sparseml.tensorflow_v1.nn package

Submodules

sparseml.tensorflow_v1.nn.layers module

sparseml.tensorflow_v1.nn.layers.activation(x_tens: tensorflow.python.framework.ops.Tensor, act: Union[None, str], name: str = 'act')[source]

Create an activation operation in the current graph and scope.

Parameters

  • x_tens – the tensor to apply the op to

  • act – the activation type to apply, supported: [None, relu, relu6, sigmoid, softmax]

  • name – the name to give to the activation op in the graph

Returns

the created operation

sparseml.tensorflow_v1.nn.layers.conv2d(name: str, x_tens: tensorflow.python.framework.ops.Tensor, in_chan: int, out_chan: int, kernel: int, stride: int, padding: str, act: Optional[str] = None)[source]

Create a convolutional layer with the proper ops and variables.

Parameters

  • name – the name scope to create the layer under

  • x_tens – the tensor to apply the layer to

  • in_chan – the number of input channels

  • out_chan – the number of output channels

  • kernel – the kernel size to create a convolution for

  • stride – the stride to apply to the convolution

  • padding – the padding to apply to the convolution

  • act – an activation type to add into the layer, supported: [None, relu, sigmoid, softmax]

Returns

the created layer

sparseml.tensorflow_v1.nn.layers.conv2d_block(name: str, x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], channels: int, kernel_size: int, padding: Union[str, int, Tuple[int, ...]] = 'same', stride: int = 1, data_format: str = 'channels_last', include_bn: bool = True, include_bias: Optional[bool] = None, act: Union[None, str] = 'relu', kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>)[source]

Create a convolution op and supporting ops (batch norm, activation, etc) in the current graph and scope.

Parameters

  • name – The name to group all ops under in the graph

  • x_tens – The input tensor to apply a convolution and supporting ops to

  • training – A bool or tensor to indicate if the net is being run in training mode or not. Used for batch norm

  • channels – The number of output channels from the conv op

  • kernel_size – The size of the kernel to use for the conv op

  • padding – Any padding to apply to the tensor before the convolution; if string then uses tensorflows built in padding, else uses symmetric_pad2d

  • stride – The stride to apply for the convolution

  • data_format – Either channels_last or channels_first

  • include_bn – True to include a batch norm operation after the conv, False otherwise

  • include_bias – If left unset, will add a bias if not include_bn. Otherwise can be set to True to include a bias after the convolution, False otherwise.

  • act – The activation to apply after the conv op and batch norm (if included). Default is “relu”, set to None for no activation.

  • kernel_initializer – The initializer to use for the convolution kernels

  • bias_initializer – The initializer to use for the bias variable, if a bias is included

  • beta_initializer – The initializer to use for the beta variable, if batch norm is included

  • gamma_initializer – The initializer to use for the gamma variable, if gamma is included

Returns

the tensor after all ops have been applied

sparseml.tensorflow_v1.nn.layers.dense_block(name: str, x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], channels: int, include_bn: bool = False, include_bias: Optional[bool] = None, dropout_rate: Optional[float] = None, act: Union[None, str] = 'relu', kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>)[source]

Create a dense or fully connected op and supporting ops (batch norm, activation, etc) in the current graph and scope.

Parameters

  • name – The name to group all ops under in the graph

  • x_tens – The input tensor to apply a fully connected and supporting ops to

  • training – A bool or tensor to indicate if the net is being run in training mode or not. Used for batch norm and dropout

  • channels – The number of output channels from the dense op

  • include_bn – True to include a batch norm operation after the conv, False otherwise

  • include_bias – If left unset, will add a bias if not include_bn. Otherwise can be set to True to include a bias after the convolution, False otherwise.

  • dropout_rate – The dropout rate to apply after the fully connected and batch norm if included. If none, will not include batch norm

  • act – The activation to apply after the conv op and batch norm (if included). Default is “relu”, set to None for no activation.

