549 lines
20 KiB
Python
549 lines
20 KiB
Python
import sys
|
|
|
|
sys.path.append(".")
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore as ms
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.common.initializer import Normal
|
|
from mindspore.nn import TrainOneStepCell, WithLossCell
|
|
|
|
from mindcv.optim import create_optimizer
|
|
|
|
|
|
class SimpleCNN(nn.Cell):
|
|
def __init__(self, num_classes=10, in_channels=1, include_top=True):
|
|
super(SimpleCNN, self).__init__()
|
|
self.include_top = include_top
|
|
|
|
self.conv1 = nn.Conv2d(in_channels, 6, 5, pad_mode="valid")
|
|
self.conv2 = nn.Conv2d(6, 16, 5, pad_mode="valid")
|
|
self.relu = nn.ReLU()
|
|
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
|
|
|
|
if self.include_top:
|
|
self.flatten = nn.Flatten()
|
|
self.fc = nn.Dense(16 * 5 * 5, num_classes, weight_init=Normal(0.02))
|
|
|
|
def construct(self, x):
|
|
x = self.conv1(x)
|
|
x = self.relu(x)
|
|
x = self.max_pool2d(x)
|
|
x = self.conv2(x)
|
|
x = self.relu(x)
|
|
x = self.max_pool2d(x)
|
|
if self.include_top:
|
|
x = self.flatten(x)
|
|
x = self.fc(x)
|
|
return x
|
|
|
|
|
|
@pytest.mark.parametrize("opt", ["sgd", "momentum"])
|
|
@pytest.mark.parametrize("nesterov", [True, False])
|
|
@pytest.mark.parametrize("filter_bias_and_bn", [True, False])
|
|
def test_sgd_optimizer(opt, nesterov, filter_bias_and_bn):
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
|
|
net_opt = create_optimizer(
|
|
network.trainable_params(),
|
|
opt,
|
|
lr=0.01,
|
|
weight_decay=1e-5,
|
|
momentum=0.9,
|
|
nesterov=nesterov,
|
|
filter_bias_and_bn=filter_bias_and_bn,
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f"{opt}, begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
@pytest.mark.parametrize("bs", [1, 2, 4, 8, 16])
|
|
@pytest.mark.parametrize("opt", ["adam", "adamW", "rmsprop", "adagrad"])
|
|
def test_bs_adam_optimizer(opt, bs):
|
|
network = SimpleCNN(num_classes=10)
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
|
|
net_opt = create_optimizer(network.trainable_params(), opt, lr=0.01, weight_decay=1e-5)
|
|
|
|
bs = bs
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
|
|
print(f"{opt}, begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
@pytest.mark.parametrize("loss_scale", [0.1, 0.2, 0.3, 0.5, 0.9, 1.0])
|
|
@pytest.mark.parametrize("weight_decay", [0.00001, 0.0001, 0.001, 0.005, 0.01, 0.05])
|
|
@pytest.mark.parametrize("lr", [0.0001, 0.001, 0.005, 0.05, 0.1, 0.2])
|
|
def test_lr_weight_decay_loss_scale_optimizer(lr, weight_decay, loss_scale):
|
|
network = SimpleCNN(num_classes=10)
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
|
|
net_opt = create_optimizer(
|
|
network.trainable_params(), "adamW", lr=lr, weight_decay=weight_decay, loss_scale=loss_scale
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
|
|
print(f"{lr}, {weight_decay}, {loss_scale}, begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
@pytest.mark.parametrize("momentum", [0.1, 0.2, 0.5, 0.9, 0.99])
|
|
def test_momentum_optimizer(momentum):
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
|
|
net_opt = create_optimizer(
|
|
network.trainable_params(), "momentum", lr=0.01, weight_decay=1e-5, momentum=momentum, nesterov=False
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f"{momentum}, begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
def test_param_lr_001_filter_bias_and_bn_optimizer():
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
conv_params = list(filter(lambda x: "conv" in x.name, network.trainable_params()))
|
|
no_conv_params = list(filter(lambda x: "conv" not in x.name, network.trainable_params()))
|
|
group_params = [
|
|
{"params": conv_params, "weight_decay": 0.01, "grad_centralization": True},
|
|
