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))