TrainΒΆ

Here is an example on how to train a convolutional neural network to identify a tomographic reconstructed image that has the best center.

The network is trained using one image off center and the best centered reconstruction. Once the training is complete the network will be able to evaulate a series of reconstructed images with different rotation center and select the one with the best center.

You can download the python scritp here or the Jupyter notebook here

To run this example please download the test data from the classify_train folder at url

%pylab inline

Populating the interactive namespace from numpy and matplotlib

import dxchange
import numpy as np
from xlearn.utils import nor_data
from xlearn.utils import extract_3d
from xlearn.utils import img_window
from xlearn.classify import train

Using Theano backend.
Using gpu device 0: Tesla M2050 (CNMeM is disabled, cuDNN not available)

np.random.seed(1337)
dim_img = 128
patch_size = (dim_img, dim_img)
batch_size = 50
nb_classes = 2
nb_epoch = 12

number of convolutional filters to use

nb_filters = 32

size of pooling area for max pooling

nb_pool = 2

convolution kernel size

nb_conv = 3

fname = '../../test/test_data/1038.tiff'
img_x = dxchange.read_tiff(fname)

plt.imshow(img_x, cmap='Greys_r')
plt.clim(-0.0005,0.0028)
plt.show()
../../_images/classify_train_10_0.png 
ind_uncenter1 = range(1038, 1047)
ind_uncenter2 = range(1049, 1057)
uncenter1 = dxchange.read_tiff_stack(fname, ind=ind_uncenter1, digit=4)
uncenter2 = dxchange.read_tiff_stack(fname, ind=ind_uncenter2, digit=4)
uncenter = np.concatenate((uncenter1, uncenter2), axis=0)
uncenter = nor_data(uncenter)

uncenter = img_window(uncenter[:, 360:1460, 440:1440], 200)

uncenter_patches = extract_3d(uncenter, patch_size, 1)

np.random.shuffle(uncenter_patches)

center_img = dxchange.read_tiff('../../test/test_data/1048.tiff')

plt.imshow(center_img, cmap='Greys_r')
plt.clim(-0.0005,0.0028)
plt.show()
../../_images/classify_train_19_0.png 
center_img = nor_data(center_img)

center_img = img_window(center_img[360:1460, 440:1440], 400)
center_patches = extract_3d(center_img, patch_size, 1)
np.random.shuffle(center_patches)

x_train = np.concatenate((uncenter_patches[0:50000], center_patches[0:50000]), axis=0)
x_test = np.concatenate((uncenter_patches[50000:60000], center_patches[50000:60000]), axis=0)
x_train = x_train.reshape(x_train.shape[0], 1, dim_img, dim_img)
x_test = x_test.reshape(x_test.shape[0], 1, dim_img, dim_img)
y_train = np.zeros(100000)
y_train[50000:99999] = 1
y_test = np.zeros(20000)
y_test[10000:19999] = 1

model = train(x_train, y_train, x_test, y_test, dim_img, nb_filters, nb_conv, batch_size, nb_epoch, nb_classes)

(100000, 1, 128, 128) (100000, 2) (20000, 1, 128, 128) (20000, 2)
Train on 100000 samples, validate on 20000 samples
Epoch 1/12
100000/100000 [==============================] - 836s - loss: 0.1251 - acc: 0.9604 - val_loss: 0.0726 - val_acc: 0.9704
Epoch 2/12
100000/100000 [==============================] - 835s - loss: 0.0085 - acc: 0.9977 - val_loss: 0.1675 - val_acc: 0.9311
Epoch 3/12
100000/100000 [==============================] - 835s - loss: 0.0045 - acc: 0.9989 - val_loss: 0.0155 - val_acc: 0.9949
Epoch 4/12
100000/100000 [==============================] - 832s - loss: 0.0034 - acc: 0.9990 - val_loss: 0.0090 - val_acc: 0.9976
Epoch 5/12
100000/100000 [==============================] - 834s - loss: 0.0018 - acc: 0.9995 - val_loss: 0.1212 - val_acc: 0.9512
Epoch 6/12
100000/100000 [==============================] - 835s - loss: 9.9921e-04 - acc: 0.9998 - val_loss: 0.0033 - val_acc: 0.9991
Epoch 7/12
100000/100000 [==============================] - 835s - loss: 5.3466e-04 - acc: 0.9999 - val_loss: 6.5040e-04 - val_acc: 1.0000
Epoch 8/12
100000/100000 [==============================] - 836s - loss: 7.6305e-04 - acc: 0.9998 - val_loss: 0.0016 - val_acc: 0.9997
Epoch 9/12
100000/100000 [==============================] - 833s - loss: 3.9566e-04 - acc: 0.9999 - val_loss: 8.2169e-04 - val_acc: 1.0000
Epoch 10/12
100000/100000 [==============================] - 835s - loss: 4.5675e-04 - acc: 0.9999 - val_loss: 8.0605e-04 - val_acc: 1.0000
Epoch 11/12
100000/100000 [==============================] - 833s - loss: 3.1511e-04 - acc: 1.0000 - val_loss: 8.0620e-04 - val_acc: 1.0000
Epoch 12/12
100000/100000 [==============================] - 833s - loss: 2.0671e-04 - acc: 1.0000 - val_loss: 8.0606e-04 - val_acc: 1.0000

Test score: 0.000806061122949
Test accuracy: 0.99995

model.save_weights('classify_training_weights.h5')