keras实现用resnet18对四种天气分类，自定义归一化方法，采用均值方差归一化方法

下面是使用Keras实现ResNet18对四种天气分类并自定义归一化方法的代码：

import keras
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Conv2D, MaxPooling2D, BatchNormalization, Add
from keras.utils import np_utils

# Define custom normalization function
def custom_normalize(x):
    mean = np.mean(x)
    std = np.std(x)
    return (x - mean) / std

# Load the data
train_data_dir = 'path/to/train/data'
validation_data_dir = 'path/to/validation/data'
test_data_dir = 'path/to/test/data'
nb_train_samples = 1000
nb_validation_samples = 400
nb_test_samples = 400
img_width, img_height = 224, 224
input_shape = (img_height, img_width, 3)
batch_size = 32
epochs = 50

# Normalize the data using custom normalization function
train_datagen = ImageDataGenerator(preprocessing_function=custom_normalize)
validation_datagen = ImageDataGenerator(preprocessing_function=custom_normalize)
test_datagen = ImageDataGenerator(preprocessing_function=custom_normalize)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = validation_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')

test_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')

# Define ResNet18 model
def resnet_block(input_data, filters, strides=1, conv_shortcut=False):
    shortcut = input_data

    # First convolution layer
    x = Conv2D(filters=filters, kernel_size=3, strides=strides, padding='same')(input_data)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    # Second convolution layer
    x = Conv2D(filters=filters, kernel_size=3, strides=1, padding='same')(x)
    x = BatchNormalization()(x)

    # Add shortcut connection
    if conv_shortcut:
        shortcut = Conv2D(filters=filters, kernel_size=1, strides=strides, padding='same')(shortcut)
        shortcut = BatchNormalization()(shortcut)

    x = Add()([x, shortcut])
    x = Activation('relu')(x)

    return x

def ResNet18(input_shape, num_classes):
    inputs = Input(shape=input_shape)

    # Stem block
    x = Conv2D(filters=64, kernel_size=7, strides=2, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

    # ResNet blocks
    x = resnet_block(x, filters=64, conv_shortcut=True)
    x = resnet_block(x, filters=64)
    x = resnet_block(x, filters=64)

    x = resnet_block(x, filters=128, strides=2, conv_shortcut=True)
    x = resnet_block(x, filters=128)
    x = resnet_block(x, filters=128)

    x = resnet_block(x, filters=256, strides=2, conv_shortcut=True)
    x = resnet_block(x, filters=256)
    x = resnet_block(x, filters=256)

    x = resnet_block(x, filters=512, strides=2, conv_shortcut=True)
    x = resnet_block(x, filters=512)
    x = resnet_block(x, filters=512)

    # Classification layer
    x = AveragePooling2D(pool_size=7)(x)
    x = Flatten()(x)
    x = Dense(units=num_classes, activation='softmax')(x)

    # Create model
    model = Model(inputs=inputs, outputs=x)

    return model

# Compile and train the model
model = ResNet18(input_shape=input_shape, num_classes=4)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit_generator(train_generator, steps_per_epoch=nb_train_samples // batch_size, epochs=epochs,
                    validation_data=validation_generator, validation_steps=nb_validation_samples // batch_size)

# Evaluate the model on the test data
score = model.evaluate_generator(test_generator, steps=nb_test_samples // batch_size)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

在以上代码中，我们定义了一个名为`custom_normalize`的自定义归一化函数，并将其作为参数传递给`ImageDataGenerator`。在`ResNet18`函数中，我们定义了一个ResNet块函数，该函数包含两个卷积层和一个shortcut连接。然后，我们在`ResNet18`函数中使用这个ResNet块函数来构建整个ResNet18模型。最后，我们使用`compile`和`fit_generator`方法来编译和训练模型，并使用`evaluate_generator`方法在测试数据上评估模型的性能。