def fit(data, filter_sizes, out_channels, strides, paddings,
smooth_weights, sparse_weights, readout_sparse_weight,
learning_rate=0.001, max_iter=10000, val_steps=50,
early_stopping_steps=10):
'''Fit CNN model.
Parameters:
data: Dataset object (see load_data())
filter_sizes: Filter sizes (list containing one number per conv layer)
out_channels: Number of output channels (list; one number per conv layer)
strides: Strides (list; one number per conv layer)
paddings: Paddings (list; one number per conv layer; VALID|SAME)
smooth_weights: Weights for smoothness regularizer (list; one number per conv layer)
sparse_weights: Weights for group sparsity regularizer (list; one number per conv layer)
readout_sparse_weight: Sparisty of readout weights (scalar)
learning_rate: Learning rate (default: 0.001)
max_iter: Max. number of iterations (default: 10000)
val_steps: Validation interval (number of iterations; default: 50)
early_stopping_steps: Tolerance for early stopping. Will stop optimizing
after this number of validation steps without decrease of loss.
Output:
cnn: A fitted ConvNet object
'''
cnn = ConvNet(data, log_dir='cnn', log_hash='manual')
cnn.build(filter_sizes=filter_sizes,
out_channels=out_channels,
strides=strides,
paddings=paddings,
smooth_weights=smooth_weights,
sparse_weights=sparse_weights,
readout_sparse_weight=readout_sparse_weight)
for lr_decay in range(3):
training = cnn.train(max_iter=max_iter,
val_steps=val_steps,
early_stopping_steps=early_stopping_steps,
learning_rate=learning_rate)
for (i, (logl, readout_sparse, conv_sparse, smooth, total_loss, pred)) in training:
print('Step %d | Loss: %.2f | Poisson: %.2f | L1 readout: %.2f | Sparse: %.2f | Smooth: %.2f | Var(y): %.3f' % \
(i, total_loss, logl, readout_sparse, conv_sparse, smooth, np.mean(np.var(pred, axis=0))))
learning_rate /= 3
print('Reducing learning rate to %f' % learning_rate)
print('Done fitting')
return cnn
