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 testAssertModelCloneSameObjectsThrowError(self):
input_arr = np.random.random((1, 3))
target_arr = np.random.random((1, 3))
model_graph = tf.Graph()
clone_graph = tf.Graph()
# Create two models with the same layers but different optimizers.
with tf.compat.v1.Session(graph=model_graph):
inputs = keras.layers.Input(shape=(3, ))
x = keras.layers.Dense(2)(inputs)
x = keras.layers.Dense(3)(x)
model = keras.models.Model(inputs, x)
model.compile(loss='mse',
optimizer=tf.compat.v1.train.AdadeltaOptimizer())
model.train_on_batch(input_arr, target_arr)
with tf.compat.v1.Session(graph=clone_graph):
inputs = keras.layers.Input(shape=(3, ))
x = keras.layers.Dense(2)(inputs)
x = keras.layers.Dense(4)(x)
x = keras.layers.Dense(3)(x)
clone = keras.models.Model(inputs, x)
clone.compile(loss='mse',
optimizer=optimizer_v1.RMSprop(lr=0.0001))
clone.train_on_batch(input_arr, target_arr)
def test_rmsprop(self):
with self.cached_session():
self._test_optimizer(optimizer_v1.RMSprop())
self._test_optimizer(optimizer_v1.RMSprop(decay=1e-3))
def testRMSpropCompatibility(self): opt_v1 = optimizer_v1.RMSprop() opt_v2 = rmsprop.RMSprop() self._testOptimizersCompatibility(opt_v1, opt_v2)