def dnn_autoencoder(
tensor_in, hidden_units, activation=nn.relu, add_noise=None, dropout=None,
scope=None):
"""Creates fully connected autoencoder subgraph.
Args:
tensor_in: tensor or placeholder for input features.
hidden_units: list of counts of hidden units in each layer.
activation: activation function used to map inner latent layer onto
reconstruction layer.
add_noise: a function that adds noise to tensor_in,
e.g. def add_noise(x):
return(x + np.random.normal(0, 0.1, (len(x), len(x[0]))))
dropout: if not None, will add a dropout layer with given
probability.
scope: the variable scope for this op.
Returns:
Tensors for encoder and decoder.
"""
with vs.variable_op_scope([tensor_in], scope, "autoencoder"):
if add_noise is not None:
tensor_in = add_noise(tensor_in)
with vs.variable_scope("encoder"):
# build DNN encoder
encoder = dnn_ops.dnn(
tensor_in, hidden_units, activation=activation, dropout=dropout)
with vs.variable_scope("decoder"):
# reverse hidden_units and built DNN decoder
decoder = dnn_ops.dnn(
encoder, hidden_units[::-1], activation=activation, dropout=dropout)
return encoder, decoder
def dnn_autoencoder(tensor_in,
hidden_units,
activation=nn.relu,
add_noise=None,
dropout=None,
scope=None):
"""Creates fully connected autoencoder subgraph.
Args:
tensor_in: tensor or placeholder for input features.
hidden_units: list of counts of hidden units in each layer.
activation: activation function used to map inner latent layer onto
reconstruction layer.
add_noise: a function that adds noise to tensor_in,
e.g. def add_noise(x):
return(x + np.random.normal(0, 0.1, (len(x), len(x[0]))))
dropout: if not None, will add a dropout layer with given
probability.
scope: the variable scope for this op.
Returns:
Tensors for encoder and decoder.
"""
with vs.variable_scope(scope, "autoencoder", [tensor_in]):
if add_noise is not None:
tensor_in = add_noise(tensor_in)
with vs.variable_scope("encoder"):
# build DNN encoder
encoder = dnn_ops.dnn(tensor_in,
hidden_units,
activation=activation,
dropout=dropout)
with vs.variable_scope("decoder"):
# reverse hidden_units and built DNN decoder
decoder = dnn_ops.dnn(encoder,
hidden_units[::-1],
activation=activation,
dropout=dropout)
return encoder, decoder
def dnn_estimator(x, y): """DNN estimator with target predictor function on top.""" layers = dnn_ops.dnn(x, hidden_units, dropout=dropout) return target_predictor_fn(layers, y)
def dnn_estimator(x, y):
"""DNN estimator with target predictor function on top."""
layers = dnn_ops.dnn(x, hidden_units, dropout=dropout)
return target_predictor_fn(layers, y)
def dnn_estimator(X, y): # pylint: disable=invalid-name """DNN estimator with target predictor function on top.""" layers = dnn_ops.dnn(X, hidden_units, dropout=dropout) return target_predictor_fn(layers, y)
def dnn_estimator(X, y): # pylint: disable=invalid-name """DNN estimator with target predictor function on top.""" layers = dnn_ops.dnn(X, hidden_units, dropout=dropout) return target_predictor_fn(layers, y)
