import nn_extra_student
from config_rnn import defaults
batch_size = 32
sample_batch_size = 1
n_samples = 4
rng = np.random.RandomState(42)
rng_test = np.random.RandomState(317070)
seq_len = defaults.seq_len
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,
dataset='fashion_mnist')
test_data_iter = data_iter.BaseExchSeqDataIterator(seq_len=seq_len,
batch_size=batch_size,
set='test',
dataset='fashion_mnist',
rng=rng_test)
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
batch_size = 16
sample_batch_size = 1
n_samples = 4
rng = np.random.RandomState(42)
test_rng = np.random.RandomState(317070)
seq_len = defaults.seq_len
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,
dataset='cifar10',
set='train',
rng=rng)
test_data_iter = data_iter.BaseExchSeqDataIterator(seq_len=seq_len,
batch_size=batch_size,
dataset='cifar10',
set='test',
rng=test_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
optimizer = 'rmsprop'
learning_rate = 0.001
from config_rnn import defaults
batch_size = 64
sample_batch_size = 1
n_samples = 4
rng = np.random.RandomState(42)
rng_test = np.random.RandomState(317070)
seq_len = defaults.seq_len
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)
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)
import nn_extra_student
from config_rnn import defaults
batch_size = 32
sample_batch_size = 1
n_samples = 4
rng = np.random.RandomState(42)
rng_test = np.random.RandomState(317070)
seq_len = defaults.seq_len
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:])