def test_all(self, n):
_dbn=DBN([784,1000,500,250,30],learning_rate=0.01,cd_k=1)
_dbn.pretrain(mnist.train.images,128,50)
_nnet = NN([784, 1000, 500, 250, 30, 250, 500, 1000, 784], 0.01, 128, 50)
_nnet.load_from_dbn_to_reconstructNN(_dbn)
_nnet.train(mnist.train.images, mnist.train.images)
_nnet.test_linear(mnist.test.images, mnist.test.images)
x_in = mnist.test.images[:30]
_predict = _nnet.predict(x_in)
_predict_img = np.concatenate(np.reshape(_predict, [-1, 28, 28]), axis=1)
x_in = np.concatenate(np.reshape(x_in, [-1, 28, 28]), axis=1)
img = Image.fromarray(
(1.0-np.concatenate((_predict_img, x_in), axis=0))*255.0)
img = img.convert('L')
img.save(str(n)+'_.jpg')
img2 = Image.fromarray(
(np.concatenate((_predict_img, x_in), axis=0))*255.0)
img2 = img2.convert('L')
img2.save(str(n)+'.jpg')
nnet_encoder=NN()
nnet_encoder.load_layers_from_NN(_nnet,0,4)
# featrue=nnet_encoder.predict(mnist.test.images)
nnet_decoder=NN()
nnet_decoder.load_layers_from_NN(_nnet,5,8)
def test_another_rbmtrain(self, n):
_dbn = DBN([784, 1000, 500, 250, 30], learning_rate=0.01, cd_k=1)
print(len(mnist.train.images))
for j in range(5):
for i in range(10):
_dbn.pretrain(mnist.train.images[i * 5500:i * 5500 + 5500],
128, 5)
_nnet = NN([784, 1000, 500, 250, 30, 250, 500, 1000, 784], 0.01, 128,
50)
_nnet.load_from_dbn_to_reconstructNN(_dbn)
_nnet.train(mnist.train.images, mnist.train.images)
_nnet.test_linear(mnist.test.images, mnist.test.images)
x_in = mnist.test.images[:30]
_predict = _nnet.predict(x_in)
_predict_img = np.concatenate(np.reshape(_predict, [-1, 28, 28]),
axis=1)
x_in = np.concatenate(np.reshape(x_in, [-1, 28, 28]), axis=1)
img = Image.fromarray((1.0 - np.concatenate(
(_predict_img, x_in), axis=0)) * 255.0)
img = img.convert('L')
img.save(str(n) + '_.jpg')
img2 = Image.fromarray((np.concatenate(
(_predict_img, x_in), axis=0)) * 255.0)
img2 = img2.convert('L')
img2.save(str(n) + '.jpg')