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