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Using TensorFlow backend. | |
WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1264: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version. | |
Instructions for updating: | |
keep_dims is deprecated, use keepdims instead | |
WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1349: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version. | |
Instructions for updating: | |
keep_dims is deprecated, use keepdims instead | |
Failed loading existing training data. | |
Unable to open file (truncated file: eof = 203175792, sblock->base_addr = 0, stored_eoa = 203176624) | |
Loading Trainer from Model_Original plugin... |
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def get_upsampling_weight(in_channels, out_channels, kernel_size): | |
"""Make a 2D bilinear kernel suitable for upsampling""" | |
factor = (kernel_size + 1) // 2 | |
if kernel_size % 2 == 1: | |
center = factor - 1 | |
else: | |
center = factor - 0.5 | |
og = np.ogrid[:kernel_size, :kernel_size] | |
filt = (1 - abs(og[0] - center) / factor) * \ | |
(1 - abs(og[1] - center) / factor) |
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def filter_rgb_to_hed(np_img, output_type="uint8"): | |
""" | |
Filter RGB channels to HED (Hematoxylin - Eosin - Diaminobenzidine) channels. | |
Args: | |
np_img: RGB image as a NumPy array. | |
output_type: Type of array to return (float or uint8). | |
Returns: | |
NumPy array (float or uint8) with HED channels. | |
""" | |
#t = Time() |
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player_df = df | |
player_df['mins_played'] = player_df['mins_played'].astype('int') | |
player_df['mins_played_2'] = player_df['mins_played'].astype('int') | |
player_df['ontarget_scoring_att'] = player_df['ontarget_scoring_att'].astype( | |
'int') | |
player_df['total_att_assist'] = player_df['total_att_assist'].astype('int') | |
player_df['aerial_won'] = player_df['aerial_won'].astype('int') | |
player_df['outfielder_block'] = player_df['outfielder_block'].astype('int') | |
player_df['ball_recovery'] = player_df['ball_recovery'].astype('int') | |
player_df['att_ibox_blocked'] = player_df['att_ibox_blocked'].astype('int') |
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<!doctype html> | |
<meta charset="utf-8"> | |
<head> | |
<script type="text/javascript" src="{{ url_for('static', filename='jquery.js') }}"></script> | |
<script type="text/javascript" src="{{ url_for('static', filename='jquery.js') }}"></script> | |
<script type="text/javascript" src="{{ url_for('static', filename='openseadragon.js') }}"></script> | |
<script type="text/javascript" src="{{ url_for('static', filename='openseadragon-scalebar.js') }}"></script> | |
<link rel="stylesheet" href="//code.jquery.com/ui/1.11.4/themes/smoothness/jquery-ui.css"> | |
<script src="//code.jquery.com/jquery-1.11.3.min.js"></script> |
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saved_model_schema_version: 1 | |
meta_graphs { | |
meta_info_def { | |
stripped_op_list { | |
op { | |
name: "Add" | |
input_arg { | |
name: "x" | |
type_attr: "T" | |
} |
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import argparse | |
def main(args): | |
import os | |
import sys | |
import glob | |
import random | |
import pickle | |
import numpy as np | |
from PIL import Image |
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import torch | |
import torch.nn as nn | |
import torch.optim as optim | |
import torch.backends.cudnn as cudnn | |
from torch.autograd import Variable | |
import torchvision | |
import torchvision.transforms as transforms | |
import os | |
import numpy as np | |
import argparse |
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# Path to oh-my-zsh installation. | |
export ZSH=$HOME/.oh-my-zsh | |
# Theme for zsh | |
ZSH_THEME="agnoster" | |
# oh-my-zsh framework plugins | |
plugins=(git) | |
source $ZSH/oh-my-zsh.sh |
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<?php | |
//This file is included in every .php file | |
session_start(); | |
error_reporting(E_ERROR | E_WARNING); | |
//LOCAL | |
$BASE_DIR = '/home/luiscosta/PhpstormProjects/LBAW-FEUP/'; //CHANGE WITH YOURS | |
$BASE_URL = '/'; |
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