deepmd.tf.utils.tabulate

deepmd.tf.utils.tabulate#

Attributes#

log

Classes#

DPTabulate

Class for tabulation.

Module Contents#

deepmd.tf.utils.tabulate.log[source]#

class deepmd.tf.utils.tabulate.DPTabulate(descrpt: deepmd.tf.descriptor.Descriptor, neuron: list[int], graph: deepmd.tf.env.tf.Graph, graph_def: deepmd.tf.env.tf.GraphDef, type_one_side: bool = False, exclude_types: list[list[int]] = [], activation_fn: Callable[[deepmd.tf.env.tf.Tensor], deepmd.tf.env.tf.Tensor] = tf.nn.tanh, suffix: str = '')[source]#

Bases: deepmd.utils.tabulate.BaseTabulate

Class for tabulation.

Compress a model, which including tabulating the embedding-net. The table is composed of fifth-order polynomial coefficients and is assembled from two sub-tables. The first table takes the stride(parameter) as it’s uniform stride, while the second table takes 10 * stride as it’s uniform stride The range of the first table is automatically detected by deepmd-kit, while the second table ranges from the first table’s upper boundary(upper) to the extrapolate(parameter) * upper.

Parameters:

descrpt: Descriptor of the original model
neuron: Number of neurons in each hidden layers of the embedding net \(\\mathcal{N}\)
graphtf.Graph: The graph of the original model
graph_deftf.GraphDef: The graph_def of the original model
type_one_side: Try to build N_types tables. Otherwise, building N_types^2 tables
exclude_typeslist[list[int]]: The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.
activation_function: The activation function in the embedding net. Supported options are {“tanh”,”gelu”} in common.ACTIVATION_FN_DICT.
suffixstr, optional: The suffix of the scope