def __init__(self, input_width, input_height, channel_number,
learning_rate, cost_function):
self.cost_function = cost_function
self.predict_output_list = []
self.conv1 = ConvLayer(input_width, input_height, channel_number, 3, 3,
16, 1, 1, ReluActivator(), learning_rate)
self.conv3 = MaxPoolingLayer(input_width, input_height, 16, 3, 3, 1, 2)
self.conv4 = ConvLayer(input_width // 2, input_height // 2, 16, 3, 3,
32, 1, 1, ReluActivator(), learning_rate)
self.conv5 = MaxPoolingLayer(input_width // 2, input_height // 2, 32,
3, 3, 1, 2)
self.conv6 = ConvLayer(input_width // 4, input_height // 4, 32, 3, 3,
32, 1, 1, ReluActivator(), learning_rate)
self.conv8 = UpsamplingLayer(input_width // 4, input_height // 4, 32)
self.conv9 = ConvLayer(input_width // 2, input_height // 2, 32, 3, 3,
16, 1, 1, ReluActivator(), learning_rate)
self.conv10 = UpsamplingLayer(input_width // 2, input_height // 2, 16)
self.conv12 = ConvLayer(input_width, input_height, 16, 3, 3, 2, 1, 1,
TanhActivator(), learning_rate)
def __init__(self, in_dim, n_class):
super(Lenet_numpy, self).__init__()
self.conv1 = ConvLayer(in_dim, 6, 5,5, zero_padding=2, stride=1, method='SAME')
self.conv2 = ConvLayer(6, 16, 5,5, zero_padding=0, stride=1, method='VALID')
self.conv3 = ConvLayer(16, 120, 5,5, zero_padding=0, stride=1, method='VALID')
self.maxpool1 = MaxPooling(pool_shape=(2,2), stride=(2,2))
self.maxpool2 = MaxPooling(pool_shape=(2,2), stride=(2,2))
self.relu1 = ReLU()
self.relu2 = ReLU()
self.relu3 = ReLU()
self.relu4 = ReLU()
self.fc1 = FullyConnect(120, 84)
self.fc2 = FullyConnect(84, n_class)
self.logsoftmax = Logsoftmax()
def __init__(self):
super(Discriminator, self).__init__()
# 输入1*28*28 MNIST
# 1*28*28 -> 64*16*16
self.conv1 = ConvLayer(nc,
ndf,
4,
4,
zero_padding=1,
stride=2,
method='SAME',
bias_required=False)
self.lrelu1 = Activators.LeakyReLU(0.2)
# 64*16*16 -> 128*8*8
self.conv2 = ConvLayer(ndf,
ndf * 2,
4,
4,
zero_padding=1,
stride=2,
method='SAME',
bias_required=False)
self.bn1 = BatchNorm(ndf * 2)
self.lrelu2 = Activators.LeakyReLU(0.2)
# 128*8*8 -> 256*4*4
self.conv3 = ConvLayer(ndf * 2,
ndf * 4,
4,
4,
zero_padding=1,
stride=2,
method='SAME',
bias_required=False)
self.bn2 = BatchNorm(ndf * 4)
self.lrelu3 = Activators.LeakyReLU(0.2)
# 256*4*4 -> 1*1
self.conv4 = ConvLayer(ndf * 4,
1,
4,
4,
zero_padding=0,
stride=1,
method='VALID',
bias_required=False)
self.sigmoid = Activators.Sigmoid_CE()
def Conv(filters,
kernel_size,
input_shape,
strides=(1, 1),
padding="VALID",
activation='none'):
return ConvLayer(filters, kernel_size, input_shape, strides, padding,
activation)
def __init__(self, in_dim, n_class):
super(CryptoNet_fivelayer, self).__init__()
self.conv = ConvLayer(in_dim, 5, 5,5, zero_padding=1, stride=2, method='SAME')
self.sq1 = Activators.Square()
self.fc1 = FullyConnect(845, 100)
self.sq2 = Activators.Square()
self.fc2 = FullyConnect(100, n_class)
self.logsoftmax = Logsoftmax()
def __init__(self, input_width, input_height, channel_number,
learning_rate, cost_function):
self.cost_function = cost_function
self.predict_output_list = []
self.conv1 = ConvLayer(input_width, input_height, channel_number, 3, 3,
64, 1, 1, ReluActivator(), learning_rate)
# self.conv2 = ConvLayer(input_width, input_height, 64,
# 3, 3, 64, input_width // 2 + 1, 2, ReluActivator(), learning_rate)
# self.conv2 = ConvLayer(input_width, input_height, 8,
# 3, 3, 8, 1, 1, ReluActivator(), learning_rate)
self.conv3 = MaxPoolingLayer(input_width, input_height, 64, 3, 3, 1, 2)
# self.conv4 = ConvLayer(input_width, input_height, 16,
# 3, 3, 16, 1, 1, ReluActivator(), learning_rate)
self.conv5 = ConvLayer(input_width // 2, input_height // 2, 64, 3, 3,
128, 1, 1, ReluActivator(), learning_rate)
self.conv6 = MaxPoolingLayer(input_width // 2, input_height // 2, 128,
3, 3, 1, 2)