{"params": no_conv_params, "lr": 0.01},
|
|
{"order_params": network.trainable_params()},
|
|
]
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
group_params, "adamW", lr=0.01, weight_decay=1e-5, momentum=0.9, nesterov=False, filter_bias_and_bn=False
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f" begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
def test_param_lr_0001_filter_bias_and_bn_optimizer():
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
conv_params = list(filter(lambda x: "conv" in x.name, network.trainable_params()))
|
|
no_conv_params = list(filter(lambda x: "conv" not in x.name, network.trainable_params()))
|
|
group_params = [
|
|
{"params": conv_params, "weight_decay": 0.01, "grad_centralization": True},
|
|
{"params": no_conv_params, "lr": 0.001},
|
|
{"order_params": network.trainable_params()},
|
|
]
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
group_params, "adamW", lr=0.01, weight_decay=1e-5, momentum=0.9, nesterov=False, filter_bias_and_bn=False
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f" begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
@pytest.mark.parametrize("momentum", [-0.1, -1.0, -2])
|
|
def test_wrong_momentum_optimizer(momentum):
|
|
with pytest.raises((RuntimeError, TypeError, ValueError)):
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
network.trainable_params(),
|
|
"momentum",
|
|
lr=0.01,
|
|
weight_decay=0.0001,
|
|
momentum=momentum,
|
|
loss_scale=1.0,
|
|
nesterov=False,
|
|
filter_bias_and_bn=True,
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f"{momentum}, begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
@pytest.mark.parametrize("loss_scale", [-0.1, -1.0])
|
|
def test_wrong_loss_scale_optimizer(loss_scale):
|
|
with pytest.raises((RuntimeError, TypeError, ValueError)):
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
network.trainable_params(),
|
|
"momentum",
|
|
lr=0.01,
|
|
weight_decay=0.0001,
|
|
momentum=0.9,
|
|
loss_scale=loss_scale,
|
|
nesterov=False,
|
|
filter_bias_and_bn=True,
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f"{loss_scale}, begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
if cur_loss < begin_loss:
|
|
raise ValueError
|
|
|
|
|
|
@pytest.mark.parametrize("weight_decay", [-0.1, -1.0])
|
|
def test_wrong_weight_decay_optimizer(weight_decay):
|
|
with pytest.raises((RuntimeError, TypeError, ValueError)):
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
network.trainable_params(),
|
|
"adamW",
|
|
lr=0.01,
|
|
weight_decay=weight_decay,
|
|
momentum=0.9,
|
|
loss_scale=1.0,
|
|
nesterov=False,
|
|
filter_bias_and_bn=True,
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f"{weight_decay}, begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
@pytest.mark.parametrize("lr", [-1.0, -0.1])
|
|
def test_wrong_lr_optimizer(lr):
|
|
with pytest.raises((RuntimeError, TypeError, ValueError)):
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
network.trainable_params(),
|
|
"adamW",
|
|
lr=lr,
|
|
weight_decay=1e-5,
|
|
momentum=0.9,
|
|
loss_scale=1.0,
|
|
nesterov=False,
|
|
filter_bias_and_bn=True,
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f"{lr}, begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
def test_param_lr_01_filter_bias_and_bn_optimizer():
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
conv_params = list(filter(lambda x: "conv" in x.name, network.trainable_params()))
|
|
no_conv_params = list(filter(lambda x: "conv" not in x.name, network.trainable_params()))
|
|
group_params = [
|
|
{"params": conv_params, "weight_decay": 0.01, "grad_centralization": True},
|
|
{"params": no_conv_params, "lr": 0.1},
|
|
{"order_params": network.trainable_params()},
|
|
]
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
