def __init__(self,
num_label_columns,
hidden_units,
optimizer=None,
activation_fn=nn.relu,
dropout=None,
gradient_clip_norm=None,
num_ps_replicas=0,
scope=None):
"""Initializes DNNComposableModel objects.
Args:
num_label_columns: The number of label/target columns.
hidden_units: List of hidden units per layer. All layers are fully
connected.
optimizer: An instance of `tf.Optimizer` used to apply gradients to
the model. If `None`, will use a FTRL optimizer.
activation_fn: Activation function applied to each layer. If `None`,
will use `tf.nn.relu`.
dropout: When not None, the probability we will drop out
a given coordinate.
gradient_clip_norm: A float > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
tf.clip_by_global_norm for more details.
num_ps_replicas: The number of parameter server replicas.
scope: Optional scope for variables created in this model. If not scope
is supplied, one is generated.
"""
scope = "dnn" if not scope else scope
super(DNNComposableModel, self).__init__(
num_label_columns=num_label_columns,
optimizer=optimizer,
gradient_clip_norm=gradient_clip_norm,
num_ps_replicas=num_ps_replicas,
scope=scope)
self._hidden_units = hidden_units
self._activation_fn = activation_fn
self._dropout = dropout
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