Pros of char-rnn

• Character-level modeling allows for more flexibility in generating diverse text
• Simpler implementation, easier to understand and modify
• Can generate text in any language without preprocessing

Cons of char-rnn

• May produce less coherent output compared to word-level models
• Requires more training data and computational resources
• Limited context understanding due to character-level processing

Code Comparison

char-rnn (Lua):

local model = nn.Sequential()
model:add(nn.LookupTable(vocab_size, rnn_size))
model:add(nn.LSTM(rnn_size, rnn_size, num_layers))
model:add(nn.Linear(rnn_size, vocab_size))
model:add(nn.LogSoftMax())

word-rnn-tensorflow (Python):

cell = rnn_cell.BasicLSTMCell(rnn_size, state_is_tuple=True)
self.cell = rnn_cell.MultiRNNCell([cell] * num_layers, state_is_tuple=True)
self.initial_state = self.cell.zero_state(batch_size, tf.float32)
softmax_w = tf.get_variable("softmax_w", [rnn_size, vocab_size])
softmax_b = tf.get_variable("softmax_b", [vocab_size])

The code snippets show the core model architecture for both projects. char-rnn uses Lua with the Torch framework, while word-rnn-tensorflow uses Python with TensorFlow. The main difference is the level of abstraction, with word-rnn-tensorflow using higher-level TensorFlow constructs.

Pros of models

• Comprehensive collection of various ML models and examples
• Actively maintained by TensorFlow team with frequent updates
• Extensive documentation and tutorials for each model

Cons of models

• Large repository size, potentially overwhelming for beginners
• May require more setup and configuration for specific tasks
• Not focused solely on RNN text generation like word-rnn-tensorflow

Code Comparison

word-rnn-tensorflow:

def sample(self, sess, chars, vocab, num=200, prime='The '):
    state = sess.run(self.cell.zero_state(1, tf.float32))
    for char in prime[:-1]:
        x = np.zeros((1, 1))
        x[0, 0] = vocab[char]
        feed = {self.input_data: x, self.initial_state: state}
        [state] = sess.run([self.final_state], feed)

models (text generation example):

def generate_text(model, start_string):
    num_generate = 1000
    input_eval = [char2idx[s] for s in start_string]
    input_eval = tf.expand_dims(input_eval, 0)
    text_generated = []
    temperature = 1.0
    model.reset_states()
    for i in range(num_generate):
        predictions = model(input_eval)

Pros of torch-rnn

• Utilizes Torch, which is known for its efficiency in deep learning tasks
• Supports both CPU and CUDA, allowing for GPU acceleration
• Offers more customization options for network architecture

Cons of torch-rnn

• Less active maintenance and updates compared to word-rnn-tensorflow
• Requires Lua knowledge, which may be less common among developers
• More complex setup process, especially for users new to Torch

Code Comparison

word-rnn-tensorflow:

def sample(self, sess, chars, vocab, num=200, prime='The ', sampling_type=1):
    state = sess.run(self.cell.zero_state(1, tf.float32))
    for char in prime[:-1]:
        x = np.zeros((1, 1))
        x[0, 0] = vocab[char]
        feed = {self.input_data: x, self.initial_state: state}
        [state] = sess.run([self.final_state], feed)

torch-rnn:

function LSTM:updateOutput(input)
  local h_prev = self.output[{{}, {1, self.hidden_dim}}]
  local c_prev = self.cell
  local i2h = self.i2h:updateOutput(input)
  local h2h = self.h2h:updateOutput(h_prev)
  local all_input_sums = i2h:add(h2h)

The code snippets show differences in syntax and structure between TensorFlow (Python) and Torch (Lua) implementations. word-rnn-tensorflow uses TensorFlow's session-based approach, while torch-rnn employs Torch's object-oriented style.

Pros of char-rnn-tensorflow

• Character-level modeling allows for more fine-grained text generation
• Simpler implementation, easier to understand and modify
• Can handle out-of-vocabulary words and generate novel words

Cons of char-rnn-tensorflow

• May require more training data and computational resources
• Less coherent output at the word level compared to word-based models
• Potentially slower inference due to character-by-character generation

Code Comparison

word-rnn-tensorflow:

def sample(self, sess, words, vocab, num=200, prime='first all', sampling_type=1, pick=0):
    state = self.cell.zero_state(1, tf.float32).eval()
    for word in prime.split()[:-1]:
        x = np.zeros((1, 1))
        x[0, 0] = vocab.get(word, 0)
        feed = {self.input_data: x, self.initial_state: state}
        [state] = sess.run([self.final_state], feed)

char-rnn-tensorflow:

def sample(self, sess, chars, vocab, num=200, prime='The ', sampling_type=1):
    state = sess.run(self.cell.zero_state(1, tf.float32))
    for char in prime[:-1]:
        x = np.zeros((1, 1))
        x[0, 0] = vocab[char]
        feed = {self.input_data: x, self.initial_state: state}
        [state] = sess.run([self.final_state], feed)

Both implementations use similar approaches for sampling, but char-rnn-tensorflow operates on individual characters, while word-rnn-tensorflow processes whole words.

Pros of textgenrnn

• Easier to use with a higher-level API
• Supports both word-level and character-level text generation
• Includes pre-trained models for quick start

Cons of textgenrnn

• Less flexible for customization compared to word-rnn-tensorflow
• May have lower performance on specific tasks due to generalization

Code Comparison

textgenrnn:

from textgenrnn import textgenrnn

textgen = textgenrnn()
textgen.train_from_file('path/to/file.txt', num_epochs=1)
textgen.generate()

word-rnn-tensorflow:

import train

args = train.parse_args()
args.data_dir = 'data/tinyshakespeare'
args.save_dir = 'save'
train.main(args)

The textgenrnn code is more concise and user-friendly, while word-rnn-tensorflow offers more control over the training process but requires more setup and configuration.