Пример #1
0
                  batch_size=arglist.batch_size,
                  learning_rate=arglist.lr,
                  kl_tolerance=arglist.kl_tolerance,
                  is_training=True,
                  reuse=False,
                  gpu_mode=True)

    # train loop:
    print("train", "step", "loss", "recon_loss", "kl_loss")
    for epoch in range(arglist.epoch):
      np.random.shuffle(dataset)
      for idx in range(num_batches):
        batch = dataset[idx*arglist.batch_size:(idx+1)*arglist.batch_size]

        obs = np.array(batch).astype(np.float)/255.0

        feed = {vae.x: obs,}

        (train_loss, r_loss, kl_loss, train_step, _) = vae.sess.run([
          vae.loss, vae.r_loss, vae.kl_loss, vae.global_step, vae.train_op
        ], feed)
      
        print("step", (train_step+1), train_loss, r_loss, kl_loss)
        log.write('{}\n'.format(','.join(map(str, 
                    [train_loss, r_loss, kl_loss, train_step]))))
        if ((train_step+1) % arglist.save_period == 0):
          vae.save_json(os.path.join(arglist.model_save_path,"vae.json"))

    # finished, final model:
    vae.save_json(os.path.join(arglist.model_save_path,"vae.json"))
Пример #2
0
vae = ConvVAE(z_size=z_size,
              batch_size=batch_size,
              learning_rate=learning_rate,
              kl_tolerance=kl_tolerance,
              is_training=True,
              reuse=False,
              gpu_mode=True)

# train loop:
print("train", "step", "loss", "recon_loss", "kl_loss")
for epoch in range(NUM_EPOCH):
  np.random.shuffle(dataset)
  for idx in range(num_batches):
    batch = dataset[idx*batch_size:(idx+1)*batch_size]

    obs = batch.astype(np.float)

    feed = {vae.x: obs,}

    (train_loss, r_loss, kl_loss, train_step, _) = vae.sess.run([
      vae.loss, vae.r_loss, vae.kl_loss, vae.global_step, vae.train_op
    ], feed)
  
    if ((train_step+1) % 500 == 0):
      print("step", (train_step+1), train_loss, r_loss, kl_loss)
    if ((train_step+1) % 5000 == 0):
      vae.save_json("tf_vae/vae.json")

# finished, final model:
vae.save_json("tf_vae/vae.json")
Пример #3
0
def main( dirs, z_size=32, batch_size=100, learning_rate=0.0001, kl_tolerance=0.5, epochs=100, save_model=False, verbose=True, optimizer="Adam" ):

    if save_model:
        model_save_path = "tf_vae"
        if not os.path.exists(model_save_path):
          os.makedirs(model_save_path)

    gen = DriveDataGenerator(dirs, image_size=(64,64), batch_size=batch_size, shuffle=True, max_load=10000, images_only=True )
        
    num_batches = len(gen)

    reset_graph()

    vae = ConvVAE(z_size=z_size,
                  batch_size=batch_size,
                  learning_rate=learning_rate,
                  kl_tolerance=kl_tolerance,
                  is_training=True,
                  reuse=False,
                  gpu_mode=True,
                  optimizer=optimizer)

    early = EarlyStopping(monitor='loss', min_delta=0.1, patience=5, verbose=verbose, mode='auto')
    early.set_model(vae)
    early.on_train_begin()

    best_loss = sys.maxsize

    if verbose:
        print("epoch\tstep\tloss\trecon_loss\tkl_loss")
    for epoch in range(epochs):
        for idx in range(num_batches):
            batch = gen[idx]

            obs = batch.astype(np.float)/255.0

            feed = {vae.x: obs,}

            (train_loss, r_loss, kl_loss, train_step, _) = vae.sess.run([
              vae.loss, vae.r_loss, vae.kl_loss, vae.global_step, vae.train_op
            ], feed)
            
            if train_loss < best_loss:
                best_loss = train_loss

            if save_model:
                if ((train_step+1) % 5000 == 0):
                  vae.save_json("tf_vae/vae.json")
        if verbose:
            print("{} of {}\t{}\t{:.2f}\t{:.2f}\t{:.2f}".format( epoch, epochs, (train_step+1), train_loss, r_loss, kl_loss) )
        gen.on_epoch_end()
        early.on_epoch_end(epoch, logs={"loss": train_loss})
        if vae.stop_training:
            break
    early.on_train_end()


