Zaid Alyafeai zaidalyafeai

## tensor.js
const input = tf.tensor([2,2]);

## zero-tensor.js
const input = tf.zeros([2,2]);

## tf-square.js
const a = tf.tensor([1,2,3]);
a.square().print();

## tf-square-square.js
const x = tf.tensor([1,2,3]);
const x2 = x.square().square();

## tf-dispose.js
const x = tf.tensor([1,2,3]);
x.dispose();

## tf-tidy.js
function f(x)
{
	return tf.tidy(()=>{
		const y = x.square();
		const z = x.mul(y);
		return z
        });
}

## tf-sequential.js
model = tf.sequential();

## tf-convlayer.js
const convlayer = tf.layers.conv2d({
  inputShape: [28,28,1],
  kernelSize: 5,
  filters: 8,
  strides: 1,
  activation: 'relu',
  kernelInitializer: 'VarianceScaling'
});

## tf-addconvlayer.js
model.add(convlayer);

## tf-simplemodel.js
const model = tf.sequential();

//create the first layer
model.add(tf.layers.conv2d({
  inputShape: [28, 28, 1],
  kernelSize: 5,
  filters: 8,
  strides: 1,
  activation: 'relu',
  kernelInitializer: 'VarianceScaling'
	const x = tf.tensor([1,2,3]);
	const x2 = x.square().square();
	function f(x)
	{
	return tf.tidy(()=>{
	const y = x.square();
	const z = x.mul(y);
	return z
	});
	}
	const convlayer = tf.layers.conv2d({
	inputShape: [28,28,1],
	kernelSize: 5,
	filters: 8,
	strides: 1,
	activation: 'relu',
	kernelInitializer: 'VarianceScaling'
	});
	const model = tf.sequential();

	//create the first layer
	model.add(tf.layers.conv2d({
	inputShape: [28, 28, 1],
	kernelSize: 5,
	filters: 8,
	strides: 1,
	activation: 'relu',
	kernelInitializer: 'VarianceScaling'