Tensorflow Shape Error（最新版本）多元线性回归

请记住，我对tf非常陌生。Tensorflow Shape Error（最新版本）多元线性回归

import tensorflow as tf 
import numpy as np 
import matplotlib.pyplot as plt 
from sklearn.datasets import load_boston 

np.set_printoptions(suppress=True) 

boston = load_boston() 

m = boston.data.shape[0] - 1 

bt_unfixed = np.transpose(boston.data) 
bt = np.insert(bt_unfixed, 0, 1) 

Y = tf.placeholder(tf.float64) 
X = tf.placeholder(tf.float64, [None, 13]) 
print X.shape 
W = tf.Variable(np.transpose(np.array([0.00,0,0,0,0,0,0,0,0,0,0,0,0]).flatten()), name='weights') 
b = tf.Variable(0.5, name='bias') 

hypothesis = tf.add(tf.matmul(X, W), tf.cast(b, tf.float64)) 

loss = tf.reduce_sum(tf.square(hypothesis - Y))/(2 * m) 

optimizer = tf.train.GradientDescentOptimizer(0.01) 

train_op = optimizer.minimize(loss) 

with tf.Session() as sess: 
    sess.run(tf.initialize_all_variables()) 
    for i in range(0, 1200): 
     for (x, y) in zip(boston.data, boston.target.data): 
      sess.run(train_op, feed_dict={X:x, Y:y}) 
    print "Done!\n" 
    print "Running test...\n" 
    t = sess.run(cost, feed_dict={X:boston.data[504], Y:boston.target.data[504]}) 
    print "loss =" + str(t) + "Real value" + str(boston.target.data[504]) + "Pred " +str(sess.run(hypothesis, feed_dict={X:boston.data[504]}))

请随时纠正我的代码中的任何其他错误！

当我定义假设时会引发错误，我需要转置我的向量以将它们相乘，但它不起作用。

Traceback (most recent call last): 
    File "multihouse.py", line 20, in <module> 
    hypothesis = tf.add(tf.matmul(X, W), tf.cast(b, tf.float64)) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1398, in matmul 
    name=name) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1348, in _mat_mul 
    transpose_b=transpose_b, name=name) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 749, in apply_op 
    op_def=op_def) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2382, in create_op 
    set_shapes_for_outputs(ret) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1783, in set_shapes_for_outputs 
    shapes = shape_func(op) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/common_shapes.py", line 596, in call_cpp_shape_fn 
    raise ValueError(err.message) 

    ValueError: Shape must be rank 2 but is rank 1

来源

2016-11-25 Ricky

问题是没有这样的事情，换位向量在numpy。你必须定义一个平凡的尺寸为这种事工作的一个矩阵，例如

np.array([0.00,0,0,0,0,0,0,0,0,0,0,0,0]) # this cannot be transposed 
np.array([[0.00,0,0,0,0,0,0,0,0,0,0,0,0]]) # this can

就像在这个例子中

>>> import numpy as np 
>>> np.transpose(np.array([1,1,1])) 
array([1, 1, 1]) 
>>> np.transpose(np.array([[1,1,1]])) 
array([[1], 
     [1], 
     [1]])

一旦你解决这个问题，你的假设会被正确定义（右现在您尝试将矩阵与TF中未定义的矢量相乘）。

来源

2016-11-25 20:09:50 lejlot

Tensorflow Shape Error（最新版本）多元线性回归

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