def test_batchrenorm_mode_0_or_2_twice():
# This is a regression test for issue #4881 with the old
# batch normalization functions in the Theano backend.
model = Sequential()
model.add(normalization.BatchRenormalization(input_shape=(10, 5, 5), axis=1))
model.add(normalization.BatchRenormalization(input_shape=(10, 5, 5), axis=1))
model.compile(loss='mse', optimizer='sgd')
X = np.random.normal(loc=5.0, scale=10.0, size=(20, 10, 5, 5))
model.fit(X, X, epochs=1, verbose=0)
model.predict(X)
def test_shared_batchrenorm():
'''Test that a BN layer can be shared
across different data streams.
'''
# Test single layer reuse
bn = normalization.BatchRenormalization(input_shape=(10, ))
x1 = Input(shape=(10, ))
bn(x1)
x2 = Input(shape=(10, ))
y2 = bn(x2)
x = np.random.normal(loc=5.0, scale=10.0, size=(2, 10))
model = Model(x2, y2)
assert len(model.updates) == 5
model.compile('sgd', 'mse')
model.train_on_batch(x, x)
# Test model-level reuse
x3 = Input(shape=(10, ))
y3 = model(x3)
new_model = Model(x3, y3)
assert len(model.updates) == 5
new_model.compile('sgd', 'mse')
new_model.train_on_batch(x, x)
def test_batchrenorm_mode_1():
norm_m1 = normalization.BatchRenormalization(input_shape=(10, ), mode=1)
norm_m1.build(input_shape=(None, 10))
for inp in [input_1, input_2, input_3]:
out = (norm_m1.call(K.variable(inp)) - norm_m1.beta) / norm_m1.gamma
assert_allclose(K.eval(K.mean(out)), 0.0, atol=1e-1)
if inp.std() > 0.:
assert_allclose(K.eval(K.std(out)), 1.0, atol=1e-1)
else:
assert_allclose(K.eval(K.std(out)), 0.0, atol=1e-1)
def test_batchrenorm_mode_0_convnet():
model = Sequential()
norm_m0 = normalization.BatchRenormalization(axis=1, input_shape=(3, 4, 4), momentum=0.8)
model.add(norm_m0)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
X = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
model.fit(X, X, epochs=4, verbose=0)
out = model.predict(X)
out -= np.reshape(K.eval(norm_m0.beta), (1, 3, 1, 1))
out /= np.reshape(K.eval(norm_m0.gamma), (1, 3, 1, 1))
assert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
assert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)
def test_batchrenorm_mode_0_or_2():
for training in [1, 0]:
model = Sequential()
norm_m0 = normalization.BatchRenormalization(input_shape=(10,), momentum=0.8)
model.add(norm_m0)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
X = np.random.normal(loc=5.0, scale=10.0, size=(1000, 10))
model.fit(X, X, epochs=4, verbose=0)
out = model.predict(X)
out -= K.eval(norm_m0.beta)
out /= K.eval(norm_m0.gamma)
assert_allclose(out.mean(), 0.0, atol=1e-1)
assert_allclose(out.std(), 1.0, atol=1e-1)
def test_batchrenorm_clipping_schedule():
'''Test that the clipping schedule isn't fixed at r_max=1, d_max=0'''
inp = Input(shape=(10, ))
bn = normalization.BatchRenormalization(t_delta=1.)
out = bn(inp)
model = Model(inp, out)
model.compile('sgd', 'mse')
x = np.random.normal(5, 10, size=(2, 10))
y = np.random.normal(5, 10, size=(2, 10))
r_max, d_max = K.get_value(bn.r_max), K.get_value(bn.d_max)
assert r_max == 1
assert d_max == 0
for i in range(10):
model.train_on_batch(x, y)
r_max, d_max = K.get_value(bn.r_max), K.get_value(bn.d_max)
assert_allclose([r_max, d_max], [3, 5], atol=1e-1)
def test_batchrenorm_get_config():
'''Test that get_config works on a model with a batchrenorm layer.'''
x = Input(shape=(10, ))
y = normalization.BatchRenormalization()(x)
model = Model(x, y)
model.get_config()
