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capsuleTraining.py
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462 lines (357 loc) · 225 KB
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from __future__ import division, print_function, unicode_literals
import random
import numpy as np
import tensorflow as tf
from flask import Flask, redirect
from flask import request, jsonify, json, make_response, current_app
from flask_cors import CORS, cross_origin
from datetime import datetime as dt
from collections import defaultdict
app = Flask(__name__)
app.config['SECRET_KEY'] = 'the quick brown fox jumps over the lazy dog'
app.config['CORS_HEADERS'] = 'Content-Type'
myIp = '192.168.1.204'
savePath_ = "./saveModel/"
cors = CORS(app, resources={r"/test": {"origins": myIp + ":5000"}}) # 13.228.19.240
myWebUrl_ = "http://" + myIp + ":5000/"
trainingSetFileName = "sansiriTrainingSet.txt"
# -----------------------------------------------------------------------------------------------------------------------
tf.reset_default_graph()
np.random.seed(42)
tf.set_random_seed(42)
classDict = {0: 'ant', 1: 'bat', 2: 'bear', 3: 'bee', 4: 'bird', 5: 'butterfly', 6: 'camel', 7: 'cat', 8: 'cow',
9: 'crab', 10: 'crocodile', 11: 'dog', 12: 'dolphin', 13: 'dragon', 14: 'duck', 15: 'elephant', 16: 'fish',
17: 'flamingo', 18: 'frog', 19: 'giraffe', 20: 'hedgehog', 21: 'horse', 22: 'kangaroo', 23: 'lion',
24: 'lobster', 25: 'mermaid', 26: 'monkey', 27: 'mosquito', 28: 'mouse', 29: 'octopus', 30: 'owl',
31: 'panda', 32: 'penguin', 33: 'pig', 34: 'rabbit', 35: 'raccoon', 36: 'rhinoceros', 37: 'scorpion',
38: 'sea turtle', 39: 'shark', 40: 'sheep', 41: 'snail', 42: 'snake', 43: 'spider', 44: 'squirrel',
45: 'swan', 46: 'tiger', 47: 'whale', 48: 'zebra'}
classNum = len(classDict)
inputSize = 64
def get_cropReSizeParam(arr):
boxes = []
boxes_id = []
rpow = random.uniform(0.5, 2)
for i in range(len(arr)):
boxes.append([random.uniform(0.0, 0.15625), random.uniform(0.0, 0.15625), random.uniform(0.84375, 1.0),
random.uniform(0.84375, 1.0)])
boxes_id.append(i)
return boxes, boxes_id, rpow
def squash(s, axis=-1, epsilon=1e-7, name=None):
with tf.name_scope(name, default_name="squash"):
squared_norm = tf.reduce_sum(tf.square(s), axis=axis,
keep_dims=True)
safe_norm = tf.sqrt(squared_norm + epsilon)
squash_factor = squared_norm / (1. + squared_norm)
unit_vector = s / safe_norm
return squash_factor * unit_vector
def safe_norm(s, axis=-1, epsilon=1e-7, keep_dims=False, name=None):
with tf.name_scope(name, default_name="safe_norm"):
squared_norm = tf.reduce_sum(tf.square(s), axis=axis,
keep_dims=keep_dims)
return tf.sqrt(squared_norm + epsilon)
def model():
# BEGIN_MODEL-----------------------------------------------------------------------------------------------------------------
caps1_n_maps = 32
caps1_n_caps = caps1_n_maps * 6 * 6 # 512 primary capsules
caps1_n_dims = 8
x_ = tf.placeholder(tf.float32, shape=[None, inputSize * inputSize], name="image")
y_ = tf.placeholder(tf.float32, shape=[None, classNum], name="target")
boxes_ = tf.placeholder(tf.float32, shape=[None, 4])
boxes_id_ = tf.placeholder(tf.int32, shape=[None])
rpow_ = tf.placeholder(tf.float32)
