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 self.test_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=keras.optimizers.RMSprop(lr=0.0001), metrics=[keras.metrics.categorical_accuracy]) f_ref_model = h5py.File(h5_path, 'w') saving.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=keras.optimizers.RMSprop(lr=0.0001), metrics=[keras.metrics.categorical_accuracy]) with self.assertRaisesRegexp( 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 \(3, 5\)\.'): saving.load_weights_from_hdf5_group_by_name( f_model, model.layers)
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.test_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=keras.optimizers.RMSprop(lr=0.0001), metrics=[keras.metrics.categorical_accuracy]) f_ref_model = h5py.File(h5_path, 'w') saving.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=keras.optimizers.RMSprop(lr=0.0001), metrics=[keras.metrics.categorical_accuracy]) with self.assertRaisesRegexp( ValueError, r'Layer #0 \(named \"d1\"\) expects 1 ' r'weight\(s\), but the saved weights have 2 ' r'element\(s\)\.'): saving.load_weights_from_hdf5_group_by_name(f_model, model.layers)
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 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=keras.optimizers.RMSprop(lr=0.0001), metrics=[keras.metrics.categorical_accuracy]) f_ref_model = h5py.File(h5_path, 'w') saving.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=keras.optimizers.RMSprop(lr=0.0001), metrics=[keras.metrics.categorical_accuracy]) with self.assertRaisesRegexp(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 \(3, 5\)\.'): saving.load_weights_from_hdf5_group_by_name(f_model, model.layers)
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=keras.optimizers.RMSprop(lr=0.0001), metrics=[keras.metrics.categorical_accuracy]) f_ref_model = h5py.File(h5_path, 'w') saving.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=keras.optimizers.RMSprop(lr=0.0001), metrics=[keras.metrics.categorical_accuracy]) with self.assertRaisesRegexp(ValueError, r'Layer #0 \(named \"d1\"\) expects 1 ' r'weight\(s\), but the saved weights have 2 ' r'element\(s\)\.'): saving.load_weights_from_hdf5_group_by_name(f_model, model.layers)