model的形状不兼容怎么解决？

Question

This is my train & test split shape:

print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
----------------------
(120000, 72)
(12000, 72)
(120000, 6)
(12000, 6)

I reshape the data for CNN:

X_train = X_train.reshape(len(X_train), X_train.shape[1], 1)
X_test = X_test.reshape(len(X_test), X_test.shape[1], 1)
X_train.shape, X_test.shape
-------------------------------------------------------------------
((120000, 72, 1), (12000, 72, 1))

Making the deep learning function:

def model():
    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=6, activation='relu', 
                    padding='same', input_shape=(72, 1)))
    model.add(BatchNormalization())
    
    # adding a pooling layer
    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))
    
    model.add(Conv1D(filters=64, kernel_size=6, activation='relu', 
                    padding='same', input_shape=(72, 1)))
    model.add(BatchNormalization())
    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))
    
    model.add(Conv1D(filters=64, kernel_size=6, activation='relu', 
                    padding='same', input_shape=(72, 1)))
    model.add(BatchNormalization())
    model.add(MaxPooling1D(pool_size=(3), strides=2, padding='same'))
    
    model.add(Flatten())
    model.add(Dense(64, activation='relu'))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(3, activation='softmax'))
    
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

When i fit it shows error: Shapes (32, 6) and (32, 3) are incompatible

model = model()
model.summary()
logger = CSVLogger('logs.csv', append=True)
his = model.fit(X_train, y_train, epochs=30, batch_size=32, 
          validation_data=(X_test, y_test), callbacks=[logger])

---------------------------------------------------------------
 ValueError: Shapes (32, 6) and (32, 3) are incompatible

What problem and how can i solve it?

Answer 1

Your targets are 6 dimensional, but your prediction is 3 dimensional, thus the mismatch. You should have Dense(6) as the last layer of your model, not Dense(3)