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Learning the digits

You're going to build a model on the digits dataset, a sample dataset that comes pre-loaded with scikit learn. The digits dataset consist of 8x8 pixel handwritten digits from 0 to 9:

You want to distinguish between each of the 10 possible digits given an image, so we are dealing with multi-class classification.

The dataset has already been partitioned into X_train, y_train, X_test, and y_test, using 30% of the data as testing data. The labels are already one-hot encoded vectors, so you don't need to use Keras to_categorical() function.

Let's build this new model!

Diese Übung ist Teil des Kurses

Introduction to Deep Learning with Keras

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Anleitung zur Übung

  • Add a Dense layer of 16 neurons with relu activation and an input_shape that takes the total number of pixels of the 8x8 digit image.
  • Add a Dense layer with 10 outputs and softmax activation.
  • Compile your model with adam, categorical_crossentropy, and accuracy metrics.
  • Make sure your model works by predicting on X_train.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Instantiate a Sequential model
model = Sequential()

# Input and hidden layer with input_shape, 16 neurons, and relu 
model.add(Dense(____, input_shape = (____,), activation = ____))

# Output layer with 10 neurons (one per digit) and softmax
model.____(____)

# Compile your model
model.____(optimizer = ____, loss = ____, metrics = [____])

# Test if your model is well assembled by predicting before training
print(model.predict(____))
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