class_docstr: Basic CNN model for tensorflow eager execution.
class_docstr_tok: ["Basic","CNN","model","for","tensorflow","eager","execution","."]
docstr: Forward propagation throught the network
  :return: dictionary with layer names mapping to activation values.
docstr_tok: ["Forward","propagation","throught","the","network",":","return",":","dictionary","with","layer","names","mapping","to","activation","values","."]
code: def fprop(self, x):
      """
      Forward propagation throught the network
      :return: dictionary with layer names mapping to activation values.
      """
  
      # Feed forward through the network layers
      for layer_name in self.layer_names:
          if layer_name == "input":
              prev_layer_act = x
              continue
          else:
              self.layer_acts[layer_name] = self.layers[layer_name](
                  prev_layer_act
              )
              prev_layer_act = self.layer_acts[layer_name]
  
      # Adding softmax values to list of activations.
      self.layer_acts["probs"] = tf.nn.softmax(
          logits=self.layer_acts["logits"]
      )
      return self.layer_acts
  
file_path: cleverhans/cleverhans_v3.1.0/cleverhans_tutorials/tutorial_models_tfe.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import tensorflow
  from cleverhans.model import Model
label_desc: Use image node_id 2 for calling the ModelBasicCNNTFE obj's underlying member method code with example usage: obj.fprop(x) and returns: self