keras 极简搭建VGG16 手写数字识别

使用VGG16网络 完成迁移学习案例

from keras.applications.vgg16 import VGG16
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Flatten, Dense
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import numpy as np
from keras.utils import  np_utils
import cv2
import pickle
import matplotlib.pyplot as plt
from keras.datasets import mnist

# 得到适合网络的数据
(X_train_data, Y_train), (X_test_data, Y_test) = mnist.load_data()  # 下载数据
X_train_data = X_train_data.astype('float32')  # uint8-->float32
X_test_data = X_test_data.astype('float32')
X_train_data /= 255  # 归一化到0~1区间
X_test_data /= 255
# (60000, 48, 48, 3)
X_train = []
# (10000, 48, 48, 3)
X_test = []
# 把(27, 27, 1)维的数据转化成(48, 48, 3)维的数据
for i in range(X_train_data.shape[0]):
    X_train.append(cv2.cvtColor(cv2.resize(X_train_data[i], (48, 48)), cv2.COLOR_GRAY2RGB))
for i in range(X_test_data.shape[0]):
    X_test.append(cv2.cvtColor(cv2.resize(X_test_data[i], (48, 48)), cv2.COLOR_GRAY2RGB))

X_train = np.array(X_train)
X_test = np.array(X_test)
# 独热编码
y_train = np_utils.to_categorical(Y_train, num_classes=10)
y_test = np_utils.to_categorical(Y_test, num_classes=10)

# 构建网络
vgg16_model = VGG16(weights='imagenet', include_top=False, input_shape=(48, 48, 3))
for layer in vgg16_model.layers:
    layer.trainable = False # 别去调整之前的卷积层的参数

top_model = Sequential()
top_model.add(Flatten(input_shape=vgg16_model.output_shape[1:]))
top_model.add(Dense(512, activation='relu'))
top_model.add(Dropout(0.4))
top_model.add(Dense(10, activation='softmax'))

model = Sequential()
model.add(vgg16_model)
model.add(top_model)
sgd = SGD(learning_rate=0.05, decay=1e-5)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
model.fit(X_train, y_train, batch_size=128, epochs=15)
model.evaluate(X_test, y_test)

我跑了30轮数据，测试集上准确率在0.9833左右