| # Copyright 2018 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Tests specific to `Sequential` model.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| from absl.testing import parameterized |
| import numpy as np |
| |
| from tensorflow.python import keras |
| from tensorflow.python.data.ops import dataset_ops |
| from tensorflow.python.eager import context |
| from tensorflow.python.eager import function |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.framework import test_util as tf_test_util |
| from tensorflow.python.keras import keras_parameterized |
| from tensorflow.python.keras import testing_utils |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.platform import test |
| |
| |
| class TestSequential(keras_parameterized.TestCase): |
| """Most Sequential model API tests are covered in `training_test.py`. |
| """ |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_basic_methods(self): |
| model = keras.models.Sequential() |
| model.add(keras.layers.Dense(1, input_dim=2)) |
| model.add(keras.layers.Dropout(0.3, name='dp')) |
| model.add(keras.layers.Dense(2, kernel_regularizer='l2', |
| kernel_constraint='max_norm')) |
| self.assertEqual(len(model.layers), 3) |
| self.assertEqual(len(model.weights), 2 * 2) |
| self.assertEqual(model.get_layer(name='dp').name, 'dp') |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_input_defined_first_layer(self): |
| model = keras.models.Sequential() |
| model.add(keras.Input(shape=(2,), name='input_layer')) |
| model.add(keras.layers.Dense(1)) |
| model.add(keras.layers.Dropout(0.3, name='dp')) |
| model.add(keras.layers.Dense(2, kernel_regularizer='l2', |
| kernel_constraint='max_norm')) |
| self.assertLen(model.layers, 3) |
| self.assertLen(model.weights, 2 * 2) |
| self.assertEqual(model.get_layer(name='dp').name, 'dp') |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_single_layer_in_init(self): |
| model = keras.models.Sequential(keras.layers.Dense(1)) |
| self.assertLen(model.layers, 1) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_sequential_pop(self): |
| num_hidden = 5 |
| input_dim = 3 |
| batch_size = 5 |
| num_classes = 2 |
| |
| model = testing_utils.get_small_sequential_mlp( |
| num_hidden, num_classes, input_dim) |
| model.compile( |
| loss='mse', |
| optimizer='rmsprop', |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| x = np.random.random((batch_size, input_dim)) |
| y = np.random.random((batch_size, num_classes)) |
| model.fit(x, y, epochs=1) |
| model.pop() |
| self.assertEqual(len(model.layers), 1) |
| self.assertEqual(model.output_shape, (None, num_hidden)) |
| model.compile( |
| loss='mse', |
| optimizer='rmsprop', |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| y = np.random.random((batch_size, num_hidden)) |
| model.fit(x, y, epochs=1) |
| |
| # Test popping single-layer model |
| model = keras.models.Sequential() |
| model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) |
| model.pop() |
| self.assertEqual(model.layers, []) |
| self.assertEqual(model.outputs, None) |
| |
| # Invalid use case |
| model = keras.models.Sequential() |
| with self.assertRaises(TypeError): |
| model.pop() |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_sequential_deferred_build_with_np_arrays(self): |
| num_hidden = 5 |
| input_dim = 3 |
| batch_size = 5 |
| num_classes = 2 |
| |
| model = testing_utils.get_small_sequential_mlp(num_hidden, num_classes) |
| model.compile( |
| loss='mse', |
| optimizer='rmsprop', |
| metrics=[keras.metrics.CategoricalAccuracy()], |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| self.assertEqual(len(model.layers), 2) |
| with self.assertRaisesRegexp( |
| ValueError, 'Weights for model .* have not yet been created'): |
| len(model.weights) |
| self.assertFalse(model.built) |
| |
| x = np.random.random((batch_size, input_dim)) |
| y = np.random.random((batch_size, num_classes)) |
| model.fit(x, y, epochs=1) |
| self.assertTrue(model.built) |
| self.assertFalse(model._is_graph_network) |
