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')
def train_classifier(X, y):
"""
Trains a classifier using best known
parameters on given data / labels.
:param X: Samples, a numpy array of
(N, n_vis) shape where N is number of
samples and n_vis number of visible
varliables (sample dimensionality).
:param y: Labels, a numpy array of
(N, 1) shape. Each lable should be
a label index.
"""
# split data into minibatches
X_mnb, y_mnb = util.create_minibatches(X, y, __CLASS_COUNT * 20)
# create a DBN and pretrain
dbn = DBN([32 * 24, 600, 600], __CLASS_COUNT)
pretrain_params = [[80, 0.05, True, 1, 0.085, 0.1],
[80, 0.05, True, 1, 0.000, 0.0]]
dbn.pretrain(X_mnb, y_mnb, pretrain_params)
# fine-tuning
mlp = dbn.to_mlp()
mlp.train(X_mnb, y_mnb, 1000, 0.1)
return mlp
def _perform(self):
p = self.params
class_count = p[0]
layer_sizes = p[1]
# make a copy of rbm-param list because we might
# modify it for training, and we don't want to
# affect the original
rbm_params = list(p[2])
dbn = DBN(layer_sizes, class_count)
# train the first RBM as an RbmJob (to be able to reuse)
# assuming the RBN is more then one layer deep
if len(layer_sizes) >= 2:
first_rbm_params = [class_count, layer_sizes[0], layer_sizes[1]]
first_rbm_params.extend(rbm_params[0])
first_rbm_job = RbmJob(first_rbm_params, self.X_train)
if not first_rbm_job.is_done():
first_rbm_job.perform()
# create the DBN, then replace the first
# RBM with the one already trained
dbn.rbms[0] = first_rbm_job.results()[0]
# make sure it's skipped in DBN training
rbm_params[0] = None
train_res = dbn.pretrain(self.X_train, self.y_train, rbm_params)
return (dbn, train_res)
