ValuError：检查目标时出错：预期activation_6具有形状（70，）但得到形状为（71，）的数组

Question

I am creating face recognition using CNN. I was following a tutorial. I am using Tensorflow==1.15.

The programme will take 70 snaps of the user's face and save them in the folder 'dataset'

I keep getting the error:

ValueError: Error when checking target: expected activation_6 to have shape (70,) but got array with shape (71,)

input shapes - (32,32,1)

classes(n_classes) - 70

K.clear_session()
n_faces = len(set(ids))

model = model((32,32,1),n_faces) #Calling Model given in next code block
faces = np.asarray(faces)
faces = np.array([downsample_image(ab) for ab in faces])
ids = np.asarray(ids)
faces = faces[:,:,:,np.newaxis]
print("Shape of Data: " + str(faces.shape))
print("Number of unique faces : " + str(n_faces))


ids = to_categorical(ids)

faces = faces.astype('float32')
faces /= 255.


x_train, x_test, y_train, y_test = train_test_split(faces,ids, test_size = 0.2, random_state = 0)

print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)

checkpoint = callbacks.ModelCheckpoint('trained_model.h5', monitor='val_acc',
                                           save_best_only=True, save_weights_only=True, verbose=1)
                                    
model.fit(x_train, y_train,
             batch_size=32,
             epochs=10,
             validation_data=(x_test, y_test),
             shuffle=True,callbacks=[checkpoint])

def model(input_shape,num_classes):    

    model = Sequential()

    model.add(Conv2D(32, (3, 3), input_shape=input_shape))
    model.add(Activation("relu"))

    model.add(Conv2D(64, (3, 3)))
    model.add(BatchNormalization())
    model.add(Activation("relu"))

    model.add(Conv2D(64, (1, 1)))
    model.add(Dropout(0.5))
    model.add(BatchNormalization())
    model.add(Activation("relu"))

    model.add(Conv2D(128, (3, 3)))
    model.add(Dropout(0.5))
    model.add(Activation("relu"))

    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(64, (1, 1)))
    model.add(Activation("relu"))

    model.add(Flatten())
    model.add(Dense(32))
    model.add(Dense(num_classes))
    model.add(Activation("softmax"))
    
    model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])

    model.summary()
    return model

Output

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 30, 30, 32)        320       
_________________________________________________________________
activation_1 (Activation)    (None, 30, 30, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 28, 28, 64)        18496     
_________________________________________________________________
batch_normalization_1 (Batch (None, 28, 28, 64)        256       
_________________________________________________________________
activation_2 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 28, 28, 64)        4160      
_________________________________________________________________
dropout_1 (Dropout)          (None, 28, 28, 64)        0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 28, 28, 64)        256       
_________________________________________________________________
activation_3 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 26, 26, 128)       73856     
_________________________________________________________________
dropout_2 (Dropout)          (None, 26, 26, 128)       0         
_________________________________________________________________
activation_4 (Activation)    (None, 26, 26, 128)       0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 128)       0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 13, 13, 64)        8256      
_________________________________________________________________
activation_5 (Activation)    (None, 13, 13, 64)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 10816)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 32)                346144    
_________________________________________________________________
dense_2 (Dense)              (None, 70)                2310      
_________________________________________________________________
activation_6 (Activation)    (None, 70)                0         
=================================================================
Total params: 454,054
Trainable params: 453,798
Non-trainable params: 256
_________________________________________________________________
Shape of Data: (70, 32, 32, 1)
Number of unique faces : 70

I am calculating x_train, x_test, y_train, y_test as shown below

x_train, x_test, y_train, y_test = train_test_split(faces,ids, test_size = 0.2, random_state = 0)

Output

x_train - (56, 32, 32, 1)

y_train - (56, 71)

x_test - (14, 32, 32, 1)

y_test - (14, 71)

What I am doing wrong with the dimensions of CNN layers? Please Help

Answer 1

ValueError: Error when checking target: expected activation_6 to have shape (70,) but got array with shape (71,)

You are feeding in 71 classes, but 70 are expected.

Either change num_classes to 71, or see why you are feeding in 71 classes to x_train , y_train instead of 70.

Which is correct depends on your specific use case we can't know from the given question.