diff --git a/DeepSpeech.py b/DeepSpeech.py
index 13e5501151..069abe510f 100755
--- a/DeepSpeech.py
+++ b/DeepSpeech.py
@@ -23,7 +23,6 @@
 from util.feeding import create_dataset, samples_to_mfccs, audiofile_to_features
 from util.flags import create_flags, FLAGS
 from util.logging import log_info, log_error, log_debug, log_warn
-from util.text import Alphabet
 
 
 # Graph Creation
@@ -382,6 +381,7 @@ def train(server=None):
     # It will automagically get incremented by the optimizer.
     global_step = tf.Variable(0, trainable=False, name='global_step')
 
+    # One rate per layer
     dropout_rates = [tf.placeholder(tf.float32, name='dropout_{}'.format(i)) for i in range(6)]
 
     # Create training and validation datasets
@@ -463,12 +463,7 @@ def end(self, session):
         hooks.append(tf.train.CheckpointSaverHook(checkpoint_dir=FLAGS.checkpoint_dir, save_secs=FLAGS.checkpoint_secs, saver=saver))
 
     no_dropout_feed_dict = {
-        dropout_rates[0]: 0.,
-        dropout_rates[1]: 0.,
-        dropout_rates[2]: 0.,
-        dropout_rates[3]: 0.,
-        dropout_rates[4]: 0.,
-        dropout_rates[5]: 0.,
+        rate: 0. for rate in dropout_rates
     }
 
     # Progress Bar
@@ -667,6 +662,7 @@ def create_inference_graph(batch_size=1, n_steps=16, tflite=False):
 
         previous_state = tf.contrib.rnn.LSTMStateTuple(previous_state_c, previous_state_h)
 
+    # One rate per layer
     no_dropout = [None] * 6
 
     if tflite:
diff --git a/evaluate.py b/evaluate.py
index ebcd244379..e60abc09b4 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -5,27 +5,24 @@
 import itertools
 import json
 import numpy as np
-import os
-import pandas
 import progressbar
-import sys
 import tensorflow as tf
 
 from ds_ctcdecoder import ctc_beam_search_decoder_batch, Scorer
-from multiprocessing import Pool, cpu_count
+from multiprocessing import cpu_count
 from six.moves import zip, range
 from util.config import Config, initialize_globals
+from util.evaluate_tools import calculate_report
+from util.feeding import create_dataset
 from util.flags import create_flags, FLAGS
 from util.logging import log_error
-from util.feeding import create_dataset
-from util.text import Alphabet, levenshtein, text_to_char_array
-from util.evaluate_tools import process_decode_result, calculate_report
+from util.text import levenshtein
 
 
 def sparse_tensor_value_to_texts(value, alphabet):
     r"""
     Given a :class:`tf.SparseTensor` ``value``, return an array of Python strings
-    representing its values.
+    representing its values, converting tokens to strings using ``alphabet``.
     """
     return sparse_tuple_to_texts((value.indices, value.values, value.dense_shape), alphabet)
 
@@ -53,6 +50,7 @@ def evaluate(test_csvs, create_model):
 
     (batch_x, batch_x_len), batch_y = it.get_next()
 
+    # One rate per layer
     no_dropout = [None] * 6
     logits, _ = create_model(batch_x=batch_x,
                              seq_length=batch_x_len,