03.模型的加载

程序说明

时间：2016年11月16日

说明：该程序是一个包含两个隐藏层的神经网络。演示如何加载一个保存好的模型。

数据集：MNIST

1.加载keras模块

from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, Adam, RMSprop
from keras.utils import np_utils

Using TensorFlow backend.

需要加载load_model

from keras.models import load_model

2.变量初始化

batch_size = 128 
nb_classes = 10
nb_epoch = 20

3.准备数据

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

60000 train samples
10000 test samples

转换类标号

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

4.建立模型

在现有的文件中加载模型

model = load_model('mnist-mpl.h5')

打印模型

model.summary()

____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
dense_1 (Dense)                  (None, 512)           401920      dense_input_1[0][0]              
____________________________________________________________________________________________________
activation_1 (Activation)        (None, 512)           0           dense_1[0][0]                    
____________________________________________________________________________________________________
dropout_1 (Dropout)              (None, 512)           0           activation_1[0][0]               
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 512)           262656      dropout_1[0][0]                  
____________________________________________________________________________________________________
activation_2 (Activation)        (None, 512)           0           dense_2[0][0]                    
____________________________________________________________________________________________________
dropout_2 (Dropout)              (None, 512)           0           activation_2[0][0]               
____________________________________________________________________________________________________
dense_3 (Dense)                  (None, 10)            5130        dropout_2[0][0]                  
____________________________________________________________________________________________________
activation_3 (Activation)        (None, 10)            0           dense_3[0][0]                    
====================================================================================================
Total params: 669706
____________________________________________________________________________________________________

5.训练与评估

编译模型

model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])

模型评估

score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])

Test score: 0.113199677604
Test accuracy: 0.9831