def perlin_experiment(numpy_rng, run_index):
recon = []
ims_train, ims_test, ims_valid = load_data(image_dataset_path)
spec_train, spec_test, spec_valid = load_data(spect_dataset_path)
print ""
print "Experiment 9: layer-wise model on images, initialized with perlin noise"
model = SdA(numpy_rng, 1024, [256, 64, 16], [0.3, 0.3, 0.3], perlin=True)
model.train(ims_train, max_epochs=200, max_runtime=7200, layer_wise=True)
save_path = os.path.join(model_save_dir,
"perlin_layerwise_images_only_%i.sda" % run_index)
print "Training complete, saving model at %s" % save_path
pickle.dump(model, open(save_path, 'wb'))
recon_error = model.test(ims_test)
print "Average reconstruction error: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 10: layer-wise model on audio, initialized with perlin noise"
model = SdA(numpy_rng, 1024, [256, 64, 16], [0.3, 0.3, 0.3], perlin=True)
model.train(spec_train, max_epochs=200, max_runtime=7200, layer_wise=False)
save_path = os.path.join(model_save_dir,
"perlin_layerwise_audio_only_%i.sda" % run_index)
print "Training complete, saving model at %s" % save_path
pickle.dump(model, open(save_path, 'wb'))
recon_error = model.test(spec_test)
print "Average reconstruction error: %0.9f\n" % recon_error
recon.append(recon_error)
return recon
def perlin_experiment(numpy_rng,run_index):
recon = []
ims_train, ims_test, ims_valid = load_data(image_dataset_path)
spec_train, spec_test, spec_valid = load_data(spect_dataset_path)
print ""
print "Experiment 9: layer-wise model on images, initialized with perlin noise"
model = SdA( numpy_rng, 1024, [256,64,16], [0.3,0.3,0.3], perlin=True )
model.train( ims_train, max_epochs=200, max_runtime=7200, layer_wise=True)
save_path = os.path.join(model_save_dir, "perlin_layerwise_images_only_%i.sda"%run_index)
print "Training complete, saving model at %s" % save_path
pickle.dump(model,open(save_path,'wb'))
recon_error = model.test( ims_test )
print "Average reconstruction error: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 10: layer-wise model on audio, initialized with perlin noise"
model = SdA( numpy_rng, 1024, [256,64,16], [0.3,0.3,0.3], perlin=True )
model.train( spec_train, max_epochs=200, max_runtime=7200, layer_wise=False)
save_path =os.path.join(model_save_dir, "perlin_layerwise_audio_only_%i.sda"%run_index)
print "Training complete, saving model at %s" % save_path
pickle.dump(model,open(save_path,'wb'))
recon_error = model.test( spec_test )
print "Average reconstruction error: %0.9f\n" % recon_error
recon.append(recon_error)
return recon
def combined(numpy_rng):
recon = []
ims_train, ims_test, ims_valid = load_data(image_dataset_path)
spec_train, spec_test, spec_valid = load_data(spect_dataset_path)
print ""
print "Experiment 1: run ordinary model on images"
model = SdA(numpy_rng, 1024, [256, 64, 16], [0.3, 0.3, 0.3])
model.train(ims_train, max_epochs=200, max_runtime=7200, layer_wise=False)
save_path = os.path.join(model_save_dir, "interleaved_images_only.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test(ims_test)
print "Average reconstruction error: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 2: run greedy layer-wise pre-training on images"
model = SdA(numpy_rng, 1024, [256, 64, 16], [0.3, 0.3, 0.3])
model.train(ims_train, max_epochs=200, max_runtime=7200, layer_wise=True)
save_path = os.path.join(model_save_dir, "layerwise_images_only.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test(ims_test)
print "Average reconstruction error: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 3: run ordinary model on spectrograms"
model = SdA(numpy_rng, 1024, [256, 64, 16], [0.3, 0.3, 0.3])
model.train(spec_train, max_epochs=200, max_runtime=7200, layer_wise=False)
save_path = os.path.join(model_save_dir, "interleaved_spect_only.sda")
print "Training complete, saving model at %s" % save_path
pickle.dump(model, open(save_path, 'wb'))
recon_error = model.test(spec_test)
print "Average reconstruction error: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 4: run greedy layer-wise pre-training on spectrograms"
model = SdA(numpy_rng, 1024, [256, 64, 16], [0.3, 0.3, 0.3])
model.train(spec_train, max_epochs=200, max_runtime=7200, layer_wise=True)
save_path = os.path.join(model_save_dir, "layerwise_spect_only.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test(spec_test)
print "Average reconstruction error: %0.9f\n" % recon_error
print ""
recon.append(recon_error)
print ""
print "Experiment 5: train interleaved model on images, then spectrograms"
model = SdA(numpy_rng, 1024, [256, 64, 16], [0.3, 0.3, 0.3])
model.train(ims_train, max_epochs=100, max_runtime=7200, layer_wise=False)
model.train(spec_train, max_epochs=100, max_runtime=7200, layer_wise=False)
save_path = os.path.join(model_save_dir,
"interleaved_images_then_spec.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test(spec_test)
print "Average reconstruction error on spectrograms: %0.9f" % recon_error
recon_error = model.test(ims_test)
print "Average reconstruction error on images: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 6: train interleaved model on spectrograms, then images"
model = SdA(numpy_rng, 1024, [256, 64, 16], [0.3, 0.3, 0.3])
model.train(spec_train, max_epochs=100, max_runtime=7200, layer_wise=False)
model.train(ims_train, max_epochs=100, max_runtime=7200, layer_wise=False)
save_path = os.path.join(model_save_dir,
"interleaved_spec_then_images.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test(spec_test)
print "Average reconstruction error on spectrograms: %0.9f" % recon_error
recon_error = model.test(ims_test)
print "Average reconstruction error on images: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 7: train layer-wise model on images, then spectrograms"