X = tf.reshape(x_, [-1, inputSize, inputSize, 1], name='X')
X_crop_and_resize = tf.image.crop_and_resize(X, boxes=boxes_, box_ind=boxes_id_, crop_size=[28, 28])
X_contrast = tf.pow(X_crop_and_resize, rpow_)
W_conv1 = tf.Variable(tf.truncated_normal([9, 9, 1, 256], stddev=0.1), name='w_cov1')
conv1 = tf.nn.conv2d(X_contrast, W_conv1, strides=[1, 1, 1, 1], padding='VALID')
relu1 = tf.maximum(conv1, 0.1 * conv1)
W_conv2 = tf.Variable(tf.truncated_normal([9, 9, 256, 256], stddev=0.1), name='w_cov2')
conv2 = tf.nn.conv2d(relu1, W_conv2, strides=[1, 2, 2, 1], padding='VALID') # [128 6 6 256]
relu2 = tf.maximum(conv2,
0.1 * conv2) # can't find out any words from the paper whether the PrimaryCap convolution does a ReLU activation or not before squashing function, but experiment show that using ReLU get a higher test accuracy
caps1_raw = tf.reshape(relu2, [-1, caps1_n_caps, caps1_n_dims], name="caps1_raw") # [ 128 1152 8]
caps1_output = squash(caps1_raw, name="caps1_output") # [ 128 1152 8]
caps2_n_caps = classNum # 10
caps2_n_dims = 16
init_sigma = 0.01
W_init = tf.random_normal(shape=(1, caps1_n_caps, caps2_n_caps, caps2_n_dims, caps1_n_dims), stddev=init_sigma,
dtype=tf.float32, name="W_init")
W = tf.Variable(W_init, name="W")
batch_size = tf.shape(X_contrast)[0]
W_tiled = tf.tile(W, [batch_size, 1, 1, 1, 1], name="W_tiled")
caps1_output_expanded = tf.expand_dims(caps1_output, -1, name="caps1_output_expanded")
caps1_output_tile = tf.expand_dims(caps1_output_expanded, 2, name="caps1_output_tile")
caps1_output_tiled = tf.tile(caps1_output_tile, [1, 1, caps2_n_caps, 1, 1],
name="caps1_output_tiled") # [ 128 512 10 8 1]
caps2_predicted = tf.matmul(W_tiled, caps1_output_tiled, name="caps2_predicted")
raw_weights = tf.zeros([batch_size, caps1_n_caps, caps2_n_caps, 1, 1], dtype=np.float32, name="raw_weights")
# Round 1
routing_weights = tf.nn.softmax(raw_weights, dim=2, name="routing_weights")
weighted_predictions = tf.multiply(routing_weights, caps2_predicted, name="weighted_predictions")
weighted_sum = tf.reduce_sum(weighted_predictions, axis=1, keep_dims=True, name="weighted_sum")
caps2_output_round_1 = squash(weighted_sum, axis=-2, name="caps2_output_round_1")
# Round 2
caps2_output_round_1_tiled = tf.tile(caps2_output_round_1, [1, caps1_n_caps, 1, 1, 1],
name="caps2_output_round_1_tiled")
agreement = tf.matmul(caps2_predicted, caps2_output_round_1_tiled, transpose_a=True, name="agreement")
raw_weights_round_2 = tf.add(raw_weights, agreement, name="raw_weights_round_2")
routing_weights_round_2 = tf.nn.softmax(raw_weights_round_2, dim=2, name="routing_weights_round_2")
weighted_predictions_round_2 = tf.multiply(routing_weights_round_2, caps2_predicted,
name="weighted_predictions_round_2")
weighted_sum_round_2 = tf.reduce_sum(weighted_predictions_round_2, axis=1, keep_dims=True,
name="weighted_sum_round_2")
caps2_output_round_2 = squash(weighted_sum_round_2, axis=-2, name="caps2_output_round_2")
# Round 3
caps2_output_round_2_tiled = tf.tile(caps2_output_round_2, [1, caps1_n_caps, 1, 1, 1],
name="caps2_output_round_2_tiled")
agreement = tf.matmul(caps2_predicted, caps2_output_round_2_tiled, transpose_a=True, name="agreement")
