[英]ValuError: Error when checking target: expected activation_6 to have shape (70,) but got array with shape (71,)
[英]ValueError: Error when checking target: expected activation_6 to have shape (70,) but got array with shape (71,)
我正在使用 CNN 创建人脸识别。 我正在学习教程。 我正在使用 Tensorflow==1.15。
该程序将拍摄 70 张用户面部快照并将它们保存在“数据集”文件夹中
我不断收到错误:
ValueError: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
输入形状 - (32,32,1)
类(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
我正在计算 x_train、x_test、y_train、y_test,如下所示
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)
我在 CNN 层的尺寸上做错了什么? 请帮忙
在您的 model.summary() output 中,您会看到最终的密集层具有形状(无,70)。 None 代表您的批量大小,目前未知。 然后 70 是每个图像的 output 的维度。
从您的 y_train 和 y_pred 来看,您似乎想要 output 71 个类,而不是 70 个,因此尺寸不匹配。 您可以尝试将最后一个密集层更改为
model.add(Dense(num_classes+1))
这应该有效。 我不知道为什么您的 y 值的长度与您的班级数量不同。 一个原因可能是,有一个 class 表示“无”,因此应该在其他 class 中选择的 class 是正确的。 这可以解释为什么如果你有 70 个类,你需要一个 71 维的 output。
我怀疑ids
的形状 (row, col) 为(70,71)
- 其中 70 是实例数,71 是 class 的 softmax 向量。(我通过添加 x_train.shape[0]=56 得到了这个和 x_test.shape[0]=14)
在这一行n_faces = len(set(ids))
中, set
方法正在检查唯一列表(每个类的 softmax 向量),然后len
方法为您提供实例数,即 70。
在train_test_split
中, y
参数是整个ids
,因此它沿行(70 个实例)拆分,同时保留每个实例的 softmax 向量(71 维向量)。
这可以解释为什么你的 model 有 70 个维度 output 而你实际上需要 71 个维度 output。
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