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Ich bin der Anfänger von Theano.And wenn ich die logistischen Regression des Code ausführen (http://deeplearning.net/tutorial/code/logistic_sgd.py),i haben ein Problem, am Ende des Codes gibt eine Vorhersagefunktion:Theano Tutorial: Vorhersage ein geschultes Modell unter Verwendung von
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)
Es kann das Modell neu zu laden und vorhersagen Etikett von neuen Daten. Aber ich kann die Ausgabe der Vorhersage .My Ausgabe wie diese nicht erhalten ist, wenn ich die alle
/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
Debug-Code ausführen es in pycharm, es zeigt keinen fehler. Und wenn ich cre aß einen neuen py.file, Code wie folgt:
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
der Ausgang ist:
/usr/bin/python2.7 /home/daiy/PycharmProjects/MNISTdigitclassification/yuce.py
... loading data
Process finished with exit code0
es noch keine prädiktive output.but ist, wenn ich das Debuggen, es ist:
/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
ich denke, ist eine einfache Frage, aber ich kann die Antwort nicht finden. und ich benutze python2.7 in ubuntun14.04.1.