我正在尝试创建一个用于二进制分类的卷积神经网络，该网络可以区分正常人脸和中风患者的人脸之间的差异。
按照本教程中使用的方法，我创建了CNN，它在使用MNIST数据集时可以工作。我还遵循了这个StackOverflow线程中建议的方法，将我的图像导入神经网络。下面显示的是我的代码。
Strokes = glob.glob('C:\Users\Colin\CNNImages\Strokes\*.*')
RegularFaces = glob.glob('C:\Users\Colin\CNNImages\RegularFaces\*.*')
data = []
labels = []
for i in Strokes:
image=tf.keras.preprocessing.image.load_img(i, color_mode='rgb',
target_size= (128,128))
image=np.array(image)
data.append(image)
labels.append(0)
for i in RegularFaces:
image=tf.keras.preprocessing.image.load_img(i, color_mode='rgb',
target_size= (128,128))
image=np.array(image)
data.append(image)
labels.append(1)
data = np.array(data)
labels = np.array(labels)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2,
random_state=42)
batch_size = 128
num_classes = 2
epochs = 12
# input image dimensions
img_rows, img_cols = 128, 128
x_train = x_train.reshape(864,128,128,3)
x_test = x_test.reshape(216,128,128,3)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=(28,28,1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
当我运行代码时，我收到以下错误：ValueError: Input 0 of layer sequential_6 is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape (None, 128, 128, 3)。我该如何阻止此错误的发生？
更改这行代码：-
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=(28,28,1)))
到此：-
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=(28,28,3)))
你在模型l中的input_shape论点必须与你在上面转化的图像维度一致。