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- Original paper
Draft
Draft
package main | |
import ( | |
"encoding/hex" | |
"fmt" | |
"github.com/btcsuite/btcd/btcec" | |
) | |
func privkeyToPubkey(priv int) int { |
package main | |
import ( | |
"testing" | |
"github.com/btcsuite/btcd/btcec" | |
"github.com/btcsuite/btcd/chaincfg" | |
"github.com/btcsuite/btcd/chaincfg/chainhash" | |
"github.com/btcsuite/btcd/txscript" | |
"github.com/btcsuite/btcd/wire" |
package main | |
import ( | |
"testing" | |
"github.com/btcsuite/btcd/btcec" | |
"github.com/btcsuite/btcd/chaincfg" | |
"github.com/btcsuite/btcd/chaincfg/chainhash" | |
"github.com/btcsuite/btcd/txscript" | |
"github.com/btcsuite/btcd/wire" |
// main.rs | |
mod request; | |
use request::request; | |
fn main() { | |
match request() { | |
Ok(text) => println!("{}", text), | |
Err(err) => println!("{}", err) | |
} |
import tensorflow as tf | |
# model1 | |
def model1_fn(inputs, labels, learning_rate=0.001): | |
preds = tf.layers.dense(inputs, 1) | |
cost = tf.losses.mean_squared_error(labels, preds) | |
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) | |
return optimizer, cost, preds | |
# model2 |
var reactionImageUrls = {'angry': 'http://url.com/', 'wow': 'http://url.com/'}; | |
firebase.database().ref('Points').once('value', function(points) { | |
points.forEach(function(point) { | |
var location = data.val().Location; | |
var reaction = data.val().Reaction; | |
imageUrl = reactionImageUrls[reaction]; | |
// display a point |
git clone https://github.com/Jwata/tensorflow-pet-detector-quickstart | |
cd tensorflow-pet-detector-quickstart |