kerasロードモデル

説明:このプログラムは2つの非表示層を含むニューラルネットワークです.保存したモデルをロードする方法を示します.データセット:MNIST

1.kerasモジュールのロード

from __future__ import print_function
#Python __future__ ， ， 。
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

load_model

from keras.models import load_model

変数初期化

batch_size = 128 
nb_classes = 10
nb_epoch = 20 

準備データ

# 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')

# 
# 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)

モデル

を構築

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

訓練と評価

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

モデル評価

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