def test_get_weights(tf_graph, mocker):
params = get_params()
rnn = RNN(params)
with pytest.raises(UserWarning) as excinfo:
rnn.get_weights()
assert 'No weights to return yet -- model has not yet been initialized.' in str(
excinfo.value)
mocker.patch.object(RNN, 'forward_pass')
RNN.forward_pass.return_value = tf.fill(
[params['N_batch'], params['N_steps'], params['N_out']],
float('nan')), tf.fill(
[params['N_batch'], params['N_steps'], params['N_rec']],
float('nan'))
rnn.build()
with pytest.raises(UserWarning) as excinfo:
rnn.get_weights()
assert 'No weights to return yet -- model has not yet been initialized.' in str(
excinfo.value)
rdm1 = rd.RDM(dt=params['dt'],
tau=params['tau'],
T=2000,
N_batch=params['N_batch'])
gen1 = rdm1.batch_generator()
rnn.train(gen1)
assert type(rnn.get_weights()) is dict
def test_get_weights(tf_graph,mocker):
params = get_params()
rnn = RNN(params)
mocker.patch.object(RNN, 'forward_pass')
RNN.forward_pass.return_value = tf.fill([params['N_batch'], params['N_steps'], params['N_out']], float('nan')), tf.fill([params['N_batch'], params['N_steps'], params['N_rec']], float('nan'))
assert type(rnn.get_weights()) is dict
