Python nets.resnet_v2.resnet_v2_152() Examples

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Example #1
Source File: attack_iter.py    From Translation-Invariant-Attacks with Apache License 2.0 4 votes vote down vote up 
def graph(x, y, i, x_max, x_min, grad):
    eps = 2.0 * FLAGS.max_epsilon / 255.0
    num_iter = FLAGS.num_iter
    alpha = eps / num_iter
    momentum = FLAGS.momentum
    num_classes = 1001

    # should keep original x here for output

    with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
        logits_v3, end_points_v3 = inception_v3.inception_v3(
            input_diversity(x), num_classes=num_classes, is_training=False)

    with slim.arg_scope(inception_v4.inception_v4_arg_scope()):
        logits_v4, end_points_v4 = inception_v4.inception_v4(
            input_diversity(x), num_classes=num_classes, is_training=False)

    with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
        logits_res_v2, end_points_res_v2 = inception_resnet_v2.inception_resnet_v2(
            input_diversity(x), num_classes=num_classes, is_training=False, reuse=True)

    with slim.arg_scope(resnet_v2.resnet_arg_scope()):
        logits_resnet, end_points_resnet = resnet_v2.resnet_v2_152(
            input_diversity(x), num_classes=num_classes, is_training=False)

    logits = (logits_v3 + logits_v4 + logits_res_v2 + logits_resnet) / 4
    auxlogits = (end_points_v3['AuxLogits'] + end_points_v4['AuxLogits'] + end_points_res_v2['AuxLogits']) / 3
    cross_entropy = tf.losses.softmax_cross_entropy(y,
                                                    logits,
                                                    label_smoothing=0.0,
                                                    weights=1.0)
    cross_entropy += tf.losses.softmax_cross_entropy(y,
                                                     auxlogits,
                                                     label_smoothing=0.0,
                                                     weights=0.4)
    noise = tf.gradients(cross_entropy, x)[0]
    noise = tf.nn.depthwise_conv2d(noise, stack_kernel, strides=[1, 1, 1, 1], padding='SAME')
    noise = noise / tf.reduce_mean(tf.abs(noise), [1, 2, 3], keep_dims=True)
    noise = momentum * grad + noise
    x = x + alpha * tf.sign(noise)
    x = tf.clip_by_value(x, x_min, x_max)
    i = tf.add(i, 1)
    return x, y, i, x_max, x_min, noise