Added README.rst

MANIFEST.in

@@ -0,0 +1 @@
+include LICENSE.txt

README.md

@@ -6,8 +6,10 @@ Generate Shakespeare poems with 4 lines of code.
 
 ## Installation
 
-    pip install tensorflow>=1.1
-    pip install tensorlm
+`tensorlm` is written in / for Python 3.4+
+
+    pip3 install tensorflow>=1.1
+    pip3 install tensorlm
 
 ## Basic Usage
 

README.rst

@@ -0,0 +1,99 @@
+tensorlm
+========
+
+Generate Shakespeare poems with 4 lines of code.
+
+<a href="http://www.mlowl.com/post/character-language-model-lstm-tensorflow/" target="_blank">[![showcase
+of the package]]</a>
+
+Installation
+------------
+
+`tensorlm` is written in / for Python 3.
+
+    pip3 install tensorflow>=1.1
+    pip3 install tensorlm
+
+Basic Usage
+-----------
+
+Use the `CharLM` or `WordLM` class:
+
+``` {.python}
+import tensorflow as tf
+from tensorlm import CharLM
+
+with tf.Session() as session:
+
+    # Create a new model. You can also use WordLM
+    model = CharLM(session, "datasets/sherlock/train.txt", max_vocab_size=96,
+                   neurons_per_layer=100, num_layers=3, num_timesteps=15)
+
+    # Train it
+    model.train(session, max_epochs=5, max_steps=500, print_logs=True)
+
+    # Let it generate a text
+    generated = model.sample(session, "The ", num_steps=100)
+    print("The " + generated)
+```
+
+This should output something like:
+
+    The     eee       ee      ee      ee     e e     ee      ee     e  e    e  e    e  e    e  e    e  e
+
+Command Line Usage
+------------------
+
+**Train:**\
+`python3 -m tensorlm.cli --train=True --level=char --train_text_path=datasets/sherlock/train.txt --max_vocab_size=96 --neurons_per_layer=100 --num_layers=3 --batch_size=10 --num_timesteps=160 --save_dir=out/model --max_epochs=300 --save_interval_hours=0.5`
+
+**Sample:**\
+`python3 -m tensorlm.cli --sample=True --level=char --neurons_per_layer=400 --num_layers=3 --num_timesteps=160 --save_dir=out/model`
+
+**Evaluate:**\
+`python3 -m tensorlm.cli --evaluate=True --level=char --evaluate_text_path=datasets/sherlock/valid.txt --neurons_per_layer=400 --num_layers=3 --batch_size=10 --num_timesteps=160 --save_dir=out/model`
+
+See `python3 -m tensorlm.cli --help` for all options.
+
+Advanced Usage
+--------------
+
+### Custom Input Data
+
+The inputs and targets don’t have to be text. `GeneratingLSTM` only
+expects token ids, so you can use any data type for the sequences, as
+long as you can encode the data to integer ids.
+
+``` {.python}
+# We use integer ids from 0 to 19, so the vocab size is 20. The range of ids must always start
+# at zero.
+batch_inputs = np.array([[1, 2, 3, 4], [15, 16, 17, 18]])  # 2 batches, 4 time steps each
+batch_targets = np.array([[2, 3, 4, 5], [16, 17, 18, 19]])
+
+# Create the model in a TensorFlow graph
+model = GeneratingLSTM(vocab_size=20, neurons_per_layer=10, num_layers=2, max_batch_size=2)
+
+# Initialize all defined TF Variables
+session.run(tf.global_variables_initializer())
+
+for _ in range(5000):
+    model.train_step(session, batch_inputs, batch_targets)
+
+sampled = model.sample_ids(session, [15], num_steps=3)
+print("Sampled: " + str(sampled))
+```
+
+This should output something like:
+
+    Sampled: [16, 18, 19]
+
+### Custom Training, Dropout etc.
+
+Use the `GeneratingLSTM` class directly. This class is agnostic to the
+dataset type. It expects integer ids and returns integer ids.
+
+\`\`\`python\
+import tensorflow
+
+  [showcase of the package]: http://i.cubeupload.com/8Cm5RQ.gif
+  [![showcase of the package]]: http://www.mlowl.com/post/character-language-model-lstm-tensorflow/

setup.py

@@ -1,16 +1,32 @@
-from distutils.core import setup
+from setuptools import setup, find_packages  # Always prefer setuptools over distutils
+from codecs import open  # To use a consistent encoding
+from os import path
+
+here = path.abspath(path.dirname(__file__))
+
+# Get the long description from the relevant file
+with open(path.join(here, "README.rst"), encoding="utf-8") as f:
+    long_description = f.read()
 
 setup(
     name="tensorlm",
-    packages=["tensorlm"],
-    version="0.1",
+    packages=find_packages(exclude=["examples"]),
+    version="0.2",
     description="TensorFlow wrapper for deep neural text generation on character or word level "
                 "with RNNs / LSTMs",
+    long_description=long_description,
     author="Kilian Batzner",
     author_email="info@kilians.net",
+    license="MIT",
     url="https://github.com/batzner/tensorlm",
-    download_url="https://github.com/batzner/tensorlm/archive/v0.1.tar.gz",
+    download_url="https://github.com/batzner/tensorlm/archive/v0.2.tar.gz",
     keywords=["tensorflow", "text", "generation", "language", "model", "rnn", "lstm", "deep",
               "neural", "char", "word"],
-    classifiers=[],
+    classifiers=[
+        "Development Status :: 3 - Alpha",
+        "Intended Audience :: Developers",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3 :: Only",
+    ],
 )