Python DBN.predict Examples

Example #1

def run_dbn(geral, teste):

    #epochs = np.array(([5,10,15,20,200]))

    hidden = np.array(([150]))

    epochs = np.array(([15, 4]))

    ann = DBN(geral.shape[1], geral.shape[1], hidden, epochs)

    ann.training_set(geral, geral)

    ann.train_dbn()

    #ann = DBN.load('dbn_teste.pk1')

    dbn_output1 = result(ann.predict(geral), size)

    dbn_output2 = result(ann.predict(teste), size)

    print ann.predict(geral)

    #ann.save(ann, 'dbn_teste')

    cv2.imwrite('dbn_result1.png', dbn_output1)
    cv2.imwrite('dbn_result2.png', dbn_output2)

Example #2

File: 8_autoencoder.py Project: parasitew/python-dbn

import sys,re,random
import numpy as np
from dbn        import DBN 
from dbn.layers import *
import theano.tensor as T
import theano
if __name__ == '__main__':
	data = np.hstack((np.eye(8),np.arange(8).reshape((8,1))))
	data = np.vstack(100*(data,))
	np.random.shuffle(data)
	
	net = DBN([
				OneHotSoftmax(8),
				Sigmoid(3)
			],8,max_epochs=1000)
	net.fit(data[:,:-1],data[:,-1])
	print net.predict(np.eye(8,dtype=np.float32))

Example #3

#build model
model = DBN(n_nodes=5005,rbm_epoch=100,max_epoch=2000, alpha=0.001)

#import training data
imgs_train = imread_collection('images/train/*.jpg')
print("Imported", len(imgs_train), "images")
print("The first one is",len(imgs_train[0]), "pixels tall, and",
     len(imgs_train[0][0]), "pixels wide")
imgs_train = [resize(x,(77,65),mode='constant', anti_aliasing=False) for x in imgs_train]
imgs_train = [rgb2gray(x) for x in imgs_train]
imgsarr_train = [x.flatten('C') for x in imgs_train]
print(np.array(imgsarr_train).shape)
'''
X = np.array([[0.2157, 0.1255, 0.4039, 1.0, 0.0941, 0.2550],
                [0.1686, 0.9529, 0.0824, 0.0980, 1.0, 0.3529],
                [0.3529, 0.0824, 0.4275, 1.0, 0.1255, 0.2941],
                [0.1255, 1.0, 0.1216, 0.0471, 1.0, 0.2431]])
'''
y = []
for i in range(250):
     y.append(1)
for i in range(250):
     y.append(0)
y = np.array([y])

model.fit(np.array(imgsarr_train), y)
model.predict(np.array([imgsarr_train[0]]))
filename = '8aug2020p250n250e100_2000a0-001_0-01_0-1.pkl'
pickle.dump(model, open(filename, 'wb'))

Example #4

import sys, re, random
import numpy as np
from dbn import DBN
from dbn.layers import *
import theano.tensor as T
import theano
if __name__ == '__main__':
    data = np.hstack((np.eye(8), np.arange(8).reshape((8, 1))))
    data = np.vstack(100 * (data, ))
    np.random.shuffle(data)

    net = DBN([OneHotSoftmax(8), Sigmoid(3)], 8, max_epochs=1000)
    net.fit(data[:, :-1], data[:, -1])
    print net.predict(np.eye(8, dtype=np.float32))