TfLearn Confusion Matrix training completed at std :: bad_alloc

Question

Cannot decide how to get the confusion matrix when using TFLearn to create a convolutional neural network. The code I still have is as follows:

 from __future__ import division, print_function, absolute_import

    import tflearn
    from tflearn.layers.core import input_data, dropout, fully_connected
    from tflearn.layers.conv import conv_2d, max_pool_2d
    from tflearn.layers.normalization import local_response_normalization
    from tflearn.layers.estimator import regression

    from 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))

Every time I try to run this code, I get the following error message:

termination of the call after calling the instance 'std :: bad_alloc' what (): std :: bad_alloc Canceled (the kernel is reset)

Any advice would be great, new to TFLearn.

Answer 1

, , , . , :

# Predict Classes
predictions = []
count = 0
length = len(X)
for line in X:
  print('Line ' + str(count) + ' of ' + str(length))
  tmp = model.predict_label([line])
  predictions.append(tmp[0])
  count += 1

Sklearn :

predictedClasses = np.argmin(predictions, axis=1)
actualClasses = np.argmax(Y, axis=1)
print(confusion_matrix(actualClasses, predictedClasses))

... , TFLearn , .