shuffle_batches=shuffle_batches,
shuffle_examples=shuffle_examples)
print 'Building'
trainer.build()
print 'Training'
start_time = time.clock()
trainer.train()
end_time = time.clock()
print 'The training took %i seconds' % (end_time - start_time)
print 'Display weights'
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from filter_plot import tile_raster_images
W = np.transpose(model.layer[0].W.get_value())
# histogram = np.histogram(W,bins=1000,range=(-.2,.2))
# np.savetxt(core_path + "_hist0.csv", histogram[0], delimiter=",")
# np.savetxt(core_path + "_hist1.csv", histogram[1], delimiter=",")
W = tile_raster_images(W, (28, 28), (4, 4), (2, 2))
plt.imshow(W, cmap=cm.Greys_r)
# plt.show()
plt.savefig(core_path + '_features.png')
monitor_step = 3
model = PI_MNIST_model(rng = rng)
trainer = Trainer(rng = rng,
train_set = train_set, valid_set = valid_set, test_set = test_set,
model = model,
LR = LR, LR_decay = 0.98, LR_fin = LR/100.,
batch_size = batch_size, gpu_batches = gpu_batches,
n_epoch = n_epoch, monitor_step = monitor_step,
shuffle_batches = False, shuffle_examples = True)
print 'Building'
trainer.build()
print 'Training'
trainer.train()
print 'Display weights'
W = np.transpose(model.layer[0].W.get_value())
print np.max((W==0.))
W = tile_raster_images(W,(28,28),(10,10),(2, 2))
plt.imshow(W, cmap = cm.Greys_r)
plt.show()
end_time = time.clock()
print 'The code ran for %i seconds'%(end_time - start_time)
n_epoch = n_epoch, monitor_step = monitor_step,
shuffle_batches = shuffle_batches, shuffle_examples = shuffle_examples)
print 'Building'
trainer.build()
print 'Training'
start_time = time.clock()
trainer.train()
end_time = time.clock()
print 'The training took %i seconds'%(end_time - start_time)
print 'Display weights'
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from filter_plot import tile_raster_images
W = np.transpose(model.layer[0].W.get_value())
# histogram = np.histogram(W,bins=1000,range=(-.2,.2))
# np.savetxt(core_path + "_hist0.csv", histogram[0], delimiter=",")
# np.savetxt(core_path + "_hist1.csv", histogram[1], delimiter=",")
W = tile_raster_images(W,(28,28),(4,4),(2, 2))
plt.imshow(W, cmap = cm.Greys_r)
# plt.show()
plt.savefig(core_path + '_features.png')
