In [1]:

    

import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist

import syft as sy


    

In [2]:

    

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255


    

In [3]:

    

num_classes = 10
input_shape = (1, 28, 28, 1)
output_shape = (1, num_classes)


    

In [4]:

    

client = sy.TFEWorker()

alice = sy.TFEWorker(host='localhost:4000')
bob = sy.TFEWorker(host='localhost:4001')
carol = sy.TFEWorker(host='localhost:4002')
cluster = sy.TFECluster(alice, bob, carol)

client.connect_to_model(input_shape, output_shape, cluster)


    

    




INFO:tf_encrypted:Starting session on target 'grpc://localhost:4000' using config graph_options {
}

In [5]:

    

# User inputs
num_tests = 3
images, expected_labels = x_test[:num_tests], y_test[:num_tests]


    

In [6]:

    

for image, expected_label in zip(images, expected_labels):

    res = client.query_model(image.reshape(1, 28, 28, 1))
    predicted_label = np.argmax(res)

    print("The image had label {} and was {} classified as {}".format(
        expected_label,
        "correctly" if expected_label == predicted_label else "wrongly",
        predicted_label))


    

    




The image had label 7 and was correctly classified as 7
The image had label 2 and was correctly classified as 2
The image had label 1 and was correctly classified as 1