def propdown(self, hid): """ This function propagates the hidden units activation downwords to the visible units :param hid: Variable Matrix(batch_size, out_channels, image_height_out, image_width_out) - given h_sample :return: Variable Matrix(batch_size, in_channels, image_height, image_width) - probability for each visible units to be v_j = 1 """ batch_size = hid.data.shape[0] if self.real == 0: W_flipped = F.swapaxes(CF.flip(self.conv.W, axes=(2, 3)), axis1=0, axis2=1) pre_sigmoid_activation = F.convolution_2d(hid, W_flipped, self.conv.a, pad=self.ksize-1) # F.matmul(hid, self.l.W) + F.broadcast_to(self.l.a, (batch_size, self.n_visible)) v_mean = F.sigmoid(pre_sigmoid_activation) #print('W info ', self.conv.W.data.shape, 'W_flipped info ', W_flipped.data.shape) #print('W info ', self.conv.W.data[3, 0, 2, 3], 'W_flipped info ', W_flipped.data[0, 3, 8, 7]) #print('W info ', self.conv.W.data[3, 0, 8, 7], 'W_flipped info ', W_flipped.data[0, 3, 2, 3]) #print('W info ', self.conv.W.data[19, 0, 4, 0], 'W_flipped info ', W_flipped.data[0, 19, 6, 10]) #print('pre_sigmoidactivation', F.sum(pre_sigmoid_activation).data) #print('v_mean', v_mean.data.shape) #print('v_mean sum', F.sum(v_mean).data) #print('hid', hid.data.shape) else: # TODO: check W_flipped = F.swapaxes(CF.flip(self.conv.W, axes=(2, 3)), axis1=0, axis2=1) v_mean = F.convolution_2d(hid, W_flipped, self.conv.a, pad=self.ksize-1) return v_mean
def fp(self,text): global img global flag if flag: x=functions.flip() if x: img=colour_effect.flip_top_bottom(img) else: img=colour_effect.flip_left_right(img) img.save("trash.jpg") self.show_picture('trash.jpg') else: mt=easygui.msgbox('You do not insert any file, please open a file and try again','Detail',' ok ')
def reconstruct(self, v): """ :param v: Variable Matrix(batch_size, in_channels, image_height, image_width) :return: reconstructed_v, Variable Matrix(batch_size, in_channels, image_height, image_width) """ batch_size = v.data.shape[0] xp = cuda.get_array_module(v.data) if self.real == 0: h = F.sigmoid(self.conv(v)) else: std_ch = xp.reshape(self.std, (1, self.in_channels, 1, 1)) h = F.sigmoid(self.conv(v / std_ch)) # F.sigmoid(F.matmul(v, self.l.W, transb=True) + F.broadcast_to(self.l.b, (batch_size, self.n_hidden))) W_flipped = F.swapaxes(CF.flip(self.conv.W, axes=(2, 3)), axis1=0, axis2=1) reconstructed_v = F.sigmoid(F.convolution_2d(h, W_flipped, self.conv.a, pad=self.ksize-1)) # = F.sigmoid(F.matmul(h, self.l.W) + F.broadcast_to(self.l.a, (batch_size, self.n_visible))) return reconstructed_v
def command_flip(message): my_flip = functions.flip() bot.reply_to(message, my_flip)
def copy(list_f): return foldl(flip(cons))(Nil)(reverse(list_f))
# 15. def copy(list_f): return foldl(flip(cons))(Nil)(reverse(list_f)) # 16. copy2 = foldr(cons)(Nil) # 17. def func_map2(f): return foldr(compose(cons)(f))(Nil) # 18. reverse2 = foldl(flip(cons))(Nil) # 19. def zip_f(a_lst_f): return ( lambda b_lst_f: Nil if nil(a_lst_f) or nil(b_lst_f) else cons(pair(head(a_lst_f))(head(b_lst_f)))(zip_f(tail(a_lst_f))(tail(b_lst_f))) ) # 20. def unzip_func(list_pairs): if nil(list_pairs): return pair(Nil)(Nil)