DenseNet 11*11图像大小分类代码

以下是使用DenseNet在11*11图像上进行分类的示例代码：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, BatchNormalization, Activation, AveragePooling2D, Flatten
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import LearningRateScheduler
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import backend as K
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Concatenate

def densenet_block(x, num_filters):
    # Bottleneck layer
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(4*num_filters, kernel_size=1, padding='same', kernel_initializer='he_normal')(x)
    # Composite function
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(num_filters, kernel_size=3, padding='same', kernel_initializer='he_normal')(x)
    return x

def densenet_stack(x, num_layers, num_filters):
    for i in range(num_layers):
        y = densenet_block(x, num_filters)
        x = Concatenate()([x, y])
    return x

# Define hyperparameters
num_classes = 10
num_layers_per_block = 4
num_filters = 32
compression_factor = 0.5

# Define model
inputs = Input(shape=(11, 11, 3))
x = Conv2D(num_filters, kernel_size=3, padding='same', kernel_initializer='he_normal')(inputs)

for i in range(3):
    x = densenet_stack(x, num_layers_per_block, num_filters)
    num_filters = int(num_filters * compression_factor)
    x = Conv2D(num_filters, kernel_size=1, padding='same', kernel_initializer='he_normal')(x)
    x = AveragePooling2D(pool_size=2)(x)

x = Flatten()(x)
outputs = Dense(num_classes, activation='softmax')(x)

model = Model(inputs=inputs, outputs=outputs)

# Compile model
model.compile(loss='categorical_crossentropy',
              optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999),
              metrics=['accuracy'])

# Load data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

# Define image augmentations
datagen = ImageDataGenerator(
    rotation_range=15,
    width_shift_range=0.1,
    height_shift_range=0.1,
    horizontal_flip=True
)

# Define learning rate schedule
def lr_schedule(epoch):
    lr = 1e-3
    if epoch > 50:
        lr *= 0.1
    elif epoch > 25:
        lr *= 0.5
    return lr

# Train model
model.fit_generator(
    datagen.flow(x_train, y_train, batch_size=32),
    epochs=75,
    validation_data=(x_test, y_test),
    callbacks=[LearningRateScheduler(lr_schedule)]
)

这个模型基于DenseNet的思想，使用了一个堆积的DenseNet块来构建网络。每个块由多个DenseNet层组成，每个层都通过连接前面所有层的方式进行连接，以最大化信息流动。这个代码片段还包括了数据增强和动态学习率调整的功能，以帮助提高模型的性能。