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from datetime import datetime
import sys

class SubLogger(object):
    def __init__(self, name, source, logger):
        self.name = name
        self.source = source
        self.logger = logger

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import numpy as np

# Creating some random data with 4 columns centered around 10
data = np.random.randn(4, 512) + 10
data = data.astype(np.float32) #np.float64
column_means = np.mean(data, axis=1, keepdims=True)
print(column_means.reshape(-1))
# [ 9.975992   9.9283495 10.003674  10.012635 ]

# Subtracting the column mean should yield zero-centered columns

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from PIL import Image
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import os

from object_detection.utils import label_map_util
from object_detection.utils import config_util

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# Author: Ygor Rebouças
# 


### The Training Loop
#
# 0) Imports
import tensorflow as tf
import numpy as np

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# Reproducing the main findings of the paper "Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs" 
# Goal: Train a ResNet model to solve the CIFAR-10 dataset using only batchnorm layers, all else is frozen at their random initial state.
# https://medium.com/@ygorrebouasserpa
# https://www.linkedin.com/in/ygor-rebouças-serpa-11606093/


import tensorflow as tf
import numpy as np
import pandas as pd