raw_weights_round_3 = tf.add(raw_weights, agreement, name="raw_weights_round_3")
routing_weights_round_3 = tf.nn.softmax(raw_weights_round_3, dim=2, name="routing_weights_round_3")
weighted_predictions_round_3 = tf.multiply(routing_weights_round_3, caps2_predicted,
name="weighted_predictions_round_3")
weighted_sum_round_3 = tf.reduce_sum(weighted_predictions_round_3, axis=1, keep_dims=True,
name="weighted_sum_round_3")
caps2_output_round_3 = squash(weighted_sum_round_3, axis=-2, name="caps2_output_round_3") # [128 1 10 16 1]
caps2_output = caps2_output_round_3
# ROUND_END
y_proba = safe_norm(caps2_output, axis=-2, name="y_proba") # [128 1 10 1]
y_proba_argmax = tf.argmax(y_proba, axis=2, name="y_proba") # [128 1 1]
y_pred = tf.squeeze(y_proba_argmax, axis=[1, 2], name="y_pred") # [128]
m_plus = 0.9
m_minus = 0.1
lambda_ = 0.5
caps2_output_norm = safe_norm(caps2_output, axis=-2, keep_dims=True,
name="caps2_output_norm") # [128 1 10 1 1]
present_error_raw = tf.square(tf.maximum(0., m_plus - caps2_output_norm), name="present_error_raw")
present_error = tf.reshape(present_error_raw, shape=(-1, classNum), name="present_error")
absent_error_raw = tf.square(tf.maximum(0., caps2_output_norm - m_minus), name="absent_error_raw")
absent_error = tf.reshape(absent_error_raw, shape=(-1, classNum), name="absent_error")
L = tf.add(y_ * present_error, lambda_ * (1.0 - y_) * absent_error, name="L")
margin_loss = tf.reduce_mean(tf.reduce_sum(L, axis=1), name="margin_loss")
mask_with_labels = tf.placeholder_with_default(False, shape=(), name="mask_with_labels")
reconstruction_targets = tf.cond(mask_with_labels, # condition
lambda: tf.argmax(y_, axis=1), # if True
lambda: y_pred, # if False
name="reconstruction_targets")
reconstruction_mask = tf.one_hot(reconstruction_targets,
depth=caps2_n_caps,
name="reconstruction_mask")
reconstruction_mask_reshaped = tf.reshape(
reconstruction_mask, [-1, 1, caps2_n_caps, 1, 1],
name="reconstruction_mask_reshaped")
caps2_output_masked = tf.multiply(
caps2_output, reconstruction_mask_reshaped,
name="caps2_output_masked")
decoder_input = tf.reshape(caps2_output_masked,
[-1, caps2_n_caps * caps2_n_dims],
name="decoder_input")
n_hidden1 = 512
n_hidden2 = 1024
n_output = 28 * 28
with tf.name_scope("decoder"):
hidden1 = tf.layers.dense(decoder_input, n_hidden1,
activation=tf.nn.relu,
name="hidden1")
hidden2 = tf.layers.dense(hidden1, n_hidden2,
activation=tf.nn.relu,
name="hidden2")
decoder_output = tf.layers.dense(hidden2, n_output,
activation=tf.nn.sigmoid,
name="decoder_output")
X_flat = tf.reshape(X_crop_and_resize, [-1, n_output], name="X_flat")
squared_difference = tf.square(X_flat - decoder_output,
name="squared_difference")
reconstruction_loss = tf.reduce_mean(squared_difference,
name="reconstruction_loss")
alpha = 0.0005
loss = tf.add(margin_loss, alpha * reconstruction_loss, name="loss")
correct = tf.equal(tf.argmax(y_, axis=1), y_pred, name="correct")
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name="accuracy")
optimizer = tf.train.AdamOptimizer()
training_op = optimizer.minimize(loss, name="training_op")
# END_MODEL-----------------------------------------------------------------------------------------------------------------
restoreNum = 224
