docstr: Initializes a Dragonnet.
docstr_tok: ["Initializes","a","Dragonnet","."]
code: def __init__(
      self,
      neurons_per_layer=200,
      targeted_reg=True,
      ratio=1.0,
      val_split=0.2,
      batch_size=64,
      epochs=100,
      learning_rate=1e-5,
      momentum=0.9,
      reg_l2=0.01,
      use_adam=True,
      adam_epochs=30,
      adam_learning_rate=1e-3,
      loss_func=dragonnet_loss_binarycross,
      verbose=True,
  ):
      """
      Initializes a Dragonnet.
      """
      self.neurons_per_layer = neurons_per_layer
      self.targeted_reg = targeted_reg
      self.ratio = ratio
      self.val_split = val_split
      self.batch_size = batch_size
      self.epochs = epochs
      self.learning_rate = learning_rate
      self.momentum = momentum
      self.use_adam = use_adam
      self.adam_learning_rate = adam_learning_rate
      self.adam_epochs = adam_epochs
      self.reg_l2 = reg_l2
      self.loss_func = loss_func
      self.verbose = verbose
  
file_path: causalml/causalml/inference/tf/dragonnet.py
imports: import numpy
  from tensorflow.keras import Input, Model
  from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TerminateOnNaN
  from tensorflow.keras.layers import Dense, Concatenate
  from tensorflow.keras.optimizers import SGD, Adam
  from tensorflow.keras.regularizers import l2
  from causalml.inference.tf.utils import dragonnet_loss_binarycross, EpsilonLayer, regression_loss, binary_classification_loss, treatment_accuracy, track_epsilon, make_tarreg_loss
  from causalml.inference.meta.utils import convert_pd_to_np
label_desc: Use image node_id 1 to create a new DragonNet object  with example: obj = DragonNet(neurons_per_layer, targeted_reg, ratio, val_split, batch_size, epochs, learning_rate, momentum, reg_l2, use_adam, adam_epochs, adam_learning_rate, loss_func, verbose)