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 forward(x, t):
y = conv1(x)
y = autograd.relu(y)
y = conv2(y)
y = autograd.relu(y)
y = autograd.max_pool_2d(y)
y = autograd.flatten(y)
y = linear(y)
y = autograd.soft_max(y)
loss = autograd.cross_entropy(y, t)
return loss, y
def forward(x, t):
y = conv1(x)
y = autograd.relu(y)
y = conv2(y)
y = autograd.relu(y)
y = autograd.max_pool_2d(y)
y = autograd.flatten(y)
y = linear(y)
y = autograd.soft_max(y)
loss = autograd.cross_entropy(y, t)
return loss, y
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])
n = y.shape[0]
categorical = np.zeros((n, num_classes))
categorical[np.arange(n), y] = 1
return categorical
label = to_categorical(label, 2).astype(np.float32)
print('train_data_shape:', data.shape)
print('train_label_shape:', label.shape)
inputs = Tensor(data=data)
target = Tensor(data=label)
model = onnx.load('mlp.onnx')
print('finish init')
sgd = optimizer.SGD(0.00)
#####backend run multiple times
# training process
rep = sonnx.BackendRep(model)
for epoch in range(1):
outputs = rep.run([inputs])
loss = autograd.cross_entropy(outputs[0], target)
if (epoch % 100 == 0):
print('training loss = ', tensor.to_numpy(loss)[0])
#####backend run only one time
outputs = sonnx.Backend.run_model(model, [inputs])
loss = autograd.cross_entropy(outputs[0], target)
print('training loss = ', tensor.to_numpy(loss)[0])