python-kerasHow can I use Python Keras to create a machine learning model for the MNIST dataset?

To use Python Keras to create a machine learning model for the MNIST dataset, you can follow the steps below:

1. Import the necessary libraries:

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

2. Load the data:

(x_train, y_train), (x_test, y_test) = mnist.load_data()

3. Preprocess the data:

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

4. Create the model:

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dense(10, activation='softmax'))

5. Compile the model:

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

6. Fit the model:

model.fit(x_train, y_train, batch_size=128, epochs=10, verbose=1)

7. Evaluate the model:

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

Output example

Test loss: 0.07912476190745235
Test accuracy: 0.9776

Helpful links

• Keras Documentation
• MNIST Dataset

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