def create_model(self, model_input, vocab_size, l2_penalty=1e-08, **unused_params):
    "Creates a CNN model.\n\n    Args:\n      model_input: 'batch' x 'num_features' matrix of input features.\n      vocab_size: The number of classes in the dataset.\n\n    Returns:\n      A dictionary with a tensor containing the probability predictions of the\n      model in the 'predictions' key. The dimensions of the tensor are\n      batch_size x num_classes.\n    "
    model_input = tf.reshape(model_input, [(- 1), 32, 32])
    model_input = tf.expand_dims(model_input, 3)
    net = slim.conv2d(model_input, 3, [3, 3], scope='conv1', activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty))
    net = slim.conv2d(net, 3, [4, 4], scope='conv2', activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty))
    net = slim.conv2d(net, 3, [5, 5], scope='conv3', activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty))
    net = slim.max_pool2d(net, [2, 2], scope='pool2')
    net = tf.reshape(net, [(- 1), ((16 * 16) * 3)])
    output = slim.fully_connected(net, vocab_size, activation_fn=tf.nn.sigmoid, weights_regularizer=slim.l2_regularizer(l2_penalty))
    return {'predictions': output}
Creates a CNN model.

Args:
  model_input: 'batch' x 'num_features' matrix of input features.
  vocab_size: The number of classes in the dataset.

Returns:
  A dictionary with a tensor containing the probability predictions of the
  model in the 'predictions' key. The dimensions of the tensor are
  batch_size x num_classes.
def create_model(self, model_input, vocab_size, l2_penalty=1e-08, **unused_params):
    "Creates a CNN model.\n\n    Args:\n      model_input: 'batch' x 'num_features' matrix of input features.\n      vocab_size: The number of classes in the dataset.\n\n    Returns:\n      A dictionary with a tensor containing the probability predictions of the\n      model in the 'predictions' key. The dimensions of the tensor are\n      batch_size x num_classes.\n    "
    model_input = tf.reshape(model_input, [(- 1), 32, 32])
    model_input = tf.expand_dims(model_input, 3)
    net = slim.conv2d(model_input, 3, [3, 3], scope='conv1', activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty))
    net = slim.conv2d(net, 3, [4, 4], scope='conv2', activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty))
    net = slim.conv2d(net, 3, [5, 5], scope='conv3', activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(l2_penalty))
    net = slim.max_pool2d(net, [2, 2], scope='pool2')
    net = tf.reshape(net, [(- 1), ((16 * 16) * 3)])
    output = slim.fully_connected(net, vocab_size, activation_fn=tf.nn.sigmoid, weights_regularizer=slim.l2_regularizer(l2_penalty))
    return {'predictions': output}