Shallow Neural Network in Keras

Build a shallow neural network to classify MNIST digits

Set seed for reproducibility



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import numpy as np
np.random.seed(42)

Load dependencies



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import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD

Load data



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(X_train, y_train), (X_test, y_test) = mnist.load_data()



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X_train.shape



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y_train.shape



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y_train[0:99]



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X_test.shape



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X_test[0]



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y_test.shape



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y_test[0]

Preprocess data



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X_train = X_train.reshape(60000, 784).astype('float32')
X_test = X_test.reshape(10000, 784).astype('float32')



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X_train /= 255
X_test /= 255



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X_test[0]



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n_classes = 10
y_train = keras.utils.to_categorical(y_train, n_classes)
y_test = keras.utils.to_categorical(y_test, n_classes)



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y_test[0]

Design neural network architecture



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# add architecture here



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model.summary()



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(64*784)



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(64*784)+64



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(10*64)+10

Configure model



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# compile model here

Train!



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# train model here



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model.evaluate(X_test, y_test)



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