【发布时间】：2016-03-24 13:12:42
【问题描述】：
我正在熟悉 Theano 和机器学习。为此，我想计算一个线性回归。我的代码灵感来自 Theano 简介中的 logistic regression example。
我写了以下代码：
import numpy
import theano
import theano.tensor as T
class LinearRegression(object):
""" Calculate Linear Regression """
def __init__(self, input):
""" Initialize the parameters of the logistic regression
Parameters:
-----------
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
"""
self.W = theano.shared(
value=numpy.zeros(1, dtype=theano.config.floatX),
name='W', borrow=True
)
self.b = theano.shared(
value=numpy.zeros(1, dtype=theano.config.floatX),
name='b', borrow=True
)
self.y_pred = T.dot(input, self.W) + self.b
def errors(self, y):
""" The squared distance
Parameters:
----------
:y input: array_like:
:param input: the sample data
"""
errors = y- self.y_pred
return T.sum(T.pow(errors, 2))
def sgd_optimization(learning_rate=0.0013, n_epochs=100):
"""
Demonstrate stochastic gradient descent optimization of a linear model
Parameters:
-----
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
"""
x_train = numpy.random.uniform(low=-2, high = 2, size=(50,1))
epsilon = numpy.random.normal(scale=0.01, size=50)
y_train = numpy.squeeze(2*x_train) + epsilon
costs = []
eta0, x, y = T.scalar('eta0'), T.matrix(name='x'), T.vector(name='y')
classifier = LinearRegression(input = x)
cost = classifier.errors(y)
g_W = T.grad(cost=cost, wrt=classifier.W)
g_b = T.grad(cost=cost, wrt=classifier.b)
update = [(classifier.W, classifier.W - eta0 * g_W),
(classifier.b, classifier.b - eta0 * g_b)]
train = theano.function(inputs = [eta0],
outputs = cost,
updates = update,
givens = {x: x_train, y: y_train})
for _ in range(n_epochs):
costs.append(train(learning_rate))
return costs, w
SSE, regressor = sgd_optimization()
不幸的是，当我运行代码时，Python 返回以下错误消息：
ValueError: Input dimension mis-match. (input[0].shape[0] = 1, input[1].shape[0] = 50)
Apply node that caused the error: Elemwise{Composite{((-i0) + i1)}}[(0, 1)](b, CGemv{no_inplace}.0)
Inputs types: [TensorType(float64, vector), TensorType(float64, vector)]
Inputs shapes: [(1,), (50,)]
Inputs strides: [(8,), (8,)]
Inputs values: [array([ 0.]), 'not shown']
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.
我怀疑该错误与维度 (50,1) 的样本数据和仅维度 (1,1) 的回归量有关。尽管如此，一段时间以来我一直未能纠正我的代码中的错误。有人可以提供如何纠正错误的提示吗？感谢您的帮助！
【问题讨论】：
标签： python machine-learning linear-regression theano