using the last hidden state for Fc

using the last hidden state for Fc rather than average pooling
brightmart · May 10, 2018 · d64e229 · d64e229
1 parent a552e11
commit d64e229
Showing 1 changed file with 2 additions and 1 deletion.
diff --git a/a03_TextRNN/p8_TextRNN_model.py b/a03_TextRNN/p8_TextRNN_model.py
@@ -64,7 +64,8 @@ def inference(self):
         print("outputs:===>",outputs) #outputs:(<tf.Tensor 'bidirectional_rnn/fw/fw/transpose:0' shape=(?, 5, 100) dtype=float32>, <tf.Tensor 'ReverseV2:0' shape=(?, 5, 100) dtype=float32>))
         #3. concat output
         output_rnn=tf.concat(outputs,axis=2) #[batch_size,sequence_length,hidden_size*2]
-        self.output_rnn_last=tf.reduce_mean(output_rnn,axis=1) #[batch_size,hidden_size*2] #output_rnn_last=output_rnn[:,-1,:] ##[batch_size,hidden_size*2] #TODO
+        #self.output_rnn_last=tf.reduce_mean(output_rnn,axis=1) #[batch_size,hidden_size*2] 
+        self.output_rnn_last=output_rnn[:,-1,:] ##[batch_size,hidden_size*2] #TODO
         print("output_rnn_last:", self.output_rnn_last) # <tf.Tensor 'strided_slice:0' shape=(?, 200) dtype=float32>
         #4. logits(use linear layer)
         with tf.name_scope("output"): #inputs: A `Tensor` of shape `[batch_size, dim]`.  The forward activations of the input network.