Layers

Module that implements custom layers. Mainly needed for handling periodicity, backmapping or sparsity.

class encodermap.models.layers.BackMapLayer(*args, **kwargs)

Bases: Layer

Layer that implements backmapping from torsions-angles-distances to euclidean coordinates.

__init__()

Instantiate the layer

call(inputs)

Call the layers, inputs should be a tuple shaped, so that it can be split into distances, angles, dihedrals = inputs

class encodermap.models.layers.MeanAngles(*args, **kwargs)

Bases: Layer

Layer that implements the mean of periodic angles.

__init__(parameters, print_name, multiples_shape)

Instantiate the layer.

call(inputs)

Call the layer

class encodermap.models.layers.PairwiseDistances(*args, **kwargs)

Bases: Layer

Layer that implements pairwise distances.

__init__(parameters, print_name)

Instantiate the layer.

call(inputs)

Call the layer

class encodermap.models.layers.PeriodicInput(*args, **kwargs)

Bases: Layer

Layer that handles periodic input. Needed, if angles are treated. Input angles will be split into sin and cos components and a tensor with shape[0] = 2 * inp_shape[0] will be returned

__init__(parameters, print_name, trainable=False)

Instantiate the layer. Need parameters to get the info about the periodicity. Although angles are most often used, who knows what hyper-toroidal manifold your data lies in.

call(inputs)

Call the layer.

class encodermap.models.layers.PeriodicOutput(*args, **kwargs)

Bases: Layer

Layer that reverses the PeriodicInputLayer.

__init__(parameters, print_name, trainable=False)

Instantiate the layer, We also need to know here, what periodicity is needed.

call(inputs)

Calls the layer, Inputs shold be a tuple of (sin, cos) of the same angles

class encodermap.models.layers.Sparse(*args, **kwargs)

Bases: Dense

Simple subclass of tf.keras.layers.Dense, which implements sparse_dense_matmul

call(inputs)

Call the layer.

class encodermap.models.layers.SparseReshape(*args, **kwargs)

Bases: Reshape

Layer that can reshapa a sparse Tensor.

call(inputs)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Parameters:

• inputs –

  Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

  arguments, and inputs cannot be provided via the default value of a keyword argument.

  • NumPy array or Python scalar values in inputs get cast as tensors.

  • Keras mask metadata is only collected from inputs.

  • Layers are built (build(input_shape) method) using shape info from inputs only.

  • input_spec compatibility is only checked against inputs.

  • Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

  • The SavedModel input specification is generated using inputs only.

  • Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

• *args – Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.

• **kwargs –

  Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

  whether the call is meant for training or inference.

  • mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.