shouldRestore = True
isTesting = True
for a, b in classDict.items():
print(a, b)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
if shouldRestore:
saver.restore(sess, savePath_ + 'my-model-' + str(restoreNum))
# ----------------------------------------------------------------------------------------------------------------------------------------START WEB
@app.route('/training', methods=['GET', 'POST'])
@cross_origin(origin='localhost', headers=['Content-Type', 'Authorization'])
def trainingWeb():
if request.method == 'GET':
return '''<!DOCTYPE html>
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.4/jquery.min.js"></script>
</head>
<body>
<center>
<h1>
<button onclick=chain();>Click</button>
</h1>
<br>
<div>
Empty
</div>
<br>
<div id="image">
<svg xmlns='http://www.w3.org/2000/svg' width='600' height='600' viewBox='0 0 600 600'> <rect width='600' height='600' x='0' y='0' fill='white' /> <g transform='translate(0, 0)'> <polyline fill='none' points='432.5,112.5 432.505859375,112.4921875 432.5234375,112.46875 432.55859375,112.421875 432.6171875,112.34375 432.705078125,112.2265625 432.828125,112.0625 432.9921875,111.84375 433.203125,111.5625 433.451171875,111.23046875 433.7265625,110.859375 434.01953125,110.4609375 434.3203125,110.046875 434.619140625,109.62890625 434.90625,109.21875 435.171875,108.828125 435.40625,108.46875 435.61328125,108.1328125 435.796875,107.8125 435.9609375,107.5 436.109375,107.1875 436.24609375,106.8671875 436.375,106.53125 436.5,106.171875 436.625,105.78125 436.748046875,105.359375 436.8671875,104.90625 436.98046875,104.421875 437.0859375,103.90625 437.181640625,103.359375 437.265625,102.78125 437.3359375,102.171875 437.390625,101.53125 437.431640625,100.861328125 437.4609375,100.1640625 437.48046875,99.44140625 437.4921875,98.6953125 437.498046875,97.927734375 437.5,97.140625 437.5,96.3359375 437.5,95.515625 437.4921875,94.673828125 437.46875,93.8046875 437.421875,92.90234375 437.34375,91.9609375 437.2265625,90.974609375 437.0625,89.9375 436.84375,88.84375 436.5625,87.6875 436.22265625,86.482421875 435.828125,85.2421875 435.3828125,83.98046875 434.890625,82.7109375 434.35546875,81.447265625 433.78125,80.203125 433.171875,78.9921875 432.53125,77.828125 431.869140625,76.7109375 431.1953125,75.640625 430.51953125,74.6171875 429.8515625,73.640625 429.201171875,72.7109375 428.578125,71.828125 427.9921875,70.9921875 427.453125,70.203125 426.955078125,69.4609375 426.4921875,68.765625 426.05859375,68.1171875 425.6484375,67.515625 425.255859375,66.9609375 424.875,66.453125 424.5,65.9921875 424.125,65.578125 423.748046875,65.203125 423.3671875,64.859375 422.98046875,64.5390625 422.5859375,64.234375 422.181640625,63.9375 421.765625,63.640625 421.3359375,63.3359375 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function _(x) {
return document.getElementById(x);
}
function chain() {
processString(s)
}
function processString(x) {
class LinkedList {
constructor(value) {
const newNode = { value };
newNode.next = null;
newNode.prev = null;
this.head = newNode;
this.tail = newNode;
this.length = 1;
}
addToHead(value) {
const newNode = { value };
