Python Tensorflow Slim:&x27；模块'；对象没有属性'；平方和'；

我现在正在学习教程。然而在

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

我似乎得到了

属性错误：“模块”对象没有属性“平方和”

。我已经在Ubuntu 16.04上安装了TF版本0.12head，CPU版本。我正在运行的完整代码如下所示：

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)

---

显然由于某种原因，它已经在最新的版本中被删除了，正如在上所看到的。我切换到

loss=slim.loss.mean_squared_error（预测、目标）

，这应该符合我的假设