Neural Networks in Keras

Here are some core concepts you need to know for working with Keras. All Keras exercises will be performed in JUPYTER workspaces, using python 3.5, Tensorflow 1.3, and Keras 2.09. More information on using JUPYTER in workspaces, can be found earlier in the term in the Workspaces lesson.

Sequential Model

    from keras.models import Sequential

    # Create the Sequential model
    model = Sequential()

The keras.models.Sequential class is a wrapper for the neural network model. It provides common functions like fit() , evaluate() , and compile() . We'll cover these functions as we get to them. Let's start looking at the layers of the model.

See the documentation for keras.models.Sequential in Keras 2.09 here .

Layers

A Keras layer is just like a neural network layer. There are fully connected layers, max pool layers, and activation layers. You can add a layer to the model using the model's add() function. For example, a simple model would look like this:

    from keras.models import Sequential
    from keras.layers.core import Dense, Activation, Flatten

    # Create the Sequential model
    model = Sequential()

    #1st Layer - Add a flatten layer
    model.add(Flatten(input_shape=(32, 32, 3)))

    #2nd Layer - Add a fully connected layer
    model.add(Dense(100))

    #3rd Layer - Add a ReLU activation layer
    model.add(Activation('relu'))

    #4th Layer - Add a fully connected layer
    model.add(Dense(60))

    #5th Layer - Add a ReLU activation layer
    model.add(Activation('relu'))

Keras will automatically infer the shape of all layers after the first layer. This means you only have to set the input dimensions for the first layer.

The first layer from above, model.add(Flatten(input_shape=(32, 32, 3))) , sets the input dimension to (32, 32, 3) and output dimension to (3072=32 x 32 x 3). The second layer takes in the output of the first layer and sets the output dimensions to (100). This chain of passing output to the next layer continues until the last layer, which is the output of the model.

Quiz

In this quiz you will build a multi-layer feedforward neural network to classify traffic sign images using Keras.

1. Set the first layer to a Flatten() layer with the input_shape set to (32, 32, 3).
2. Set the second layer to a Dense() layer with an output width of 128.
3. Use a ReLU activation function after the second layer.
4. Set the output layer width to 5, because for this data set there are only 5 classes.
5. Use a softmax activation function after the output layer.
6. Train the model for 3 epochs. You should be able to get over 50% training accuracy.

To get started, review the Keras documentation about models and layers.
The Keras example of a Multi-Layer Perceptron network is similar to what you need to do here. Use that as a guide, but keep in mind that there are a number of differences.

Data Download

The data set used in these quizzes can be downloaded here .