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View brazilNamesGenderRatio4.tsv
firstName total female male totalFound prFemale
0SEAS 1 0 1 1 0.0000000000
0SMAR 1 0 1 1 0.0000000000
A 1 0 1 1 0.0000000000
AARAO 2 0 2 2 0.0000000000
DIARONE 6 0 6 6 0.0000000000
DIARONI 4 0 4 4 0.0000000000
DIASIS 1 0 1 1 0.0000000000
DIASLINS 1 0 1 1 0.0000000000
@lucasdavid
lucasdavid / cifar100-sequence-training-example.py
Created Jun 10, 2018
Training a classifier on cifar100, using full TB functionality
View cifar100-sequence-training-example.py
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
lucasdavid / cifar100-sequence-training-example.py
Created Jun 10, 2018
Training a classifier on cifar100, using full TB functionality
View cifar100-sequence-training-example.py
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')
View cifar100-training-example.py
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
lucasdavid / model_retraining.py
Created Dec 11, 2017
Demonstrates correct behavior of TensorBoard callback on model re-training.
View model_retraining.py
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
lucasdavid / show_triplet_model_throws_error.py
Created Feb 24, 2017
Show that we cannot call model.predict inside the generator that feeds model.fit.
View show_triplet_model_throws_error.py
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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