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lucasdavid / test_voc_colormap.py

Last active December 16, 2022 18:46

Test which option translates segmentation masks pixels faster

	import argparse
	import os
	import time

	import numpy as np
	import tensorflow as tf

	parser = argparse.ArgumentParser()
	parser.add_argument('--opt', default='a', choices=['a', 'b'])
	parser.add_argument('--batch_size', type=int, default=32)

lucasdavid / brazilNamesGenderRatio4.tsv

Last active February 6, 2024 19:48

lucasdavid / cifar100-sequence-training-example.py

Created June 10, 2018 23:04

Training a classifier on cifar100, using full TB functionality

	import tensorflow as tf
	from keras.callbacks import TensorBoard
	from keras.datasets import cifar100
	from keras.layers import Dense, Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization, Activation, Dropout
	from keras.layers import Input
	from keras.models import Model
	from keras.preprocessing.image import ImageDataGenerator
	from sacred import Experiment

	ex = Experiment('tb-efficiency')

lucasdavid / cifar100-sequence-training-example.py

Created June 10, 2018 23:04

Training a classifier on cifar100, using full TB functionality

	import tensorflow as tf
	from keras.callbacks import TensorBoard
	from keras.datasets import cifar100
	from keras.layers import Dense, Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization, Activation, Dropout
	from keras.layers import Input
	from keras.models import Model
	from keras.preprocessing.image import ImageDataGenerator
	from sacred import Experiment

	ex = Experiment('tb-efficiency')

lucasdavid / cifar100-training-example.py

Created May 15, 2018 00:41

	import tensorflow as tf
	from keras.callbacks import TensorBoard
	from keras.datasets import cifar100
	from keras.layers import Dense, Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization, Activation, Dropout
	from keras.layers import Input
	from keras.models import Model
	from sacred import Experiment

	ex = Experiment('tb-efficiency')

lucasdavid / model_retraining.py

Created December 11, 2017 18:08

Demonstrates correct behavior of TensorBoard callback on model re-training.

	import numpy as np
	from keras import Input
	from keras.layers import Dense
	from keras.models import Model
	from keras import callbacks
	from sklearn.datasets import load_digits
	from sklearn.model_selection import train_test_split

	def build_model(shape, name=None):
	x = Input(shape)

lucasdavid / show_triplet_model_throws_error.py

Created February 24, 2017 20:56

Show that we cannot call model.predict inside the generator that feeds model.fit.

	import numpy as np
	from keras import backend as K
	from keras.datasets import cifar10
	from keras.engine import Input, Model
	from keras.models import Sequential
	from keras.layers import Dense, Lambda


	def build_model(x_shape):
	b_net = Sequential([

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