mindscience/SciAI/tutorial

• Example
• • Setup Neural Networks
  • Loss Definition
  • Networks Training

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Example

• Setup Neural Networks
• Loss Definition
• Networks Training

SciAI base framework consists of several modules covering network setup, network training, validation and auxiliary functions.
The following simple example, indicating the fundamental processes in using SciAI.

Setup Neural Networks

The principle of setting up a neural networks in ScAI is the same as in MindSpore,
but in SciAI it is much easier. The following code segment creates a neural networks with 2-D input, 1-D output and two 5-D hidden layers.

from sciai.architecture import MLP

example_net = MLP(layers=[2, 5, 5, 1], weight_init=XavierTruncNormal(), bias_init='zeros', activation="tanh")

MLP accepts various initialization method and all activation functions provided by MindSpore.

Loss Definition

We define the loss function as a sub-class of Cell, and calculate the loss in method construct.

from mindspore import nn
from sciai.architecture import MSE

class ExampleLoss(nn.Cell):
    def __init__(self, network):
        super().__init__()
        self.network = network
        self.mse = MSE()

    def construct(self, x, y_true):
        y_predict = self.network(x)
        return self.mse(y_predict - y_true)

example_loss = ExampleLoss(example_net)

At this moment, we can calculate the prediction loss by calling example_loss with input x and ground truth y_true.

Networks Training

Then, by creating instance of trainer class provided by SciAI, we can start training with datasets.

from mindspore import nn
from sciai.common import TrainCellWithCallBack
from sciai.context.context import init_project

# Get the correct platform automatically and set to GRAPH_MODE by default.
init_project()

example_optimizer = nn.Adam(example_loss.trainable_params())
example_trainer = TrainCellWithCallBack(example_loss, example_optimizer, loss_interval=100, time_interval=100)
for _ in range(num_iters):
    example_trainer(x_train, y_true)

When the training of example_net is finished and loss converges, we can use the net to predict the value at x by calling y = example_net(x).
The complete example code can be found in ./example_net.py.
Ths example network only indicates the basic ability of SciAI framework and fundamental training process for a neural network.
With this exmpale, you can understand the models in the following model library better, how each model are trained and validated with SciAI and MindSpore.