def test_reset():
sj.current_graph().reset()
w = symjax.tensor.Variable(1.0, name="w", dtype="float32")
x = symjax.tensor.Variable(2.0, name="x", dtype="float32")
f = symjax.function(outputs=[w, x], updates={w: w + 1, x: x + 1})
for i in range(10):
print(i)
assert np.array_equal(np.array(f()), np.array([1, 2]) + i)
# reset only the w variable
symjax.reset_variables("*w")
assert np.array_equal(np.array(f()), np.array([1, 2 + i + 1]))
# reset all variables
symjax.reset_variables("*")
assert np.array_equal(np.array(f()), np.array([1, 2]))
import symjax.tensor as T
import matplotlib.pyplot as plt
# GRADIENT DESCENT
z = T.Variable(3.0, dtype="float32")
loss = (z - 1) ** 2
g_z = symjax.gradients(loss, [z])[0]
symjax.current_graph().add_updates({z: z - 0.1 * g_z})
train = symjax.function(outputs=[loss, z], updates=symjax.get_updates())
losses = list()
values = list()
for i in range(200):
if (i + 1) % 50 == 0:
symjax.reset_variables("*")
a, b = train()
losses.append(a)
values.append(b)
plt.figure()
plt.subplot(121)
plt.plot(losses, "-x")
plt.ylabel("loss")
plt.xlabel("number of gradient updates")
plt.subplot(122)
plt.plot(values, "-x")
plt.axhline(1, c="red")
plt.ylabel("value")
})
# pretend we train for a while
for i in range(4):
print(train())
# [0.]
# [8.]
# [16.]
# [24.]
# now say we wanted to reset the variables and retrain, we can do
# either with g, as it contains all the variables
g.reset()
# or we can do
symjax.reset_variables("*")
# or if we wanted to only reset say variables from layer2
symjax.reset_variables("*layer2*")
# now that all has been reset, let's retrain for a while
# pretend we train for a while
for i in range(2):
print(train())
# [0.]
# [8.]
# now resetting is nice, but we might want to save the model parameters, to
# keep training later or do some other analyses. We can do so as follows:
g.save_variables("model1_saved")
# this would save all variables as they are contained in g. Now say we want to