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')