（python 3）
1 import numpy 2 from scipy import sparse as S 3 from matplotlib import pyplot as plt 4 from scipy.sparse.csr import csr_matrix 5 import pandas 6 7 def normalize(x): 8 V = x.copy() 9 V -= x.min(axis=1).reshape(x.shape[0],1) 10 V /= V.max(axis=1).reshape(x.shape[0],1) 11 return V 12 13 def sigmoid(x): 14 #return x*(x > 0) 15 #return numpy.tanh(x) 16 return 1.0/(1+numpy.exp(-x)) 17 18 class RBM(): 19 def __init__(self, n_visible=None, n_hidden=None, W=None, learning_rate = 0.1, weight_decay=1,cd_steps=1,momentum=0.5): 20 if W == None: 21 self.W = numpy.random.uniform(-.1,0.1,(n_visible, n_hidden)) / numpy.sqrt(n_visible + n_hidden) 22 self.W = numpy.insert(self.W, 0, 0, axis = 1) 23 self.W = numpy.insert(self.W, 0, 0, axis = 0) 24 else: 25 self.W=W 26 self.learning_rate = learning_rate 27 self.momentum = momentum 28 self.last_change = 0 29 self.last_update = 0 30 self.cd_steps = cd_steps 31 self.epoch = 0 32 self.weight_decay = weight_decay 33 self.Errors = [] 34 35 36 def fit(self, Input, max_epochs = 1, batch_size=100): 37 if isinstance(Input, S.csr_matrix): 38 bias = S.csr_matrix(numpy.ones((Input.shape[0], 1))) 39 csr = S.hstack([bias, Input]).tocsr() 40 else: 41 csr = numpy.insert(Input, 0, 1, 1) 42 for epoch in range(max_epochs): 43 idx = numpy.arange(csr.shape[0]) 44 numpy.random.shuffle(idx) 45 idx = idx[:batch_size] 46 47 self.V_state = csr[idx] 48 self.H_state = self.activate(self.V_state) 49 pos_associations = self.V_state.T.dot(self.H_state) 50 51 for i in range(self.cd_steps): 52 self.V_state = self.sample(self.H_state) 53 self.H_state = self.activate(self.V_state) 54 55 neg_associations = self.V_state.T.dot(self.H_state) 56 self.V_state = self.sample(self.H_state) 57 58 # Update weights. 59 w_update = self.learning_rate * ((pos_associations - neg_associations) / batch_size) 60 total_change = numpy.sum(numpy.abs(w_update)) 61 self.W += self.momentum * self.last_change + w_update 62 self.W *= self.weight_decay 63 64 self.last_change = w_update 65 66 RMSE = numpy.mean((csr[idx] - self.V_state)**2)**0.5 67 self.Errors.append(RMSE) 68 self.epoch += 1 69 print("Epoch %s: RMSE = %s; ||W||: %6.1f; Sum Update: %f" % (self.epoch, RMSE, numpy.sum(numpy.abs(self.W)), total_change)) 70 return self 71 72 def learning_curve(self): 73 plt.ion() 74 #plt.figure() 75 plt.show() 76 E = numpy.array(self.Errors) 77 plt.plot(pandas.rolling_mean(E, 50)[50:]) 78 79 def activate(self, X): 80 if X.shape[1] != self.W.shape[0]: 81 if isinstance(X, S.csr_matrix): 82 bias = S.csr_matrix(numpy.ones((X.shape[0], 1))) 83 csr = S.hstack([bias, X]).tocsr() 84 else: 85 csr = numpy.insert(X, 0, 1, 1) 86 else: 87 csr = X 88 p = sigmoid(csr.dot(self.W)) 89 p[:,0] = 1.0 90 return p 91 92 def sample(self, H, addBias=True): 93 if H.shape[1] == self.W.shape[0]: 94 if isinstance(H, S.csr_matrix): 95 bias = S.csr_matrix(numpy.ones((H.shape[0], 1))) 96 csr = S.hstack([bias, H]).tocsr() 97 else: 98 csr = numpy.insert(H, 0, 1, 1) 99 else: 100 csr = H 101 p = sigmoid(csr.dot(self.W.T)) 102 p[:,0] = 1 103 return p 104 105 if __name__=="__main__": 106 data = numpy.random.uniform(0,1,(100,10)) 107 rbm = RBM(10,15) 108 rbm.fit(data,1000) 109 rbm.learning_curve()