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 ops.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 testAssertModelCloneSameObjectsThrowError(self):
input_arr = np.random.random((1, 3))
target_arr = np.random.random((1, 3))
model_graph = ops.Graph()
clone_graph = ops.Graph()
# Create two models with the same layers but different optimizers.
with session.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=training_module.AdadeltaOptimizer())
model.train_on_batch(input_arr, target_arr)
with session.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_nested_model_with_tensor_input(self):
gpus = 2
input_dim = 10
shape = (input_dim, )
num_samples = 16
num_classes = 10
if not check_if_compatible_devices(gpus=gpus):
self.skipTest('multi gpu only')
with ops.Graph().as_default(), self.cached_session():
input_shape = (num_samples, ) + shape
x_train = np.random.randint(0, 255, input_shape)
y_train = np.random.randint(0, num_classes, (input_shape[0], ))
y_train = np_utils.to_categorical(y_train, num_classes)
x_train = x_train.astype('float32')
y_train = y_train.astype('float32')
dataset = data.Dataset.from_tensor_slices((x_train, y_train))
dataset = dataset.repeat()
dataset = dataset.batch(4)
iterator = data.make_one_shot_iterator(dataset)
inputs, targets = iterator.get_next()
input_tensor = keras.layers.Input(tensor=inputs)
model = keras.models.Sequential()
model.add(keras.layers.Dense(3, input_shape=(input_dim, )))
model.add(keras.layers.Dense(num_classes))
output = model(input_tensor)
outer_model = keras.Model(input_tensor, output)
parallel_model = multi_gpu_utils.multi_gpu_model(outer_model,
gpus=gpus)
parallel_model.compile(loss='categorical_crossentropy',
optimizer=optimizer_v1.RMSprop(lr=0.0001,
decay=1e-6),
metrics=['accuracy'],
target_tensors=[targets])
parallel_model.fit(epochs=1, steps_per_epoch=3)
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)
