sample_dimension = training_set.get_value(borrow=True).shape[1]
label_dimension = training_labels.get_value(borrow=True).shape[1]
print_flush("... sample dimension %d" % sample_dimension)
print_flush("... label dimension %d" % label_dimension)
stacked_autoencoder = StackedAutoencoder(numpy_rng=numpy_rng, n_ins=sample_dimension, n_outs=label_dimension,
hidden_layer_sizes=hidden_layer_sizes, tied_weights=tied_weights,
sigmoid_compressions=sigmoid_compressions,
sigmoid_reconstructions=sigmoid_reconstructions,
supervised_sigmoid_activation=supervised_sigmoid_activation)
print_flush("... getting the pre-training functions")
pretraining_fns = stacked_autoencoder.pretraining_functions(training_set=training_set, batch_size=batch_size)
if ENABLE_FINE_TUNING:
print_flush("... getting the fine-tune function")
if fine_tune_supervised:
finetune_fn, validate_model = stacked_autoencoder.finetune_functions(training_set=training_set,
training_labels=training_labels,
test_set=test_set, test_labels=test_labels,
batch_size=batch_size,
learning_rate=fine_tune_learning_rate)
else:
finetune_fn, validate_model = stacked_autoencoder.finetune_functions_unsupervised(training_set=training_set,
test_set=test_set,
batch_size=batch_size,
learning_rate=fine_tune_learning_rate)
