def test_save(tf_graph, mocker, tmpdir):
save_weights_path = str(tmpdir.dirpath("save_weights.npz"))
params = get_params()
rnn = RNN(params)
with pytest.raises(UserWarning) as excinfo:
rnn.save(save_weights_path)
assert "No weights to return" 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()
rdm1 = rd.RDM(dt=params['dt'],
tau=params['tau'],
T=2000,
N_batch=params['N_batch'])
gen1 = rdm1.batch_generator()
rnn.train(gen1)
assert not tmpdir.dirpath("save_weights.npz").check(exists=1)
rnn.save(save_weights_path)
assert tmpdir.dirpath("save_weights.npz").check(exists=1)
tmpdir.dirpath("save_weights.npz").remove()
def test_save(tf_graph, mocker, tmpdir):
save_weights_path = str(tmpdir.dirpath("save_weights.npz"))
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'))
pd1 = PerceptualDiscrimination(dt = params['dt'], tau = params['tau'], T = 2000, N_batch = params['N_batch'])
gen1 = pd1.batch_generator()
rnn.train(gen1)
assert not tmpdir.dirpath("save_weights.npz").check(exists=1)
rnn.save(save_weights_path)
assert tmpdir.dirpath("save_weights.npz").check(exists=1)
tmpdir.dirpath("save_weights.npz").remove()