我按照这个指南学习图像分类与CNN和我实现了这个代码到我的数据集：
https://www.tensorflow.org/tutorials/images/classification

train_image_generator = ImageDataGenerator(rescale=1. / 255)  # Generator for our training data
validation_image_generator = ImageDataGenerator(rescale=1. / 255)  # Generator for our validation data

train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=train_img_folder,
                                                           shuffle=True,
                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                           class_mode='categorical',
                                                           color_mode='grayscale')

val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,
                                                              directory=valid_img_folder,
                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                              class_mode='categorical',
                                                              color_mode='grayscale'
                                                              )

model = Sequential([
    Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 1)),
    MaxPooling2D(),
    Conv2D(32, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Conv2D(64, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Flatten(),
    Dense(512, activation='relu'),
    Dense(3, activation='softmax')
])

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

history = model.fit_generator(
    train_data_gen,
    steps_per_epoch=total_train_value // batch_size,
    epochs=epochs,
    validation_data=val_data_gen,
    validation_steps=total_valid_value // batch_size
)

# Single prediction
img = []
temp = np.array(Image.open('path/to/pic.jpg').resize((256, 256), Image.ANTIALIAS))
temp.shape = temp.shape + (1,) # now its (256, 256, 1)
img.append(temp)
test = np.array(img) # (1, 1024, 1024, 1)
prediction = model.predict(test)

当我尝试predict_generator函数时：

test_datagen = ImageDataGenerator(rescale=1 / 255.)

test_generator = test_datagen.flow_from_directory('test_images/',
                                                  classes=['0', '1', '2'],
                                                  color_mode='grayscale',
                                                  shuffle=True,
                                                  # use same size as in training
                                                  target_size=(256, 256))

preds = model.predict_generator(test_generator, steps=4) # I dont know what is steps doing. I put there because of error.

我的第一个问题是：我可以得到训练和验证精度，但我想得到单幅图像的预测结果。我该怎么做？示例：

foo = model.predict(path/to/pic.jpg)
# foo returns 0-> 0.70 | 1-> 0.30

补充：当我尝试使用模型.预测这样我得到了这个错误：

ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (1024, 1024)

或者转换到2D（以及3D）NP阵列仍然得到相同的结果。

• • 我的第二个问题是：**有没有办法预测没有完成%100？我的意思是，如果我们有2类（猫和狗）和测试月亮图片，我想得到这样的结果：

%15 cat | %10 dog

不

%50 cat | %50 dog

我试图把垃圾类如下更改。当我运行history = model.fit_generator行，我得到了以下错误：

ValueError: Error when checking target: expected dense_2 to have shape (3,) but got array with shape (2,)