def dnn(tensor_in, hidden_units, activation=nn.relu, dropout=None):
"""Creates fully connected deep neural network subgraph.
Args:
tensor_in: tensor or placeholder for input features.
hidden_units: list of counts of hidden units in each layer.
activation: activation function between layers. Can be None.
dropout: if not None, will add a dropout layer with given probability.
Returns:
A tensor which would be a deep neural network.
"""
with vs.variable_scope('dnn'):
for i, n_units in enumerate(hidden_units):
with vs.variable_scope('layer%d' % i):
# Weight initializer was set to None to replicate the behavior of
# rnn_cell.linear. Using fully_connected's default initializer gets
# slightly worse quality results on unit tests.
tensor_in = layers.legacy_fully_connected(
tensor_in,
n_units,
weight_init=None,
weight_collections=['dnn_weights'],
bias_collections=['dnn_biases'])
if activation is not None:
tensor_in = activation(tensor_in)
if dropout is not None:
is_training = array_ops_.squeeze(ops.get_collection('IS_TRAINING'))
tensor_in = control_flow_ops.cond(
is_training,
lambda: dropout_ops.dropout(tensor_in, prob=(1.0 - dropout)),
lambda: tensor_in)
return tensor_in
def dnn(tensor_in, hidden_units, activation=nn.relu, dropout=None):
"""Creates fully connected deep neural network subgraph.
Args:
tensor_in: tensor or placeholder for input features.
hidden_units: list of counts of hidden units in each layer.
activation: activation function between layers. Can be None.
dropout: if not None, will add a dropout layer with given probability.
Returns:
A tensor which would be a deep neural network.
"""
with vs.variable_scope('dnn'):
for i, n_units in enumerate(hidden_units):
with vs.variable_scope('layer%d' % i):
tensor_in = rnn_cell.linear(tensor_in, n_units, True)
if activation is not None:
tensor_in = activation(tensor_in)
if dropout is not None:
tensor_in = dropout_ops.dropout(tensor_in, prob=(1.0 - dropout))
return tensor_in