class_docstr: Neural Network. Deep Learning base model.
  
  Parameters:
  -----------
  optimizer: class
      The weight optimizer that will be used to tune the weights in order of minimizing
      the loss.
  loss: class
      Loss function used to measure the model's performance. SquareLoss or CrossEntropy.
  validation: tuple
      A tuple containing validation data and labels (X, y)
class_docstr_tok: ["Neural","Network",".","Deep","Learning","base","model",".","Parameters",":","--","--","--","--","--","-","optimizer",":","class","The","weight","optimizer","that","will","be","used","to","tune","the","weights","in","order","of","minimizing","the","loss",".","loss",":","class","Loss","function","used","to","measure","the","model","'s","performance",".","SquareLoss","or","CrossEntropy",".","validation",":","tuple","A","tuple","containing","validation","data","and","labels","(","X",",","y",")"]
docstr: Method which enables freezing of the weights of the network's layers. 
docstr_tok: ["Method","which","enables","freezing","of","the","weights","of","the","network","'s","layers","."]
code: def set_trainable(self, trainable):
      """Method which enables freezing of the weights of the network's layers."""
      for layer in self.layers:
          layer.trainable = trainable
  
file_path: ML-From-Scratch/mlfromscratch/deep_learning/neural_network.py
imports: from __future__ import print_function, division
  from terminaltables import AsciiTable
  import numpy
  import progressbar
  from mlfromscratch.utils import batch_iterator
  from mlfromscratch.utils.misc import bar_widgets
label_desc: Use image node_id 2 for calling the NeuralNetwork obj's underlying member method code with example usage: obj.set_trainable(trainable) without return types