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你好,我在tensorflow是新的排名使我的圖,我嘗試運行,但我得到這個錯誤:你如何改變tensorflow對象
ValueError: Shape (3,) must have rank 2
其來源於此行
tf.matmul(tf.matmul(phix, tf.transpose(param)), B)
我檢查了我的變量phix的等級,結果是0
,我不明白爲什麼,因爲它的形狀是(3,3)
。這是我的劇本,請你幫助我。
import tensorflow as tf
def phi(x, b, w, B):
z = tf.matmul(x,w)
phix = tf.cos(z) + b # attention shapes
phix /= tf.sqrt(float(float(int(w.get_shape()[0]))/2.))
return phix, B
def model(phix, B, param) :
return tf.matmul(tf.matmul(phix, tf.transpose(param)), B)
B = tf.constant(1., shape=[1]) # constant (non trainable)
x2 = tf.placeholder(tf.float32, shape=[3,1]) # variable
W2 = tf.Variable(initial_value = tf.random_normal(shape=[1, 3]),trainable=False ,name="W2")
b2 = tf.Variable(tf.random_uniform(shape=[3]),trainable=False , name="b2")
y = tf.Variable(tf.random_uniform(shape=[3,3]),trainable=False)
param = tf.Variable(tf.random_uniform(shape=[3])) # variable trainable
norm = tf.sqrt(tf.reduce_sum(tf.square(param)))**2 ## attention ici c'est par ce que param est un vecteur de une dimmention
phix, B = phi (x2,b2,W2,B)
lamda = tf.constant(1. , shape=[3])
cost = tf.nn.l2_loss(y - model(phix, B, param)) + lamda * norm
opt = tf.train.GradientDescentOptimizer(0.5).minimize(cost)
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for i in range(10):
sess.run(opt)
z4_op = sess.run(opt , feed_dict = {x2: [[1.0],[2.0],[3.0]]})
print(z4_op)