to_categorical(train_labels, 5)
print('Shape of categorical_labels:', categorical_labels.shape) # -> Shape of categorical_labels: (439, 5)

# I make a model to have 3277-elements data and classify data into 5 labels.
model = keras.Sequential([
    keras.layers.Dense(30, activation='relu', input_shape=(3277,)),
    keras.layers.Dense(30, activation='relu'),
    keras.layers.Dense(5, activation='softmax')
])
model.summary()
model.compile(optimizer='adam',
          loss='sparse_categorical_crossentropy',
          metrics=['accuracy'])
model.fit(data, categorical_labels, epochs=5, verbose=1) # A
#model.fit(data, train_labels, epochs=5, verbose=1) # B

当我尝试使用标记为“A”的行时，我得到了这个错误

ValueError: Error when checking target: expected dense_3 to have shape (1,) but got array with shape (5,)

使用“B”，它运行正常（无明显错误，机器返回高分）

显然，这个错误与形状的不同有关。。。当我想使用

keras.utils.to_category

时，如何修改我的代码

另一个问题是为什么这个案例有效（）而我的案例无效

---

结构看起来很相似。。。对我来说。

因为

sparse\u categorical\u crossentropy

不希望标签采用一种热编码格式，所以应该使用

loss='categorical\u crossentropy'

简言之，关于你的情况：

• train\u标签

  =>

  loss='sparse'u categorical\u crossentropy'

• categorical\u标签

  =>

  loss='categorical\u交叉熵'