def build_model(input_data_tensor, input_label_tensor):
num_classes = config["num_classes"]
images = tf.image.resize_images(input_data_tensor, [224, 224])
logits = vgg.build(images, n_classes=num_classes, training=True)
probs = tf.nn.softmax(logits)
loss = L.loss(logits, tf.one_hot(input_label_tensor, num_classes))
error_top5 = L.topK_error(probs, input_label_tensor, K=5)
error_top1 = L.topK_error(probs, input_label_tensor, K=1)
# you must return a dictionary with at least the "loss" as a key
return dict(loss=loss,
logits=logits,
error_top5=error_top5,
error_top1=error_top1)
def build_model(input_data_tensor, input_label_tensor):
num_classes = config["num_classes"]
weight_decay = config["weight_decay"]
images = tf.image.resize_images(input_data_tensor, [224, 224])
logits = vgg.build(images, n_classes=num_classes, training=True)
probs = tf.nn.softmax(logits)
loss_classify = L.loss(logits, tf.one_hot(input_label_tensor, num_classes))
loss_weight_decay = tf.reduce_sum(tf.stack([tf.nn.l2_loss(i) for i in tf.get_collection('variables')]))
loss = loss_classify + weight_decay*loss_weight_decay
error_top5 = L.topK_error(probs, input_label_tensor, K=5)
error_top1 = L.topK_error(probs, input_label_tensor, K=1)
# you must return a dictionary with loss as a key, other variables
return dict(loss=loss,
probs=probs,
logits=logits,
error_top5=error_top5,
error_top1=error_top1)