def forward(self, inputs):
x = autograd.matmul(inputs, self.w0)
x = autograd.add_bias(x, self.b0)
x = autograd.relu(x)
x = autograd.matmul(x, self.w1)
x = autograd.add_bias(x, self.b1)
return x
def onnx_loss(a,model,target):
'''
input:
a graph node dictionary
model: graph model
target: label
load other nodes of onnx
'''
for i in model.graph.node:
if (i.op_type == 'Constant'):
pass
# do nothing
if (i.op_type == 'LeakyRelu'):
a[str(i.output[0])] = autograd.relu(a[str(i.input[0])])
elif (i.op_type == 'Relu'):
a[str(i.output[0])] = autograd.relu(a[str(i.input[0])])
elif (i.op_type == 'Softmax'):
a[str(i.output[0])] = autograd.softmax(a[str(i.input[0])])
elif (i.op_type == 'Add'):
if(str(i.input[1])[-1] == 'b'):
a[str(i.output[0])] = autograd.add_bias(a[str(i.input[0])], a[str(i.input[1])])
else:
a[str(i.output[0])] = autograd.add(a[str(i.input[0])],a[str(i.input[1])])
elif (i.op_type == 'MatMul'):
a[str(i.output[0])] = autograd.matmul(a[str(i.input[0])], a[str(i.input[1])])
loss = autograd.cross_entropy(a['Y'], target)
return loss
def singa_to_onnx(niter, use_cpu=False):
if use_cpu:
print("Using CPU")
dev = device.get_default_device()
else:
print("Using GPU")
dev = device.create_cuda_gpu()
inputs = Tensor(
data=data,
device=dev,
requires_grad=False,
stores_grad=False,
name="input",
)
target = Tensor(
data=label,
device=dev,
requires_grad=False,
stores_grad=False,
name="target",
)
w0 = Tensor(shape=(2, 3), device=dev, requires_grad=True, stores_grad=True)
w0.gaussian(0.0, 0.1)
b0 = Tensor(shape=(3,), device=dev, requires_grad=True, stores_grad=True)
b0.set_value(0.0)
w1 = Tensor(shape=(3, 2), device=dev, requires_grad=True, stores_grad=True)
w1.gaussian(0.0, 0.1)
b1 = Tensor(shape=(2,), device=dev, requires_grad=True, stores_grad=True)
b1.set_value(0.0)
sgd = opt.SGD(0.1)
# training process
for i in range(100):
x = autograd.matmul(inputs, w0)
x = autograd.add_bias(x, b0)
x = autograd.relu(x)
x = autograd.matmul(x, w1)
x = autograd.add_bias(x, b1)
loss = autograd.softmax_cross_entropy(x, target)
for p, gp in autograd.backward(loss):
sgd.update(p, gp)
print("training loss = ", tensor.to_numpy(loss)[0])
sonnx.export([inputs], [x], file_path="mlp.onnx")
print("train_label_shape:", label.shape)
inputs = Tensor(data=data)
target = Tensor(data=label)
w0 = Tensor(shape=(2, 3), requires_grad=True, stores_grad=True)
w0.gaussian(0.0, 0.1)
b0 = Tensor(shape=(1, 3), requires_grad=True, stores_grad=True)
b0.set_value(0.0)
w1 = Tensor(shape=(3, 2), requires_grad=True, stores_grad=True)
w1.gaussian(0.0, 0.1)
b1 = Tensor(shape=(1, 2), requires_grad=True, stores_grad=True)
b1.set_value(0.0)
sgd = optimizer.SGD(0.05)
# training process
for i in range(1001):
x = autograd.matmul(inputs, w0)
x = autograd.add_bias(x, b0)
x = autograd.relu(x)
x = autograd.matmul(x, w1)
x = autograd.add_bias(x, b1)
x = autograd.softmax(x)
loss = autograd.cross_entropy(x, target)
for p, gp in autograd.backward(loss):
sgd.apply(0, gp, p, "")
if i % 100 == 0:
print("training loss = ", tensor.to_numpy(loss)[0])
print('train_label_shape:', label.shape)
inputs = Tensor(data=data)
target = Tensor(data=label)
w0 = Tensor(shape=(2, 3), requires_grad=True, stores_grad=True)
w0.gaussian(0.0, 0.1)
b0 = Tensor(shape=(1, 3), requires_grad=True, stores_grad=True)
b0.set_value(0.0)
w1 = Tensor(shape=(3, 2), requires_grad=True, stores_grad=True)
w1.gaussian(0.0, 0.1)
b1 = Tensor(shape=(1, 2), requires_grad=True, stores_grad=True)
b1.set_value(0.0)
sgd = optimizer.SGD(0.05)
# training process
for i in range(1001):
x = autograd.matmul(inputs, w0)
x = autograd.add_bias(x, b0)
x = autograd.relu(x)
x = autograd.matmul(x, w1)
x = autograd.add_bias(x, b1)
x = autograd.softmax(x)
loss = autograd.cross_entropy(x, target)
for p, gp in autograd.backward(loss):
sgd.apply(0, gp, p, '')
if (i % 100 == 0):
print('training loss = ', tensor.to_numpy(loss)[0])
w1 = Tensor(shape=(2, 2), requires_grad=True, stores_grad=True)
w1.gaussian(0.0, 0.1)
b1 = Tensor(shape=(1, 2), requires_grad=True, stores_grad=True)
b1.set_value(0.0)
w2 = Tensor(shape=(2, 2), requires_grad=True, stores_grad=True)
w2.gaussian(0.0, 0.1)
b2 = Tensor(shape=(1, 2), requires_grad=True, stores_grad=True)
b2.set_value(0.0)
sgd = optimizer.SGD(0.00)
# training process
for i in range(1):
x = autograd.matmul(inputs, w0)
x = autograd.add_bias(x, b0)
x = autograd.relu(x)
x2 = autograd.matmul(x, w2)
x2 = autograd.add_bias(x2, b2)
x1 = autograd.matmul(x, w1)
x1 = autograd.add_bias(x1, b1)
x = autograd.add(x1, x2)
y = autograd.softmax(x)
loss = autograd.cross_entropy(y, target)
gradient = autograd.backward(loss)
for p, gp in gradient:
sgd.apply(0, gp, p, '')
if (i % 100 == 0):
print('training loss = ', tensor.to_numpy(loss)[0])
model = sonnx.to_onnx_model([inputs], [y])