newNode.prev = this.tail;
if (this.tail) {
this.tail.next = newNode;
}
newNode.next = null;
this.tail = newNode;
this.length++;
return this;
}
}
if (x.length < 1) return "";
var indexOfPoints = 1;
var indexOfBegin = 1;
var indexOfEnd = 1;
var count = (x.match(/points/g) || []).length;
console.log(count);
var pathsArr = new Array(count);
var pCount = 0;
while (indexOfPoints > 0) {
indexOfPoints = x.indexOf("points",indexOfPoints);
indexOfPoints += 1;
indexOfBegin = x.indexOf("'",indexOfPoints);
indexOfEnd = x.indexOf("'",indexOfBegin + 1);
tmp = "";
for (var i = indexOfBegin + 1; i < indexOfEnd; i++) {
tmp += x.charAt(i);
}
var xyArr = tmp.split(' ');
var firstXY = xyArr[0].split(',');
if (firstXY.length != 2) {
console.log(firstXY);
continue;
}
var fxInt = Math.round(parseFloat(firstXY[0]));
var fyInt = Math.round(parseFloat(firstXY[1]));
if (isNaN(fxInt) || isNaN(fyInt)) {
console.log(firstXY)
return;
}
const list = new LinkedList([fxInt,fyInt]);
var xInt = fxInt;
var yInt = fyInt;
var xIntOld = fxInt;
var yIntOld = fyInt;
for (var i = 0; i < xyArr.length; i++) {
var xy = xyArr[i].split(',');
if (xy.length != 2) {
console.log(xy);
continue;
}
xInt = Math.round(parseFloat(xy[0]));
yInt = Math.round(parseFloat(xy[1]));
if (isNaN(xInt) || isNaN(yInt)) {
console.log(xy)
return;
}
var dis = (xInt - xIntOld)*(xInt - xIntOld) + (yInt - yIntOld)*(yInt - yIntOld);
xIntOld = xInt;
yIntOld = yInt;
if (dis > 0) {
list.addToHead([xInt,yInt]);
}
}
var arr = new Array(list.length);
console.log(list);
lh = list.head;
var i = 0;
while (lh) {
arr[i++] = lh.value;
lh = lh.next;
}
pathsArr[pCount++] = arr;
if (indexOfPoints >= x.length || indexOfPoints < 1) {
break;
}
}
console.log(pathsArr);
sendPaths('elephant', pathsArr);
return pathsArr;
}
function sendPaths(className, paths) {
$.ajax({
url: ''' + "'" + myWebUrl_ + 'training' + "'" + ''' ,
contentType: 'application/json;charset=UTF-8',
method: 'POST',
dataType: "html",
data: JSON.stringify({'className': className, 'paths': paths}),
success: function (response) {
console.log(response);
}
})
}
</script>
</body>
</html>
'''
elif request.method == 'POST':
data = request.get_json()
if not data.get('paths'):
return "data does not contain field 'paths'"
if not data.get('className'):
return "data does not contain field 'className'"
if len(data.get('className')) > 100:
return "className is too long"
paths = data['paths']
cn = data['className']
tsp = dt.now().strftime("%Y-%m-%d %H:%M:%S")
pathsT = []
pathCount = 0
for path in paths:
try:
pt = np.transpose(path).tolist()
if len(pt) != 2:
return "path must contain a set of [x,y] only"
if len(pt[0]) != len(pt[1]):
return "fail numX != numY : " + str(len(pt[0])) + " != " + len(pt[1])
pathsT.append(pt)
except:
return "exception has occurred during transform path " + str(pathCount)
pathCount += 1
pathsT = str(pathsT)
print(cn)
print(tsp)
with open(trainingSetFileName, "a") as myfile:
myfile.write('{"word":"' + cn + '","timestamp":"' + tsp + '","drawing":' + pathsT + "}\n")
return "success"
# ----------------------------------------------------------------------------------------------------------------------------------------END WEB
app.run(host=myIp)
model()