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])