sugartensor.sg_net module

sugartensor.sg_net.sg_densenet_121(x, opt)

Applies dense net 121 model.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

x: A Tensor. opt:

k: An integer. The Growth rate of densenet. Default is 32. num_class: An integer. Number of class. Default is 1000. conv_only: Boolean. If True, fc layers are not applied. Default is False. squeeze: Boolean. If True (default), the dimensions with size 1 in the final outputs will be removed. act: String. ‘relu’ (default). the activation function name reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String(Optional). If provided, used as the scope name of this network

Returns:

A Tensor.

sugartensor.sg_net.sg_densenet_161(x, opt)

Applies dense net 161 model.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

x: A Tensor. opt:

k: An integer. The Growth rate of densenet. Default is 48. num_class: An integer. Number of class. Default is 1000. conv_only: Boolean. If True, fc layers are not applied. Default is False. squeeze: Boolean. If True (default), the dimensions with size 1 in the final outputs will be removed. act: String. ‘relu’ (default). the activation function name reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String(Optional). If provided, used as the scope name of this network

Returns:

A Tensor.

sugartensor.sg_net.sg_densenet_169(x, opt)

Applies dense net 169 model.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

x: A Tensor. opt:

k: An integer. The Growth rate of densenet. Default is 32. num_class: An integer. Number of class. Default is 1000. conv_only: Boolean. If True, fc layers are not applied. Default is False. squeeze: Boolean. If True (default), the dimensions with size 1 in the final outputs will be removed. act: String. ‘relu’ (default). the activation function name reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String(Optional). If provided, used as the scope name of this network

Returns:

A Tensor.

sugartensor.sg_net.sg_densenet_201(x, opt)

Applies dense net 201 model.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

x: A Tensor. opt:

k: An integer. The Growth rate of densenet. Default is 32. num_class: An integer. Number of class. Default is 1000. conv_only: Boolean. If True, fc layers are not applied. Default is False. squeeze: Boolean. If True (default), the dimensions with size 1 in the final outputs will be removed. act: String. ‘relu’ (default). the activation function name reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String(Optional). If provided, used as the scope name of this network

Returns:

A Tensor.

sugartensor.sg_net.sg_densenet_layer(x, opt)

Applies basic architecture of densenet layer.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

x: A Tensor. opt:

dim: An integer. Dimension for this resnet layer num: Number of times to repeat act: String. ‘relu’ (default). the activation function name trans: Boolean. If True(default), transition layer will be applied. reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String. (optional) Used as convolution layer prefix

Returns:

A Tensor.

sugartensor.sg_net.sg_resnet_101(x, opt)

Applies residual net 101 model.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

x: A Tensor. opt:

num_class: An integer. Number of class. Default is 1000. conv_only: Boolean. If True, fc layers are not applied. Default is False. squeeze: Boolean. If True (default), the dimensions with size 1 in the final outputs will be removed. act: String. ‘relu’ (default). the activation function name reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String(Optional). If provided, used as the scope name of this network

Returns:

A Tensor.

sugartensor.sg_net.sg_resnet_152(x, opt)

Applies residual net 152 model.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

x: A Tensor. opt:

num_class: An integer. Number of class. Default is 1000. conv_only: Boolean. If True, fc layers are not applied. Default is False. squeeze: Boolean. If True (default), the dimensions with size 1 in the final outputs will be removed. act: String. ‘relu’ (default). the activation function name reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String(Optional). If provided, used as the scope name of this network

Returns:

A Tensor.

sugartensor.sg_net.sg_resnet_200(x, opt)

Applies residual net 200 model.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

x: A Tensor. opt:

num_class: An integer. Number of class. Default is 1000. conv_only: Boolean. If True, fc layers are not applied. Default is False. squeeze: Boolean. If True (default), the dimensions with size 1 in the final outputs will be removed. act: String. ‘relu’ (default). the activation function name reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String(Optional). If provided, used as the scope name of this network

Returns:

A Tensor.

sugartensor.sg_net.sg_resnet_50(x, opt)

Applies residual net 50 model.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

x: A Tensor. opt:

num_class: An integer. Number of class. Default is 1000. conv_only: Boolean. If True, fc layers are not applied. Default is False. squeeze: Boolean. If True (default), the dimensions with size 1 in the final outputs will be removed. act: String. ‘relu’ (default). the activation function name reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String(Optional). If provided, used as the scope name of this network

Returns:

A Tensor.

sugartensor.sg_net.sg_resnet_layer(x, opt)

Applies basic architecture of residual net.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

x: A Tensor. opt:

dim: An integer. Dimension for this resnet layer num: Number of times to repeat act: String. ‘relu’ (default). the activation function name reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String. (optional) Used as convolution layer prefix

Returns:

A Tensor.

sugartensor.sg_net.sg_vgg_16(tensor, opt)

Applies vgg 16 model.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

tensor: A Tensor opt:

num_class: An integer. Number of class. Default is 1000. conv_only: Boolean. If True, fc layers are not applied. Default is False. squeeze: Boolean. If True (default), the dimensions with size 1 in the final outputs will be removed. act: String. ‘relu’ (default). the activation function name bn: True or False(default). If True, batch normal will be applied reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String(Optional). If provided, used as the scope name of this network

Returns:

A Tensor.

sugartensor.sg_net.sg_vgg_19(tensor, opt)

Applies vgg 19 model.

Note that the fc layers in the original architecture

will be replaced with fully convolutional layers. For convenience, We still call them fc layers, though.

Args:

tensor: A Tensor. opt:

num_class: An integer. Number of class. Default is 1000. conv_only: Boolean. If True, fc layers are not applied. Default is False. squeeze: Boolean. If True (default), the dimensions with size 1 in the final outputs will be removed. act: String. ‘relu’ (default). the activation function name bn: True or False(default). If True, batch normal will be applied reuse: Boolean(Optional). If True, all variables will be loaded from previous network. name: String(Optional). If provided, used as the scope name of this network

Returns:

A Tensor.