58 lines
1.6 KiB
JavaScript
58 lines
1.6 KiB
JavaScript
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
|
|
const tf = require('@tensorflow/tfjs');
|
|
|
|
const model = tf.sequential();
|
|
model.add(tf.layers.conv2d({
|
|
inputShape: [28, 28, 1],
|
|
filters: 32,
|
|
kernelSize: 3,
|
|
activation: 'relu',
|
|
}));
|
|
model.add(tf.layers.conv2d({
|
|
filters: 32,
|
|
kernelSize: 3,
|
|
activation: 'relu',
|
|
}));
|
|
model.add(tf.layers.maxPooling2d({poolSize: [2, 2]}));
|
|
model.add(tf.layers.conv2d({
|
|
filters: 64,
|
|
kernelSize: 3,
|
|
activation: 'relu',
|
|
}));
|
|
model.add(tf.layers.conv2d({
|
|
filters: 64,
|
|
kernelSize: 3,
|
|
activation: 'relu',
|
|
}));
|
|
model.add(tf.layers.maxPooling2d({poolSize: [2, 2]}));
|
|
model.add(tf.layers.flatten());
|
|
model.add(tf.layers.dropout({rate: 0.25}));
|
|
model.add(tf.layers.dense({units: 512, activation: 'relu'}));
|
|
model.add(tf.layers.dropout({rate: 0.5}));
|
|
model.add(tf.layers.dense({units: 10, activation: 'softmax'}));
|
|
|
|
const optimizer = 'rmsprop';
|
|
model.compile({
|
|
optimizer: optimizer,
|
|
loss: 'categoricalCrossentropy',
|
|
metrics: ['accuracy'],
|
|
});
|
|
|
|
module.exports = model;
|