【python keras実戦】VGGボリュームニューラルネットワークによる手書きフォント認識

# encoding: utf-8
import sys
reload(sys)
sys.setdefaultencoding('utf-8')

import numpy as np
from keras.datasets import mnist
import gc

from keras.models import Sequential, Model
from keras.layers import Input, Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.applications.vgg16 import VGG16
from keras.optimizers import SGD


import cv2
import h5py as h5py 
import numpy as np


##       10   ，         0-9  one Hot     。
def tran_y(y): 
    y_ohe = np.zeros(10) 
    y_ohe[y] = 1 
    return y_ohe


#         ，      32GB    ，GPU  8GB    ，         。VGG    48  
ishape=48
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = [cv2.cvtColor(cv2.resize(i, (ishape, ishape)), cv2.COLOR_GRAY2BGR) for i in X_train] 
X_train = np.concatenate([arr[np.newaxis] for arr in X_train]).astype('float32') 
X_train /= 255.0

X_test = [cv2.cvtColor(cv2.resize(i, (ishape, ishape)), cv2.COLOR_GRAY2BGR) for i in X_test] 
X_test = np.concatenate([arr[np.newaxis] for arr in X_test]).astype('float32')
X_test /= 255.0

y_train_ohe = np.array([tran_y(y_train[i]) for i in range(len(y_train))]) 
y_test_ohe = np.array([tran_y(y_test[i]) for i in range(len(y_test))])
y_train_ohe = y_train_ohe.astype('float32')
y_test_ohe = y_test_ohe.astype('float32')


print X_train.shape

# VGG16         

#                     。    ，             
for i in range(10):
    gc.collect()



ishape=224
model_vgg = VGG16(include_top = False, weights = 'imagenet', input_shape = (ishape, ishape, 3)) 

for layer in model_vgg.layers:
        layer.trainable = False
model = Flatten()(model_vgg.output) 
model = Dense(4096, activation='relu', name='fc1')(model)
model = Dense(4096, activation='relu', name='fc2')(model)
model = Dropout(0.5)(model)
model = Dense(10, activation = 'softmax', name='prediction')(model) 
model_vgg_mnist_pretrain = Model(model_vgg.input, model, name = 'vgg16_pretrain')
print model_vgg_mnist_pretrain.summary()


##       25    ，       60 。
sgd = SGD(lr = 0.05, decay = 1e-5) 
model_vgg_mnist_pretrain.compile(loss = 'categorical_crossentropy', optimizer = sgd, metrics = ['accuracy'])

model_vgg_mnist_pretrain.fit(X_train, y_train_ohe, validation_data = (X_test, y_test_ohe), epochs = 10, batch_size = 64)


#######            
scores=model_vgg_mnist_pretrain.evaluate(X_test,y_test_ohe,verbose=0)

#####     
print scores


time2 = time.time()
print u'ok,  !'
print u'    ：' + str(time2 - time1) + 's'