print("\n")
print("Loading model parameters from:", dump_file)
with open(dump_file, 'rb') as fp:
params = pickle.load(fp)
if isinstance(params[0], list):
# old redundant dump
for i_layer, layer in enumerate(layers):
lasagne.layers.set_all_param_values(layer, params[i_layer])
else:
# non-redundant dump
lasagne.layers.set_all_param_values(layers, params)
# select data
print("\nLoading data...")
data = select_data(args.data, args.train_split, args.config, args.seed, test_only=True)
print("\nCompiling prediction functions...")
l_view1, l_view2, l_v1latent, l_v2latent = layers
input_1 = input_2 = [l_view1.input_var, l_view2.input_var]
compute_v1_latent = theano.function(inputs=input_1,
outputs=lasagne.layers.get_output(l_v1latent, deterministic=True))
compute_v2_latent = theano.function(inputs=input_2,
outputs=lasagne.layers.get_output(l_v2latent, deterministic=True))
# iterate test data
print("Evaluating on test set...")
# compute output on test set
eval_set = 'test'
# load model parameters
if args.estimate_UV:
model.EXP_NAME += "_est_UV"
out_path = os.path.join(os.path.join(EXP_ROOT), model.EXP_NAME)
dump_file = 'params.pkl' if args.tag is None else 'params_%s.pkl' % args.tag
dump_file = os.path.join(out_path, dump_file)
print("\n")
print("Loading model parameters from:", dump_file)
with open(dump_file, 'r') as fp:
params = pickle.load(fp)
lasagne.layers.set_all_param_values(layers, params)
print("\nLoading data...")
data = select_data(args.data, seed=args.seed)
print("\nCompiling prediction functions...")
l_view1, l_view2, l_v1latent, l_v2latent = layers
input_1 = input_2 = [l_view1.input_var, l_view2.input_var]
compute_v1_latent = theano.function(inputs=input_1,
outputs=lasagne.layers.get_output(
l_v1latent, deterministic=True))
compute_v2_latent = theano.function(inputs=input_2,
outputs=lasagne.layers.get_output(
l_v2latent, deterministic=True))
print("Evaluating on test set...")
print("\n")
print("Loading model parameters from:", dump_file)
with open(dump_file, 'r') as fp:
params = pickle.load(fp)
lasagne.layers.set_all_param_values(layers, params)
# reset model parameter file
model.EXP_NAME += "_est_UV"
out_path = os.path.join(os.path.join(EXP_ROOT), model.EXP_NAME)
if not os.path.exists(out_path):
os.mkdir(out_path)
dump_file = os.path.join(out_path, dump_file_name)
# select data
print("\nLoading data...")
data = select_data(args.data, args.train_split, args.config, args.seed)
print("\nCompiling prediction functions...")
l_view1, l_view2, l_v1latent, l_v2latent = layers
# get network input variables
input_1, input_2 = [l_view1.input_var], [l_view2.input_var]
# get cca layer input
for l in lasagne.layers.helper.get_all_layers(l_v1latent):
if isinstance(l, CCALayer1) or isinstance(l, CCALayer2):
print("CCALayer found!")
cca_layer = l
l_v1_cca = cca_layer.input_layers[0]
l_v2_cca = cca_layer.input_layers[1]
break