如何在keras CNN中使用黑白图像？

Question

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPool2D, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import matplotlib.pyplot as plt
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

os.chdir('C:/Users/dancu/PycharmProjects/firstCNN/data/ad-vs-cn')

physical_devices = tf.config.experimental.list_physical_devices('GPU')
print("Num GPUs Available: ", len(physical_devices))
tf.config.experimental.set_memory_growth(physical_devices[0], True)

train_path = "C:/Users/dancu/PycharmProjects/firstCNN\data/ad-vs-cn/train"
test_path = "C:/Users/dancu/PycharmProjects/firstCNN\data/ad-vs-cn/test"
valid_path = "C:/Users/dancu/PycharmProjects/firstCNN\data/ad-vs-cn/valid"

train_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
    .flow_from_directory(directory=train_path, target_size=(256,256), classes=['cn', 'ad'], batch_size=10, color_mode="rgb")
valid_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
    .flow_from_directory(directory=valid_path, target_size=(256,256), classes=['cn', 'ad'], batch_size=10, color_mode="rgb")
test_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
    .flow_from_directory(directory=test_path, target_size=(256,256), classes=['cn', 'ad'], batch_size=10, color_mode="rgb", shuffle=False)


# def plotImages(images_arr):
#     fig, axes = plt.subplots(1, 10, figsize=(20,20))
#     axes = axes.flatten()
#     for img, ax in zip( images_arr, axes):
#         ax.imshow(img)
#         ax.axis('off')
#     plt.tight_layout()
#     plt.show()
#
#
# imgs, labels = next(train_batches)
# plotImages(imgs)

model = Sequential([
    Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding = 'same', input_shape=(256,256,3)),
    MaxPool2D(pool_size=(2, 2), strides=2),
    Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding = 'same'),
    MaxPool2D(pool_size=(2, 2), strides=2),
    Flatten(),
    Dense(units=2, activation='softmax')
])

#print(model.summary())

model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(x=train_batches,
    steps_per_epoch=len(train_batches),
    validation_data=valid_batches,
    validation_steps=len(valid_batches),
    epochs=10,
    verbose=2
)

这段代码运行得非常好，但我使用的图像实际上是灰度的，因此由于图像的显示方式，我的输出精度非常差。 当我将颜色模式更改为“灰度”时，会出现以下错误：

Traceback (most recent call last):
  File "C:/Users/dancu/PycharmProjects/firstCNN/finalData.py", line 56, in <module>
    model.fit(x=train_batches,
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\tensorflow\python\keras\engine\training.py", line 66, in _method_wrapper
    return method(self, *args, **kwargs)
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\tensorflow\python\keras\engine\training.py", line 802, in fit
    data_handler = data_adapter.DataHandler(
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py", line 1100, in __init__
    self._adapter = adapter_cls(
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py", line 901, in __init__
    super(KerasSequenceAdapter, self).__init__(
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py", line 772, in __init__
    peek, x = self._peek_and_restore(x)
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py", line 912, in _peek_and_restore
    return x[0], x
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\keras_preprocessing\image\iterator.py", line 65, in __getitem__
    return self._get_batches_of_transformed_samples(index_array)
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\keras_preprocessing\image\iterator.py", line 239, in _get_batches_of_transformed_samples
    x = self.image_data_generator.standardize(x)
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\keras_preprocessing\image\image_data_generator.py", line 708, in standardize
    x = self.preprocessing_function(x)
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\tensorflow\python\keras\applications\vgg16.py", line 232, in preprocess_input
    return imagenet_utils.preprocess_input(
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\tensorflow\python\keras\applications\imagenet_utils.py", line 106, in preprocess_input
    return _preprocess_numpy_input(
  File "C:\Users\dancu\PycharmProjects\firstCNN\venv\lib\site-packages\tensorflow\python\keras\applications\imagenet_utils.py", line 223, in _preprocess_numpy_input
    x[..., 1] -= mean[1]
IndexError: index 1 is out of bounds for axis 2 with size 1

Process finished with exit code 1

我还将Conv2D层的输入_形状更改为只有1个通道，而不是3个，但出现了相同的错误

有人能帮我吗？谢谢大家!

Answer 1

设置color_mode='grayscale'时出现错误，因为tf.keras.applications.vgg16.preprocess_input根据其documentation使用具有3个通道的输入张量。你不需要这个函数，因为你是从头开始训练你的模型，所以在ImageNet图片中把输入归零没有多大意义。您只需在ImageDataGenerator调用中传递rescale=1/255就可以了，这对于基本的预处理很好

train_batches = ImageDataGenerator(
    rescale=1/255).flow_from_directory(directory=train_path, 
        target_size=(256,256), classes=['cn', 'ad'], batch_size=10,
            color_mode="grayscale")

如果精度较低，我建议如下：

1. 使用优化器学习率的默认值
2. 添加更多conv/max_池层，增加更多神经元
3. 在展平层后添加一个或两个致密层
4. 使用verbose=1以便跟踪验证度量，它将提供信息