def __init__(self,
units,
hidden_units,
feature_columns,
activation_fn,
dropout,
input_layer_partitioner,
batch_norm,
name=None,
**kwargs):
super(_DNNModel, self).__init__(name=name, **kwargs)
if feature_column_lib.is_feature_column_v2(feature_columns):
self._input_layer = feature_column_lib.DenseFeatures(
feature_columns=feature_columns, name='input_layer')
else:
self._input_layer = feature_column.InputLayer(
feature_columns=feature_columns,
name='input_layer',
create_scope_now=False)
self._add_layer(self._input_layer, 'input_layer')
self._dropout = dropout
self._batch_norm = batch_norm
self._hidden_layers = []
self._dropout_layers = []
self._batch_norm_layers = []
self._hidden_layer_scope_names = []
for layer_id, num_hidden_units in enumerate(hidden_units):
with variable_scope.variable_scope(
'hiddenlayer_%d' % layer_id) as hidden_layer_scope:
hidden_layer = core_layers.Dense(
units=num_hidden_units,
activation=activation_fn,
kernel_initializer=init_ops.glorot_uniform_initializer(),
name=hidden_layer_scope,
_scope=hidden_layer_scope)
self._add_layer(hidden_layer, hidden_layer_scope.name)
self._hidden_layer_scope_names.append(hidden_layer_scope.name)
self._hidden_layers.append(hidden_layer)
if self._dropout is not None:
dropout_layer = core_layers.Dropout(rate=self._dropout)
self._add_layer(dropout_layer, dropout_layer.name)
self._dropout_layers.append(dropout_layer)
if self._batch_norm:
batch_norm_layer = normalization.BatchNormalization(
# The default momentum 0.99 actually crashes on certain
# problem, so here we use 0.999, which is the default of
# tf.contrib.layers.batch_norm.
momentum=0.999,
trainable=True,
name='batchnorm_%d' % layer_id,
_scope='batchnorm_%d' % layer_id)
self._add_layer(batch_norm_layer, batch_norm_layer.name)
self._batch_norm_layers.append(batch_norm_layer)
with variable_scope.variable_scope('logits') as logits_scope:
self._logits_layer = core_layers.Dense(
units=units,
activation=None,
kernel_initializer=init_ops.glorot_uniform_initializer(),
name=logits_scope,
_scope=logits_scope)
self._add_layer(self._logits_layer, logits_scope.name)
self._logits_scope_name = logits_scope.name
self._input_layer_partitioner = input_layer_partitioner
def __init__(self,
units,
hidden_units,
feature_columns,
activation_fn,
dropout,
batch_norm,
name=None,
**kwargs):
super(_DNNModelV2, self).__init__(name=name, **kwargs)
# Add this name_scope for backward compatibility, as previously it's used
# in variable_scope
with ops.name_scope(
'input_from_feature_columns') as input_feature_column_scope:
layer_name = input_feature_column_scope + 'input_layer'
if feature_column_lib.is_feature_column_v2(feature_columns):
self._input_layer = feature_column_lib.DenseFeatures(
feature_columns=feature_columns, name=layer_name)
else:
self._input_layer = feature_column.InputLayer(
feature_columns=feature_columns,
name=layer_name,
create_scope_now=False)
self._add_layer(self._input_layer, self._input_layer.name)
self._dropout = dropout
self._batch_norm = batch_norm
self._hidden_layers = []
self._dropout_layers = []
self._batch_norm_layers = []
self._hidden_layer_scope_names = []
for layer_id, num_hidden_units in enumerate(hidden_units):
with ops.name_scope('hiddenlayer_%d' %
layer_id) as hidden_layer_scope:
# Get scope name without the trailing slash.
hidden_shared_name = _name_from_scope_name(hidden_layer_scope)
hidden_layer = core_layers.Dense(
units=num_hidden_units,
activation=activation_fn,
kernel_initializer=init_ops.glorot_uniform_initializer(),
name=hidden_shared_name)
self._add_layer(hidden_layer, hidden_shared_name)
self._hidden_layer_scope_names.append(hidden_shared_name)
self._hidden_layers.append(hidden_layer)
if self._dropout is not None:
dropout_layer = core_layers.Dropout(rate=self._dropout)
self._add_layer(dropout_layer, dropout_layer.name)
self._dropout_layers.append(dropout_layer)
if self._batch_norm:
batch_norm_name = hidden_shared_name + '/batchnorm_%d' % layer_id
batch_norm_layer = normalization.BatchNormalization(
# The default momentum 0.99 actually crashes on certain
# problem, so here we use 0.999, which is the default of
# tf.contrib.layers.batch_norm.
momentum=0.999,
trainable=True,
name=batch_norm_name)
self._add_layer(batch_norm_layer, batch_norm_name)
self._batch_norm_layers.append(batch_norm_layer)
with ops.name_scope('logits') as logits_scope:
logits_shared_name = _name_from_scope_name(logits_scope)
self._logits_layer = core_layers.Dense(
units=units,
activation=None,
kernel_initializer=init_ops.glorot_uniform_initializer(),
name=logits_shared_name)
self._add_layer(self._logits_layer, logits_shared_name)
self._logits_scope_name = logits_shared_name
