VGG16（学习）

2021-05-22 12:00:07 729 阅读 AI 写同款 · GEO 优化AI 选题数据监测一键分发 ›

深度学习——VGG16模型详解 1、网络结构 VGG16模型很好的适用于分类和定位任务，其名称来自牛津大学几何组（Visual Geometry Group）的缩写。根据卷积核的大小核卷积层数，VGG共有6种配置，分别为A、A-LRN、B、C、D、E，其中D和E两种是最为常用的VGG16和VGG19。介绍结构图： conv3-64 ：是指第三层卷积后维度变成64，同样地，conv3-128指的是第三层卷积后维度变成128； input（224x224 RGB image）：指的是输入图片大小为224244的彩色图像，通道为阅读详情

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
from keras.datasets import fashion_mnist
from keras.layers import Input, Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from keras.optimizers import Adam
from keras.models import Model
def vgg16(input_shape):
input_tensor=Input(shape=input_shape)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(input_tensor)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
print(x.shape)
# 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)
print(x.shape)
# 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)
print(x.shape)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
print(x.shape)
# # Block 5
# x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
# x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
# x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
# x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# print(x.shape)

# Classification block
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dropout(0.5)(x)
x = Dense(10, activation='softmax', name='predictions')(x)
model = Model(inputs=input_tensor, outputs=x, name='VGG16')
return model

model = vgg16(input_shape = (28, 28, 1))

model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
x_train = x_train/255
x_test = x_test/255
x_train, x_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size=0.2, random_state=0)
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_validate = x_validate.reshape(x_validate.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

print('x_train shape: {}'.format(x_train.shape))
print('x_test shape: {}'.format(x_test.shape))
print('x_validate shape: {}'.format(x_validate.shape))
print ("y_train shape: " + str(y_train.shape))
print ("y_test shape: " + str(y_test.shape))
model.fit(x_train, y_train, epochs = 10, batch_size = 512)

preds = model.evaluate(x_test, y_test)
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))

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