def test_adam(self):
with self.cached_session():
self._test_optimizer(optimizer_v1.Adam())
# Accuracy seems dependent on the seed initialization.
# TODO(b/121051441): fix test flakiness.
self._test_optimizer(optimizer_v1.Adam(decay=1e-3), target=0.73)
self._test_optimizer(optimizer_v1.Adam(amsgrad=True))
def testSetWeightsFromV1AdamWithoutMinimize(self):
keras_v1_adam = optimizer_v1.Adam()
keras_v2_adam = adam.Adam()
keras_v2_adam.set_weights(keras_v1_adam.get_weights())
keras_v1_iteration = keras_v1_adam.iterations
keras_v2_iteration = keras_v2_adam.iterations
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertEqual(
self.evaluate(keras_v1_iteration), self.evaluate(keras_v2_iteration))
def test_model_saving_to_pre_created_h5py_file(self):
saved_model_dir = self._save_model_dir()
save_format = test_utils.get_save_format()
with tf.Graph().as_default(), self.cached_session():
inputs = keras.Input(shape=(3,))
x = keras.layers.Dense(2)(inputs)
outputs = keras.layers.Dense(3)(x)
model = keras.Model(inputs, outputs)
model.compile(
loss=keras.losses.MSE,
optimizer=optimizer_v1.Adam(),
metrics=[
keras.metrics.categorical_accuracy,
keras.metrics.CategoricalAccuracy()
])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
keras.models.save_model(model, saved_model_dir, save_format=save_format)
loaded_model = keras.models.load_model(saved_model_dir)
out1 = loaded_model.predict(x)
self.assertAllClose(out, out1, atol=1e-05)
if save_format in ['tf', 'tensorflow']:
return
# Test h5 format specifically
fd, fname = tempfile.mkstemp('.h5')
with h5py.File(fname, mode='r+') as h5file:
keras.models.save_model(model, h5file)
loaded_model = keras.models.load_model(h5file)
out2 = loaded_model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
# Test non-default options in h5
with h5py.File(
'_', driver='core', mode='w', backing_store=False) as h5file:
keras.models.save_model(model, h5file)
loaded_model = keras.models.load_model(h5file)
out2 = loaded_model.predict(x)
self.assertAllClose(out, out2, atol=1e-05)
# Cleanup
os.close(fd)
os.remove(fname)
def testNumericEquivalenceForAmsgrad(self):
if tf.executing_eagerly():
self.skipTest(
'v1 optimizer does not run in eager mode')
np.random.seed(1331)
with test_utils.use_gpu():
train_samples = 20
input_dim = 3
num_classes = 2
(x, y), _ = test_utils.get_test_data(
train_samples=train_samples,
test_samples=10,
input_shape=(input_dim,),
num_classes=num_classes)
y = np_utils.to_categorical(y)
num_hidden = 5
model_k_v1 = test_utils.get_small_sequential_mlp(
num_hidden=num_hidden, num_classes=num_classes, input_dim=input_dim)
model_k_v2 = test_utils.get_small_sequential_mlp(
num_hidden=num_hidden, num_classes=num_classes, input_dim=input_dim)
model_k_v2.set_weights(model_k_v1.get_weights())
opt_k_v1 = optimizer_v1.Adam(amsgrad=True)
opt_k_v2 = adam.Adam(amsgrad=True)
model_k_v1.compile(
opt_k_v1,
loss='categorical_crossentropy',
metrics=[],
run_eagerly=test_utils.should_run_eagerly())
model_k_v2.compile(
opt_k_v2,
loss='categorical_crossentropy',
metrics=[],
run_eagerly=test_utils.should_run_eagerly())
hist_k_v1 = model_k_v1.fit(x, y, batch_size=5, epochs=10, shuffle=False)
hist_k_v2 = model_k_v2.fit(x, y, batch_size=5, epochs=10, shuffle=False)
self.assertAllClose(model_k_v1.get_weights(), model_k_v2.get_weights())
self.assertAllClose(opt_k_v1.get_weights(), opt_k_v2.get_weights())
self.assertAllClose(hist_k_v1.history['loss'], hist_k_v2.history['loss'])
def test_negative_clipvalue_or_clipnorm(self):
with self.assertRaises(ValueError):
_ = optimizer_v1.SGD(lr=0.01, clipvalue=-0.5)
with self.assertRaises(ValueError):
_ = optimizer_v1.Adam(clipnorm=-2.0)
def testAdamCompatibility(self): opt_v1 = optimizer_v1.Adam() opt_v2 = adam.Adam() self._testOptimizersCompatibility(opt_v1, opt_v2)