def calculate_loss_from_wals_model(self, wals_model, sp_inputs):
current_rows = embedding_ops.embedding_lookup(
wals_model.row_factors, math_ops.range(wals_model._input_rows),
partition_strategy="div")
current_cols = embedding_ops.embedding_lookup(
wals_model.col_factors, math_ops.range(wals_model._input_cols),
partition_strategy="div")
row_wts = embedding_ops.embedding_lookup(
wals_model._row_weights, math_ops.range(wals_model._input_rows),
partition_strategy="div")
col_wts = embedding_ops.embedding_lookup(
wals_model._col_weights, math_ops.range(wals_model._input_cols),
partition_strategy="div")
return factorization_ops_test_utils.calculate_loss(
sp_inputs, current_rows, current_cols, wals_model._regularization,
wals_model._unobserved_weight, row_wts, col_wts)
def calculate_loss(self):
"""Calculates the loss of the current (trained) model."""
current_rows = embedding_ops.embedding_lookup(
self._model.get_row_factors(), math_ops.range(self._num_rows),
partition_strategy='div')
current_cols = embedding_ops.embedding_lookup(
self._model.get_col_factors(), math_ops.range(self._num_cols),
partition_strategy='div')
row_wts = embedding_ops.embedding_lookup(
self._row_weights, math_ops.range(self._num_rows),
partition_strategy='div')
col_wts = embedding_ops.embedding_lookup(
self._col_weights, math_ops.range(self._num_cols),
partition_strategy='div')
sp_inputs = self.np_array_to_sparse(self.INPUT_MATRIX)
return factorization_ops_test_utils.calculate_loss(
sp_inputs, current_rows, current_cols, self._regularization_coeff,
self._unobserved_weight, row_wts, col_wts)
def calculate_loss_from_wals_model(self, wals_model, sp_inputs):
current_rows = embedding_ops.embedding_lookup(
wals_model.row_factors,
math_ops.range(wals_model._input_rows),
partition_strategy="div")
current_cols = embedding_ops.embedding_lookup(
wals_model.col_factors,
math_ops.range(wals_model._input_cols),
partition_strategy="div")
row_wts = embedding_ops.embedding_lookup(wals_model._row_weights,
math_ops.range(
wals_model._input_rows),
partition_strategy="div")
col_wts = embedding_ops.embedding_lookup(wals_model._col_weights,
math_ops.range(
wals_model._input_cols),
partition_strategy="div")
return factorization_ops_test_utils.calculate_loss(
sp_inputs, current_rows, current_cols, wals_model._regularization,
wals_model._unobserved_weight, row_wts, col_wts)