Theano教程：使用训练模型进行预测

Question

我是Theano的初学者。当我运行Logistic回归的代码（ http://deeplearning.net/tutorial/code/logistic_sgd.py）时，我有一个问题，在代码的最后，有一个预测功能：

def predict():
    """
    An example of how to load a trained model and use it
    to predict labels.
    """

    # load the saved model
    classifier = pickle.load(open('best_model.pkl'))

    # compile a predictor function
    predict_model = theano.function(
        inputs=[classifier.input],
        outputs=classifier.y_pred)

    # We can test it on some examples from test test
    dataset='mnist.pkl.gz'
    datasets = load_data(dataset)
    test_set_x, test_set_y = datasets[2]
    test_set_x = test_set_x.get_value()

    predicted_values = predict_model(test_set_x[:10])
    print("Predicted values for the first 10 examples in test set:")
    print(predicted_values)

它可以重新加载模型并预测新数据的标签。 但我无法得到预测的输出。当我运行所有代码时，我的输出是这样的

/usr/bin/python2.7 /home/daiy/PycharmProjects/MNISTdigitclassification/logistic-regression-code.py
... loading data
... building the model
... training the model
epoch 1, minibatch 83/83, validation error 12.458333 %
epoch 2, minibatch 83/83, validation error 11.010417 % 
.
.
.   
epoch 73, minibatch 83/83, validation error 7.500000 %
Optimization complete with best validation score of 7.500000 %,
The code run for 74 epochs, with 3.189913 epochs/sec
The code for file logistic-regression-code.py ran for 23.2s

    Process finished with exit code 0

我在pycharm中调试它，它没有显示错误。当我创建一个新的py.file时，代码如下：

import pickle,numpy
import theano
import six.moves.cPickle as pickle
import gzip
import os
import theano.tensor as T

def load_data(dataset):
    ''' Loads the dataset

    :type dataset: string
    :param dataset: the path to the dataset (here MNIST)
    '''

    #############
    # LOAD DATA #
    #############

    # Download the MNIST dataset if it is not present
    data_dir, data_file = os.path.split(dataset)
    if data_dir == "" and not os.path.isfile(dataset):
        # Check if dataset is in the data directory.
        new_path = os.path.join(
            os.path.split(__file__)[0],
            "..",
            "data",
            dataset
        )
        if os.path.isfile(new_path) or data_file == 'mnist.pkl.gz':
            dataset = new_path



    print('... loading data')

    with gzip.open(dataset, 'rb') as f:
        try:
            train_set, valid_set, test_set = pickle.load(f, encoding='latin1')
        except:
            train_set, valid_set, test_set = pickle.load(f)

    def shared_dataset(data_xy, borrow=True):
        """ Function that loads the dataset into shared variables

        The reason we store our dataset in shared variables is to allow
        Theano to copy it into the GPU memory (when code is run on GPU).
        Since copying data into the GPU is slow, copying a minibatch everytime
        is needed (the default behaviour if the data is not in a shared
        variable) would lead to a large decrease in performance.
        """
        data_x, data_y = data_xy
        shared_x = theano.shared(numpy.asarray(data_x,
                                               dtype=theano.config.floatX),
                                 borrow=borrow)
        shared_y = theano.shared(numpy.asarray(data_y,
                                               dtype=theano.config.floatX),
                                 borrow=borrow)
        # When storing data on the GPU it has to be stored as floats
        # therefore we will store the labels as ``floatX`` as well
        # (``shared_y`` does exactly that). But during our computations
        # we need them as ints (we use labels as index, and if they are
        # floats it doesn't make sense) therefore instead of returning
        # ``shared_y`` we will have to cast it to int. This little hack
        # lets ous get around this issue
        return shared_x, T.cast(shared_y, 'int32')

    test_set_x, test_set_y = shared_dataset(test_set)
    valid_set_x, valid_set_y = shared_dataset(valid_set)
    train_set_x, train_set_y = shared_dataset(train_set)


    rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y),
            (test_set_x, test_set_y)]
    return rval

dataset = 'mnist.pkl.gz'
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]

def predict():
    '''

    :return:
    '''

    classifier = pickle.load(open('best_model.pkl','rb'))

    predict_model = theano.function(inputs=[classifier.input],outputs=classifier.y_pred)

    dataset = 'mnist.pkl.gz'
    datasets = load_data(dataset)
    test_set_x ,test_set_y = datasets[2]
    test_set_x = test_set_x.get_value()

    predicted_values = predict_model(test_set_x[:10])
    print('predicted values for the first 10 examples in test data:')
    print predicted_values

输出是：

/usr/bin/python2.7 /home/daiy/PycharmProjects/MNISTdigitclassification/yuce.py
... loading data

Process finished with exit code0

仍然没有预测输出。但是当我调试它时，它是：

/usr/bin/python2.7 /raid/pycharm-community-2016.2.3/helpers/pydev/pydevd.py --multiproc --qt-support --client 127.0.0.1 --port 43960 --file /home/daiy/PycharmProjects/MNISTdigitclassification/yuce.py
warning: Debugger speedups using cython not found. Run '"/usr/bin/python2.7" "/raid/pycharm-community-2016.2.3/helpers/pydev/setup_cython.py" build_ext --inplace' to build.
pydev debugger: process 4506 is connecting

Connected to pydev debugger (build 162.1967.10)
... loading data
Exception TypeError: TypeError("'NoneType' object is not callable",) in <function _remove at 0x7fe6444f1668> ignored

Process finished with exit code 0

我认为这是一个简单的问题，但我找不到答案。 我在ubuntun14.04.1中使用python2.7。

Answer 1

您可能希望尝试更改预测功能的以下部分
（如箭头所示， - >）

def predict():
    """
    An example of how to load a trained model and use it
    to predict labels.
    """

    # load the saved model
    classifier = pickle.load(open('best_model.pkl'))

    # compile a predictor function
    predict_model = theano.function(
    inputs=[classifier.input],
    outputs=classifier.y_pred)

    # We can test it on some examples from test test
    dataset='mnist.pkl.gz'
    datasets = load_data(dataset)
    test_set_x, test_set_y = datasets[2]
    test_set_x = test_set_x.get_value()

--> predicted_values = predict_model(test_set_x)
    print("Predicted values for the first 10 examples in test set:")         
--> return predicted_values