Update hyperparams from trained model

.compute

@@ -16,11 +16,10 @@ python3 -u DeepSpeech.py \
   --test_batch_size 48 \
   --n_hidden 2048 \
   --learning_rate 0.0001 \
-  --dropout_rate 0.2367 \
+  --dropout_rate 0.2 \
   --epoch 13 \
   --display_step 0 \
   --validation_step 1 \
-  --log_level 0 \
   --checkpoint_dir "../keep" \
   --summary_dir "../keep/summaries" \
   --decoder_library_path "../tmp/native_client/libctc_decoder_with_kenlm.so"

DeepSpeech.py

@@ -162,8 +162,8 @@ def create_flags():
     tf.app.flags.DEFINE_string  ('lm_binary_path',       'data/lm/lm.binary', 'path to the language model binary file created with KenLM')
     tf.app.flags.DEFINE_string  ('lm_trie_path',         'data/lm/trie', 'path to the language model trie file created with native_client/generate_trie')
     tf.app.flags.DEFINE_integer ('beam_width',        1024,       'beam width used in the CTC decoder when building candidate transcriptions')
-    tf.app.flags.DEFINE_float   ('lm_weight',         1.75,       'the alpha hyperparameter of the CTC decoder. Language Model weight.')
-    tf.app.flags.DEFINE_float   ('valid_word_count_weight', 1.00, 'valid word insertion weight. This is used to lessen the word insertion penalty when the inserted word is part of the vocabulary.')
+    tf.app.flags.DEFINE_float   ('lm_weight',         1.50,       'the alpha hyperparameter of the CTC decoder. Language Model weight.')
+    tf.app.flags.DEFINE_float   ('valid_word_count_weight', 2.25, 'valid word insertion weight. This is used to lessen the word insertion penalty when the inserted word is part of the vocabulary.')
 
     # Inference mode