API - Model Training

TensorLayerX provides two model training interfaces, which can satisfy the training of various deep learning tasks.

Model(network[, loss_fn, optimizer, metrics])	High-Level API for Training or Testing.
WithLoss(backbone, loss_fn)	High-Level API for Training or Testing.
WithGrad(network[, loss_fn, optimizer])	Module that returns the gradients.
TrainOneStep(net_with_loss, optimizer, …)	High-Level API for Training One Step.

Model

tensorlayerx.model.Model(network, loss_fn=None, optimizer=None, metrics=None, **kwargs)

High-Level API for Training or Testing.

Model groups layers into an object with training and inference features.

Parameters

• network (tensorlayer model) – The training or testing network.

• loss_fn (function) – Objective function

• optimizer (class) – Optimizer for updating the weights

• metrics (class) – Dict or set of metrics to be evaluated by the model during

tensorlayerx.model.trin()

Model training.

tensorlayerx.model.eval()

Model prediction.

tensorlayerx.model.save_weights()

Input file_path, save model weights into a file of given format. Use load_weights() to restore.

tensorlayerx.model.load_weights()

Load model weights from a given file, which should be previously saved by save_weights().

Examples

>>> import tensorlayerx as tlx
>>> class Net(Module):
>>>     def __init__(self):
>>>         super(Net, self).__init__()
>>>         self.conv = tlx.nn.Conv2d(out_channels=32, kernel_size=(3, 3), stride=(2, 2), in_channels=5, name='conv2d')
>>>         self.bn = tlx.nn.BatchNorm2d(num_features=32, act=tlx.ReLU)
>>>         self.flatten = tlx.nn.Flatten()
>>>         self.fc = tlx.nn.Linear(out_features=12, in_features=32*224*224) # padding=0
>>>
>>>     def construct(self, x):
>>>         x = self.conv(x)
>>>         x = self.bn(x)
>>>         x = self.flatten(x)
>>>         out = self.fc(x)
>>>         return out
>>>
>>> net = Net()
>>> loss = tlx.losses.softmax_cross_entropy_with_logits
>>> optim = tlx.optimizers.Momentum(params=net.trainable_weights, learning_rate=0.1, momentum=0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
>>> dataset = get_dataset()
>>> model.train(2, dataset)

WithLoss

tensorlayerx.model.WithLoss(backbone, loss_fn)

High-Level API for Training or Testing.

Wraps the network with loss function. This Module accepts data and label as inputs and the computed loss will be returned.

Parameters

• backbone (tensorlayer model) – The tensorlayer network.

• loss_fn (function) – Objective function

tensorlayerx.model.forward()

Model inference.

Examples

>>> import tensorlayerx as tlx
>>> net = vgg16()
>>> loss_fn = tlx.losses.softmax_cross_entropy_with_logits
>>> net_with_loss = tlx.model.WithLoss(net, loss_fn)

WithGrad

tensorlayerx.model.WithGrad(network, loss_fn=None, optimizer=None)

Module that returns the gradients.

Parameters

• network (tensorlayer model) – The tensorlayer network.

• loss_fn (function) – Objective function

• optimizer (class) – Optimizer for updating the weights

Examples

>>> import tensorlayerx as tlx
>>> net = vgg16()
>>> loss_fn = tlx.losses.softmax_cross_entropy_with_logits
>>> optimizer = tlx.optimizers.Adam(learning_rate=1e-3)
>>> net_with_grad = tlx.model.WithGrad(net, loss_fn, optimizer)
>>> inputs, labels = tlx.nn.Input((128, 784), dtype=tlx.float32), tlx.nn.Input((128, 1), dtype=tlx.int32)
>>> net_with_grad(inputs, labels)

TrainOneStep

tensorlayerx.model.TrainOneStep(net_with_loss, optimizer, train_weights)

High-Level API for Training One Step.

Wraps the network with an optimizer. It can be trained in one step using the optimizer to get the loss.

Parameters

• net_with_loss (tensorlayer WithLoss) – The training or testing network.

• optimizer (class) – Optimizer for updating the weights

• train_weights (class) – Dict or set of metrics to be evaluated by the model during

Examples

>>> import tensorlayerx as tlx
>>> net = vgg16()
>>> train_weights = net.trainable_weights
>>> loss_fn = tlx.losses.softmax_cross_entropy_with_logits
>>> optimizer = tlx.optimizers.Adam(learning_rate=1e-3)
>>> net_with_loss = tlx.model.WithLoss(net, loss_fn)
>>> train_one_step = tlx.model.TrainOneStep(net_with_loss, optimizer, train_weights)
>>> inputs, labels = tlx.nn.Input((128, 784), dtype=tlx.float32), tlx.nn.Input((128, 1), dtype=tlx.int32)
>>> train_one_step(inputs, labels)