How to use the numjs.dot function in numjs
To help you get started, we’ve selected a few numjs examples, based on popular ways it is used in public projects.
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var prev_synapse_1_weight_update = nj.zeros(synapse_1.shape);
var synapse_0_direction_count = nj.zeros(synapse_0.shape);
var synapse_1_direction_count = nj.zeros(synapse_1.shape);
for(var j = 0; j < epochs + 1; j++) {
var layer_0 = X;
var layer_1 = nj.sigmoid(nj.dot(layer_0, synapse_0));
if(dropout) {
// I don't understand what this does yet
// layer_1 *= nj.random.binomial([np.ones((len(X),hidden_neurons))], 1-dropout_percent)[0] * (1.0/(1-dropout_percent));
}
var layer_2 = nj.sigmoid(nj.dot(layer_1, synapse_1));
var layer_2_error = y.subtract(layer_2);
if( (j % 10000) == 0 && j > 5000 ) {
// if this 10k iteration's error is greater than
// the last iteration, break out
if (nj.mean(nj.abs(layer_2_error)) < last_mean_error) {
console.log("delta after " + j + " iterations:" + nj.mean(nj.abs(layer_2_error)) );
last_mean_error = nj.mean(nj.abs(layer_2_error));
} else {
console.log ("break:" + nj.mean(nj.abs(layer_2_error)) + ">" + last_mean_error );
break;
}
}
var layer_2_delta = layer_2_error.multiply( curve(layer_2) );
var layer_1_error = layer_2_delta.dot(synapse_1.T);var last_mean_error = 1;
var synapse_0 = nj.array( rand(X_arr[0].length, hidden_neurons) );
var synapse_1 = nj.array( rand(hidden_neurons, classes.length) );
var prev_synapse_0_weight_update = nj.zeros(synapse_0.shape);
var prev_synapse_1_weight_update = nj.zeros(synapse_1.shape);
var synapse_0_direction_count = nj.zeros(synapse_0.shape);
var synapse_1_direction_count = nj.zeros(synapse_1.shape);
for(var j = 0; j < epochs + 1; j++) {
var layer_0 = X;
var layer_1 = nj.sigmoid(nj.dot(layer_0, synapse_0));
if(dropout) {
// I don't understand what this does yet
// layer_1 *= nj.random.binomial([np.ones((len(X),hidden_neurons))], 1-dropout_percent)[0] * (1.0/(1-dropout_percent));
}
var layer_2 = nj.sigmoid(nj.dot(layer_1, synapse_1));
var layer_2_error = y.subtract(layer_2);
if( (j % 10000) == 0 && j > 5000 ) {
// if this 10k iteration's error is greater than
// the last iteration, break out
if (nj.mean(nj.abs(layer_2_error)) < last_mean_error) {
console.log("delta after " + j + " iterations:" + nj.mean(nj.abs(layer_2_error)) );
last_mean_error = nj.mean(nj.abs(layer_2_error));
} else {function think(sentence) {
var x = bow(sentence, words);
var l0 = x;
var l1 = nj.sigmoid(nj.dot(l0, synapse_0));
var l2 = nj.sigmoid(nj.dot(l1, synapse_1));
return l2;
}
