[pplm] split classif head into its own file

examples/pplm/pplm_classification_head.py

+import torch
+
+class ClassificationHead(torch.nn.Module):
+    """Classification Head for  transformer encoders"""
+
+    def __init__(self, class_size, embed_size):
+        super(ClassificationHead, self).__init__()
+        self.class_size = class_size
+        self.embed_size = embed_size
+        # self.mlp1 = torch.nn.Linear(embed_size, embed_size)
+        # self.mlp2 = (torch.nn.Linear(embed_size, class_size))
+        self.mlp = torch.nn.Linear(embed_size, class_size)
+
+    def forward(self, hidden_state):
+        # hidden_state = F.relu(self.mlp1(hidden_state))
+        # hidden_state = self.mlp2(hidden_state)
+        logits = self.mlp(hidden_state)
+        return logits

examples/pplm/run_pplm.py

@@ -33,10 +33,10 @@ import torch.nn.functional as F
 from torch.autograd import Variable
 from tqdm import trange
 
-from examples.run_pplm_discrim_train import ClassificationHead
 from transformers import GPT2Tokenizer
 from transformers.file_utils import cached_path
 from transformers.modeling_gpt2 import GPT2LMHeadModel
+from pplm_classification_head import ClassificationHead
 
 PPLM_BOW = 1
 PPLM_DISCRIM = 2

examples/pplm/run_pplm_discrim_train.py

@@ -21,6 +21,7 @@ from torchtext import datasets
 from tqdm import tqdm, trange
 
 from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from pplm_classification_head import ClassificationHead
 
 torch.manual_seed(0)
 np.random.seed(0)
@@ -29,22 +30,6 @@ example_sentence = "This is incredible! I love it, this is the best chicken I ha
 max_length_seq = 100
 
 
-class ClassificationHead(torch.nn.Module):
-    """Classification Head for  transformer encoders"""
-
-    def __init__(self, class_size, embed_size):
-        super(ClassificationHead, self).__init__()
-        self.class_size = class_size
-        self.embed_size = embed_size
-        # self.mlp1 = torch.nn.Linear(embed_size, embed_size)
-        # self.mlp2 = (torch.nn.Linear(embed_size, class_size))
-        self.mlp = torch.nn.Linear(embed_size, class_size)
-
-    def forward(self, hidden_state):
-        # hidden_state = F.relu(self.mlp1(hidden_state))
-        # hidden_state = self.mlp2(hidden_state)
-        logits = self.mlp(hidden_state)
-        return logits
 
 
 class Discriminator(torch.nn.Module):