import numpy
import theano
import theano.tensor as T

from mlpv import *
if __name__ == '__main__':
  

    train_mlp()  

    '''

    prob = test2.classify_image(img=image, normMean=0.5, norm_std=1.0)
    plt.imshow(1-prob)
    plt.show()
    mahotas.imsave('tmp_output_05.png', np.uint8((1-prob)*255))

    hl = test2.hiddenLayers[0]
    plt.imshow(hl.visualize_filters())
    plt.show()
    mahotas.imsave('filter_output_05.png', np.uint8(hl.visualize_filters()))

    plt.plot(np.array(test2.trainingCost), label='training')
    plt.plot(np.array(test2.validationError), label='validation')
    plt.legend()
    plt.show()

    if len(test2.validationError) > 5000:
        plt.plot(np.array(test2.trainingCost)[-5000:], label='training')
        plt.plot(np.array(test2.validationError)[-5000:], label='validation')
        plt.legend()
        plt.show()


    print "best validation score: ", test2.validationError[-1]
    '''

import numpy
import theano
import theano.tensor as T

from mlpv import *
if __name__ == '__main__':
  

    train_mlp()  

    '''

    prob = test2.classify_image(img=image, normMean=0.5, norm_std=1.0)
    plt.imshow(1-prob)
    plt.show()
    mahotas.imsave('tmp_output_05.png', np.uint8((1-prob)*255))

    hl = test2.hiddenLayers[0]
    plt.imshow(hl.visualize_filters())
    plt.show()
    mahotas.imsave('filter_output_05.png', np.uint8(hl.visualize_filters()))

    plt.plot(np.array(test2.trainingCost), label='training')
    plt.plot(np.array(test2.validationError), label='validation')
    plt.legend()
    plt.show()

    if len(test2.validationError) > 5000:
        plt.plot(np.array(test2.trainingCost)[-5000:], label='training')
        plt.plot(np.array(test2.validationError)[-5000:], label='validation')
        plt.legend()
        plt.show()


    print "best validation score: ", test2.validationError[-1]
    '''

prob = test2.classify_image(img=image, normMean=0.5, norm_std=1.0)     plt.imshow(1-prob)     plt.show()     mahotas.imsave('tmp_output_05.png', np.uint8((1-prob)*255))      hl = test2.hiddenLayers[0]     plt.imshow(hl.visualize_filters())     plt.show()     mahotas.imsave('filter_output_05.png', np.uint8(hl.visualize_filters()))      plt.plot(np.array(test2.trainingCost), label='training')     plt.plot(np.array(test2.validationError), label='validation')     plt.legend()     plt.show()      if len(test2.validationError) > 5000:         plt.plot(np.array(test2.trainingCost)[-5000:], label='training')         plt.plot(np.array(test2.validationError)[-5000:], label='validation')         plt.legend()         plt.show()       print "best validation score: ", test2.validationError[-1]