ypeleg

import tensorflow as tf
from copy import deepcopy
import tensorflow.keras.backend as K
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Model, clone_model
from tensorflow.keras.layers import Input, Dropout, Dense, ReLU, BatchNormalization, Activation, Concatenate

class DynamicNet(object):
    def __init__(self, c0 = None, lr = None, concat_input=False, additive_boosting=False, encoder_layers=None):

JiaminXuan

import numpy as np
from numpy import bincount,ravel,log
from scipy.sparse import coo_matrix
import pandas as pd
from keras import backend as K
from keras.layers import *
from keras.models import Model,Sequential

def imf_loss(y_true, y_pred):
    C=1+2*y_true

awjuliani

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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rgreenjr

-- show running queries (pre 9.2)
SELECT procpid, age(clock_timestamp(), query_start), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(clock_timestamp(), query_start), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%'