densenet识别mnist

DenseNet是一种卷积神经网络，可以用于图像分类和目标识别。MNIST是一个手写数字数据集，包含60000个训练图像和10000个测试图像。下面是使用DenseNet识别MNIST的简单示例：

1.导入必要的库和数据集

import tensorflow as tf
from tensorflow.keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

2.数据预处理

# 将图像转换为浮点数，并将像素值归一化到0到1之间
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# 将标签转换为独热编码
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

# 将图像转换为3维张量
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)

3.创建模型

from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Activation, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, concatenate
from tensorflow.keras.models import Model

def dense_block(x, blocks, name):
    for i in range(blocks):
        x = conv_block(x, 32, name=name + '_block' + str(i + 1))
    return x

def conv_block(x, growth_rate, name):
    bn_axis = 3
    x1 = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(x)
    x1 = Activation('relu', name=name + '_0_relu')(x1)
    x1 = Conv2D(4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(x1)
    x1 = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x1)
    x1 = Activation('relu', name=name + '_1_relu')(x1)
    x1 = Conv2D(growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(x1)
    x = concatenate([x, x1], axis=bn_axis, name=name + '_concat')
    return x

def transition_block(x, reduction, name):
    bn_axis = 3
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x)
    x = Activation('relu', name=name + '_relu')(x)
    x = Conv2D(int(tf.keras.backend.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, name=name + '_conv')(x)
    x = AveragePooling2D(2, strides=2, name=name + '_pool')(x)
    return x

input_shape = (28, 28, 1)
img_input = Input(shape=input_shape)

x = Conv2D(64, 7, strides=2, padding='same', name='conv1')(img_input)
x = BatchNormalization(axis=3, epsilon=1.001e-5, name='bn_conv1')(x)
x = Activation('relu', name='relu_conv1')(x)
x = MaxPooling2D(3, strides=2, padding='same', name='pool1')(x)

x = dense_block(x, 6, name='dense_block1')
x = transition_block(x, 0.5, name='transition_block1')

x = dense_block(x, 12, name='dense_block2')
x = transition_block(x, 0.5, name='transition_block2')

x = dense_block(x, 24, name='dense_block3')
x = transition_block(x, 0.5, name='transition_block3')

x = dense_block(x, 16, name='dense_block4')

x = BatchNormalization(axis=3, epsilon=1.001e-5, name='bn')(x)
x = Activation('relu', name='relu')(x)

x = AveragePooling2D(7, name='pool')(x)
x = Flatten()(x)
x = Dense(10, activation='softmax', name='fc')(x)

model = Model(img_input, x, name='densenet')

4.编译和训练模型

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))

5.评估模型

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])