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Simplify Keras unit tests by removing unnecessary session scopes and introducing a utility function for repeated code.

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PiperOrigin-RevId: 209523944
This commit is contained in:
Francois Chollet 2018-08-20 18:48:13 -07:00 committed by TensorFlower Gardener
parent c5f27df3a5
commit d29759fa53
4 changed files with 701 additions and 760 deletions
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55
tensorflow/python/keras/callbacks_test.py
View File

@ -235,11 +235,8 @@ class KerasCallbacksTest(test.TestCase):
num_classes=NUM_CLASSES)
y_test = keras.utils.to_categorical(y_test)
y_train = keras.utils.to_categorical(y_train)
model = keras.models.Sequential()
model.add(
keras.layers.Dense(
NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=NUM_HIDDEN, num_classes=NUM_CLASSES, input_dim=INPUT_DIM)
model.compile(
loss='categorical_crossentropy',
optimizer='rmsprop',
@ -298,9 +295,8 @@ class KerasCallbacksTest(test.TestCase):
test_samples=50,
input_shape=(1,),
num_classes=NUM_CLASSES)
model = keras.models.Sequential((keras.layers.Dense(
1, input_dim=1, activation='relu'), keras.layers.Dense(
1, activation='sigmoid'),))
model = testing_utils.get_small_sequential_mlp(
num_hidden=1, num_classes=1, input_dim=1)
model.compile(
optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])
@ -334,11 +330,8 @@ class KerasCallbacksTest(test.TestCase):
num_classes=NUM_CLASSES)
y_test = keras.utils.to_categorical(y_test)
y_train = keras.utils.to_categorical(y_train)
model = keras.models.Sequential()
model.add(
keras.layers.Dense(
NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=NUM_HIDDEN, num_classes=NUM_CLASSES, input_dim=INPUT_DIM)
model.compile(
loss='categorical_crossentropy',
optimizer='sgd',
@ -388,12 +381,8 @@ class KerasCallbacksTest(test.TestCase):
def make_model():
random_seed.set_random_seed(1234)
np.random.seed(1337)
model = keras.models.Sequential()
model.add(
keras.layers.Dense(
NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=NUM_HIDDEN, num_classes=NUM_CLASSES, input_dim=INPUT_DIM)
model.compile(
loss='categorical_crossentropy',
optimizer=keras.optimizers.SGD(lr=0.1),
@ -498,12 +487,8 @@ class KerasCallbacksTest(test.TestCase):
def make_model():
np.random.seed(1337)
model = keras.models.Sequential()
model.add(
keras.layers.Dense(
NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=NUM_HIDDEN, num_classes=NUM_CLASSES, input_dim=INPUT_DIM)
model.compile(
loss='categorical_crossentropy',
optimizer=keras.optimizers.SGD(lr=0.1),
@ -985,9 +970,8 @@ class KerasCallbacksTest(test.TestCase):
yield x, y
with self.test_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(10, input_dim=100, activation='relu'))
model.add(keras.layers.Dense(10, activation='softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=10, num_classes=10, input_dim=100)
model.compile(
loss='categorical_crossentropy',
optimizer='sgd',
@ -1083,11 +1067,8 @@ class KerasCallbacksTest(test.TestCase):
y_test = keras.utils.to_categorical(y_test)
y_train = keras.utils.to_categorical(y_train)
model = keras.models.Sequential()
model.add(
keras.layers.Dense(
NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=NUM_HIDDEN, num_classes=NUM_CLASSES, input_dim=INPUT_DIM)
model.compile(
loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])
@ -1179,7 +1160,6 @@ class KerasCallbacksTest(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def test_Tensorboard_eager(self):
with self.test_session():
temp_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
@ -1191,11 +1171,8 @@ class KerasCallbacksTest(test.TestCase):
y_test = keras.utils.to_categorical(y_test)
y_train = keras.utils.to_categorical(y_train)
model = keras.models.Sequential()
model.add(
keras.layers.Dense(
NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=NUM_HIDDEN, num_classes=NUM_CLASSES, input_dim=INPUT_DIM)
model.compile(
loss='binary_crossentropy',
optimizer=adam.AdamOptimizer(0.01),

