def __init__(self, hidden_units, n_classes=0, tf_master="", batch_size=32,
steps=50, optimizer="SGD", learning_rate=0.1, tf_random_seed=42):
model_fn = models.get_dnn_model(hidden_units,
models.linear_regression)
super(TensorFlowDNNRegressor, self).__init__(
model_fn=model_fn,
n_classes=n_classes, tf_master=tf_master,
batch_size=batch_size, steps=steps, optimizer=optimizer,
learning_rate=learning_rate, tf_random_seed=tf_random_seed)
def __init__(self, hidden_units, n_classes, tf_master="", batch_size=32,
steps=50, optimizer="SGD", learning_rate=0.1,
tf_random_seed=42, continue_training=False):
model_fn = models.get_dnn_model(hidden_units,
models.logistic_regression)
super(TensorFlowDNNClassifier, self).__init__(
model_fn=model_fn,
n_classes=n_classes, tf_master=tf_master,
batch_size=batch_size, steps=steps, optimizer=optimizer,
learning_rate=learning_rate, tf_random_seed=tf_random_seed,
continue_training=continue_training)
def __init__(self,
hidden_units,
n_classes=0,
tf_master="",
batch_size=32,
steps=50,
optimizer="SGD",
learning_rate=0.1,
tf_random_seed=42):
model_fn = models.get_dnn_model(hidden_units, models.linear_regression)
super(TensorFlowDNNRegressor,
self).__init__(model_fn=model_fn,
n_classes=n_classes,
tf_master=tf_master,
batch_size=batch_size,
steps=steps,
optimizer=optimizer,
learning_rate=learning_rate,
tf_random_seed=tf_random_seed)
def __init__(self,
hidden_units,
n_classes,
tf_master="",
batch_size=32,
steps=50,
optimizer="SGD",
learning_rate=0.1,
tf_random_seed=42,
continue_training=False):
model_fn = models.get_dnn_model(hidden_units,
models.logistic_regression)
super(TensorFlowDNNClassifier,
self).__init__(model_fn=model_fn,
n_classes=n_classes,
tf_master=tf_master,
batch_size=batch_size,
steps=steps,
optimizer=optimizer,
learning_rate=learning_rate,
tf_random_seed=tf_random_seed,
continue_training=continue_training)
def _model_fn(self, X, y):
return models.get_dnn_model(self.hidden_units,
models.logistic_regression)(X, y)