def test_sequential_weight_loading_group_name_with_incorrect_shape(self):
if h5py is None:
return
h5_path = self._save_model_dir("test.h5")
num_hidden = 5
input_dim = 3
num_classes = 2
with tf.Graph().as_default(), self.cached_session():
ref_model = keras.models.Sequential()
ref_model.add(
keras.layers.Dense(num_hidden, input_dim=input_dim, name="d1"))
ref_model.add(keras.layers.Dense(num_classes, name="d2"))
ref_model.compile(
loss=keras.losses.MSE,
optimizer=optimizer_v1.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy],
)
f_ref_model = h5py.File(h5_path, "w")
keras.backend.set_value(ref_model.layers[1].bias,
[3.5] * num_classes)
hdf5_format.save_weights_to_hdf5_group(f_ref_model, ref_model)
f_model = h5py.File(h5_path, "r")
model = keras.models.Sequential()
model.add(
keras.layers.Dense(num_hidden + 5,
input_dim=input_dim,
name="d1"))
model.add(keras.layers.Dense(num_classes, name="d2"))
model.compile(
loss=keras.losses.MSE,
optimizer=optimizer_v1.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy],
)
with self.assertRaises(
ValueError,
msg=
"Shape mismatch in layer #0 (named d1) for weight d1_1/kernel:0. "
"Weight expects shape (3, 10). "
"Received saved weight with shape (3, 5)",
):
hdf5_format.load_weights_from_hdf5_group_by_name(
f_model, model)
hdf5_format.load_weights_from_hdf5_group_by_name(
f_model, model, skip_mismatch=True)
self.assertAllClose(
[3.5] * num_classes,
keras.backend.get_value(model.layers[1].bias),
)
def test_sequential_weight_loading_group_name_with_incorrect_length(self):
if h5py is None:
return
h5_path = self._save_model_dir("test.h5")
num_hidden = 5
input_dim = 3
num_classes = 2
with self.cached_session():
ref_model = keras.models.Sequential()
ref_model.add(
keras.layers.Dense(num_hidden, input_dim=input_dim, name="d1"))
ref_model.add(keras.layers.Dense(num_classes, name="d2"))
ref_model.compile(
loss=keras.losses.MSE,
optimizer="rmsprop",
metrics=[keras.metrics.categorical_accuracy],
)
f_ref_model = h5py.File(h5_path, "w")
hdf5_format.save_weights_to_hdf5_group(f_ref_model, ref_model)
f_model = h5py.File(h5_path, "r")
model = keras.models.Sequential()
model.add(
keras.layers.Dense(num_hidden,
use_bias=False,
input_dim=input_dim,
name="d1"))
model.add(keras.layers.Dense(num_classes, name="d2"))
model.compile(
loss=keras.losses.MSE,
optimizer="rmsprop",
metrics=[keras.metrics.categorical_accuracy],
)
with self.assertRaises(
ValueError,
msg="Weight count mismatch for layer #0 (named d1). "
"Layer expects 1 weight(s). Received 2 saved weight(s)",
):
hdf5_format.load_weights_from_hdf5_group_by_name(
f_model, model)
hdf5_format.load_weights_from_hdf5_group_by_name(
f_model, model, skip_mismatch=True)
self.assertAllClose(
keras.backend.get_value(ref_model.layers[1].kernel),
keras.backend.get_value(model.layers[1].kernel),
)
def test_sequential_weight_loading_group_name_with_incorrect_shape(self):
if h5py is None:
return
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir)
h5_path = os.path.join(temp_dir, 'test.h5')
num_hidden = 5
input_dim = 3
num_classes = 2
with tf.Graph().as_default(), self.cached_session():
ref_model = keras.models.Sequential()
ref_model.add(
keras.layers.Dense(num_hidden, input_dim=input_dim, name='d1'))
ref_model.add(keras.layers.Dense(num_classes, name='d2'))
ref_model.compile(loss=keras.losses.MSE,
optimizer=optimizer_v1.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy])
f_ref_model = h5py.File(h5_path, 'w')
keras.backend.set_value(ref_model.layers[1].bias,
[3.5] * num_classes)
hdf5_format.save_weights_to_hdf5_group(f_ref_model,
ref_model.layers)
f_model = h5py.File(h5_path, 'r')
model = keras.models.Sequential()
model.add(
keras.layers.Dense(num_hidden + 5,
input_dim=input_dim,
name='d1'))
model.add(keras.layers.Dense(num_classes, name='d2'))
model.compile(loss=keras.losses.MSE,
optimizer=optimizer_v1.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy])
with self.assertRaisesRegex(
ValueError, r'Layer #0 \(named "d1"\), weight '
r'<tf\.Variable \'d1_1\/kernel:0\' '
r'shape=\(3, 10\) dtype=float32> has '
r'shape \(3, 10\), but the saved weight has '
r'shape \(3, 5\)\.'):
hdf5_format.load_weights_from_hdf5_group_by_name(
f_model, model.layers)
hdf5_format.load_weights_from_hdf5_group_by_name(
f_model, model.layers, skip_mismatch=True)
self.assertAllClose([3.5] * num_classes,
keras.backend.get_value(model.layers[1].bias))
def test_sequential_weight_loading_group_name_with_incorrect_length(self):
if h5py is None:
return
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir)
h5_path = os.path.join(temp_dir, 'test.h5')
num_hidden = 5
input_dim = 3
num_classes = 2
with self.cached_session():
ref_model = keras.models.Sequential()
ref_model.add(
keras.layers.Dense(num_hidden, input_dim=input_dim, name='d1'))
ref_model.add(keras.layers.Dense(num_classes, name='d2'))
ref_model.compile(loss=keras.losses.MSE,
optimizer='rmsprop',
metrics=[keras.metrics.categorical_accuracy])
f_ref_model = h5py.File(h5_path, 'w')
hdf5_format.save_weights_to_hdf5_group(f_ref_model,
ref_model.layers)
f_model = h5py.File(h5_path, 'r')
model = keras.models.Sequential()
model.add(
keras.layers.Dense(num_hidden,
use_bias=False,
input_dim=input_dim,
name='d1'))
model.add(keras.layers.Dense(num_classes, name='d2'))
model.compile(loss=keras.losses.MSE,
optimizer='rmsprop',
metrics=[keras.metrics.categorical_accuracy])
with self.assertRaisesRegex(
ValueError, r'Layer #0 \(named \"d1\"\) expects 1 '
r'weight\(s\), but the saved weights have 2 '
r'element\(s\)\.'):
hdf5_format.load_weights_from_hdf5_group_by_name(
f_model, model.layers)
hdf5_format.load_weights_from_hdf5_group_by_name(
f_model, model.layers, skip_mismatch=True)
self.assertAllClose(
keras.backend.get_value(ref_model.layers[1].kernel),
keras.backend.get_value(model.layers[1].kernel))