import tensorflow as tf import normalizer as no import tokenizer as to import mlp categories = ["rail", "other"] num_of_steps = 500 tokenizer = to.NormalizedTokenizer(normalizer=no.BasicNormalizer(), tokenizer=to.LetterBigramTokenizer()) def make_model(num_of_terms): optimizer = tf.train.AdamOptimizer(0.01) model = mlp.MultiLayerPerceptron(optimizer=optimizer, categories=categories, num_of_terms=num_of_terms, num_of_hidden_nodes=100) print("model.optimizer = " + str(type(model.optimizer))) print("model.num_of_categories = " + str(model.num_of_categories)) print("model.num_of_terms = " + str(model.num_of_terms)) print("model.num_of_hidden_nodes = " + str(model.num_of_hidden_nodes)) return model
import tensorflow as tf import normalizer as no import tokenizer as to import sr categories = ["rail", "other"] num_of_steps = 500 tokenizer = to.NormalizedTokenizer( normalizer=no.BasicNormalizer(), tokenizer=to.JanomeBigramTokenizer()) def make_model(num_of_terms): optimizer = tf.train.GradientDescentOptimizer(0.01) model = sr.SoftmaxRegressions(optimizer=optimizer, categories=categories, num_of_terms=num_of_terms) print("model.optimizer = " + str(type(model.optimizer))) print("model.num_of_categories = " + str(model.num_of_categories)) print("model.num_of_terms = " + str(model.num_of_terms)) return model