In this post, we'll be looking at how to build convolutional neural networks (CNNs) in TensorFlow.js and Node.js. We'll be using a well-known dataset for image recognition, the MNIST dataset, to train our CNN.
Convolutional neural networks are a type of neural network that are particularly well-suited for image recognition tasks. This is because they are able to take advantage of the spatial structure of images.
We'll start by looking at how to build a CNN in TensorFlow.js. If you're not familiar with TensorFlow.js, it's a JavaScript library for machine learning that runs in the browser.
First, we need to load the MNIST dataset. The MNIST dataset is a collection of images of handwritten digits, each of which is 28x28 pixels.
We can load the MNIST dataset using TensorFlow.js's tf.data.mnist
function. This function returns a Dataset
object, which we can then use to train our CNN.
const mnistData = tf.data.mnist({
train: true,
testData: false
});
Next, we need to define the convolutional neural network. We'll be using a Sequential model, which is a type of neural network that is composed of a linear stack of layers.
We'll start by adding a Flatten
layer, which takes our 28x28 images and flattens them into a single vector of 784 elements.
const model = tf.sequential();
model.add(tf.layers.flatten({
inputShape: [28, 28]
}));
Next, we'll add a Dense
layer. This is a fully-connected layer, which means that each neuron in the layer is connected to every neuron in the previous layer.
We'll configure the Dense
layer to have 128 neurons and to use the relu
activation function. The relu
activation function is a common choice for neural networks. It outputs 0 for any input less than 0 and outputs the input for any input greater than or equal to 0.
model.add(tf.layers.dense({
units: 128,
activation: 'relu',
inputShape: [784]
}));
Finally, we'll add a Dense
layer with 10 neurons and the softmax
activation function. The softmax
activation function is a common choice for the output layer of a neural network. It outputs a probability distribution over the 10 possible digits.
model.add(tf.layers.dense({
units: 10,
activation: 'softmax',
inputShape: [128]
}));
Now that we've defined our model, we need to compile it. When we compile a model, we specify the loss function and the optimizer.
The loss function is a measure of how well the model is performing. We want to minimize the loss function, which means that we want the model to make predictions that are as close to the true labels as possible.
The optimizer is a algorithm that is used to update the model in order to minimize the loss function. There are many different optimizers available, but we'll be using the sgd
optimizer.
model.compile({
loss: 'categoricalCrossentropy',
optimizer: 'sgd'
});
Now that our model is compiled, we can train it. We'll train it for 10 epochs, which means that the model will see the entire MNIST dataset 10 times.
model.fit(mnistData, {
epochs: 10
});
Now that we've seen how to build a CNN in TensorFlow.js, let's take a look at how to do it in Node.js.
First, we need to load the MNIST dataset. The MNIST dataset is a collection of images of handwritten digits, each of which is 28x28 pixels.
We can load the MNIST dataset using the mnist
package. This package returns a Dataset
object, which we can then use to train our CNN.
const mnist = require('mnist');
const mnistData = mnist.training(0, 60000);
Next, we need to define the convolutional neural network. We'll be using a Sequential model, which is a type of neural network that is composed of a linear stack of layers.
We'll start by adding a Flatten
layer, which takes our 28x28 images and flattens them into a single vector of 784 elements.
const model = tf.sequential();
model.add(tf.layers.flatten({
inputShape: [28, 28]
}));
Next, we'll add a Dense
layer. This is a fully-connected layer, which means that each neuron in the layer is connected to every neuron in the previous layer.
We'll configure the Dense
layer to have 128 neurons and to use the relu
activation function. The relu
activation function is a common choice for neural networks. It outputs 0 for any input less than 0 and outputs the input for any input greater than or equal to 0.
model.add(tf.layers.dense({
units: 128,
activation: 'relu',
inputShape: [784]
}));
Finally, we'll add a Dense
layer with 10 neurons and the softmax
activation function. The softmax
activation function is a common choice for the output layer of a neural network. It outputs a probability distribution over the 10 possible digits.
model.add(tf.layers.dense({
units: 10,
activation: 'softmax',
inputShape: [128]
}));
Now that we've defined our model, we need to compile it. When we compile a model, we specify the loss function and the optimizer.
The loss function is a measure of how well the model is performing. We want to minimize the loss function, which means that we want the model to make predictions that are as close to the true labels as possible.
The optimizer is a algorithm that is used to update the model in order to minimize the loss function. There are many different optimizers available, but we'll be using the sgd
optimizer.
model.compile({
loss: 'categoricalCrossentropy',
optimizer: 'sgd'
});
Now that our model is compiled, we can train it. We'll train it for 10 epochs, which means that the model will see the entire MNIST dataset 10 times.
model.fit(mnistData, {
epochs: 10
});
In this post, we've seen how to build convolutional neural networks in TensorFlow.js and Node.js. We've also seen how to train these networks on the MNIST dataset.