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)