Pros of tensorflow/models

• Comprehensive collection of state-of-the-art models and examples for various machine learning tasks
• Active community with frequent updates and contributions
• Extensive documentation and tutorials for each model

Cons of tensorflow/models

• Larger codebase and more complex to navigate compared to a single-purpose project like char-rnn-tensorflow
• May require more setup and configuration to get a specific model running

Code Comparison

tensorflow/models (Transformer model):

def create_masks(inp, tar):
    # Encoder padding mask
    enc_padding_mask = create_padding_mask(inp)

    # Used in the 2nd attention block in the decoder.
    # This padding mask is used to mask the encoder outputs.
    dec_padding_mask = create_padding_mask(inp)

    # Used in the 1st attention block in the decoder.
    # It is used to pad and mask future tokens in the input received by
    # the decoder.
    look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])
    dec_target_padding_mask = create_padding_mask(tar)
    combined_mask = tf.maximum(dec_target_padding_mask, look_ahead_mask)

    return enc_padding_mask, combined_mask, dec_padding_mask

sherjilozair/char-rnn-tensorflow (Character-level RNN):

def sample(self, n=200, prime='The '):
    """
    Generate sample text (n characters) from the model.
    """
    states = self.sess.run(self.initial_state)
    x = np.array([self.char_to_ix[c] for c in prime])
    txt = prime
    for i in range(n):
        feed = {self.x: x[None, None], self.initial_state: states}
        preds, states = self.sess.run([self.proba, self.final_state], feed)
        p = preds[0, -1]
        x = np.random.choice(len(p), p=p)
        txt += self.ix_to_char[x]
    return txt

Pros of char-rnn

• Supports multiple programming languages (Python, Lua, and Torch)
• Provides a more comprehensive set of features, including support for different types of RNNs (LSTM, GRU) and optimization methods
• Includes more detailed documentation and examples

Cons of char-rnn

• Requires more setup and configuration compared to char-rnn-tensorflow
• May be more complex for beginners to understand and use
• Potentially slower performance due to the overhead of the Torch framework

Code Comparison

Here's a brief comparison of the code for training a character-level language model in both repositories:

char-rnn-tensorflow:

model = CharRNNModel(
    num_classes=len(vocab),
    batch_size=batch_size,
    num_steps=num_steps,
    lstm_size=lstm_size,
    num_layers=num_layers,
    learning_rate=learning_rate)
model.train(data_loader.train_data, data_loader.valid_data)

char-rnn:

local opt = {
  data_dir = 'data/tinyshakespeare',
  rnn_size = 128,
  num_layers = 2,
  batch_size = 50,
  seq_length = 50,
  num_epochs = 50,
  learning_rate = 2e-3,
  decay_rate = 0.97,
  dropout = 0.5,
}

local model = require 'model'(opt)
model:train()

The char-rnn-tensorflow code is more concise and straightforward, while the char-rnn code is more verbose and requires more configuration options. However, the char-rnn code provides more flexibility and customization options.

Pros of TensorFlow-Examples

• Covers a wide range of TensorFlow examples, from basic to advanced, making it a comprehensive resource for learning and experimentation.
• Provides clear and well-documented code, making it easier for beginners to understand and follow along.
• Includes examples for various machine learning tasks, such as classification, regression, and generative models.

Cons of TensorFlow-Examples

• May not be as focused or specialized as char-rnn-tensorflow, which is dedicated to a specific task (character-level language modeling).
• The examples may not be as up-to-date with the latest TensorFlow versions and best practices compared to a more focused project.
• The project may not have the same level of active maintenance and community support as a more specialized repository.

Code Comparison

Here's a brief comparison of the code structure between the two repositories:

char-rnn-tensorflow

def build_rnn_graph(num_classes, num_seqs=50, num_steps=50):
    """
    Builds the computation graph for the character-level language model.
    """
    # ...
    cell = tf.nn.rnn_cell.BasicLSTMCell(rnn_size)
    initial_state = cell.zero_state(num_seqs, tf.float32)
    # ...

TensorFlow-Examples

def conv_net(x_dict, n_classes, dropout, reuse, is_training):
    """
    Convolution Neural Network Model.
    """
    # Create the network
    with tf.variable_scope('ConvNet', reuse=reuse):
        # Convolution Layer
        conv1 = tf.layers.conv2d(x_dict['images'], 32, 5, activation=tf.nn.relu)
        # Max Pooling (down-sampling)
        conv1 = tf.layers.max_pooling2d(conv1, 2, 2)
    # ...

The char-rnn-tensorflow code focuses on building a character-level language model using an LSTM-based recurrent neural network, while the TensorFlow-Examples code demonstrates a convolutional neural network for image classification.