deepcell.layers

Custom Layers

location

Layers to encode location data

class deepcell.layers.location.Location2D(*args: Any, **kwargs: Any)

Bases: Layer

Location Layer for 2D cartesian coordinate locations.

Parameters:

data_format (str) – A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

call(inputs)

compute_output_shape(input_shape)

get_config()

class deepcell.layers.location.Location3D(*args: Any, **kwargs: Any)

Bases: Layer

Location Layer for 3D cartesian coordinate locations.

Parameters:

data_format (str) – A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

call(inputs)

compute_output_shape(input_shape)

get_config()

normalization

Layers to noramlize input images for 2D and 3D images

class deepcell.layers.normalization.ImageNormalization2D(*args: Any, **kwargs: Any)

Bases: Layer

Image Normalization layer for 2D data.

Parameters:

• norm_method (str) – Normalization method to use, one of: “std”, “max”, “whole_image”, None.

• filter_size (int) – The length of the convolution window.

• data_format (str) – A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

• activation (function) – Activation function to use. If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

• use_bias (bool) – Whether the layer uses a bias.

• kernel_initializer (function) – Initializer for the kernel weights matrix, used for the linear transformation of the inputs.

• bias_initializer (function) – Initializer for the bias vector. If None, the default initializer will be used.

• kernel_regularizer (function) – Regularizer function applied to the kernel weights matrix.

• bias_regularizer (function) – Regularizer function applied to the bias vector.

• activity_regularizer (function) – Regularizer function applied to.

• kernel_constraint (function) – Constraint function applied to the kernel weights matrix.

• bias_constraint (function) – Constraint function applied to the bias vector.

build(input_shape)

call(inputs)

compute_output_shape(input_shape)

get_config()

class deepcell.layers.normalization.ImageNormalization3D(*args: Any, **kwargs: Any)

Bases: Layer

Image Normalization layer for 3D data.

Parameters:

• norm_method (str) – Normalization method to use, one of: “std”, “max”, “whole_image”, None.

• filter_size (int) – The length of the convolution window.

• data_format (str) – A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

• activation (function) – Activation function to use. If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

• use_bias (bool) – Whether the layer uses a bias.

• kernel_initializer (function) – Initializer for the kernel weights matrix, used for the linear transformation of the inputs.

• bias_initializer (function) – Initializer for the bias vector. If None, the default initializer will be used.

• kernel_regularizer (function) – Regularizer function applied to the kernel weights matrix.

• bias_regularizer (function) – Regularizer function applied to the bias vector.

• activity_regularizer (function) – Regularizer function applied to.

• kernel_constraint (function) – Constraint function applied to the kernel weights matrix.

• bias_constraint (function) – Constraint function applied to the bias vector.

build(input_shape)

call(inputs)

compute_output_shape(input_shape)

get_config()

padding

Layers for padding for 2D and 3D images

class deepcell.layers.padding.ReflectionPadding2D(*args: Any, **kwargs: Any)

Bases: ZeroPadding2D

Reflection-padding layer for 2D input (e.g. picture).

This layer can add rows and columns of reflected values at the top, bottom, left and right side of an image tensor.

Parameters:

• padding (int, tuple) – If int, the same symmetric padding is applied to height and width. If tuple of 2 ints, interpreted as two different symmetric padding values for height and width: (symmetric_height_pad, symmetric_width_pad). If tuple of 2 tuples of 2 ints, interpreted as ((top_pad, bottom_pad), (left_pad, right_pad)).

• data_format (str) – A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

call(inputs)

class deepcell.layers.padding.ReflectionPadding3D(*args: Any, **kwargs: Any)

Bases: ZeroPadding3D

Reflection-padding layer for 3D data (spatial or spatio-temporal).

Parameters:

• padding (int, tuple) – The pad-width to add in each dimension. If an int, the same symmetric padding is applied to height and width. If a tuple of 3 ints, interpreted as two different symmetric padding values for height and width: (symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad). If tuple of 3 tuples of 2 ints, interpreted as ((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad))

• data_format (str) – A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

call(inputs)

pooling

Layers to encode location data

class deepcell.layers.pooling.DilatedMaxPool2D(*args: Any, **kwargs: Any)

Bases: Layer

Dilated max pooling layer for 2D inputs (e.g. images).

Parameters:

• pool_size (int) – An integer or tuple/list of 2 integers: (pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions.

• strides (int) – An integer or tuple/list of 2 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions.

• dilation_rate (int) – An integer or tuple/list of 2 integers, specifying the dilation rate for the pooling.

• padding (str) – The padding method, either "valid" or "same" (case-insensitive).

• data_format (str) – A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

call(inputs)

compute_output_shape(input_shape)

get_config()

class deepcell.layers.pooling.DilatedMaxPool3D(*args: Any, **kwargs: Any)

Bases: Layer

Dilated max pooling layer for 3D inputs.

Parameters:

• pool_size (int) – An integer or tuple/list of 2 integers: (pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions.

• strides (int) – An integer or tuple/list of 2 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions.

• dilation_rate (int) – An integer or tuple/list of 2 integers, specifying the dilation rate for the pooling.

• padding (str) – The padding method, either "valid" or "same" (case-insensitive).

• data_format (str) – A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

call(inputs)

compute_output_shape(input_shape)

get_config()

tensor_product

Layers to generate tensor products for 2D and 3D data

class deepcell.layers.tensor_product.TensorProduct(*args: Any, **kwargs: Any)

Bases: Layer

Just your regular densely-connected NN layer.

Dense implements the operation:

output = activation(dot(input, kernel) + bias)

where activation is the element-wise activation function passed as the activation argument, kernel is a weights matrix created by the layer, and bias is a bias vector created by the layer (only applicable if use_bias is True).

Note: if the input to the layer has a rank greater than 2, then it is flattened prior to the initial dot product with kernel.

Parameters:

• output_dim (int) – Positive integer, dimensionality of the output space.

• data_format (str) – A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

• activation (function) – Activation function to use. If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

• use_bias (bool) – Whether the layer uses a bias.

• kernel_initializer (function) – Initializer for the kernel weights matrix, used for the linear transformation of the inputs.

• bias_initializer (function) – Initializer for the bias vector. If None, the default initializer will be used.

• kernel_regularizer (function) – Regularizer function applied to the kernel weights matrix.

• bias_regularizer (function) – Regularizer function applied to the bias vector.

• activity_regularizer (function) – Regularizer function applied to.

• kernel_constraint (function) – Constraint function applied to the kernel weights matrix.

• bias_constraint (function) – Constraint function applied to the bias vector.

Input shape:

nD tensor with shape: (batch_size, …, input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim).

Output shape:

nD tensor with shape: (batch_size, …, output_dim). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, output_dim).

build(input_shape)

call(inputs)

compute_output_shape(input_shape)

get_config()

upsample

Upsampling layers

class deepcell.layers.upsample.UpsampleLike(*args: Any, **kwargs: Any)

Bases: Layer

Layer for upsampling a Tensor to be the same shape as another Tensor.

Adapted from https://github.com/fizyr/keras-retinanet.

Parameters:

data_format (str) – A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

call(inputs, **kwargs)

compute_output_shape(input_shape)

get_config()

resize_volumes(volume, size)