Ich versuche MXNet Gradientenabstiegsoptimierer zu verwenden, um eine Funktion zu minimieren. Das äquivalente Beispiel in Tensorflow wäre:Einfacher Gradientabstieg mit mxnet
import tensorflow as tf
x = tf.Variable(2, name='x', dtype=tf.float32)
log_x = tf.log(x)
log_x_squared = tf.square(log_x)
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(log_x_squared)
init = tf.initialize_all_variables()
def optimize():
with tf.Session() as session:
session.run(init)
print("starting at", "x:", session.run(x), "log(x)^2:", session.run(log_x_squared))
for step in range(10):
session.run(train)
print("step", step, "x:", session.run(x), "log(x)^2:", session.run(log_x_squared))
Ich bin nicht sicher, wie man das gleiche in MxNet zu erreichen. Die Optimierer-API documentation scheint keine äquivalente Methode zu haben. Hier ist, was ich bisher versucht habe. Die Haupt Verwirrung um die Notwendigkeit, Trainingsdaten weitergeben müssen:
import mxnet as mx
x = mx.sym.Variable('data')
log_x = mx.sym.log(x)
log_x_squared = mx.sym.square(log_x)
mod = mx.mod.Module(log_x_squared) # Create a module where the loss function
# is the one we want to optimize
mod.bind(data_shapes=[('data', (1,1))]) # ?? not sure if this is correct - we
# are saying our input is a scalar
mod.init_params()
mod.init_optimizer() # SGD is default
mod.fit() # ?? must pass data_iter to fit
Es scheint, wie die x
Variable sollte irgendwie in als data_iter
zurückgeführt werden, aber ich weiß nicht, wie dies zu erreichen.
Update: dank kevinthesun für ihre ausgezeichnete Antwort! Hier ist eine Arbeitsminimierungsroutine auf einer einzelnen versteckten Schicht des neuronalen Netzes gebaut:
import mxnet as mx
import numpy as np
def minimize(objective_function,
initial_params,
max_iters=1000,
optimizer='sgd',
optimizer_params=(('learning_rate', 0.1),),
tol=1e-8):
class InitialParam(mx.init.Initializer):
def __init__(self, vals):
super(InitialParam, self).__init__()
self._vals = vals
def _init_weight(self, _, arr):
arr[:] = self._vals.asnumpy()[:, np.newaxis]
x = mx.sym.Variable('data')
params_len = initial_params.shape[0]
fc = mx.sym.FullyConnected(data=x, name='fc1',
num_hidden=params_len,
no_bias=True)
# Passing the FullyConnected layer into the objective function
# is difficult to manipulate. If the fully connected layer represents
# [x, y] for optimizing a 2 dimensional function f(x, y) it is easier
# to work with x, and y. So we split the fully connected layer into a
# number of symbols for each parameter:
param_syms = []
for i in range(params_len):
ps = mx.sym.slice(fc, begin=(0, i), end=(1, i + 1))
param_syms.append(ps)
# The loss function for the network is our objective function.
loss = mx.sym.MakeLoss(objective_function(param_syms))
mod = mx.mod.Module(loss)
mod.bind(data_shapes=[('data', (1,))])
mod.init_params(InitialParam(initial_params))
mod.init_optimizer(optimizer=optimizer,
optimizer_params=optimizer_params)
(o_name, o_shape), = mod.output_shapes
i = 0
params = initial_params
old_val = np.full(o_shape, np.nan)
while i < max_iters:
mod.forward_backward(mx.io.DataBatch(
data=[mx.nd.ones((1,))]))
mod.update()
params = mod.get_params()[0]['fc1_weight']
val = mod.get_outputs()[0].asnumpy()
if np.allclose(old_val, val, atol=tol):
print 'Function value: {}'.format(val)
print 'Iterations: {}'.format(i)
return params
old_val = val
i += 1
return params
und dessen Verwendung:
def my_func(x):
return (x[0] + 1) ** 2
p = minimize(my_func, mx.nd.array([1.0]))
p.asnumpy()
>>> array([[-0.99999988]], dtype=float32)
und eine andere:
def my_func(x):
return (x[0] + 1) ** 2 + (x[1] - 2) ** 2 + (x[2] + 3) ** 2
p = minimize(my_func, mx.nd.array([1.0, 1.5, 2.0]))
p.asnumpy()
>>> array([[-0.99996436],
[ 1.99999106],
[-2.99991083]], dtype=float32)