一、保存模型

from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense

# 数据集
(X_train, y_train), (X_test, y_test) = mnist.load_data()  # 读取并划分MNIST训练集、测试集

X_train = X_train.reshape(len(X_train), -1)  # 二维变一维
X_test = X_test.reshape(len(X_test), -1)

X_train = X_train.astype('float32')  # 转为float类型
X_test = X_test.astype('float32')

X_train = (X_train - 127) / 127  # 灰度像素数据归一化
X_test = (X_test - 127) / 127

y_train = np_utils.to_categorical(y_train, num_classes=10)  # 独热编码。如原来为5，转换后[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
y_test = np_utils.to_categorical(y_test, num_classes=10)

# 定义模型
model = Sequential()  # Keras序列模型

model.add(Dense(20, input_shape=(784,), activation='relu'))  # 添加全连接层（隐藏层），隐藏层数20层，激活函数为ReLU
model.add(Dense(10, activation='sigmoid'))  # 添加输出层，结果10类，激活函数为Sigmoid

print(model.summary())  # 模型基本信息

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])  # 编译模型

# 训练
model.fit(X_train, y_train, epochs=20, batch_size=64, verbose=1, validation_split=0.05)  # 迭代20次

# 评估
loss, accuracy = model.evaluate(X_test, y_test)
print('Test loss:', loss)
print('Accuracy:', accuracy)

# 保存
model.save('mnistmodel.h5')

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 20)                15700     
_________________________________________________________________
dense_2 (Dense)              (None, 10)                210       
=================================================================
Total params: 15,910
Trainable params: 15,910
Non-trainable params: 0
_________________________________________________________________


Test loss: 0.2107365175232291
Accuracy: 0.938

可以看到保存了一个.h5文件
在这里插入图片描述

二、预测

import random
import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.models import load_model

# 数据集
(_, _), (X_test, y_test) = mnist.load_data()  # 划分MNIST训练集、测试集

# 随机数
index = random.randint(0, X_test.shape[0])
x = X_test[index]
y = y_test[index]

# 显示该数字
plt.imshow(x, cmap='gray_r')
plt.title("original {}".format(y))
plt.show()

# 加载
mymodel = load_model('mnistmodel.h5')

# 预测
x.shape = (1,784)#变成[[]]
# x = x.flatten()[None]  # 也可以用这个
predict = mymodel.predict(x)
predict = np.argmax(predict)#取最大值的位置

print('index:', index)
print('original:', y)
print('predicted:', predict)

在这里插入图片描述

index 8991
original: 0
predicted: 0

三、参考文献

Numpy 改变数组维度的几种方法 - m0_37586991的博客 - CSDN博客 https://blog.csdn.net/m0_37586991/article/details/79758168
Keras-2 Keras Mnist - 记录学习的过程 - CSDN博客 https://blog.csdn.net/weiwei9363/article/details/78570390

四、IPython

import random
import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.models import load_model

# 数据集
(_, _), (X_test, y_test) = mnist.load_data()  # 划分MNIST训练集、测试集

# 加载模型
mymodel = load_model('mnistmodel.h5')

# 随机数
index = random.randint(0, X_test.shape[0])
x = X_test[index]
y = y_test[index]

# 显示该数字
plt.imshow(x, cmap='gray_r')
plt.title("original {}".format(y))
plt.show()

# 预测
x.shape = (1,784)#变成[[]]
predict = mymodel.predict(x)
predict = np.argmax(predict)#取最大值的位置

print('index', index)
print('original:', y)
print('predicted:', predict)