# finished, final model:
    if save_model:
        vae.save_json("tf_vae/vae.json")

    return best_loss
Пример #4
0
              gpu_mode=True)
print('Training beta-VAE with beta={:.1f}'.format(beta))

# train loop:
print("train", "step", "loss", "recon_loss", "kl_loss")
for epoch in range(NUM_EPOCH):
    np.random.shuffle(dataset)
    for idx in range(num_batches):
        batch = dataset[idx * batch_size:(idx + 1) * batch_size]

        obs = batch.astype(np.float) / 255.0

        feed = {
            vae.x: obs,
        }

        (train_loss, r_loss, kl_loss, train_step, _) = vae.sess.run(
            [vae.loss, vae.r_loss, vae.kl_loss, vae.global_step, vae.train_op],
            feed)

        if ((train_step + 1) % 500 == 0):
            print("step", (train_step + 1), train_loss, r_loss, kl_loss)
        if ((train_step + 1) % 5000 == 0):
            vae.save_json(
                os.path.join(model_save_path,
                             args.name + '_vae.json'.format(beta)))

# finished, final model:
vae.save_json(
    os.path.join(model_save_path, args.name + '_vae.json'.format(beta)))
Пример #5
0
    # train loop:
    print("train", "step", "loss", "recon_loss", "kl_loss")
    for epoch in range(arglist.epoch):
        np.random.shuffle(dataset)
        for idx in range(num_batches):
            batch = dataset[idx * arglist.batch_size:(idx + 1) *
                            arglist.batch_size]

            obs = np.array(batch).astype(np.float) / 255.0

            feed = {
                vae.x: obs,
            }

            (train_loss, r_loss, kl_loss, train_step, _) = vae.sess.run([
                vae.loss, vae.r_loss, vae.kl_loss, vae.global_step,
                vae.train_op
            ], feed)

            print("step", (train_step + 1), train_loss, r_loss, kl_loss)
            # log.write('{}\n'.format(','.join(map(str,
            # [train_loss, r_loss, kl_loss, train_step]))))
            if ((train_step + 1) % arglist.save_period == 0):
                vae.save_json(
                    os.path.join(arglist.model_save_path,
                                 "vae_{}.json".format(train_step + 1)))

    # finished, final model:
    vae.save_json(os.path.join(arglist.model_save_path, "vae.json"))
Пример #6
0
              kl_tolerance=kl_tolerance,
              is_training=True,
              reuse=False,
              gpu_mode=True)

# train loop:
print("train", "step", "loss", "recon_loss", "kl_loss")
for epoch in range(NUM_EPOCH):
    np.random.shuffle(dataset)
    for idx in range(num_batches):
        batch = dataset[idx * batch_size:(idx + 1) * batch_size]

        obs = batch.astype(np.float) / 255.0

        feed = {
            vae.x: obs,
        }

        (train_loss, r_loss, kl_loss, train_step, _) = vae.sess.run(
            [vae.loss, vae.r_loss, vae.kl_loss, vae.global_step, vae.train_op],
            feed)

        if ((train_step + 1) % 500 == 0):
            print("step", (train_step + 1), train_loss, r_loss, kl_loss)
        if ((train_step + 1) % 5000 == 0):
            vae.save_json("vae/vae.json")

# finished, final model:
vae.save_json("vae/vae.json")
print("Finish time: \t", datetime.datetime.now())