import tensorflow as tf
import numpy as np
# logging
import logging
from segmentpy.tf114 import log
logger = log.setup_custom_logger(__name__)
logger.setLevel(logging.DEBUG)
def init_weights(shape, name='weights', reuse=False, uniform=True):
"""
input:
-------
shape: (list) shape of the weight matrix e.g. [5, 5, 1, 32]
name: (string) name of the node
return:
-------
tensorflow variable initialized by xavier method
"""
with tf.variable_scope(name, reuse=reuse):
return tf.get_variable('w',
shape=shape,
initializer=tf.initializers.glorot_uniform()
if uniform else tf.initializers.glorot_normal())
def init_bias(shape, name='bias', reuse=False, uniform=True):
"""
input:
-------
from tensorboard.backend.event_processing import event_accumulator
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import re
import os
from segmentpy.tf114.util import check_N_mkdir
import logging
from segmentpy.tf114 import log
logger = log.setup_custom_logger('root')
logger.setLevel(logging.DEBUG)
def gradient_extractor(event_dir: str, write_rlt=True):
if not event_dir.endswith('train/'):
_dir = os.path.join(event_dir, 'train/')
else:
_dir = event_dir
accumulator = event_accumulator.EventAccumulator(_dir,
size_guidance={
event_accumulator.SCALARS: 0,
event_accumulator.HISTOGRAMS: 0,
})
accumulator.Reload()
tags = accumulator.Tags()
l_grad_tag = []
for param_name in tags['histograms']:
