Model Module Customization

Basic Usage Examples

The neural network model is composed of various layers. MindSpore provides Cell, the base unit for constructing neural network layers, and performs neural network encapsulation based on Cell. In the following, the classical model AlexNet is constructed by using Cell.

As shown in the figure, AlexNet consists of five convolutional layers in series with three fully-connected layers. We construct it by using the neural network layer interface provided by mindspore.nn.

from mindspore import nn

The following code shows how to quickly construct AlexNet by using nn.Cell.

Top-level neural networks inherit from nn.Cell as a nested structure.
Each neural network layer is a subclass of nn.Cell.
nn.SequentialCell can be simplified when defining models for sequential structures.

class AlexNet(nn.Cell):
    def __init__(self, num_classes=1000, dropout=0.5):
        super().__init__()
        self.features = nn.SequentialCell(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, pad_mode='pad', padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, pad_mode='pad', padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, pad_mode='pad', padding=1),
            nn.ReLU(),
            nn.Conv2d(384, 256, kernel_size=3, pad_mode='pad', padding=1),
            nn.ReLU(),
            nn.Conv2d(256, 256, kernel_size=3, pad_mode='pad', padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.classifier = nn.SequentialCell(
            nn.Dropout(p=dropout),
            nn.Dense(256 * 6 * 6, 4096),
            nn.ReLU(),
            nn.Dropout(p=dropout),
            nn.Dense(4096, 4096),
            nn.ReLU(),
            nn.Dense(4096, num_classes),
        )

    def construct(self, x):
        x = self.features(x)
        x = x.view(x.shape[0], 256 * 6 * 6)
        x = self.classifier(x)
        return x

In the process of defining a model, the construct method can be used within Python syntax for any construction of the model structure, such as conditional, looping, and other control flow statements. However, when compiling Just In Time, the syntax needs to be parsed by the compiler. For a syntax restriction, refer to: Static diagram syntax support .

After completing the model construction, we construct a single sample of data and send it to the instantiated AlexNet to find the positive results.

import numpy as np
import mindspore
from mindspore import Tensor

x = Tensor(np.random.randn(1, 3, 224, 224), mindspore.float32)

network = AlexNet()
logits = network(x)
print(logits.shape)

(1, 1000)

More Usage Scenarios

In addition to the basic network structure construction, we introduce the neural network layer (Layer), loss function (Loss) and optimizer (Optimizer), the parameters (Parameter) required by the neural network layer and the construction of its initialization method (Initializer), and other scenarios respectively in detail.