def test_functional_model_saving(self):
if h5py is None:
self.skipTest('h5py required to run this test')
with self.cached_session():
inputs = keras.layers.Input(shape=(3, ))
x = keras.layers.Dense(2)(inputs)
output = keras.layers.Dense(3)(x)
model = keras.models.Model(inputs, output)
model.compile(loss=keras.losses.MSE,
optimizer=optimizers.Kfac(model=model,
**_KFAC_KWARGS),
metrics=[
keras.metrics.categorical_accuracy,
keras.metrics.CategoricalAccuracy()
],
weighted_metrics=[
keras.metrics.categorical_accuracy,
keras.metrics.CategoricalAccuracy()
])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
fd, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
model = saving_utils.load_model(fname, optimizer_name='new')
os.close(fd)
os.remove(fname)
out2 = model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
def test_sequential_model_saving(self):
if h5py is None:
self.skipTest('h5py required to run this test')
with self.cached_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(2, )))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(3))
model.compile(loss=keras.losses.MSE,
optimizer=optimizers.Kfac(model=model,
**_KFAC_KWARGS),
metrics=[
keras.metrics.categorical_accuracy,
keras.metrics.CategoricalAccuracy()
])
x = np.random.random((1, 2))
y = np.random.random((1, 3))
# TODO(b/136561651): Since we use TFP distributions to sample from the
# output distribution, optimizer's won't match exactly unless they are run
# for the same number of steps. Even with a random seed, the internal
# state of TFP changes with each call. We must switch to a stateless
# sampler. Uncomment the train line below once this is implemented.
# model.train_on_batch(x, y)
out = model.predict(x)
fd, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
new_model = saving_utils.load_model(fname, optimizer_name='new')
os.close(fd)
os.remove(fname)
out2 = new_model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
# test that new updates are the same with both models
x = np.random.random((1, 2))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
new_model.train_on_batch(x, y)
x = np.random.random((1, 2))
y = np.random.random((1, 3))
eval_out = model.evaluate(x, y)
eval_out2 = new_model.evaluate(x, y)
self.assertArrayNear(eval_out, eval_out2, 1e-03)
out = model.predict(x)
out2 = new_model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
def test_saving_model_with_long_weights_names(self):
self.skipTest('KFAC does not support nested models yet.')
if h5py is None:
self.skipTest('h5py required to run this test')
with self.cached_session():
x = keras.Input(shape=(2, ), name='nested_model_input')
f = x
for i in range(4):
f = keras.layers.Dense(2,
name='nested_model_dense_%d' % (i, ))(f)
# This layer name will make the `weights_name`
# HDF5 attribute blow out of proportion.
f = keras.layers.Dense(2,
name='nested_model_output' + ('x' *
(2**14)))(f)
nested_model = keras.Model(inputs=[x],
outputs=[f],
name='nested_model')
x = keras.Input(shape=(2, ), name='outer_model_input')
f = nested_model(x)
f = keras.layers.Dense(2, name='outer_model_output')(f)
model = keras.Model(inputs=[x], outputs=[f])
model.compile(loss='mse',
optimizer=optimizers.Kfac(model=model,
**_KFAC_KWARGS),
metrics=['acc'])
x = np.random.random((1, 2))
y = np.random.random((1, 2))
model.train_on_batch(x, y)
out = model.predict(x)
fd, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
model = saving_utils.load_model(fname, optimizer_name='new')
# Check that the HDF5 files contains chunked array
# of weight names.
with h5py.File(fname, 'r') as h5file:
num_weight_arrays = len([
attr
for attr in h5file['model_weights']['nested_model'].attrs
if attr.startswith('weight_names')
])
# The chunking of layer names array should have happened.
self.assertGreater(num_weight_arrays, 0)
out2 = model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
# Cleanup
os.close(fd)
os.remove(fname)
def test_model_saving_to_pre_created_h5py_file(self):
if h5py is None:
self.skipTest('h5py required to run this test')
with self.cached_session():
inputs = keras.Input(shape=(3, ))
x = keras.layers.Dense(2)(inputs)
outputs = keras.layers.Dense(3)(x)
model = keras.Model(inputs, outputs)
model.compile(loss=keras.losses.MSE,
optimizer=optimizers.Kfac(model=model,
**_KFAC_KWARGS),
metrics=[
keras.metrics.categorical_accuracy,
keras.metrics.CategoricalAccuracy()
])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
fd, fname = tempfile.mkstemp('.h5')
with h5py.File(fname, mode='r+') as h5file:
keras.models.save_model(model, h5file)
loaded_model = saving_utils.load_model(h5file,
optimizer_name='new')
out2 = loaded_model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
# Test non-default options in h5
with h5py.File('-', driver='core', mode='w',
backing_store=False) as h5file:
keras.models.save_model(model, h5file)
loaded_model = saving_utils.load_model(h5file,
optimizer_name='new2')
out2 = loaded_model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
# Cleanup
os.close(fd)
os.remove(fname)
def test_saving_model_with_long_layer_names(self):
if h5py is None:
self.skipTest('h5py required to run this test')
with self.cached_session():
# This layer name will make the `layers_name` HDF5 attribute blow
# out of proportion. Note that it fits into the internal HDF5
# attribute memory limit on its own but because h5py converts
# the list of layer names into numpy array, which uses the same
# amount of memory for every item, it increases the memory
# requirements substantially.
x = keras.Input(shape=(2, ), name='input_' + ('x' * (2**15)))
f = x
for i in range(4):
f = keras.layers.Dense(2, name='dense_%d' % (i, ))(f)
model = keras.Model(inputs=[x], outputs=[f])
model.compile(optimizers.Kfac(model=model, **_KFAC_KWARGS),
loss=keras.losses.MeanSquaredError(),
metrics=['acc'])
x = np.random.random((1, 2))
y = np.random.random((1, 2))
model.train_on_batch(x, y)
out = model.predict(x)
fd, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
model = saving_utils.load_model(fname, optimizer_name='new')
# Check that the HDF5 files contains chunked array
# of layer names.
with h5py.File(fname, 'r') as h5file:
num_names_arrays = len([
attr for attr in h5file['model_weights'].attrs
if attr.startswith('layer_names')
])
# The chunking of layer names array should have happened.
self.assertGreater(num_names_arrays, 0)
out2 = model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
# Cleanup
os.close(fd)
os.remove(fname)
def test_saving_constant_initializer_with_numpy(self):
if h5py is None:
self.skipTest('h5py required to run this test')
with self.cached_session():
model = keras.models.Sequential()
model.add(
keras.layers.Dense(
2,
input_shape=(3,),
kernel_initializer=keras.initializers.Constant(np.ones((3, 2)))))
model.add(keras.layers.Dense(3))
model.compile(loss='mse',
optimizer=optimizers.Kfac(model=model, **_KFAC_KWARGS),
metrics=['acc'])
fd, fname = tempfile.mkstemp('.h5')
keras.models.save_model(model, fname)
model = saving_utils.load_model(fname, optimizer_name='new')
os.close(fd)
os.remove(fname)
