TrainΒΆ

Here is an example on how to train a convolutional neural network to segment an image. The network is trained using one raw image and one that has been manually segmented.

Once the training is complete the network will be able to automatically segment a series of raw images.

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

%pylab inline

Populating the interactive namespace from numpy and matplotlib

import dxchange

Image data I/O in xlearn is supported by DXchange.

import matplotlib.pyplot as plt

matplotlib provide plotting of the result in this notebook.

Install xlearn then:

from xlearn.transform import train
from xlearn.transform import model

batch_size = 800
nb_epoch = 10
dim_img = 20
nb_filters = 32
nb_conv = 3
patch_step = 4
patch_size = (dim_img, dim_img)

img_x = dxchange.read_tiff('../../test/test_data/training_input.tiff')
img_y = dxchange.read_tiff('../../test/test_data/training_output.tiff')

plt.imshow(img_x, cmap='Greys_r')
plt.show()
../../_images/train_4_0.png 
plt.imshow(img_y, cmap='Greys_r')
plt.show()
../../_images/train_5_0.png 
mdl = train(img_x, img_y, patch_size, patch_step, dim_img, nb_filters, nb_conv, batch_size, nb_epoch)
mdl.save_weights('training_weights.h5')

Epoch 1/10
26068/26068 [==============================] - 39s - loss: 0.4458
Epoch 2/10
26068/26068 [==============================] - 39s - loss: 0.2074
Epoch 3/10
26068/26068 [==============================] - 39s - loss: 0.1607
Epoch 4/10
26068/26068 [==============================] - 39s - loss: 0.1428
Epoch 5/10
26068/26068 [==============================] - 39s - loss: 0.1321
Epoch 6/10
26068/26068 [==============================] - 39s - loss: 0.1258
Epoch 7/10
26068/26068 [==============================] - 39s - loss: 0.1244
Epoch 8/10
26068/26068 [==============================] - 39s - loss: 0.1169
Epoch 9/10
26068/26068 [==============================] - 39s - loss: 0.1135
Epoch 10/10
26068/26068 [==============================] - 39s - loss: 0.1106