39
tensorflow/python/keras/engine/sequential_test.py
View File

@ -25,22 +25,12 @@ from tensorflow.python import keras
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.eager import function
from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import testing_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
from tensorflow.python.training import rmsprop
def _get_small_mlp(num_hidden, num_classes, input_dim=None):
model = keras.models.Sequential()
if input_dim:
model.add(keras.layers.Dense(num_hidden, activation='relu',
input_dim=input_dim))
else:
model.add(keras.layers.Dense(num_hidden, activation='relu'))
model.add(keras.layers.Dense(num_classes, activation='softmax'))
return model
class TestSequential(test.TestCase, parameterized.TestCase):
"""Most Sequential model API tests are covered in `training_test.py`.
"""
@ -63,7 +53,8 @@ class TestSequential(test.TestCase, parameterized.TestCase):
batch_size = 5
num_classes = 2
model = _get_small_mlp(num_hidden, num_classes, input_dim)
model = testing_utils.get_small_sequential_mlp(
num_hidden, num_classes, input_dim)
model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3))
x = np.random.random((batch_size, input_dim))
y = np.random.random((batch_size, num_classes))
@ -94,7 +85,7 @@ class TestSequential(test.TestCase, parameterized.TestCase):
batch_size = 5
num_classes = 2
model = _get_small_mlp(num_hidden, num_classes)
model = testing_utils.get_small_sequential_mlp(num_hidden, num_classes)
model.compile(
loss='mse',
optimizer=rmsprop.RMSPropOptimizer(1e-3),
@ -118,7 +109,7 @@ class TestSequential(test.TestCase, parameterized.TestCase):
num_samples = 50
steps_per_epoch = 10
model = _get_small_mlp(num_hidden, num_classes)
model = testing_utils.get_small_sequential_mlp(num_hidden, num_classes)
model.compile(
loss='mse',
optimizer=rmsprop.RMSPropOptimizer(1e-3),
@ -145,9 +136,9 @@ class TestSequential(test.TestCase, parameterized.TestCase):
def get_model():
if deferred:
model = _get_small_mlp(10, 4)
model = testing_utils.get_small_sequential_mlp(10, 4)
else:
model = _get_small_mlp(10, 4, input_dim=3)
model = testing_utils.get_small_sequential_mlp(10, 4, input_dim=3)
model.compile(
optimizer=rmsprop.RMSPropOptimizer(1e-3),
loss='categorical_crossentropy',
@ -262,7 +253,7 @@ class TestSequential(test.TestCase, parameterized.TestCase):
batch_size = 5
num_classes = 2
model = _get_small_mlp(num_hidden, num_classes)
model = testing_utils.get_small_sequential_mlp(num_hidden, num_classes)
model.compile(
loss='mse',
optimizer=rmsprop.RMSPropOptimizer(1e-3),
@ -284,21 +275,21 @@ class TestSequential(test.TestCase, parameterized.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_sequential_shape_inference_deferred(self):
model = _get_small_mlp(4, 5)
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))
@tf_test_util.run_in_graph_and_eager_modes
def test_sequential_build_deferred(self):
model = _get_small_mlp(4, 5)
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 = _get_small_mlp(4, 3)
inner_model = _get_small_mlp(4, 5)
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))
@ -308,8 +299,8 @@ class TestSequential(test.TestCase, parameterized.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_sequential_nesting(self):
model = _get_small_mlp(4, 3)
inner_model = _get_small_mlp(4, 5)
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.RMSPropOptimizer(1e-3))
@ -353,7 +344,7 @@ class TestSequentialEagerIntegration(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_build_before_fit(self):
# Fix for b/112433577
model = _get_small_mlp(4, 5)
model = testing_utils.get_small_sequential_mlp(4, 5)
model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3))
model.build((None, 6))

