def _dynamic_rnn_model_fn(features, labels, mode):
"""The model to be passed to an `Estimator`."""
with ops.name_scope(name):
sequence_length = features.get(sequence_length_key)
sequence_input = build_sequence_input(features,
sequence_feature_columns,
context_feature_columns)
dropout = (dropout_keep_probabilities
if mode == model_fn.ModeKeys.TRAIN
else None)
# This class promises to use the cell type selected by that function.
cell = rnn_common.construct_rnn_cell(num_units, cell_type, dropout)
initial_state = dict_to_state_tuple(features, cell)
rnn_activations, final_state = construct_rnn(
initial_state,
sequence_input,
cell,
target_column.num_label_columns,
dtype=dtype,
parallel_iterations=parallel_iterations,
swap_memory=swap_memory)
loss = None # Created below for modes TRAIN and EVAL.
if prediction_type == rnn_common.PredictionType.MULTIPLE_VALUE:
prediction_dict = rnn_common.multi_value_predictions(
rnn_activations, target_column, problem_type, predict_probabilities)
if mode != model_fn.ModeKeys.INFER:
loss = _multi_value_loss(
rnn_activations, labels, sequence_length, target_column, features)
elif prediction_type == rnn_common.PredictionType.SINGLE_VALUE:
prediction_dict = _single_value_predictions(
rnn_activations, sequence_length, target_column,
problem_type, predict_probabilities)
if mode != model_fn.ModeKeys.INFER:
loss = _single_value_loss(
rnn_activations, labels, sequence_length, target_column, features)
state_dict = state_tuple_to_dict(final_state)
prediction_dict.update(state_dict)
eval_metric_ops = None
if mode != model_fn.ModeKeys.INFER:
eval_metric_ops = rnn_common.get_eval_metric_ops(
problem_type, prediction_type, sequence_length, prediction_dict,
labels)
train_op = None
if mode == model_fn.ModeKeys.TRAIN:
train_op = optimizers.optimize_loss(
loss=loss,
global_step=None, # Get it internally.
learning_rate=learning_rate,
optimizer=optimizer,
clip_gradients=gradient_clipping_norm,
summaries=optimizers.OPTIMIZER_SUMMARIES)
output_alternatives = _get_output_alternatives(prediction_type,
problem_type,
prediction_dict)
return model_fn.ModelFnOps(mode=mode,
predictions=prediction_dict,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
output_alternatives=output_alternatives)
def _rnn_model_fn(features, labels, mode):
"""The model to be passed to an `Estimator`."""
with ops.name_scope(name):
dropout = (dropout_keep_probabilities
if mode == model_fn.ModeKeys.TRAIN else None)
cell = rnn_common.construct_rnn_cell(num_units, cell_type, dropout)
batch = _read_batch(
cell=cell,
features=features,
labels=labels,
mode=mode,
num_unroll=num_unroll,
batch_size=batch_size,
sequence_feature_columns=sequence_feature_columns,
context_feature_columns=context_feature_columns,
num_threads=num_threads,
queue_capacity=queue_capacity,
seed=seed)
sequence_features = batch.sequences
context_features = batch.context
if mode != model_fn.ModeKeys.INFER:
labels = sequence_features.pop(rnn_common.RNNKeys.LABELS_KEY)
inputs = _prepare_inputs_for_rnn(sequence_features,
context_features,
sequence_feature_columns,
num_unroll)
state_name = _get_state_names(cell)
rnn_activations, final_state = construct_state_saving_rnn(
cell=cell,
inputs=inputs,
num_label_columns=target_column.num_label_columns,
state_saver=batch,
state_name=state_name)
loss = None # Created below for modes TRAIN and EVAL.
prediction_dict = rnn_common.multi_value_predictions(
rnn_activations, target_column, problem_type,
predict_probabilities)
if mode != model_fn.ModeKeys.INFER:
loss = _multi_value_loss(rnn_activations, labels, batch.length,
target_column, features)
eval_metric_ops = None
if mode != model_fn.ModeKeys.INFER:
eval_metric_ops = rnn_common.get_eval_metric_ops(
problem_type, rnn_common.PredictionType.MULTIPLE_VALUE,
batch.length, prediction_dict, labels)
state_dict = state_tuple_to_dict(final_state)
prediction_dict.update(state_dict)
train_op = None
if mode == model_fn.ModeKeys.TRAIN:
train_op = optimizers.optimize_loss(
loss=loss,
global_step=None, # Get it internally.
learning_rate=learning_rate,
optimizer=optimizer,
clip_gradients=gradient_clipping_norm,
summaries=optimizers.OPTIMIZER_SUMMARIES)
return model_fn.ModelFnOps(mode=mode,
predictions=prediction_dict,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
def _dynamic_rnn_model_fn(features, labels, mode):
"""The model to be passed to an `Estimator`."""
