Making predictions with a TensorFlow model

Question

I followed the given mnist tutorials and was able to train a model and evaluate its accuracy. However, the tutorials don\'t show how to make predictions given a model. I\'m

Answer 1

2.0 Compatible Answer: Suppose you have built a Keras Model as shown below:

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

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

Then Train and Evaluate the Model using the below code:

model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

After that, if you want to predict the class of a particular image, you can do it using the below code:

predictions_single = model.predict(img)

If you want to predict the classes of a set of Images, you can use the below code:

predictions = model.predict(new_images)

where new_images is an Array of Images.

For more information, refer this Tensorflow Tutorial.