Logistic regression with Keras

Keras is a high-level library that is available as part of TensorFlow. In this section, we will rebuild the same model we built earlier with TensorFlow core with Keras:

1. Keras takes data in a different format, and so we must first reformat the data using datasetslib:

x_train_im = mnist.load_images(x_train)

x_train_im, x_test_im = x_train_im / 255.0, x_test / 255.0

In the preceding code, we are loading the training images in memory before both the training and test images are scaled, which we do by dividing them by 255.

1. Then, we build the model:

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])

1. Compile the model with the sgd optimizer. Set the categorical entropy as the loss function and the accuracy as a metric to test the model:

model.compile(optimizer='sgd',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

1. Train the model for 5 epochs with the training set of images and labels:

model.fit(x_train_im, y_train, epochs=5)

Epoch 1/5
60000/60000 [==============================] - 3s 45us/step - loss: 0.7874 - acc: 0.8095
Epoch 2/5
60000/60000 [==============================] - 3s 42us/step - loss: 0.4585 - acc: 0.8792
Epoch 3/5
60000/60000 [==============================] - 2s 42us/step - loss: 0.4049 - acc: 0.8909
Epoch 4/5
60000/60000 [==============================] - 3s 42us/step - loss: 0.3780 - acc: 0.8965
Epoch 5/5
60000/60000 [==============================] - 3s 42us/step - loss: 0.3610 - acc: 0.9012
10000/10000 [==============================] - 0s 24us/step

1. Evaluate the model with the test data:

model.evaluate(x_test_im, nputil.argmax(y_test))

We get the following evaluation scores as output:

[0.33530342621803283, 0.9097]

Wow! Using Keras, we can achieve higher accuracy. We achieved approximately 90% accuracy. This is because Keras internally sets many optimal values for us so that we can quickly start building models.