我正在按照TF Slim教程进行学习。然而，在以下代码处：
loss = slim.losses.sum_of_squares(predictions, targets)我遇到了AttributeError: 'module' object has no attribute 'sum_of_squares'的错误。我安装的是TF版本0.12head，运行在Ubuntu 16.04上，CPU版本。我运行的完整代码如下：
import matplotlib.pyplot as pltimport mathimport numpy as npimport tensorflow as tfimport timefrom datasets import dataset_utils# Main slim libraryslim = tf.contrib.slimdef regression_model(inputs, is_training=True, scope="deep_regression"): """Creates the regression model. Args: inputs: A node that yields a `Tensor` of size [batch_size, dimensions]. is_training: Whether or not we're currently training the model. scope: An optional variable_op scope for the model. Returns: predictions: 1-D `Tensor` of shape [batch_size] of responses. end_points: A dict of end points representing the hidden layers. """ with tf.variable_scope(scope, 'deep_regression', [inputs]): end_points = {} # Set the default weight _regularizer and acvitation for each fully_connected layer. with slim.arg_scope([slim.fully_connected], activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(0.01)): # Creates a fully connected layer from the inputs with 32 hidden units. net = slim.fully_connected(inputs, 32, scope='fc1') end_points['fc1'] = net # Adds a dropout layer to prevent over-fitting. net = slim.dropout(net, 0.8, is_training=is_training) # Adds another fully connected layer with 16 hidden units. net = slim.fully_connected(net, 16, scope='fc2') end_points['fc2'] = net # Creates a fully-connected layer with a single hidden unit. Note that the # layer is made linear by setting activation_fn=None. predictions = slim.fully_connected(net, 1, activation_fn=None, scope='prediction') end_points['out'] = predictions return predictions, end_pointswith tf.Graph().as_default(): # Dummy placeholders for arbitrary number of 1d inputs and outputs inputs = tf.placeholder(tf.float32, shape=(None, 1)) outputs = tf.placeholder(tf.float32, shape=(None, 1)) # Build model predictions, end_points = regression_model(inputs) # Print name and shape of each tensor. print "Layers" for k, v in end_points.iteritems(): print 'name = {}, shape = {}'.format(v.name, v.get_shape()) # Print name and shape of parameter nodes (values not yet initialized) print "\n" print "Parameters" for v in slim.get_model_variables(): print 'name = {}, shape = {}'.format(v.name, v.get_shape())def produce_batch(batch_size, noise=0.3): xs = np.random.random(size=[batch_size, 1]) * 10 ys = np.sin(xs) + 5 + np.random.normal(size=[batch_size, 1], scale=noise) return [xs.astype(np.float32), ys.astype(np.float32)]x_train, y_train = produce_batch(200)x_test, y_test = produce_batch(200)plt.scatter(x_train, y_train)def convert_data_to_tensors(x, y): inputs = tf.constant(x) inputs.set_shape([None, 1]) outputs = tf.constant(y) outputs.set_shape([None, 1]) return inputs, outputs# The following snippet trains the regression model using a sum_of_squares loss.ckpt_dir = '/tmp/regression_model/'with tf.Graph().as_default(): tf.logging.set_verbosity(tf.logging.INFO) inputs, targets = convert_data_to_tensors(x_train, y_train) # Make the model. predictions, nodes = regression_model(inputs, is_training=True) # Add the loss function to the graph. loss = slim.losses.sum_of_squares(predictions, targets) # The total loss is the uers's loss plus any regularization losses. total_loss = slim.losses.get_total_loss() # Specify the optimizer and create the train op: optimizer = tf.train.AdamOptimizer(learning_rate=0.005) train_op = slim.learning.create_train_op(total_loss, optimizer) # Run the training inside a session. final_loss = slim.learning.train( train_op, logdir=ckpt_dir, number_of_steps=5000, save_summaries_secs=5, log_every_n_steps=500)print("Finished training. Last batch loss:", final_loss)print("Checkpoint saved in %s" % ckpt_dir)回答：
显然，由于某些原因，在最新的构建中它已被移除，如GitHub Repo所示。我改用了loss = slim.losses.mean_squared_error(predictions, targets)，我认为这应该能达到同样的效果。
