layer1 = SBM_Lower(session=session,
side=flags['img_shape'][0],
side_overlap=flags['layer_1_side_overlap'],
num_h=flags['layer_1_num_h'],
name=flags['layer_1_name'])
layer1.restore(flags['layer_1_ckpt'])
layer2 = RBM(session=session,
num_v=flags['layer_1_num_h'],
num_h=flags['layer_2_num_h'],
bottom=layer1,
name=flags['layer_2_name'])
layer2.restore(flags['layer_2_ckpt'])
table_path = os.path.join(flags['test_generalisation_plots_dir'], 'table')
layer2.test_generalisation(
test_data=test_data_batch,
output_table_path=table_path,
num_iterations=flags['test_generalisation_num_iterations'],
num_runs=flags['test_generalisation_num_runs'],
num_samples_to_average=flags['test_generalisation_num_samples_to_average'])
with open(table_path, 'rb') as f:
table = pickle.load(f)
assert len(table) == test_data_batch.shape[0]
min_dists = []
shuffle_first=False,
batch_size=flags['test_generalisation_batch_size'],
log_epochs=flags['data_log_epochs'],
name='TestData')
test_data_batch = test_data.next_batch()
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
session = tf.Session(config=config)
layer1 = RBM(session=session,
num_v=flags['img_shape'][0] * flags['img_shape'][1],
num_h=flags['layer_1_num_h'],
name=flags['layer_1_name'])
layer1.restore(flags['layer_1_ckpt'])
layer2 = RBM(session=session,
num_v=flags['layer_1_num_h'],
num_h=flags['layer_2_num_h'],
bottom=layer1,
name=flags['layer_2_name'])
layer2.restore(flags['layer_2_ckpt'])
layer3 = RBM(session=session,
num_v=flags['layer_2_num_h'],
num_h=flags['layer_3_num_h'],
bottom=layer2,
name=flags['layer_3_name'])