with ops.name_scope(name):
sequence_length = features.get(sequence_length_key)
sequence_input = build_sequence_input(features,
sequence_feature_columns,
context_feature_columns)
dropout = (dropout_keep_probabilities
if mode == model_fn.ModeKeys.TRAIN
else None)
# This class promises to use the cell type selected by that function.
cell = rnn_common.construct_rnn_cell(num_units, cell_type, dropout)
initial_state = dict_to_state_tuple(features, cell)
rnn_activations, final_state = construct_rnn(
initial_state,
sequence_input,
cell,
target_column.num_label_columns,
dtype=dtype,
parallel_iterations=parallel_iterations,
swap_memory=swap_memory)
loss = None # Created below for modes TRAIN and EVAL.
if prediction_type == rnn_common.PredictionType.MULTIPLE_VALUE:
prediction_dict = rnn_common.multi_value_predictions(
rnn_activations, target_column, problem_type, predict_probabilities)
if mode != model_fn.ModeKeys.INFER:
loss = _multi_value_loss(
rnn_activations, labels, sequence_length, target_column, features)
elif prediction_type == rnn_common.PredictionType.SINGLE_VALUE:
prediction_dict = _single_value_predictions(
rnn_activations, sequence_length, target_column,
problem_type, predict_probabilities)
if mode != model_fn.ModeKeys.INFER:
loss = _single_value_loss(
rnn_activations, labels, sequence_length, target_column, features)
state_dict = state_tuple_to_dict(final_state)
prediction_dict.update(state_dict)
eval_metric_ops = None
if mode != model_fn.ModeKeys.INFER:
eval_metric_ops = rnn_common.get_eval_metric_ops(
problem_type, prediction_type, sequence_length, prediction_dict,
labels)
train_op = None
if mode == model_fn.ModeKeys.TRAIN:
train_op = optimizers.optimize_loss(
loss=loss,
global_step=None, # Get it internally.
learning_rate=learning_rate,
optimizer=optimizer,
clip_gradients=gradient_clipping_norm,
summaries=optimizers.OPTIMIZER_SUMMARIES)
output_alternatives = _get_output_alternatives(prediction_type,
problem_type,
prediction_dict)
return model_fn.ModelFnOps(mode=mode,
predictions=prediction_dict,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
output_alternatives=output_alternatives)
def _rnn_model_fn(features, labels, mode):
"""The model to be passed to an `Estimator`."""
with ops.name_scope(name):
dropout = (dropout_keep_probabilities
if mode == model_fn.ModeKeys.TRAIN
else None)
cell = rnn_common.construct_rnn_cell(num_units, cell_type, dropout)
batch = _read_batch(
cell=cell,
features=features,
labels=labels,
mode=mode,
num_unroll=num_unroll,
batch_size=batch_size,
sequence_feature_columns=sequence_feature_columns,
context_feature_columns=context_feature_columns,
num_threads=num_threads,
queue_capacity=queue_capacity,
seed=seed)
sequence_features = batch.sequences
context_features = batch.context
if mode != model_fn.ModeKeys.INFER:
labels = sequence_features.pop(rnn_common.RNNKeys.LABELS_KEY)
inputs = _prepare_inputs_for_rnn(sequence_features, context_features,
sequence_feature_columns, num_unroll)
state_name = _get_state_names(cell)
rnn_activations, final_state = construct_state_saving_rnn(
cell=cell,
inputs=inputs,
num_label_columns=target_column.num_label_columns,
state_saver=batch,
state_name=state_name)
loss = None # Created below for modes TRAIN and EVAL.
prediction_dict = rnn_common.multi_value_predictions(
rnn_activations, target_column, problem_type, predict_probabilities)
if mode != model_fn.ModeKeys.INFER:
loss = _multi_value_loss(rnn_activations, labels, batch.length,
target_column, features)
eval_metric_ops = None
if mode != model_fn.ModeKeys.INFER:
eval_metric_ops = rnn_common.get_eval_metric_ops(
problem_type, rnn_common.PredictionType.MULTIPLE_VALUE,
batch.length, prediction_dict, labels)
state_dict = state_tuple_to_dict(final_state)
prediction_dict.update(state_dict)
train_op = None
if mode == model_fn.ModeKeys.TRAIN:
train_op = optimizers.optimize_loss(
loss=loss,
global_step=None, # Get it internally.
learning_rate=learning_rate,
optimizer=optimizer,
clip_gradients=gradient_clipping_norm,
summaries=optimizers.OPTIMIZER_SUMMARIES)
return model_fn.ModelFnOps(mode=mode,
predictions=prediction_dict,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
