return ' '.join(sentence)
if __name__ == '__main__':
hidden_units = 200
names = [preprocess(line.strip()) for line in open(sys.argv[1], 'r')]
random.shuffle(names)
word_counter = CountVectorizer(tokenizer=wordpunct_tokenize,
stop_words=stopwords,
binary=True,
dtype=np.byte)
data = word_counter.fit_transform(names)
words = word_counter.get_feature_names()
data = data.toarray()
print data.shape
_, vocab = data.shape
n = DBN([
Sigmoid(data.shape[1]),
Sigmoid(hidden_units),
Sigmoid(hidden_units / 2)
])
n.fit(data, None)
"""
visible = r.run_hidden(np.eye(hidden_units))
out = open('assoc_words','w')
for f in range(hidden_units):
out.write(' '.join( words[i] for i in range(len(words)) if visible[f,i] ) )
out.write('\n')
"""
if __name__ == '__main__':
hidden_units = 200
names = [ preprocess(line.strip()) for line in open(sys.argv[1],'r') ]
random.shuffle(names)
word_counter = CountVectorizer(
tokenizer=wordpunct_tokenize,
stop_words=stopwords,
binary=True,
dtype=np.byte
)
data = word_counter.fit_transform(names)
words = word_counter.get_feature_names()
data = data.toarray()
print data.shape
_,vocab = data.shape
n = DBN([
Sigmoid(data.shape[1]),
Sigmoid(hidden_units),
Sigmoid(hidden_units/2)
])
n.fit(data,None)
"""
visible = r.run_hidden(np.eye(hidden_units))
out = open('assoc_words','w')
for f in range(hidden_units):
out.write(' '.join( words[i] for i in range(len(words)) if visible[f,i] ) )
out.write('\n')
"""
#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'))
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))
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))
