def test_cyclic_lr_custom_fn_test():
X = np.random.rand(4000, 2)
y = np.random.rand(4000).reshape(-1, 1)
def clr_fn(x):
return 1 / (5**(x * 0.0001))
clr = callbacks.CyclicLR(scale_fn=clr_fn, scale_mode="iterations")
model = build_model()
model.compile(optimizer="sgd",
loss="binary_crossentropy",
metrics=["accuracy"])
model.fit(X, y, batch_size=1, epochs=2, verbose=0, callbacks=[clr])
custom_range = []
def scale_fn(i):
c = 0.006 - 0.001
return 0.001 + c * np.maximum(0, (1 - x(i))) * 1 / (5**(i * 0.0001))
for i in range(8000):
custom_range.append(scale_fn(i + 1))
assert_allclose(clr.history["lr"], np.array(custom_range))
def triangular_1_test(X, y):
clr = callbacks.CyclicLR()
model = build_model()
model.compile(
optimizer='sgd',
loss='binary_crossentropy',
metrics=['accuracy']
)
model.fit(X, y, batch_size=1, epochs=1, verbose=0, callbacks=[clr])
r = np.concatenate([
np.linspace(0.001, 0.006, num=2001)[1:],
np.linspace(0.006, 0.001, num=2001)[1:]
])
assert_allclose(clr.history['lr'], r)
def test_cyclic_lr_triangular_1():
X = np.random.rand(4000, 2)
y = np.random.rand(4000).reshape(-1, 1)
clr = callbacks.CyclicLR()
model = build_model()
model.compile(optimizer="sgd",
loss="binary_crossentropy",
metrics=["accuracy"])
model.fit(X, y, batch_size=1, epochs=1, verbose=0, callbacks=[clr])
r = np.concatenate([
np.linspace(0.001, 0.006, num=2001)[1:],
np.linspace(0.006, 0.001, num=2001)[1:],
])
assert_allclose(clr.history["lr"], r)
def exp_range_test(X, y):
clr = callbacks.CyclicLR(mode='exp_range', gamma=0.9996)
model = build_model()
model.compile(
optimizer='sgd',
loss='binary_crossentropy',
metrics=['accuracy']
)
model.fit(X, y, batch_size=1, epochs=2, verbose=0, callbacks=[clr])
exp_range = []
def scale_fn(i):
return 0.001 + (0.006 - 0.001) * \
np.maximum(0, (1 - x(i))) * (0.9996**(i))
for i in range(8000):
exp_range.append(scale_fn(i + 1))
assert_allclose(clr.history['lr'], np.array(exp_range))
def test_cyclic_lr_triangular_2():
X = np.random.rand(4000, 2)
y = np.random.rand(4000).reshape(-1, 1)
clr = callbacks.CyclicLR(mode='triangular2')
model = build_model()
model.compile(optimizer='sgd',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(X, y, batch_size=1, epochs=2, verbose=0, callbacks=[clr])
r = np.concatenate([
np.linspace(0.001, 0.006, num=2001)[1:],
np.linspace(0.006, 0.001, num=2001)[1:],
np.linspace(0.001, 0.0035, num=2001)[1:],
np.linspace(0.0035, 0.001, num=2001)[1:],
])
assert_allclose(clr.history['lr'], r)
def test_cyclic_lr_exp_range():
X = np.random.rand(4000, 2)
y = np.random.rand(4000).reshape(-1, 1)
clr = callbacks.CyclicLR(mode="exp_range", gamma=0.9996)
model = build_model()
model.compile(optimizer="sgd",
loss="binary_crossentropy",
metrics=["accuracy"])
model.fit(X, y, batch_size=1, epochs=2, verbose=0, callbacks=[clr])
exp_range = []
def scale_fn(i):
return 0.001 + (0.006 - 0.001) * np.maximum(0,
(1 - x(i))) * (0.9996**i)
for i in range(8000):
exp_range.append(scale_fn(i + 1))
assert_allclose(clr.history["lr"], np.array(exp_range))
def custom_fn_test(X, y):
def clr_fn(x):
return 1 / (5**(x * 0.0001))
clr = callbacks.CyclicLR(scale_fn=clr_fn, scale_mode='iterations')
model = build_model()
model.compile(
optimizer='sgd',
loss='binary_crossentropy',
metrics=['accuracy']
)
model.fit(X, y, batch_size=1, epochs=2, verbose=0, callbacks=[clr])
custom_range = []
def scale_fn(i):
return 0.001 + (0.006 - 0.001) * \
np.maximum(0, (1 - x(i))) * 1 / (5**(i * 0.0001))
for i in range(8000):
custom_range.append(scale_fn(i + 1))
assert_allclose(clr.history['lr'], np.array(custom_range))