faster help

train.py

@@ -1,68 +1,65 @@
 #!/usr/bin/env python
 
 from __future__ import print_function
-import tensorflow as tf
 
 import argparse
 import time
 import os
 from six.moves import cPickle
 
-from utils import TextLoader
-from model import Model
 
+parser = argparse.ArgumentParser(
+                    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+# Data and model checkpoints directories
+parser.add_argument('--data_dir', type=str, default='data/tinyshakespeare',
+                    help='data directory containing input.txt with training examples')
+parser.add_argument('--save_dir', type=str, default='save',
+                    help='directory to store checkpointed models')
+parser.add_argument('--log_dir', type=str, default='logs',
+                    help='directory to store tensorboard logs')
+parser.add_argument('--save_every', type=int, default=1000,
+                    help='Save frequency. Number of passes between checkpoints of the model.')
+parser.add_argument('--init_from', type=str, default=None,
+                    help="""continue training from saved model at this path (usually "save").
+                        Path must contain files saved by previous training process:
+                        'config.pkl'        : configuration;
+                        'chars_vocab.pkl'   : vocabulary definitions;
+                        'checkpoint'        : paths to model file(s) (created by tf).
+                                              Note: this file contains absolute paths, be careful when moving files around;
+                        'model.ckpt-*'      : file(s) with model definition (created by tf)
+                         Model params must be the same between multiple runs (model, rnn_size, num_layers and seq_length).
+                    """)
+# Model params
+parser.add_argument('--model', type=str, default='lstm',
+                    help='lstm, rnn, gru, or nas')
+parser.add_argument('--rnn_size', type=int, default=128,
+                    help='size of RNN hidden state')
+parser.add_argument('--num_layers', type=int, default=2,
+                    help='number of layers in the RNN')
+# Optimization
+parser.add_argument('--seq_length', type=int, default=50,
+                    help='RNN sequence length. Number of timesteps to unroll for.')
+parser.add_argument('--batch_size', type=int, default=50,
+                    help="""minibatch size. Number of sequences propagated through the network in parallel.
+                            Pick batch-sizes to fully leverage the GPU (e.g. until the memory is filled up)
+                            commonly in the range 10-500.""")
+parser.add_argument('--num_epochs', type=int, default=50,
+                    help='number of epochs. Number of full passes through the training examples.')
+parser.add_argument('--grad_clip', type=float, default=5.,
+                    help='clip gradients at this value')
+parser.add_argument('--learning_rate', type=float, default=0.002,
+                    help='learning rate')
+parser.add_argument('--decay_rate', type=float, default=0.97,
+                    help='decay rate for rmsprop')
+parser.add_argument('--output_keep_prob', type=float, default=1.0,
+                    help='probability of keeping weights in the hidden layer')
+parser.add_argument('--input_keep_prob', type=float, default=1.0,
+                    help='probability of keeping weights in the input layer')
+args = parser.parse_args()
 
-def main():
-    parser = argparse.ArgumentParser(
-                        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    # Data and model checkpoints directories
-    parser.add_argument('--data_dir', type=str, default='data/tinyshakespeare',
-                        help='data directory containing input.txt with training examples')
-    parser.add_argument('--save_dir', type=str, default='save',
-                        help='directory to store checkpointed models')
-    parser.add_argument('--log_dir', type=str, default='logs',
-                        help='directory to store tensorboard logs')
-    parser.add_argument('--save_every', type=int, default=1000,
-                        help='Save frequency. Number of passes between checkpoints of the model.')
-    parser.add_argument('--init_from', type=str, default=None,
-                        help="""continue training from saved model at this path (usually "save").
-                            Path must contain files saved by previous training process:
-                            'config.pkl'        : configuration;
-                            'chars_vocab.pkl'   : vocabulary definitions;
-                            'checkpoint'        : paths to model file(s) (created by tf).
-                                                  Note: this file contains absolute paths, be careful when moving files around;
-                            'model.ckpt-*'      : file(s) with model definition (created by tf)
-                             Model params must be the same between multiple runs (model, rnn_size, num_layers and seq_length).
-                        """)
-    # Model params
-    parser.add_argument('--model', type=str, default='lstm',
-                        help='lstm, rnn, gru, or nas')
-    parser.add_argument('--rnn_size', type=int, default=128,
-                        help='size of RNN hidden state')
-    parser.add_argument('--num_layers', type=int, default=2,
-                        help='number of layers in the RNN')
-    # Optimization
-    parser.add_argument('--seq_length', type=int, default=50,
-                        help='RNN sequence length. Number of timesteps to unroll for.')
-    parser.add_argument('--batch_size', type=int, default=50,
-                        help="""minibatch size. Number of sequences propagated through the network in parallel.
-                                Pick batch-sizes to fully leverage the GPU (e.g. until the memory is filled up)
-                                commonly in the range 10-500.""")
-    parser.add_argument('--num_epochs', type=int, default=50,
-                        help='number of epochs. Number of full passes through the training examples.')
-    parser.add_argument('--grad_clip', type=float, default=5.,
-                        help='clip gradients at this value')
-    parser.add_argument('--learning_rate', type=float, default=0.002,
-                        help='learning rate')
-    parser.add_argument('--decay_rate', type=float, default=0.97,
-                        help='decay rate for rmsprop')
-    parser.add_argument('--output_keep_prob', type=float, default=1.0,
-                        help='probability of keeping weights in the hidden layer')
-    parser.add_argument('--input_keep_prob', type=float, default=1.0,
-                        help='probability of keeping weights in the input layer')
-    args = parser.parse_args()
-    train(args)
-
+import tensorflow as tf
+from utils import TextLoader
+from model import Model
 
 def train(args):
     data_loader = TextLoader(args.data_dir, args.batch_size, args.seq_length)
@@ -144,4 +141,4 @@ def train(args):
 
 
 if __name__ == '__main__':
-    main()
+    train(args)