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<!DOCTYPE html>
<html>
<head>
<script src="https://aframe.io/releases/0.9.0/aframe.min.js"></script>
</head>
<body>
<a-scene>
<!-- scene.gltf is the 3D model -->
<!-- position and rotation are free to modified -->
<a-entity
<!DOCTYPE html>
<html>
<head>
<script src="https://aframe.io/releases/0.9.0/aframe.min.js"></script>
</head>
<body>
<a-scene>
<!-- scene.gltf is the 3D model -->
<!-- position and rotation are free to modified -->
<!DOCTYPE html>
<html>
<head>
<script src="https://aframe.io/releases/0.9.0/aframe.min.js"></script>
</head>
<body>
<a-scene>
<a-sphere position="-1.5 2.2 -5" radius="1.25" color="#EF2D5E"></a-sphere>
<a-cylinder position="1 0.95 -3" radius="0.5" height="1.3" color="#FFC65D"></a-cylinder>
<a-box position="-1 0.5 -3" rotation="0 45 0" color="#4CC3D9"></a-box>
<!DOCTYPE html>
<html>
<head>
<script src="https://aframe.io/releases/0.9.0/aframe.min.js"></script>
</head>
<body>
<a-scene>
<!-- rest of your code here -->
</a-scene>
</body>
<video id="video"></video>
<script>
var video = document.querySelector("#video");
var source = document.createElement('source');
source.type = "video/mp4";
source.src = YOUR_VIDEO_YOUR_HERE;
video.appendChild(source);
1. payoff.blade.php
//on date change
$('#dpDate').change(function(e){
$.ajax({
type: "get",
url: '{{ route("mytest") }}',
data: {
date:$('#dpDate').val(),
loan_id:loan_data.id
},
from keras.constraints import max_norm
# add to Dense layers
model.add(Dense(64, kernel_constraint=max_norm(2.)))
# or add to Conv layers
model.add(Conv2D(64, kernel_constraint=max_norm(2.)))
reduce = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, mode='auto')
early = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1e-4, patience=10, mode='auto')
model.fit(X, Y, callbacks=[reduce, early])
# Run it to obtain reproducible results across machines (from keras.io)
from __future__ import print_function
import numpy as np
import tensorflow as tf
import random as rn
import os
os.environ['PYTHONHASHSEED'] = '0'
np.random.seed(42)
rn.seed(12345)
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (12.0, 9.0)
idx = 1 # image index that you want to display
img = np.empty(3, dtype=object)
img[0] = X[idx]
img[1] = Y[idx].reshape(Y[idx].shape[0],Y[idx].shape[1])
img[2] = pred[idx].reshape(pred[idx].shape[0],pred[idx].shape[1])
title = ['input', 'ground-truth', 'result']