group_params, "momentum", lr=0.01, weight_decay=1e-5, momentum=0.9, nesterov=False, filter_bias_and_bn=False
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f" begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
@pytest.mark.parametrize("opt", ["test", "bdam", "mindspore"])
|
|
def test_wrong_opt_optimizer(opt):
|
|
with pytest.raises((RuntimeError, TypeError, ValueError)):
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
network.trainable_params(),
|
|
opt,
|
|
lr=0.01,
|
|
weight_decay=1e-5,
|
|
momentum=0.9,
|
|
loss_scale=1.0,
|
|
nesterov=False,
|
|
filter_bias_and_bn=True,
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f"{opt}, begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
def test_wrong_params_more_optimizer():
|
|
with pytest.raises((RuntimeError, TypeError, ValueError)):
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
conv_params = list(filter(lambda x: "conv" in x.name, network.trainable_params()))
|
|
conv_params.append("test")
|
|
no_conv_params = list(filter(lambda x: "conv" not in x.name, network.trainable_params()))
|
|
group_params = [
|
|
{"params": conv_params, "weight_decay": 0.01, "grad_centralization": True},
|
|
{"params": no_conv_params, "lr": 0.0},
|
|
{"order_params": network.trainable_params()},
|
|
]
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
group_params,
|
|
"momentum",
|
|
lr=0.01,
|
|
weight_decay=1e-5,
|
|
momentum=0.9,
|
|
loss_scale=1.0,
|
|
nesterov=False,
|
|
filter_bias_and_bn=False,
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f" begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
def test_wrong_params_input_optimizer():
|
|
with pytest.raises((RuntimeError, TypeError, ValueError)):
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
conv_params = [1, 2, 3, 4]
|
|
no_conv_params = list(filter(lambda x: "conv" not in x.name, network.trainable_params()))
|
|
group_params = [
|
|
{"params": conv_params, "weight_decay": 0.01, "grad_centralization": True},
|
|
{"params": no_conv_params, "lr": 0.0},
|
|
{"order_params": network.trainable_params()},
|
|
]
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
group_params,
|
|
"momentum",
|
|
lr=0.01,
|
|
weight_decay=1e-5,
|
|
momentum=0.9,
|
|
loss_scale=1.0,
|
|
nesterov=False,
|
|
filter_bias_and_bn=False,
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f" begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"mode",
|
|
[
|
|
ms.GRAPH_MODE,
|
|
ms.PYNATIVE_MODE,
|
|
],
|
|
)
|
|
def test_mode_mult_single_optimizer(mode):
|
|
ms.set_context(mode=mode)
|
|
network = SimpleCNN(in_channels=1, num_classes=10)
|
|
conv_params = list(filter(lambda x: "conv" in x.name, network.trainable_params()))
|
|
no_conv_params = list(filter(lambda x: "conv" not in x.name, network.trainable_params()))
|
|
group_params = [
|
|
{"params": conv_params, "weight_decay": 0.01, "grad_centralization": True},
|
|
{"params": no_conv_params, "lr": 0.1},
|
|
{"order_params": network.trainable_params()},
|
|
]
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
net_opt = create_optimizer(
|
|
group_params, "momentum", lr=0.01, weight_decay=1e-5, momentum=0.9, nesterov=False, filter_bias_and_bn=False
|
|
)
|
|
|
|
bs = 8
|
|
input_data = Tensor(np.ones([bs, 1, 32, 32]).astype(np.float32) * 0.01)
|
|
label = Tensor(np.ones([bs]).astype(np.int32))
|
|
|
|
net_with_loss = WithLossCell(network, net_loss)
|
|
train_network = TrainOneStepCell(net_with_loss, net_opt)
|
|
|
|
train_network.set_train()
|
|
|
|
begin_loss = train_network(input_data, label)
|
|
for i in range(10):
|
|
cur_loss = train_network(input_data, label)
|
|
print(f" begin loss: {begin_loss}, end loss: {cur_loss}")
|
|
|
|
# check output correctness
|
|
assert cur_loss < begin_loss, "Loss does NOT decrease"
|