View keras_metrics.py
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import tensorflow as tf | |
import torch | |
from torchmetrics import Metric | |
def tf2pt(x_tf=None): | |
if x_tf is None: | |
return None |
View batch_SGDEN.py
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from sklearn.datasets import load_boston | |
from sklearn.linear_model import (LinearRegression, Ridge, SGDRegressor, | |
Lasso, ElasticNetCV) | |
from sklearn.preprocessing import MinMaxScaler | |
import numpy as np | |
#from minepy import MINE | |
from sklearn.metrics import mean_squared_error | |
View batch_EN.py
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from sklearn.datasets import load_boston | |
from sklearn.linear_model import (LinearRegression, Ridge, LassoCV, ElasticNetCV, | |
ElasticNet, Lasso, RandomizedLasso) | |
from sklearn.feature_selection import RFE, f_regression | |
from sklearn.preprocessing import MinMaxScaler | |
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor | |
import numpy as np | |
import pdb | |
#from minepy import MINE |
View batch_embedded_GBRT.py
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from sklearn.datasets import load_boston | |
from sklearn.linear_model import (LinearRegression, Ridge, | |
Lasso, RandomizedLasso) | |
from sklearn.feature_selection import RFE, f_regression | |
from sklearn.preprocessing import MinMaxScaler | |
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor | |
import numpy as np | |
#from minepy import MINE | |
from sklearn.metrics import mean_squared_error |
View evonorm2d.py
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import torch | |
import torch.nn as nn | |
class EvoNorm2d(nn.Module): | |
__constants__ = ['num_features', 'eps', 'nonlinearity'] | |
def __init__(self, num_features, eps=1e-5, nonlinearity=True): | |
super(EvoNorm2d, self).__init__() | |
View input_fn.py
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def input_fn(file_pattern, labels, | |
image_size=(224,224), | |
shuffle=False, | |
batch_size=64, | |
num_epochs=None, | |
buffer_size=4096, | |
prefetch_buffer_size=None): | |
table = tf.contrib.lookup.index_table_from_tensor(mapping=tf.constant(labels)) | |
num_classes = len(labels) |
View acc_sgd.py
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class AccSGD(Optimizer): | |
"""AccSGD optimizer. | |
Arguments: | |
lr (float): learning rate | |
kappa (float, optional): ratio of long to short step (default: 1000) | |
xi (float, optional): statistical advantage parameter (default: 10) | |
smallConst (float, optional): any value <=1 (default: 0.7) | |
# References |
View amsgrad.py
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class AMSgrad(Optimizer): | |
"""AMSGrad optimizer. | |
Default parameters follow those provided in the Adam paper. | |
# Arguments | |
lr: float >= 0. Learning rate. | |
beta_1: float, 0 < beta < 1. Generally close to 1. | |
beta_2: float, 0 < beta < 1. Generally close to 1. | |
epsilon: float >= 0. Fuzz factor. |
View fashion_mnist_cnn.py
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'''Trains a simple convnet on the Zalando MNIST dataset. | |
Gets to 81.03% test accuracy after 30 epochs | |
(there is still a lot of margin for parameter tuning). | |
3 seconds per epoch on a GeForce GTX 980 GPU with CuDNN 5. | |
''' | |
from __future__ import print_function | |
import numpy as np | |
from mnist import MNIST |
View es.py
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import numpy as np | |
import gym | |
from gym.spaces import Discrete, Box | |
from gym.wrappers import Monitor | |
from keras.models import Sequential | |
from keras.layers import Dense, Activation, Flatten | |
# ================================================================ | |
# Policies |
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