| self.assertEqual(len(model.weights), 2 * 2) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_sequential_deferred_build_with_dataset_iterators(self): |
| num_hidden = 5 |
| input_dim = 3 |
| num_classes = 2 |
| num_samples = 50 |
| steps_per_epoch = 10 |
| |
| model = testing_utils.get_small_sequential_mlp(num_hidden, num_classes) |
| model.compile( |
| loss='mse', |
| optimizer='rmsprop', |
| metrics=[keras.metrics.CategoricalAccuracy()], |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| self.assertEqual(len(model.layers), 2) |
| with self.assertRaisesRegexp( |
| ValueError, 'Weights for model .* have not yet been created'): |
| len(model.weights) |
| self.assertFalse(model.built) |
| |
| x = array_ops.ones((num_samples, input_dim)) |
| y = array_ops.zeros((num_samples, num_classes)) |
| dataset = dataset_ops.Dataset.from_tensor_slices((x, y)) |
| dataset = dataset.repeat(100) |
| dataset = dataset.batch(10) |
| |
| model.fit(dataset, epochs=1, steps_per_epoch=steps_per_epoch) |
| self.assertTrue(model.built) |
| self.assertEqual(len(model.weights), 2 * 2) |
| self.assertFalse(model._is_graph_network) |
| |
| # TODO(kaftan) This test fails w/ run_with_all_keras_modes. File ticket |
| @parameterized.parameters((True,), (False,)) |
| @tf_test_util.run_deprecated_v1 |
| def test_training_and_eval_methods_on_symbolic_tensors(self, deferred): |
| with self.cached_session(): |
| |
| def get_model(): |
| if deferred: |
| model = testing_utils.get_small_sequential_mlp(10, 4) |
| else: |
| model = testing_utils.get_small_sequential_mlp(10, 4, input_dim=3) |
| model.compile( |
| optimizer='rmsprop', |
| loss='categorical_crossentropy', |
| metrics=['accuracy']) |
| return model |
| |
| inputs = keras.backend.zeros(shape=(10, 3)) |
| targets = keras.backend.zeros(shape=(10, 4)) |
| |
| model = get_model() |
| model.fit(inputs, targets, epochs=10, steps_per_epoch=30) |
| |
| model = get_model() |
| model.evaluate(inputs, targets, steps=2, verbose=0) |
| |
| model = get_model() |
| model.predict(inputs, steps=2) |
| |
| model = get_model() |
| model.train_on_batch(inputs, targets) |
| |
| model = get_model() |
| model.test_on_batch(inputs, targets) |
| |
| model = get_model() |
| model.fit( |
| inputs, |
| targets, |
| epochs=1, |
| steps_per_epoch=2, |
| verbose=0, |
| validation_data=(inputs, targets), |
| validation_steps=2) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_invalid_use_cases(self): |
| # Added objects must be layer instances |
| with self.assertRaises(TypeError): |
| model = keras.models.Sequential() |
| model.add(None) |
| |
| # Added layers cannot have multiple outputs |
| class MyLayer(keras.layers.Layer): |
| |
| def call(self, inputs): |
| return [3 * inputs, 2 * inputs] |
| |
| def compute_output_shape(self, input_shape): |
| return [input_shape, input_shape] |
| |
| with self.assertRaises(ValueError): |
| model = keras.models.Sequential() |
| model.add(MyLayer(input_shape=(3,))) |
| with self.assertRaises(TypeError): |
| model = keras.models.Sequential() |
| model.add(keras.layers.Dense(1, input_dim=1)) |
| model.add(MyLayer()) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_nested_sequential_trainability(self): |
| input_dim = 20 |
| num_units = 10 |
| num_classes = 2 |
| |
| inner_model = keras.models.Sequential() |
| inner_model.add(keras.layers.Dense(num_units, input_shape=(input_dim,))) |
| |
| model = keras.models.Sequential() |
| model.add(inner_model) |
| model.add(keras.layers.Dense(num_classes)) |
| |
| self.assertEqual(len(model.layers), 2) |
| |
| self.assertEqual(len(model.trainable_weights), 4) |
| inner_model.trainable = False |
| self.assertEqual(len(model.trainable_weights), 2) |
| inner_model.trainable = True |
| self.assertEqual(len(model.trainable_weights), 4) |
| |
| def test_sequential_update_disabling(self): |
| val_a = np.random.random((10, 4)) |
| val_out = np.random.random((10, 4)) |
| |
| with self.cached_session(): |
| model = keras.models.Sequential() |
| model.add(keras.layers.BatchNormalization(input_shape=(4,))) |
