我尝试在这个数据集上使用cnn-lstm模型。我已经将这个数据集存储在名为df的数据框中。这个数据集总共有11列，但我在这里只提到了9列。所有列都只有数值数据
Area book_hotel votes location hotel_type Total_Price Facilities Dine rate6 0 0 1 163 400 22 7 4.419 1 2 7 122 220 28 11 4.6X=df.drop(['rate'],axis=1) Y=df['rate']x_train, x_test, y_train, y_test = train_test_split(np.asarray(X), np.asarray(Y), test_size=0.33, shuffle= True)x_train的形状为(3350,10)，x_test的形状为(1650, 10)
# 已知输出类别的数量num_classes = 10# 输入图像的尺寸input_shape = (10,)# 将类别向量转换为二进制类别矩阵。这使用了1热编码。y_train_binary = keras.utils.to_categorical(y_train, num_classes)y_test_binary = keras.utils.to_categorical(y_test, num_classes)x_train = x_train.reshape(3350, 10,1)x_test = x_test.reshape(1650, 10,1)input_layer = Input(shape=(10, 1))conv1 = Conv1D(filters=32, kernel_size=8, strides=1, activation='relu', padding='same')(input_layer)lstm1 = LSTM(32, return_sequences=True)(conv1)output_layer = Dense(1, activation='sigmoid')(lstm1)model = Model(inputs=input_layer, outputs=output_layer)model.summary()model.compile(loss='mse',optimizer='adam')最后，当我尝试用输入来拟合模型时
model.fit(x_train,y_train)ValueError Traceback (most recent call last)<ipython-input-170-4719cf73997a> in <module>()----> 1 model.fit(x_train,y_train)2 frames/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 133 ': expected ' + names[i] + ' to have ' + 134 str(len(shape)) + ' dimensions, but got array '--> 135 'with shape ' + str(data_shape)) 136 if not check_batch_axis: 137 data_shape = data_shape[1:]ValueError: Error when checking target: expected dense_2 to have 3 dimensions, but got array with shape (3350, 1)有人可以帮助我解决这个错误吗
回答：
我看到你的代码中有一些问题…
最后一维的输出必须等于类别的数量，并且在多类任务中，你需要应用softmax激活函数：Dense(num_classes, activation='softmax')
在你的最后一个LSTM单元中，你必须设置return_sequences=False，因为你需要一个2D输出而不是3D
你必须使用categorical_crossentropy作为损失函数，并使用one-hot编码的目标
这里是一个完整的示例代码…
num_classes = 10n_sample = 1000X = np.random.uniform(0,1, (n_sample,10,1))y = tf.keras.utils.to_categorical(np.random.randint(0,num_classes, n_sample))input_layer = Input(shape=(10, 1))conv1 = Conv1D(filters=32, kernel_size=8, strides=1, activation='relu', padding='same')(input_layer)lstm1 = LSTM(32, return_sequences=False)(conv1)output_layer = Dense(num_classes, activation='softmax')(lstm1)model = Model(inputs=input_layer, outputs=output_layer)model.compile(loss='categorical_crossentropy',optimizer='adam')model.fit(X,y, epochs=5)