self.conv7 = ConvLayer(input_width // 4, input_height // 4, 128, 3, 3,
256, 1, 1, ReluActivator(), learning_rate)
self.conv8 = UpsamplingLayer(input_width // 4, input_height // 4, 256,
3, 3, 128, 1, 1, learning_rate)
self.conv9 = ConvLayer(input_width // 2, input_height // 2, 128, 3, 3,
32, 1, 1, ReluActivator(), learning_rate)
self.conv10 = UpsamplingLayer(input_width // 2, input_height // 2, 32,
3, 3, 32, 1, 1, learning_rate)
# self.conv11 = ConvLayer(input_width, input_height, 8,
# 3, 3, 4, 1, 1, ReluActivator(), learning_rate)
self.conv12 = ConvLayer(input_width, input_height, 32, 3, 3, 2, 1, 1,
NoneActivator(), learning_rate)
def __init__(self, in_dim, n_class):
super(Minionn_fivelayer, self).__init__()
self.conv = ConvLayer(in_dim,
5,
5,
5,
zero_padding=2,
stride=2,
method='SAME')
self.relu1 = Activators.ReLU()
self.fc1 = FullyConnect(980, 100)
self.relu2 = Activators.ReLU()
self.fc2 = FullyConnect(100, n_class)
self.logsoftmax = Logsoftmax()
def conv_test():
bit_length = 32
# (1,28,28)*(5,5,5)
# x_numpy = np.random.randn(1,1,28,28).astype(np.float32)
# w_numpy = np.random.randn(5,1,5,5).astype(np.float32)
# b_numpy = np.random.randn(5).astype(np.float32)
# # (1,28,28)*(5,5,5)
# x_numpy_1 = np.random.randn(1,1,28,28).astype(np.float32)
# x_numpy_2 = x_numpy-x_numpy_1
# w_numpy_1 = np.random.randn(5,1,5,5).astype(np.float32)
# w_numpy_2 = w_numpy-w_numpy_1
# b_numpy_1 = np.random.randn(5).astype(np.float32)
# b_numpy_2 = b_numpy-b_numpy_1
## (3,32,32)*(64,2,2)
# x_numpy = np.random.randn(1,3,32,32).astype(np.float32)
# w_numpy = np.random.randn(64,3,2,2).astype(np.float32)
# b_numpy = np.random.randn(64).astype(np.float32)
# x = torch.tensor(x_numpy, requires_grad=True)
# x_numpy_1 = np.random.randn(1,3,32,32).astype(np.float32)
# x_numpy_2 = x_numpy-x_numpy_1
# w_numpy_1 = np.random.randn(64,3,2,2).astype(np.float32)
# w_numpy_2 = w_numpy-w_numpy_1
# b_numpy_1 = np.random.randn(64).astype(np.float32)
# b_numpy_2 = b_numpy-b_numpy_1
x_numpy = np.random.randn(1,32,32,32).astype(np.float32)
w_numpy = np.random.randn(128,32,3,3).astype(np.float32)
b_numpy = np.random.randn(128).astype(np.float32)
x = torch.tensor(x_numpy, requires_grad=True)
x_numpy_1 = np.random.randn(1,32,32,32).astype(np.float32)
x_numpy_2 = x_numpy-x_numpy_1
w_numpy_1 = np.random.randn(128,32,3,3).astype(np.float32)
w_numpy_2 = w_numpy-w_numpy_1
b_numpy_1 = np.random.randn(128).astype(np.float32)
b_numpy_2 = b_numpy-b_numpy_1
print('input_shape: ', x_numpy.shape)
print('w_shape: ', w_numpy.shape)
# padding=0, stride=2
# cl1 = Conv_sec(1, 5, 5, 5, zero_padding=0, stride=2, method='SAME')
# cl1 = Conv_sec(3, 64, 2, 2, zero_padding=0, stride=2, method='SAME')
cl1 = Conv_sec(32, 128, 3, 3, zero_padding=0, stride=2, method='SAME')
cl_ori = ConvLayer(1, 5, 5, 5, zero_padding=1, stride=2, method='SAME')
cl_tensor = torch.nn.Conv2d(1, 5, kernel_size=5, stride=2, padding=1)
## 设置参数
cl_ori.set_weight(Parameter(w_numpy, requires_grad=True))
cl_ori.set_bias(Parameter(b_numpy, requires_grad=True))
cl1.set_weight_1(Parameter(w_numpy_1, requires_grad=True))
cl1.set_bias_1(Parameter(b_numpy_1, requires_grad=True))
cl1.set_weight_2(Parameter(w_numpy_2, requires_grad=True))
cl1.set_bias_2(Parameter(b_numpy_2, requires_grad=True))
# print('param_error: \n', w_numpy-(w_numpy_1+w_numpy_2))
# print('param_error: \n', cl_ori.weights.data-(cl1.weights_1.data+cl1.weights_2.data))
'''前向传播'''
# start_time_tensor = time.time()
# conv_out = cl_tensor(x)
# end_time_tensor = time.time()
# start_time = time.time()
# conv_out = cl_ori.forward(x_numpy)
# end_time = time.time()
test_num = 10
time_avg = 0
for i in range(test_num):
start_time_sec = time.time()
conv_out_1, conv_out_2 = cl1.forward(x_numpy_1, x_numpy_2)
end_time_sec = time.time()
time_avg+=(end_time_sec-start_time_sec)*1000
print('time avg sec: \n', time_avg/test_num)