Question

我正在学习tensorflow，想完成第一个示例，但是输入的形状或类型存在一些问题，我不知道如何解决。

import tensorflow as tf
from numpy.random import RandomState
batch_size = 8 

w1 = tf.Variable(tf.random_normal([2,3], stddev=1, seed=1))
w2 = tf.Variable(tf.random_normal([3,1], stddev=1, seed=1))

x = tf.placeholder(tf.float32, shape=(None,2), name='x-input')
y_ = tf.placeholder(tf.float32, shape=(None,1), name='y-input')

a = tf.matmul(x,w1)
y = tf.matmul(a,w2)

y = tf.sigmoid(y)
corss_entropy = -tf.reduce_mean(
y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0))
+ (1-y_) * tf.log(tf.clip_by_value(1-y, 1e-10, 1.0)))
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size,2)
Y = [[int(x1+x2<1)] for (x1,x2) in X]

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)

    print(sess.run(w1))
    print(sess.run(w2))

    STEPS = 5000
    for i in range(STEPS):
        start = (i * batch_size) % dataset_size
        end =min(start + batch_size, dataset_size)

        sess.run(train_step, 
                 feed_dict={x:X[start:end], y_:Y[start:end]})
        if i % 1000 == 0:
            total_cross_entropy = sess.run(cross_entropy, feed_dict={x:X, y_:Y})
            print('After %d training step(s), cross entropy on all data is %g'%(i, total_cross_entropy))

        print(sess.run(w1))
        print(sess.run(w2))

当我运行上面的代码时，它有一个错误：

InvalidArgumentError: You must feed a value for placeholder tensor 'input_2' with dtype float and shape [3,2]

如何修复Tensorflow中的无效参数错误

0 个答案: