Tensorflow Slim：'module'对象没有属性'sum_of_squares'

Question

我正在按照TF Slim的教程进行操作。然而在

loss = slim.losses.sum_of_squares(predictions, targets)

我似乎越来越AttributeError: 'module' object has no attribute 'sum_of_squares'.我已经安装了在 Ubuntu 16.04 上运行的 TF 版本 0.12head，CPU 版本。我正在运行的完整代码如下：

import matplotlib.pyplot as plt
import math
import numpy as np
import tensorflow as tf
import time
from datasets import dataset_utils
# Main slim library
slim = tf.contrib.slim
def 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_points

with 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)

Answer 1

显然，由于某种原因，它在最新版本中已被删除，如GitHub Repo所示。我切换到loss = slim.losses.mean_squared_error(predictions, targets)它应该符合我假设的目的。