Exemplo n.º 1
0
def test_mult_by_sigmoid_deriv():
    m = 256
    n = 128
    c_targets = np.array(np.random.randn(m, n)*10, dtype=np.float32, order='F')
    c_acts = np.array(np.random.rand(m, n), dtype=np.float32, order='F')

    g_targets = cm.CUDAMatrix(c_targets)
    g_acts = cm.CUDAMatrix(c_acts)

    c_targets = c_targets * c_acts * (1. - c_acts)
    cl.mult_by_sigmoid_deriv(g_targets, g_acts)

    assert np.max(np.abs(c_acts - g_acts.asarray())) < 10**-2, "Error in cudamat.learn.mult_by_sigmoid_deriv exceeded threshold"
Exemplo n.º 2
0
def test_mult_by_sigmoid_deriv():
    m = 256
    n = 128
    c_targets = np.array(np.random.randn(m, n) * 10,
                         dtype=np.float32,
                         order='F')
    c_acts = np.array(np.random.rand(m, n), dtype=np.float32, order='F')

    g_targets = cm.CUDAMatrix(c_targets)
    g_acts = cm.CUDAMatrix(c_acts)

    c_targets = c_targets * c_acts * (1. - c_acts)
    cl.mult_by_sigmoid_deriv(g_targets, g_acts)

    assert np.max(
        np.abs(c_acts - g_acts.asarray())
    ) < 10**-2, "Error in cudamat.learn.mult_by_sigmoid_deriv exceeded threshold"
Exemplo n.º 3
0
    def backProp(self, error):
#        print 'back propagation'
        self.dW[self.H-1].add_dot(self.vis[self.H-1],error.T)
#        print 'self.vis'
#        self.vis[self.H-1].copy_to_host()
#        print self.vis[self.H-1].numpy_array
#        print 'self.dW'
#        self.dW[self.H-1].copy_to_host()
#        print self.dW[self.H-1].numpy_array
#        print 'error 2'
#        error.copy_to_host()
#        print error.numpy_array
        self.db[self.H-1].add_sums(error,axis =1 )
        for i in list(reversed(range(self.H-1))):
            delta = cm.empty((self.W[i+1].shape[0],error.shape[1]))
            cm.dot(self.W[i+1],error,target = delta)# delta : 2000*256
            learn.mult_by_sigmoid_deriv(delta, self.vis[i+1])
            self.dW[i].add_dot(self.vis[i], delta.T)
            self.db[i].add_sums(delta, axis = 1)
            error = delta