def test_pickle_with_callbacks():
"""Test that models with callbacks (which hold a refence to the Keras model itself) are picklable."""
clf = KerasRegressor(model=get_reg,
loss="mse",
callbacks=[keras.callbacks.Callback()])
# Fit and roundtrip validating only that there are no errors
clf.fit([[1]], [1])
clf = pickle.loads(pickle.dumps(clf))
clf.predict([[1]])
clf.partial_fit([[1]], [1])
def test_partial_fit_pickle(optim):
"""
This test is implemented to make sure model pickling does not affect
training.
(this is essentially what Dask-ML does for search)
"""
X, y = make_regression(n_features=8, n_samples=100)
m1 = KerasRegressor(model=dynamic_regressor,
optimizer=optim,
random_state=42,
hidden_layer_sizes=[])
m2 = clone(m1)
# Ensure we can roundtrip before training
m2 = _reload(m2)
# Make sure start from same model
m1.partial_fit(X, y)
m2.partial_fit(X, y)
assert _weights_close(m1, m2)
# Train; make sure pickling doesn't affect it
for k in range(4):
m1.partial_fit(X, y)
m2 = _reload(m2, epoch=k + 1).partial_fit(X, y)
# Make sure the same model is produced
assert _weights_close(m1, m2)
# Make sure predictions are the same
assert np.allclose(m1.predict(X), m2.predict(X))
def test_batch_size_all_predict(length, prefix, base):
kw = prefix + base
y = np.random.random((length, ))
X = y.reshape((-1, 1))
est = KerasRegressor(dynamic_regressor, hidden_layer_sizes=[], **{kw: -1})
est.fit(X, y)
pred_orig = est.model_.predict
def check_batch_size(**kwargs):
assert kwargs[base] == X.shape[0]
return pred_orig(**kwargs)
with mock.patch.object(est.model_, "predict", new=check_batch_size):
est.predict(X)
def test_partial_fit_pickle(optim):
"""
This test is implemented to make sure model pickling does not affect
training, which is (essentially) what Dask-ML does for a model selection
search.
This test is simple for functional optimizers (like SGD without momentum),
and tricky for stateful transforms (SGD w/ momentum, Adam, Adagrad, etc).
For more detail, see https://github.com/adriangb/scikeras/pull/126 and
links within
"""
X, y = make_regression(n_features=8, n_samples=100)
m1 = KerasRegressor(model=dynamic_regressor,
optimizer=optim,
random_state=42,
hidden_layer_sizes=[])
m2 = clone(m1)
# Ensure we can roundtrip before training
m2 = _reload(m2)
# Make sure start from same model
m1.partial_fit(X, y)
m2.partial_fit(X, y)
assert _weights_close(m1, m2)
# Train; make sure pickling doesn't affect it
for k in range(4):
m1.partial_fit(X, y)
m2 = _reload(m2, epoch=k + 1).partial_fit(X, y)
# Make sure the same model is produced
assert _weights_close(m1, m2)
# Make sure predictions are the same
assert np.allclose(m1.predict(X), m2.predict(X))
