def run(**kwargs):
allow_plots = ALLOW_PLOTS
continue_training = CONTINUE_TRAINING
inference_cpu_only = INFERENCE_CPU_ONLY
use_batchnorm = USE_BATCHNORM
for k in kwargs.keys():
if k == 'allow_plots':
allow_plots = kwargs[k]
elif k == 'continue_training':
continue_training = kwargs[k]
elif k == 'inference_cpu_only':
inference_cpu_only = kwargs[k]
elif k == 'use_batchnorm':
use_batchnorm = kwargs[k]
else:
logger.warn('Keyword \'%s\' is unknown.' % k)
logger.info('### Loading dataset ...')
data = MNISTData(config.dataset_path, use_one_hot=True)
# Important! Let the network know, which dataset to use.
shared.data = data
logger.info('### Loading dataset ... Done')
logger.info('### Build, train and test network ...')
train_net = setup_network(allow_plots,
continue_training,
inference_cpu_only,
use_batchnorm,
mode='train')
train_net.train(num_iter=10001)
test_net = setup_network(allow_plots,
continue_training,
inference_cpu_only,
use_batchnorm,
mode='inference')
test_net.test()
if allow_plots:
# Example Test Samples
sample_batch = data.next_test_batch(8)
predictions = test_net.run(sample_batch[0])
shared.data.plot_samples('Example MNIST Predictions',
sample_batch[0],
outputs=sample_batch[1],
predictions=predictions,
interactive=True)
logger.info('### Build, train and test network ... Done')
def run(**kwargs):
allow_plots = ALLOW_PLOTS
for k in kwargs.keys():
if k == 'allow_plots':
allow_plots = kwargs[k]
else:
logger.warn('Keyword \'%s\' is unknown.' % k)
logger.info('### Loading dataset ...')
data = MNISTData(config.dataset_path)
# Important! Let the network know, which dataset to use.
shared.data = data
logger.info('### Loading dataset ... Done')
logger.info('### Build, train and test network ...')
# Train the network
train_net = SimpleAE(mode='train')
train_net.allow_plots = allow_plots
train_net.build()
train_net.train()
# Test the network
test_net = SimpleAE(mode='inference')
test_net.allow_plots = allow_plots
test_net.build()
test_net.test()
if allow_plots:
# Feed a random test sample through the network and display the output
# for the user.
sample = data.next_test_batch(1)
net_out = test_net.run(sample[0])
fig = plt.figure()
plt.ion()
plt.suptitle('Sample Image')
ax = fig.add_subplot(1,2,1)
ax.set_axis_off()
ax.imshow(np.squeeze(sample[0].reshape(data.in_shape)),
vmin=-1.0, vmax=1.0)
ax.set_title('Input')
ax = fig.add_subplot(1,2,2)
ax.set_axis_off()
ax.imshow(np.squeeze(net_out.reshape(data.in_shape)),
vmin=-1.0, vmax=1.0)
ax.set_title('Output')
plt.show()
logger.info('### Build, train and test network ... Done')