| assert model.updates |
| |
| model.trainable = False |
| assert not model.updates |
| |
| model.compile('sgd', 'mse') |
| assert not model.updates |
| |
| x1 = model.predict(val_a) |
| model.train_on_batch(val_a, val_out) |
| x2 = model.predict(val_a) |
| self.assertAllClose(x1, x2, atol=1e-7) |
| |
| model.trainable = True |
| model.compile('sgd', 'mse') |
| assert model.updates |
| |
| model.train_on_batch(val_a, val_out) |
| x2 = model.predict(val_a) |
| assert np.abs(np.sum(x1 - x2)) > 1e-5 |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_sequential_deferred_build_serialization(self): |
| num_hidden = 5 |
| input_dim = 3 |
| batch_size = 5 |
| num_classes = 2 |
| |
| model = testing_utils.get_small_sequential_mlp(num_hidden, num_classes) |
| model.compile( |
| loss='mse', |
| optimizer='rmsprop', |
| metrics=[keras.metrics.CategoricalAccuracy()], |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| self.assertFalse(model.built) |
| |
| x = np.random.random((batch_size, input_dim)) |
| y = np.random.random((batch_size, num_classes)) |
| model.train_on_batch(x, y) |
| self.assertTrue(model.built) |
| |
| config = model.get_config() |
| self.assertIn('build_input_shape', config) |
| |
| new_model = keras.models.Sequential.from_config(config) |
| self.assertEqual(len(new_model.layers), 2) |
| self.assertEqual(len(new_model.weights), 4) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_sequential_shape_inference_deferred(self): |
| model = testing_utils.get_small_sequential_mlp(4, 5) |
| output_shape = model.compute_output_shape((None, 7)) |
| self.assertEqual(tuple(output_shape.as_list()), (None, 5)) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_sequential_build_deferred(self): |
| model = testing_utils.get_small_sequential_mlp(4, 5) |
| |
| model.build((None, 10)) |
| self.assertTrue(model.built) |
| self.assertEqual(len(model.weights), 4) |
| |
| # Test with nested model |
| model = testing_utils.get_small_sequential_mlp(4, 3) |
| inner_model = testing_utils.get_small_sequential_mlp(4, 5) |
| model.add(inner_model) |
| |
| model.build((None, 10)) |
| self.assertTrue(model.built) |
| self.assertEqual(len(model.weights), 8) |
| |
| @keras_parameterized.run_all_keras_modes(always_skip_v1=True) |
| def test_sequential_deferred_manual_build(self): |
| model = testing_utils.get_small_sequential_mlp(4, 5) |
| self.assertFalse(model.built) |
| model(array_ops.zeros([1, 2])) |
| self.assertTrue(model.built) |
| self.assertEqual(len(model.outputs), 0) |
| model.compile( |
| 'rmsprop', |
| loss='mse', |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| self.assertEqual(len(model.outputs), 0) |
| model.train_on_batch(np.zeros((1, 2)), np.zeros((1, 5))) |
| self.assertEqual(len(model.outputs), 1) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_sequential_nesting(self): |
| model = testing_utils.get_small_sequential_mlp(4, 3) |
| inner_model = testing_utils.get_small_sequential_mlp(4, 5) |
| model.add(inner_model) |
| |
| model.compile( |
| loss='mse', |
| optimizer='rmsprop', |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| x = np.random.random((2, 6)) |
| y = np.random.random((2, 5)) |
| model.fit(x, y, epochs=1) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_variable_names(self): |
| model = keras.models.Sequential([keras.layers.Dense(3)]) |
| model.add(keras.layers.Dense(2)) |
| model(array_ops.ones([2, 4])) |
| self.assertEqual( |
| ['sequential/dense/kernel:0', 'sequential/dense/bias:0', |
| 'sequential/dense_1/kernel:0', 'sequential/dense_1/bias:0'], |
| [v.name for v in model.variables]) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_input_assumptions_propagation(self): |
| model = keras.models.Sequential() |
| model.add(keras.layers.Dense(1)) |
| if context.executing_eagerly(): |
| with self.assertRaisesRegexp(ValueError, |
| 'expected min_ndim=2, found ndim=0'): |
| model(1.0) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_string_input(self): |
| seq = keras.Sequential([ |
