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// colab link: [...]
function MnistRNN() {
var model = this;
this.weights_meta = {
'(MnistNet).dropout(Dropout).keygen(Generator)._key': [[1973249, 1973251], [2]],
'(MnistNet).lstm_core(LSTMCore).fc(Linear).b': [[266496, 268544], [2048]],
'(MnistNet).lstm_core(LSTMCore).fc(Linear).w': [[268544, 1841408], [768, 2048]],
'(MnistNet).output_head(Linear).b': [[1841408, 1841665], [257]],
'(MnistNet).output_head(Linear).w': [[1841665, 1973249], [512, 257]],
'(MnistNet).pos_embed(Embed).embeddings': [[0, 200704], [784, 256]],
'(MnistNet).value_embed(Embed).embeddings': [[200704, 266496], [257, 256]]
};
this.is_model_ready = false;
this.embed_lookup = function(index, weights) {
return tf.slice(weights, [index], [1]);
};
this.pos = 0;
this.state = null;
this.start_token = 256;
this.hidden_size = this.weights_meta['(MnistNet).lstm_core(LSTMCore).fc(Linear).b'][1][0] / 4;
this.initialize_state = function() {
this.pos = 0;
this.token = this.start_token;
var hidden = tf.zeros([1, this.hidden_size]);
var cell = tf.zeros([1, this.hidden_size]);
this.state = [hidden, cell];
};
this.lstm_core = function(inputs, state, weights) {
const [hidden, cell] = state;
const [w, b] = weights;
const i_and_h =tf.concat([inputs, hidden], 1);
const gated = tf.add(tf.matMul(i_and_h, w), b);
const [i, g, f, o] = tf.split(gated, 4, 1);
const f_ = tf.sigmoid(tf.add(f, 1.));
const i_ = tf.sigmoid(i);
const g_ = tf.tanh(g);
const c = tf.add(
tf.mul(i_, g_),
tf.mul(cell, f_)
);
const h = tf.mul(
tf.sigmoid(o),
tf.tanh(c)
);
return [h, c];
};
this.step = function() {
const [token, h, c] = tf.tidy( function() {
const lstm_b = model.MODEL_WEIGHTS['(MnistNet).lstm_core(LSTMCore).fc(Linear).b'];
const lstm_w = model.MODEL_WEIGHTS['(MnistNet).lstm_core(LSTMCore).fc(Linear).w'];
const output_b = model.MODEL_WEIGHTS['(MnistNet).output_head(Linear).b'];
const output_w = model.MODEL_WEIGHTS['(MnistNet).output_head(Linear).w'];
const pos_embed = model.MODEL_WEIGHTS['(MnistNet).pos_embed(Embed).embeddings'];
const value_embed = model.MODEL_WEIGHTS['(MnistNet).value_embed(Embed).embeddings'];
const v = model.embed_lookup(model.token, value_embed);
const p = model.embed_lookup(model.pos, pos_embed);
const x = tf.add(v, p);
const [h, c] = model.lstm_core(x, model.state, [lstm_w, lstm_b]);
tf.dispose(model.state[0]);
tf.dispose(model.state[1]);
const logits = tf.add(
tf.matMul(h, output_w),
output_b
);
const token = tf.multinomial(logits, 1).dataSync()[0];
return [token, h, c];
});
this.clean_memory();
this.token = token;
this.state = [h, c];
canvas.plot_xyc(this.pos, token);
this.pos = this.pos + 1;
};
this.MODEL_WEIGHTS = {};
this.clean_memory = function() {
tf.dispose(model.state[0]);
tf.dispose(model.state[1]);
};
this.loop = function() {
this.step();
if (this.pos >=28*28) {
setTimeout(function(){
model.clean_memory();
model.initialize_state();
canvas.plot_grid();
model.loop();
}, 3000);
} else {
canvas.plot_xyc(this.pos, 255);
setTimeout(function(){model.loop();}, 0);
}
};
this.load_model_weights = function() {
var req = new XMLHttpRequest();
req.open("GET", "weights.bin", true);
console.log('loading weights...');
req.responseType = "arraybuffer";
var this_ = this;
req.onload = function (event) {
var buff = req.response;
if (buff) {
var W = new Float32Array(buff);
for(var k in this_.weights_meta) {
info = this_.weights_meta[k];
offset = info[0];
shape = info[1];
this_.MODEL_WEIGHTS[k] = tf.tensor(W.subarray(offset[0], offset[1]), shape);
}
this_.is_model_ready = true;
} else {
alert('Error while loading weights...');
}
};
req.send(null);
};
this.load_when_ready = function() {
tf.ready().then( function() {
tf.enableProdMode();
console.log('tf is ready');
model.initialize_state()
model.load_model_weights();
console.log(model.hidden_size);
});
};
}
function MnistCanvas() {
var canvas = document.getElementById("mnist-canvas");
canvas.width = window.innerWidth;
canvas.height = window.innerHeight;
context=canvas.getContext('2d');
context.translate(canvas.width/2,canvas.height/2);
var scale = Math.floor(Math.min(canvas.width, canvas.height) / (28*2) ) * 28;
console.log(scale);
context.scale(scale, scale)
context.imageSmoothingEnabled = false;
this.clear = function() {
context.clearRect(-1, -1, 2., 2.);
context.fillStyle = "rgb(0, 0, 0)";
context.fillRect(-10, -10, 20, 20);
};
this.plot_grid = function() {
for (var i=0; i< 28*28; i++) this.plot_xyc(i, 0);
};
this.plot_xyc = function (pos, color) {
color = Math.max(20, color);
var step = 1. / 28;
var y = Math.floor(pos / 28 - 14) * step;
var x = (pos % 28 - 14) * step;
context.fillStyle = "rgb(0, " + color + ", 0)";
context.fillRect(x, y, step, step);
context.strokeStyle = "rgb(0, 0, 0)";
context.lineWidth = 0.008;
context.strokeRect(x, y, step, step);
};
this.loading_animation = function() {
var counter = 0;
var this_ = this;
this_.plot_grid();
var draw = function() {
if (model.is_model_ready) {
console.log('stopping animation.');
model.loop();
return;
}
if (counter >= 28*28) {
this_.plot_grid();
counter = 0;
}
this_.plot_xyc(counter, 255);
if (counter < 28*28-1) {
this_.plot_xyc(counter+1, 255);
}
counter = counter+1;
window.requestAnimationFrame(draw);
};
window.requestAnimationFrame(draw);
};
}
var model = null;
var canvas = null;
window.onload = function() {
setTimeout(function() {
model = new MnistRNN();
canvas = new MnistCanvas();
console.log("init...");
canvas.clear();
canvas.loading_animation();
model.load_when_ready();
}, 500);
};
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