8bf7c385c00990bf1d0de1b480336d8c1c9d74e8,train.py,,classifier,#,28

Before Change

        // Get images and labels
        with tf.device("/cpu:0"):
            images, labels = DATASET.distorted_inputs(ARGS.batch_size)
        labels = tf.squeeze(labels)
        log_io(images)
        // Build a Graph that computes the logits predictions from the
        // inference model.
        is_training_, logits = MODEL.get(

After Change

        // Train accuracy ops
        with tf.variable_scope("accuracy"):
            // handle fully convolutional classifiers
            logits_shape = logits.shape
            if len(logits_shape) == 4 and logits_shape[1:3] == [1, 1]:
                top_k_logits = tf.squeeze(logits, [1, 2])
            else:
                top_k_logits = logits
            top_k_op = tf.nn.in_top_k(top_k_logits, labels, 1)
            train_accuracy = tf.reduce_mean(tf.cast(top_k_op, tf.float32))
            // General validation summary
            accuracy_value_ = tf.placeholder(tf.float32, shape=())

Instances