tensorflow_probability/examples/vq_vae.py
def visualize_training(images_val,
                       reconstructed_images_val,
                       random_images_val,
                       log_dir, prefix, viz_n=10):
  """Helper method to save images visualizing model reconstructions.

  Args:
    images_val: Numpy array containing a batch of input images.
    reconstructed_images_val: Numpy array giving the expected output
      (mean) of the decoder.
    random_images_val: Optionally, a Numpy array giving the expected output
      (mean) of decoding samples from the prior, or `None`.
    log_dir: The directory to write images (Python `str`).
    prefix: A specific label for the saved visualizations, which
      determines their filenames (Python `str`).
    viz_n: The number of images from each batch to visualize (Python `int`).
  """
  save_imgs(images_val[:viz_n],
            os.path.join(log_dir, "{}_inputs.png".format(prefix)))
  save_imgs(reconstructed_images_val[:viz_n],
            os.path.join(log_dir,
                         "{}_reconstructions.png".format(prefix)))

  if random_images_val is not None:
    save_imgs(random_images_val[:viz_n],
              os.path.join(log_dir,
                           "{}_prior_samples.png".format(prefix)))
def visualize_training(images_val,
                       reconstructed_images_val,
                       random_images_val,
                       log_dir, prefix, viz_n=10):
  """Helper method to save images visualizing model reconstructions.

  Args:
    images_val: Numpy array containing a batch of input images.
    reconstructed_images_val: Numpy array giving the expected output
      (mean) of the decoder.
    random_images_val: Optionally, a Numpy array giving the expected output
      (mean) of decoding samples from the prior, or `None`.
    log_dir: The directory to write images (Python `str`).
    prefix: A specific label for the saved visualizations, which
      determines their filenames (Python `str`).
    viz_n: The number of images from each batch to visualize (Python `int`).
  """
  save_imgs(images_val[:viz_n],
            os.path.join(log_dir, "{}_inputs.png".format(prefix)))
  save_imgs(reconstructed_images_val[:viz_n],
            os.path.join(log_dir,
                         "{}_reconstructions.png".format(prefix)))

  if random_images_val is not None:
    save_imgs(random_images_val[:viz_n],
              os.path.join(log_dir,
                           "{}_prior_samples.png".format(prefix)))
def visualize_training ( images_val , reconstructed_images_val , random_images_val , log_dir , prefix , viz_n = 10 ) : save_imgs ( images_val [ : viz_n ] , os . path . join ( log_dir , "{}_inputs.png" . format ( prefix ) ) ) save_imgs ( reconstructed_images_val [ : viz_n ] , os . path . join ( log_dir , "{}_reconstructions.png" . format ( prefix ) ) ) if random_images_val is not None : save_imgs ( random_images_val [ : viz_n ] , os . path . join ( log_dir , "{}_prior_samples.png" . format ( prefix ) ) )
Helper method to save images visualizing model reconstructions.

  Args:
    images_val: Numpy array containing a batch of input images.
    reconstructed_images_val: Numpy array giving the expected output
      (mean) of the decoder.
    random_images_val: Optionally, a Numpy array giving the expected output
      (mean) of decoding samples from the prior, or `None`.
    log_dir: The directory to write images (Python `str`).
    prefix: A specific label for the saved visualizations, which
      determines their filenames (Python `str`).
    viz_n: The number of images from each batch to visualize (Python `int`).
Helper method to save images visualizing model reconstructions .