def create_model()

in src/run.py [0:0]

• 22 lines of code
• 4 McCabe index (conditional complexity)

def create_model(x, y, n_gpu, hparams):
    gen_logits = []
    gen_loss = []
    clf_loss = []
    tot_loss = []
    accuracy = []

    trainable_params = None
    for i in range(n_gpu):
        with tf.device("/gpu:%d" % i):
            results = model(hparams, x[i], y[i], reuse=(i != 0))

            gen_logits.append(results["gen_logits"])
            gen_loss.append(results["gen_loss"])
            clf_loss.append(results["clf_loss"])

            if hparams.clf:
                tot_loss.append(results["gen_loss"] + results["clf_loss"])
            else:
                tot_loss.append(results["gen_loss"])

            accuracy.append(results["accuracy"])

            if i == 0:
                trainable_params = tf.trainable_variables()
                print("trainable parameters:", count_parameters())

    return trainable_params, gen_logits, gen_loss, clf_loss, tot_loss, accuracy