  • kernel_initializer – The initializer to use for the fully connected kernels

  • bias_initializer – The initializer to use for the bias variable, if a bias is included

  • beta_initializer – The initializer to use for the beta variable, if batch norm is included

  • gamma_initializer – The initializer to use for the gamma variable, if gamma is included

Returns

the tensor after all ops have been applied

sparseml.tensorflow_v1.nn.layers.depthwise_conv2d_block(name: str, x_tens: tensorflow.python.framework.ops.Tensor, training: Union[bool, tensorflow.python.framework.ops.Tensor], channels: int, kernel_size: int, padding: Union[str, int, Tuple[int, ...]] = 'same', stride: int = 1, data_format: str = 'channels_last', include_bn: bool = True, include_bias: Optional[bool] = None, act: Union[None, str] = 'relu', kernel_initializer=<tensorflow.python.ops.init_ops.GlorotUniform object>, bias_initializer=<tensorflow.python.ops.init_ops.Zeros object>, beta_initializer=<tensorflow.python.ops.init_ops.Zeros object>, gamma_initializer=<tensorflow.python.ops.init_ops.Ones object>)[source]

Create a depthwise convolution op and supporting ops (batch norm, activation, etc) in the current graph and scope.

Parameters

  • name – The name to group all ops under in the graph

  • x_tens – The input tensor to apply a convolution and supporting ops to

  • training – A bool or tensor to indicate if the net is being run in training mode or not. Used for batch norm

  • channels – The number of output channels from the conv op

  • kernel_size – The size of the kernel to use for the conv op

  • padding – Any padding to apply to the tensor before the convolution; if string then uses tensorflows built in padding, else uses symmetric_pad2d

  • stride – The stride to apply for the convolution

  • data_format – Either channels_last or channels_first

  • include_bn – True to include a batch norm operation after the conv, False otherwise

  • include_bias – If left unset, will add a bias if not include_bn. Otherwise can be set to True to include a bias after the convolution, False otherwise.

  • act – The activation to apply after the conv op and batch norm (if included). Default is “relu”, set to None for no activation.

  • kernel_initializer – The initializer to use for the convolution kernels

  • bias_initializer – The initializer to use for the bias variable, if a bias is included

  • beta_initializer – The initializer to use for the beta variable, if batch norm is included

  • gamma_initializer – The initializer to use for the gamma variable, if gamma is included

Returns

the tensor after all ops have been applied

sparseml.tensorflow_v1.nn.layers.fc(name: str, x_tens: tensorflow.python.framework.ops.Tensor, in_chan: int, out_chan: int, act: Optional[str] = None)[source]

Create a fully connected layer with the proper ops and variables.

Parameters

  • name – the name scope to create the layer under

  • x_tens – the tensor to apply the layer to

  • in_chan – the number of input channels

  • out_chan – the number of output channels

  • act – an activation type to add into the layer, supported: [None, relu, sigmoid, softmax]

Returns

the created layer

sparseml.tensorflow_v1.nn.layers.pool2d(name: str, x_tens: tensorflow.python.framework.ops.Tensor, type_: str, pool_size: Union[int, Tuple[int, int]], strides: Union[int, Tuple[int, int]] = 1, padding: Union[str, int, Tuple[int, …]] = 'same', data_format: str = 'channels_last')[source]

Create a pool op with the given name in the current graph and scope. Supported are [max, avg, global_avg]

Parameters

  • name – the name to given to the pooling op in the graph

  • x_tens – the input tensor to apply pooling to

  • type – the type of pooling to apply, one of [max, avg, global_avg]

  • pool_size – the size of the pooling window to apply, if global_avg then is the desired output size

  • strides – the stride to apply for the pooling op, if global_avg then is unused

  • padding – any padding to apply to the tensor before pooling; if string then uses tensorflows built in padding, else uses symmetric_pad2d

  • data_format – either channels_last or channels_first

Returns

the tensor after pooling

Module contents

Neural Network layers and ops for TensorFlow V1