train_data_iter = data_iter.BaseExchSeqDataIterator(seq_len=seq_len, batch_size=batch_size, set='train', rng=rng) test_data_iter = data_iter.BaseExchSeqDataIterator(seq_len=seq_len, batch_size=batch_size, set='test', rng=rng_test) valid_data_iter = data_iter.BaseExchSeqDataIterator(seq_len=seq_len, batch_size=batch_size, set='test', rng=rng) test_data_iter2 = data_iter.BaseTestBatchSeqDataIterator(seq_len=seq_len, set='test', rng=rng) obs_shape = train_data_iter.get_observation_size() # (seq_len, 28,28,1) print('obs shape', obs_shape) ndim = np.prod(obs_shape[1:]) corr_init = np.ones((ndim, ), dtype='float32') * 0.1 nu_init = 1000 optimizer = 'rmsprop' learning_rate = 0.001 lr_decay = 0.999995 max_iter = 100000 save_every = 1000
eps_corr = defaults.eps_corr mask_dims = defaults.mask_dims nonlinearity = tf.nn.elu weight_norm = True train_data_iter = data_iter.BaseExchSeqDataIterator(seq_len=seq_len, batch_size=batch_size, set='train', rng=rng, digits=[0, 2, 4, 6, 8]) test_data_iter = data_iter.BaseExchSeqDataIterator(seq_len=seq_len, batch_size=batch_size, set='test', digits=[1, 3, 5, 7, 9], rng=rng_test) valid_data_iter = data_iter.BaseExchSeqDataIterator(seq_len=seq_len, batch_size=batch_size, set='test', rng=rng_test, digits=[0, 2, 4, 6, 8]) test_data_iter2 = data_iter.BaseTestBatchSeqDataIterator(seq_len=seq_len, set='test', rng=rng_test, digits=[1, 3, 5, 7, 9]) obs_shape = train_data_iter.get_observation_size() # (seq_len, 28,28,1) print('obs shape', obs_shape) ndim = np.prod(obs_shape[1:]) corr_init = np.ones((ndim,), dtype='float32') * 0.1 nu_init = 1000 optimizer = 'rmsprop' learning_rate = 0.001 lr_decay = 0.999995 max_iter = 50000 save_every = 1000