def image_summaries(dist, target, name='image', max_batch=10):
summaries = []
with tf.variable_scope(name):
empty_frame = 0 * target[:max_batch, :1]
image = dist.mode()[:max_batch]
target = target[:max_batch]
change = tf.concat([empty_frame, image[:, 1:] - image[:, :-1]], 1)
error = image - target
summaries.append(
image_strip_summary.image_strip_summary('prediction', image))
summaries.append(
image_strip_summary.image_strip_summary('change',
(change + 1) / 2))
summaries.append(
image_strip_summary.image_strip_summary('error', (error + 1) / 2))
# Concat prediction and target vertically.
frames = tf.concat([target, image], 2)
# Stack batch entries horizontally.
frames = tf.transpose(frames, [1, 2, 0, 3, 4])
s = shapelib.shape(frames)
frames = tf.reshape(frames, [s[0], s[1], s[2] * s[3], s[4]])
summaries.append(
gif_summary.gif_summary('animation',
frames[None],
max_outputs=1,
fps=20))
return summaries
def image_summaries(dist, target, name='image', max_batch=10):
summaries = []
with tf.variable_scope(name):
empty_frame = 0 * target[:max_batch, :1]
image = dist.mode()[:max_batch]
change = tf.concat([empty_frame, image[:, 1:] - image[:, :-1]], 1)
error = image[:max_batch] - target[:max_batch]
summaries.append(
image_strip_summary.image_strip_summary('prediction', image))
summaries.append(
image_strip_summary.image_strip_summary('change',
(change + 1) / 2))
summaries.append(
image_strip_summary.image_strip_summary('error', (error + 1) / 2))
return summaries
def data_summaries(data, postprocess_fn, histograms=False, name='data'):
summaries = []
with tf.variable_scope(name):
if histograms:
for key, value in data.items():
if key in ('image',):
continue
summaries.append(tf.summary.histogram(key, data[key]))
image = data['image']
if postprocess_fn:
image = postprocess_fn(image)
summaries.append(image_strip_summary.image_strip_summary('image', image))
return summaries
def data_summaries(data, postprocess_fn, histograms=False, name='data'):
summaries = []
with tf.variable_scope(name):
if histograms:
for key, value in data.items():
if key in ('image', ):
continue
summaries.append(tf.summary.histogram(key, data[key]))
image = data['image']
if postprocess_fn:
image = postprocess_fn(image)
summaries.append(
image_strip_summary.image_strip_summary('image', image))
summaries.append(
tf.summary.scalar(
'action_mean',
tf.reduce_mean(tf.reduce_sum(tf.square(data['action']), axis=-1))))
return summaries