我正在尝试在预测的测试标签和实际的测试标签之间绘制一个混淆矩阵，但出现此错误
ValueError：找到输入样本数量不一致的输入变量：[1263，12630]
数据集：GTSRB
使用的代码
图像增强
train_datagen = ImageDataGenerator(rescale=1./255,
rotation_range=20,
horizontal_flip=True,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.01,
zoom_range=[0.9, 1.25],
brightness_range=[0.5, 1.5])
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator和test_generator
batch_size = 10
train_generator = train_datagen.flow_from_directory(
directory=train_path,
target_size=(224, 224),
color_mode="rgb",
batch_size=batch_size,
class_mode="categorical",
shuffle=True,
seed=42
)
test_generator = test_datagen.flow_from_directory(
directory=test_path,
target_size=(224, 224),
color_mode="rgb",
batch_size=batch_size,
class_mode="categorical",
shuffle=False,
seed=42
)
该代码的输出
找到了属于39个类别的39209张图像。
找到了属于12个类别的12630张图像。
然后，我使用了VGG-16模型并将最新的Dense层替换为Dense(43, activation='softmax')
模型摘要
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
block1_conv1 (Conv2D) (None, 224, 224, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 224, 224, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 112, 112, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 112, 112, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 112, 112, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 56, 56, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 56, 56, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 28, 28, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 14, 14, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
flatten (Flatten) (None, 25088) 0
_________________________________________________________________
fc1 (Dense) (None, 4096) 102764544
_________________________________________________________________
fc2 (Dense) (None, 4096) 16781312
_________________________________________________________________
predictions (Dense) (None, 1000) 4097000
_________________________________________________________________
dense_1 (Dense) (None, 43) 43043
=================================================================
Total params: 138,400,587
Trainable params: 43,043
Non-trainable params: 138,357,544
_________________________________________________________________
编译模型
my_sgd = SGD(lr=0.01)
model.compile(
optimizer=my_sgd,
loss='categorical_crossentropy',
metrics=['accuracy']
)
训练模型
STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size
epochs=10
model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
epochs=epochs,
verbose=1
)
预测
STEP_SIZE_TEST=test_generator.n//test_generator.batch_size
test_generator.reset()
predictions = model.predict_generator(test_generator, steps=STEP_SIZE_TEST, verbose=1)
输出
1263/1263 [==============================]-229s 181ms / step
预测形状 打印（predictions.shape）
（12630，43）
获取test_data和test_labels
test_data = []
test_labels = []
batch_index = 0
while batch_index <= test_generator.batch_index:
data = next(test_generator)
test_data.append(data[0])
test_labels.append(data[1])
batch_index = batch_index + 1
test_data_array = np.asarray(test_data)
test_labels_array = np.asarray(test_labels)
test_data_array和test_labels_array的形状
test_data_array.shape
（1263，10，224，224，3）
test_labels_array.shape
（1263，10，43）
混淆矩阵
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(test_labels_array, predictions)
我得到了输出
ValueError：找到输入样本数量不一致的输入变量：[1263，12630]
我知道此错误是因为test_labels_array的大小与预测值不相等； 1263和12630，但是我真的不知道我在做什么错。
任何帮助将不胜感激。
PS：如果有人对我们在提高训练准确性方面有任何建议，那就太好了。
谢谢！
答案 0 :(得分：2)
您应按照以下方式重塑test_data_array和test_labels_array：
data_count, batch_count, w, h, c = test_data_array.shape
test_data_array=np.reshape(test_data_array, (data_count*batch_count, w, h, c))
test_labels_array = np.reshape(test_labels_array , (data_count*batch_count, -1))
您添加test_generator结果的方式是原因。实际上，您的test_generator的第一次调用将生成10个形状为（224，224，3）的数据。对于下一次调用，您的test_generator将生成10个形状为（224，224，3）的数据。因此，现在您应该具有20个形状数据（224、224、3），而将结果追加的方式将导致您得出2个形状数据（10、224、224、3）。这不是您所期望的。