model = SdA(numpy_rng, 1024, [256, 64, 16], [0.3, 0.3, 0.3])
model.train(ims_train, max_epochs=100, max_runtime=7200, layer_wise=True)
model.train(spec_train, max_epochs=100, max_runtime=7200, layer_wise=True)
save_path = os.path.join(model_save_dir, "layerwise_images_then_spec.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test(spec_test)
print "Average reconstruction error on spectrograms: %0.9f" % recon_error
recon_error = model.test(ims_test)
print "Average reconstruction error on images: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 8: train layer-wise model on spectrograms, then images"
model = SdA(numpy_rng, 1024, [256, 64, 16], [0.3, 0.3, 0.3])
model.train(spec_train, max_epochs=100, max_runtime=7200, layer_wise=True)
model.train(ims_train, max_epochs=100, max_runtime=7200, layer_wise=True)
save_path = os.path.join(model_save_dir, "layerwise_spec_then_images.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test(spec_test)
print "Average reconstruction error on spectrograms: %0.9f" % recon_error
recon_error = model.test(ims_test)
print "Average reconstruction error on images: %0.9f\n" % recon_error
recon.append(recon_error)
return recon
def combined(numpy_rng):
recon = []
ims_train, ims_test, ims_valid = load_data(image_dataset_path)
spec_train, spec_test, spec_valid = load_data(spect_dataset_path)
print ""
print "Experiment 1: run ordinary model on images"
model = SdA( numpy_rng, 1024, [256,64,16], [0.3,0.3,0.3] )
model.train( ims_train, max_epochs=200, max_runtime=7200, layer_wise=False)
save_path = os.path.join(model_save_dir, "interleaved_images_only.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test( ims_test )
print "Average reconstruction error: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 2: run greedy layer-wise pre-training on images"
model = SdA( numpy_rng, 1024, [256,64,16], [0.3,0.3,0.3] )
model.train( ims_train, max_epochs=200, max_runtime=7200, layer_wise=True)
save_path =os.path.join(model_save_dir, "layerwise_images_only.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test( ims_test )
print "Average reconstruction error: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 3: run ordinary model on spectrograms"
model = SdA( numpy_rng, 1024, [256,64,16], [0.3,0.3,0.3] )
model.train( spec_train, max_epochs=200, max_runtime=7200, layer_wise=False)
save_path =os.path.join(model_save_dir, "interleaved_spect_only.sda")
print "Training complete, saving model at %s" % save_path
pickle.dump(model,open(save_path,'wb'))
recon_error = model.test( spec_test )
print "Average reconstruction error: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 4: run greedy layer-wise pre-training on spectrograms"
model = SdA( numpy_rng, 1024, [256,64,16], [0.3,0.3,0.3] )
model.train( spec_train, max_epochs=200, max_runtime=7200, layer_wise=True)
save_path =os.path.join(model_save_dir, "layerwise_spect_only.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test( spec_test )
print "Average reconstruction error: %0.9f\n" % recon_error
print ""
recon.append(recon_error)
print ""
print "Experiment 5: train interleaved model on images, then spectrograms"
model = SdA( numpy_rng, 1024, [256,64,16], [0.3,0.3,0.3] )
model.train( ims_train, max_epochs=100, max_runtime=7200, layer_wise=False)
model.train( spec_train, max_epochs=100, max_runtime=7200, layer_wise=False)
save_path = os.path.join(model_save_dir, "interleaved_images_then_spec.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test( spec_test )
print "Average reconstruction error on spectrograms: %0.9f" % recon_error
recon_error = model.test( ims_test )
print "Average reconstruction error on images: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 6: train interleaved model on spectrograms, then images"
model = SdA( numpy_rng, 1024, [256,64,16], [0.3,0.3,0.3] )
model.train( spec_train, max_epochs=100, max_runtime=7200, layer_wise=False)
model.train( ims_train, max_epochs=100, max_runtime=7200, layer_wise=False)
save_path = os.path.join(model_save_dir, "interleaved_spec_then_images.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test( spec_test )
print "Average reconstruction error on spectrograms: %0.9f" % recon_error
recon_error = model.test( ims_test )
print "Average reconstruction error on images: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 7: train layer-wise model on images, then spectrograms"
model = SdA( numpy_rng, 1024, [256,64,16], [0.3,0.3,0.3] )
model.train( ims_train, max_epochs=100, max_runtime=7200, layer_wise=True)
model.train( spec_train, max_epochs=100, max_runtime=7200, layer_wise=True)
save_path = os.path.join(model_save_dir, "layerwise_images_then_spec.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test( spec_test )
print "Average reconstruction error on spectrograms: %0.9f" % recon_error
recon_error = model.test( ims_test )
print "Average reconstruction error on images: %0.9f\n" % recon_error
recon.append(recon_error)
print ""
print "Experiment 8: train layer-wise model on spectrograms, then images"
model = SdA( numpy_rng, 1024, [256,64,16], [0.3,0.3,0.3] )
model.train( spec_train, max_epochs=100, max_runtime=7200, layer_wise=True)
model.train( ims_train, max_epochs=100, max_runtime=7200, layer_wise=True)
save_path = os.path.join(model_save_dir, "layerwise_spec_then_images.sda")
#print "Training complete, saving model at %s" % save_path
#pickle.dump(model,open(save_path,'wb'))
recon_error = model.test( spec_test )
print "Average reconstruction error on spectrograms: %0.9f" % recon_error
recon_error = model.test( ims_test )
print "Average reconstruction error on images: %0.9f\n" % recon_error
recon.append(recon_error)
return recon