80
tensorflow/python/keras/engine/training_test.py
View File

@ -49,7 +49,6 @@ class TrainingTest(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_fit_on_arrays(self):
with self.test_session():
a = keras.layers.Input(shape=(3,), name='input_a')
b = keras.layers.Input(shape=(3,), name='input_b')
@ -253,7 +252,6 @@ class TrainingTest(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_evaluate_predict_on_arrays(self):
with self.test_session():
a = keras.layers.Input(shape=(3,), name='input_a')
b = keras.layers.Input(shape=(3,), name='input_b')
@ -340,12 +338,8 @@ class TrainingTest(test.TestCase):
test_samples = 1000
input_dim = 5
with self.test_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dense(num_classes))
model.add(keras.layers.Activation('softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=10, num_classes=num_classes, input_dim=input_dim)
optimizer = RMSPropOptimizer(learning_rate=0.001)
model.compile(optimizer, loss='categorical_crossentropy')
np.random.seed(1337)
@ -468,12 +462,8 @@ class LossWeightingTest(test.TestCase):
input_dim = 5
learning_rate = 0.001
with self.test_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dense(num_classes))
model.add(keras.layers.Activation('softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=10, num_classes=num_classes, input_dim=input_dim)
model.compile(
loss='categorical_crossentropy',
metrics=['acc'],
@ -541,12 +531,8 @@ class LossWeightingTest(test.TestCase):
input_dim = 5
learning_rate = 0.001
with self.test_session():
model = keras.models.Sequential()
model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dense(num_classes))
model.add(keras.layers.Activation('softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=10, num_classes=num_classes, input_dim=input_dim)
model.compile(
RMSPropOptimizer(learning_rate=learning_rate),
metrics=['acc'],
@ -1909,11 +1895,7 @@ class TestTrainingWithDatasetIterators(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_training_and_eval_methods_on_iterators_single_io(self):
with self.test_session():
x = keras.layers.Input(shape=(3,), name='input')
y = keras.layers.Dense(4, name='dense')(x)
model = keras.Model(x, y)
model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
optimizer = RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
metrics = ['mae', metrics_module.CategoricalAccuracy()]
@ -1975,11 +1957,7 @@ class TestTrainingWithDatasetIterators(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_get_next_op_created_once(self):
with self.test_session():
x = keras.layers.Input(shape=(3,), name='input')
y = keras.layers.Dense(4, name='dense')(x)
model = keras.Model(x, y)
model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
optimizer = RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
metrics = ['mae']
@ -2000,11 +1978,7 @@ class TestTrainingWithDatasetIterators(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_iterators_running_out_of_data(self):
with self.test_session():
x = keras.layers.Input(shape=(3,), name='input')
y = keras.layers.Dense(4, name='dense')(x)
model = keras.Model(x, y)
model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
optimizer = RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
metrics = ['mae']
@ -2028,11 +2002,7 @@ class TestTrainingWithDataset(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_calling_model_on_same_dataset(self):
with self.test_session():
x = keras.layers.Input(shape=(3,), name='input')
y = keras.layers.Dense(4, name='dense')(x)
model = keras.Model(x, y)
model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
optimizer = RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
metrics = ['mae']
@ -2055,11 +2025,7 @@ class TestTrainingWithDataset(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_training_and_eval_methods_on_dataset(self):
with self.test_session():
x = keras.layers.Input(shape=(3,), name='input')
y = keras.layers.Dense(4, name='dense')(x)
model = keras.Model(x, y)
model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
optimizer = RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
metrics = ['mae', metrics_module.CategoricalAccuracy()]
@ -2119,13 +2085,8 @@ class TestTrainingWithDataset(test.TestCase):
def test_dataset_input_shape_validation(self):
with self.test_session():
x = keras.layers.Input(shape=(3,), name='input')
y = keras.layers.Dense(4, name='dense')(x)
model = keras.Model(x, y)
optimizer = RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
model.compile(optimizer, loss)
model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
model.compile(optimizer=RMSPropOptimizer(learning_rate=0.001), loss='mse')
# User forgets to batch the dataset
inputs = np.zeros((10, 3))
@ -2134,7 +2095,7 @@ class TestTrainingWithDataset(test.TestCase):
dataset = dataset.repeat(100)
with self.assertRaisesRegexp(ValueError,
'expected input to have 2 dimensions'):
r'expected (.*?) to have 2 dimensions'):
model.train_on_batch(dataset)
# Wrong input shape
@ -2145,7 +2106,7 @@ class TestTrainingWithDataset(test.TestCase):
dataset = dataset.batch(10)
with self.assertRaisesRegexp(ValueError,
'expected input to have shape'):
r'expected (.*?) to have shape \(3,\)'):
model.train_on_batch(dataset)
@ -2176,7 +2137,6 @@ class TestTrainingWithMetrics(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_metrics_correctness(self):
with self.test_session():
model = keras.Sequential()
model.add(
keras.layers.Dense(
@ -2203,7 +2163,6 @@ class TestTrainingWithMetrics(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_metrics_correctness_with_iterator(self):
with self.test_session():
model = keras.Sequential()
model.add(
keras.layers.Dense(
@ -2237,7 +2196,6 @@ class TestTrainingWithMetrics(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_metrics_correctness_with_weighted_metrics(self):
with self.test_session():
np.random.seed(1337)
x = np.array([[[1.], [1.]], [[0.], [0.]]])
model = keras.models.Sequential()
@ -2266,7 +2224,6 @@ class TestTrainingWithMetrics(test.TestCase):
@tf_test_util.run_in_graph_and_eager_modes
def test_metric_state_reset_between_fit_and_evaluate(self):
with self.test_session():
model = keras.Sequential()
model.add(keras.layers.Dense(3, activation='relu', input_dim=4))
model.add(keras.layers.Dense(1, activation='sigmoid'))
@ -2291,11 +2248,8 @@ class TestTrainingWithMetrics(test.TestCase):
num_classes = 5
input_dim = 5
with self.test_session():
model = keras.models.Sequential()
model.add(
keras.layers.Dense(10, activation='relu', input_shape=(input_dim,)))
model.add(keras.layers.Dense(num_classes, activation='softmax'))
model = testing_utils.get_small_sequential_mlp(
num_hidden=10, num_classes=num_classes, input_dim=input_dim)
with self.assertRaisesRegexp(
TypeError, 'Type of `metrics` argument not understood. '

19
tensorflow/python/keras/testing_utils.py
View File

@ -184,3 +184,22 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
# for further checks in the caller function
return actual_output
def get_small_sequential_mlp(num_hidden, num_classes, input_dim=None):
model = keras.models.Sequential()
if input_dim:
model.add(keras.layers.Dense(num_hidden, activation='relu',
input_dim=input_dim))
else:
model.add(keras.layers.Dense(num_hidden, activation='relu'))
activation = 'sigmoid' if num_classes == 1 else 'softmax'
model.add(keras.layers.Dense(num_classes, activation=activation))
return model
def get_small_functional_mlp(num_hidden, num_classes, input_dim):
inputs = keras.Input(shape=(input_dim,))
outputs = keras.layers.Dense(num_hidden, activation='relu')(inputs)
activation = 'sigmoid' if num_classes == 1 else 'softmax'
outputs = keras.layers.Dense(num_classes, activation=activation)(outputs)
return keras.Model(inputs, outputs)