machine learning - TfLearn Confusion Matrix training terminated on std::bad_alloc -


having problems working out how confusion matrix when using tflearn creation of convolutional neural network. code have far follows:

 __future__ import division, print_function, absolute_import      import tflearn     tflearn.layers.core import input_data, dropout, fully_connected     tflearn.layers.conv import conv_2d, max_pool_2d     tflearn.layers.normalization import local_response_normalization     tflearn.layers.estimator import regression      sklearn.metrics import confusion_matrix     import h5py      hdf5test = h5py.file('/path', 'r')      x = hdf5test['x']     y = hdf5test['y']      # building convolutional network     network = input_data(shape=[none, 240, 320, 3], name='input')     network = conv_2d(network, 32, 3, activation='relu', regularizer="l2")     network = max_pool_2d(network, 2)     network = local_response_normalization(network)     network = conv_2d(network, 64, 3, activation='relu', regularizer="l2")     network = max_pool_2d(network, 2)     network = local_response_normalization(network)     network = fully_connected(network, 128, activation='tanh')     network = dropout(network, 0.8)     network = fully_connected(network, 256, activation='tanh')     network = dropout(network, 0.8)     network = fully_connected(network, 2, activation='softmax')     network = regression(       network,       optimizer='sgd',       learning_rate=0.01,       loss='categorical_crossentropy',       name='target'     )      # training     model = tflearn.dnn(network, tensorboard_verbose=0)     model.load('/path.tflearn')      predictions = model.predict(x)     print(confusion_matrix(y, predictions))

everytime try run code given following error message:

terminate called after throwing instance of 'std::bad_alloc' what(): std::bad_alloc aborted (core dumped)

any advice great, new tflearn.

in end, found due size of data trying predict. fixed inserting within loop:

# predict classes predictions = [] count = 0 length = len(x) line in x:   print('line ' + str(count) + ' of ' + str(length))   tmp = model.predict_label([line])   predictions.append(tmp[0])   count += 1

with formatting able use sklearn produce confusion matrix:

predictedclasses = np.argmin(predictions, axis=1) actualclasses = np.argmax(y, axis=1) print(confusion_matrix(actualclasses, predictedclasses))

this approach worked me , may work you... think tflearn should streamlined approach production of confusion matrix other don't have same problem.


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