这里的问题实际上是您的损失函数.由于您已经有1个热编码的 y 向量，因此您不应该使用稀疏分类交叉熵，而应该使用分类交叉熵.试试这个

分类交叉熵

就尺寸而言，让我们只创建一些虚拟数据.

X_train = np.zeros((2464, 150, 1))
y_train = np.zeros((2464,))

X_val = np.zeros((2464, 150, 1))
y_val = np.zeros((2464,))

X_train_r = X_train.reshape(2464,150,)
X_val_r = X_val.reshape(2464,150,)
input_shape = (150,)

print(X_train_r.shape)
print(X_val_r.shape)

(2464，150)
(2464，150)

现在，我们将为标签获得一个热编码输出.

num_classes = 5
# Convert class vectors to binary class matrices. This uses 1 hot encoding.
y_train_binary = keras.utils.to_categorical(y_train, num_classes)
y_val_binary = keras.utils.to_categorical(y_val, num_classes)

print(y_train_binary.shape)
print(y_val_binary.shape)

(2464，5)
(2464，5)

现在，我们将按照以下步骤制作模型

model = Sequential()                 
model.add(Dense(16, activation='relu',input_shape=input_shape)) 
model.add(Dense(16, activation='relu'))           
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

model.summary()

history=model.fit(X_train_r,
                  y_train_binary,
                  epochs=5,
                  batch_size=8,
                  validation_data=(X_val_r, y_val_binary))

这将为您工作.

稀疏分类交叉熵

如果要使用稀疏分类交叉熵，则不应使用1-hot编码标签.像这样

X_train = np.zeros((2464, 150, 1))
y_train = np.zeros((2464,))

X_val = np.zeros((2464, 150, 1))
y_val = np.zeros((2464,))

X_train_r = X_train.reshape(2464,150,)
X_val_r = X_val.reshape(2464,150,)
input_shape = (150,)

num_classes = 5

model = Sequential()                 
model.add(Dense(16, activation='relu',input_shape=input_shape)) 
model.add(Dense(16, activation='relu'))           
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss='sparse_categorical_crossentropy',
                       optimizer='rmsprop',
                       metrics=['mae'])

model.summary()

history=model.fit(X_train_r,
                  y_train,
                  epochs=5,
                  batch_size=8,
                  validation_data=(X_val_r, y_val))

I have ECG data which are in tensor-form of (samples, timesteps, features) with e.g. (2464,15000,1).

I aim to classify labels in the range 1 to 5. I one hot encoded the target to have the dimensions (2464,5).

Before I start with LSTM I wanted to try a basic approach. With following sequential model:

 def build_model():  
        model = models.Sequential()                 
        model.add(layers.Dense(16, activation='relu',input_shape=(X_train.shape[1],X_train.shape[2]))) 
        model.add(layers.Dense(16, activation='relu'))           
        model.add(layers.Dense(y_train.shape[1], activation='softmax'))

        model.compile(loss='sparse_categorical_crossentropy',
                       optimizer='rmsprop',
                       metrics=['mae'])
        return model

    model=build_model()
    history=model.fit(X_train,
                      y_train,
                      epochs=20,
                      batch_size=512,
                      validation_data=(X_val,y_val))

Unfortunately, I get a value error:

File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 113, in _standardize_input_data 'with shape ' + str(data_shape))

ValueError: Error when checking target: expected dense_13 to have 3 dimensions, but got array with shape (2464, 5)

I searched for other topics with the problem. Most of the case, the output layer does not fit the expected targets, in my case 5. So that should be correct. The other issue was often that the target was not one hot encoded. But that is also done. The error tells me to have three dimensions (based on the input?). But if I add one dimension to gain a target dimension of (2464,5,1), the ValueError change, expecting the same dimension as the input data, while I have to reduce it to the y-target with the softmax layer.

ValueError: Error when checking target: expected dense_23 to have shape (15000, 1) but got array with shape (5, 1)

I get confused. Could you give me a hint?

Extra: I also tried to flatten the input, but get also a (different) ValueError (...shape (1,) but got array with shape (5,)). Is flattening here the correct approach?

Thanks for your help.

The problem here is actually your loss function. Since you already have 1-hot encoded y vectors then you should not be using the sparse categorical cross entropy, but rather you should use categorical cross entropy. Try this

Categorical cross entropy

Let's just make some dummy data for dimensions sake.

X_train = np.zeros((2464, 150, 1))
y_train = np.zeros((2464,))

X_val = np.zeros((2464, 150, 1))
y_val = np.zeros((2464,))

X_train_r = X_train.reshape(2464,150,)
X_val_r = X_val.reshape(2464,150,)
input_shape = (150,)

print(X_train_r.shape)
print(X_val_r.shape)

(2464, 150)
(2464, 150)

Now we will get our one hot encoded outputs for our labels.

num_classes = 5
# Convert class vectors to binary class matrices. This uses 1 hot encoding.
y_train_binary = keras.utils.to_categorical(y_train, num_classes)
y_val_binary = keras.utils.to_categorical(y_val, num_classes)

print(y_train_binary.shape)
print(y_val_binary.shape)

(2464, 5)
(2464, 5)

Now we will make our model as follows

model = Sequential()                 
model.add(Dense(16, activation='relu',input_shape=input_shape)) 
model.add(Dense(16, activation='relu'))           
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

model.summary()

history=model.fit(X_train_r,
                  y_train_binary,
                  epochs=5,
                  batch_size=8,
                  validation_data=(X_val_r, y_val_binary))

This will work for you.

Sparse categorical cross entropy

If you want to use sparse categorical cross entropy then you should not use 1-hot encoded labels. Like this

X_train = np.zeros((2464, 150, 1))
y_train = np.zeros((2464,))

X_val = np.zeros((2464, 150, 1))
y_val = np.zeros((2464,))

X_train_r = X_train.reshape(2464,150,)
X_val_r = X_val.reshape(2464,150,)
input_shape = (150,)

num_classes = 5

model = Sequential()                 
model.add(Dense(16, activation='relu',input_shape=input_shape)) 
model.add(Dense(16, activation='relu'))           
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss='sparse_categorical_crossentropy',
                       optimizer='rmsprop',
                       metrics=['mae'])

model.summary()

history=model.fit(X_train_r,
                  y_train,
                  epochs=5,
                  batch_size=8,
                  validation_data=(X_val_r, y_val))