def test_train_iters(tf_graph, mocker, capfd):
params = get_params()
pd1 = PerceptualDiscrimination(dt=params['dt'],
tau=params['tau'],
T=2000,
N_batch=params['N_batch'])
gen1 = pd1.batch_generator()
for remainder in [0, 1]:
train_params = {}
train_params['training_iters'] = params[
'N_batch'] + remainder # number of iterations to train for Default: 10000
train_params[
'loss_epoch'] = 1 # Compute and record loss every 'loss_epoch' epochs. Default: 10
train_params['verbosity'] = True
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'))
rnn.train(gen1, train_params)
rnn.destruct()
out, _ = capfd.readouterr()
N_epochs = int(
ceil(train_params['training_iters'] / (params['N_batch'] * 1.0)))
assert "Iter " + str(N_epochs * params['N_batch']) in out
assert "Iter " + str((N_epochs + 1) * params['N_batch']) not in out
def test_custom_loss(tf_graph, mocker):
params = get_params()
params['loss_function'] = 'my_mean_squared_error'
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'))
with pytest.raises(UserWarning) as excinfo:
rnn.build()
assert 'my_mean_squared_error' in str(excinfo.value)
rnn.destruct()
params['my_mean_squared_error'] = mean_squared_error
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'))
rnn.build()
def test_destruct(tf_graph, mocker):
params = get_params()
rnn1 = RNN(params)
rnn1.destruct()
rnn2 = 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'))
rnn2.build()
rnn2.destruct()
rnn3 = RNN(params)
def test_load_weights_path_rnn(tf_graph, mocker, tmpdir, capfd):
params = get_params()
pd1 = PerceptualDiscrimination(dt=params['dt'],
tau=params['tau'],
T=2000,
N_batch=params['N_batch'])
gen1 = pd1.batch_generator()
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 = RNN(params)
rnn.build()
train_params = {}
train_params['save_weights_path'] = str(
tmpdir.dirpath("save_weights.npz")
) # Where to save the model after training. Default: None
train_params['verbosity'] = False
### save out some weights to test with and destroy the rnn that created them
assert not tmpdir.dirpath("save_weights.npz").check(exists=1)
rnn.train(gen1, train_params)
assert rnn.is_initialized is True
out, _ = capfd.readouterr()
print(out)
assert out == ""
assert tmpdir.dirpath("save_weights.npz").check(exists=1)
rnn.destruct()
### Make sure loading weights fails on nonexistent file
params['load_weights_path'] = "nonexistent"
with pytest.raises(EnvironmentError) as excinfo:
rnn = RNN(params)
assert "No such file" in str(excinfo.value)
rnn.destruct()
### Ensure success when loading weights created previously
params['load_weights_path'] = str(tmpdir.dirpath("save_weights.npz"))
rnn = RNN(params)
tmpdir.dirpath("save_weights.npz").remove()