all_scores.append(score)
if i % 500 == 0:
mean_scores = np.mean(scores)
print 'Batch #%d: %.6f' % (i, mean_scores)
scores = []
except KeyboardInterrupt:
pass
# Visualization
def marker(generator):
def marker_(params):
num_repeats_length = params['repeats'] if generator.unary else 1
return params['length'] + num_repeats_length
return marker_
markers = [{
'location': marker(generator),
'style': {
'color': 'red',
'ls': '-'
}
}]
dashboard = Dashboard(generator=generator, ntm_fn=ntm_fn, ntm_layer_fn=ntm_layer_fn, \
memory_shape=(128, 20), markers=markers, cmap='bone')
# Example
params = generator.sample_params()
dashboard.sample(**params)
for i, (example_input, example_output) in generator:
score = train_fn(example_input, example_output)
scores.append(score)
all_scores.append(score)
if i % 500 == 0:
mean_scores = np.mean(scores)
print 'Batch #%d: %.6f' % (i, mean_scores)
scores = []
except KeyboardInterrupt:
pass
# Visualization
def marker(generator):
def marker_(params):
num_repeats_length = params['repeats'] if generator.unary else 1
return params['length'] + num_repeats_length
return marker_
markers = [
{
'location': marker(generator),
'style': {'color': 'red', 'ls': '-'}
}
]
dashboard = Dashboard(generator=generator, ntm_fn=ntm_fn, ntm_layer_fn=ntm_layer_fn, \
memory_shape=(128, 20), markers=markers, cmap='bone')
# Example
params = generator.sample_params()
dashboard.sample(**params)