| keras.layers.InputLayer(input_shape=(1,), dtype=dtypes.string), |
| keras.layers.Lambda(lambda x: x[0]) |
| ]) |
| seq.run_eagerly = testing_utils.should_run_eagerly() |
| seq._experimental_run_tf_function = testing_utils.should_run_tf_function() |
| preds = seq.predict([['tensorflow eager']]) |
| self.assertEqual(preds.shape, (1,)) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_multi_output_layer_not_accepted(self): |
| |
| class MultiOutputLayer(keras.layers.Layer): |
| |
| def call(self, inputs): |
| return inputs, inputs |
| |
| with self.assertRaisesRegexp( |
| ValueError, 'should have a single output tensor'): |
| keras.Sequential([MultiOutputLayer(input_shape=(3,))]) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_layer_add_after_compile_deferred(self): |
| model = keras.Sequential([keras.layers.Dense(3)]) |
| |
| self.assertFalse(model.built) |
| self.assertFalse(model.inputs) |
| self.assertFalse(model.outputs) |
| |
| model.compile('adam', loss='mse') |
| model.fit(np.random.random((1, 3)), np.random.random((1, 3))) |
| |
| self.assertTrue(model.built) |
| self.assertTrue(model.inputs) |
| self.assertTrue(model.outputs) |
| |
| model.add(keras.layers.Dense(3)) |
| |
| self.assertTrue(model.built) |
| self.assertTrue(model.inputs) |
| self.assertTrue(model.outputs) |
| |
| model.compile('adam', loss='mse') |
| model.fit(np.random.random((1, 3)), np.random.random((1, 3))) |
| |
| def test_sequential_layer_tracking(self): |
| """Test that Sequential only tracks layers added in init or `.add`.""" |
| layer = keras.layers.Dense(1) |
| model = keras.Sequential([layer]) |
| self.assertEqual(model._layers[-1], layer) |
| |
| model.a = [keras.layers.Dense(3)] # should not be added to the layers list. |
| self.assertEqual(model._layers[-1], layer) |
| |
| layer2 = keras.layers.Dense(2) |
| model.add(layer2) |
| self.assertEqual(model._layers[-1], layer2) |
| |
| model.a = [keras.layers.Dense(3)] # should not be added to the layers list. |
| self.assertEqual(model._layers[-1], layer2) |
| |
| model.pop() |
| self.assertEqual(model._layers[-1], layer) |
| |
| |
| class TestSequentialEagerIntegration(keras_parameterized.TestCase): |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_defun_on_call(self): |
| # Check that one can subclass Sequential and place the `call` in a `defun`. |
| |
| class MySequential(keras.Sequential): |
| |
| def __init__(self, name=None): |
| super(MySequential, self).__init__(name=name) |
| self.call = function.defun(self.call) |
| |
| model = MySequential() |
| model.add(keras.layers.Dense(4, activation='relu')) |
| model.add(keras.layers.Dense(5, activation='softmax')) |
| |
| model.compile( |
| loss='mse', |
| optimizer='rmsprop', |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| |
| x = np.random.random((2, 6)) |
| y = np.random.random((2, 5)) |
| model.fit(x, y, epochs=1) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_build_before_fit(self): |
| # Fix for b/112433577 |
| model = testing_utils.get_small_sequential_mlp(4, 5) |
| model.compile( |
| loss='mse', |
| optimizer='rmsprop', |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| |
| model.build((None, 6)) |
| |
| x = np.random.random((2, 6)) |
| y = np.random.random((2, 5)) |
| model.fit(x, y, epochs=1) |
| |
| @keras_parameterized.run_all_keras_modes |
| def test_sequential_model_fails_with_dict_inputs(self): |
| num_classes = 5 |
| model = testing_utils.get_small_sequential_mlp( |
| num_hidden=10, num_classes=num_classes) |
| model.compile( |
| 'rmsprop', |
| metrics=['acc'], |
| weighted_metrics=['mae'], |
| loss='categorical_crossentropy', |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| |
| x = {'dense_input': np.random.random((10, 1))} |
| y = np.random.randint(num_classes, size=(10, 1)) |
| |
| with self.assertRaisesRegexp( |
| ValueError, 'Passing a dictionary input to a Sequential Model which ' |
| 'doesn\'t have FeatureLayer as the first layer is an error'): |
| model.fit(x, y, batch_size=5, epochs=1) |
| |
| |
| if __name__ == '__main__': |
| test.main() |
