The complete AnyNet model is constructed by initializing a sequential container and adding the stem, iterating through an architectural configuration tuple (arch) to append each stage of the body, and finally adding the head (consisting of global average pooling and a dense classification layer).

PyTorch Implementation:
```python
@d2l.add_to_class(AnyNet)
def __init__(self, arch, stem_channels, lr=0.1, num_classes=10):
    super(AnyNet, self).__init__()
    self.save_hyperparameters()
    self.net = nn.Sequential(self.stem(stem_channels))
    for i, s in enumerate(arch):
        self.net.add_module(f'stage{i+1}', self.stage(*s))
    self.net.add_module('head', nn.Sequential(
        nn.AdaptiveAvgPool2d((1, 1)), nn.Flatten(),
        nn.LazyLinear(num_classes)))
    self.net.apply(d2l.init_cnn)
```

MXNet Implementation:
```python
@d2l.add_to_class(AnyNet)
def __init__(self, arch, stem_channels, lr=0.1, num_classes=10):
    super(AnyNet, self).__init__()
    self.save_hyperparameters()
    self.net = nn.Sequential()
    self.net.add(self.stem(stem_channels))
    for i, s in enumerate(arch):
        self.net.add(self.stage(*s))
    self.net.add(nn.GlobalAvgPool2D(), nn.Dense(num_classes))
    self.net.initialize(init.Xavier())
```

JAX Implementation:
```python
@d2l.add_to_class(AnyNet)
def create_net(self):
    net = nn.Sequential([self.stem(self.stem_channels)])
    for i, s in enumerate(self.arch):
        net.layers.extend([self.stage(*s)])
    net.layers.extend([nn.Sequential([
        lambda x: nn.avg_pool(x, window_shape=x.shape[1:3],
                            strides=x.shape[1:3], padding='valid'),
        lambda x: x.reshape((x.shape[0], -1)),
        nn.Dense(self.num_classes)])])
    return net
```

TensorFlow Implementation:
```python
@d2l.add_to_class(AnyNet)
def __init__(self, arch, stem_channels, lr=0.1, num_classes=10):
    super(AnyNet, self).__init__()
    self.save_hyperparameters()
    self.net = tf.keras.models.Sequential(self.stem(stem_channels))
    for i, s in enumerate(arch):
        self.net.add(self.stage(*s))
    self.net.add(tf.keras.models.Sequential([
        tf.keras.layers.GlobalAvgPool2D(),
        tf.keras.layers.Dense(units=num_classes)]))
```

AnyNet Model Code Implementation

The head of an AnyNet architecture converts the feature representations extracted by the network body into the final desired predictions. It employs an entirely standard design consisting of global average pooling to collapse spatial dimensions, followed by a fully connected layer to emit an $$n$$-dimensional vector for $$n$$-class classification.

Claude

The AnyNet design space provides a generic architectural template for constructing and exploring families of convolutional neural networks. This macro-structure is composed of three primary components: a stem that performs the initial image processing, a body that carries out the bulk of the transformations to build object representations across multiple stages, and a head that converts these representations into the final desired outputs (e.g., via a softmax regressor). Within this generic framework, design choices such as stage depth, channel counts, and block structures (often using ResNeXt blocks) parameterize a vast space of potential network configurations.

AnyNet Design Space

Dive into Deep Learning

The stem of an AnyNet architecture serves as the initial processing stage for RGB input images ($$3$$ channels). It typically applies a $$3 	imes 3$$ convolution with a stride of $$2$$, followed by batch normalization. This sequence halves the spatial resolution of the image (from $$r 	imes r$$ to $$r/2 	imes r/2$$) and generates an initial set of $$c_0$$ channels that serve as the input for the network's body.

AnyNet Stem

The body of an AnyNet architecture conducts the majority of feature extraction by operating on images at progressively decreasing resolutions. Typically designed to process ImageNet inputs (e.g., reducing a $$224 \times 224 \times 3$$ image to a $$7 \times 7 \times c_4$$ representation), the body is divided into multiple stages (e.g., $$4$$ stages). Each stage quarters the total spatial resolution (by halving both height and width) and is constructed from a sequence of ResNeXt blocks. The initial block in a stage applies a stride of $$2$$ to downsample the feature map, which necessitates a $$1 \times 1$$ convolution on the residual branch to match dimensions, while all subsequent blocks in the stage maintain constant resolution and channel counts.

Learn Before

Related

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