作业

 

# 从keras.model中导入model模块，为函数api搭建网络做准备
from keras.models import Model
from keras.layers import Flatten, Dense, Dropout, MaxPooling2D, Conv2D, BatchNormalization, Input, ZeroPadding2D, \
Concatenate
from keras.layers.convolutional import AveragePooling2D
from keras import regularizers # 正则化
from tensorflow.python.keras.optimizers import adam_v2
from tensorflow.python.keras.optimizers import rmsprop_v2 # 优化选择器
from keras.layers import AveragePooling2D
from keras.datasets import mnist
from keras.utils import np_utils
import matplotlib.pyplot as plt
import numpy as np

# 数据处理
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()
X_test1 = X_test
Y_test1 = Y_test
X_train = X_train.reshape(-1, 28, 28, 1).astype("float32") / 255.0
X_test = X_test.reshape(-1, 28, 28, 1).astype("float32") / 255.0
Y_train = np_utils.to_categorical(Y_train, 10)
Y_test = np_utils.to_categorical(Y_test, 10)
print(X_train.shape)
print(Y_train.shape)
print(X_train.shape)

def vgg16():
x_input = Input((28, 28, 1)) # 输入数据形状28*28*1
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(x_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Con