'Creates a logistic 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):

    output = slim.fully_connected(model_input, vocab_size, activation_fn=tf.nn.sigmoid, weights_regularizer=slim.l2_regularizer(l2_penalty))
    return {'predictions': output}
