Python 张量流神经网络模型的实现

我试图在张量流上实现一个神经网络模型，但似乎占位符的形状有问题。我是TF的新手，所以这可能只是一个简单的误解。以下是我的代码和数据示例：

_data=[[0.4,0.5,0.6,1],[0.7,0.8,0.9,0],....]

数据由4列数组组成，每个数组的最后一列是标签。我想将每个数组分类为标签0、标签1或标签2

import tensorflow as tf
import numpy as np

_data = datamatrix
X = tf.placeholder(tf.float32, [None, 3])
W = tf.Variable(tf.zeros([3, 1]))
b = tf.Variable(tf.zeros([3]))

init = tf.global_variables_initializer()
Y = tf.nn.softmax(tf.matmul(X, W) + b)

# placeholder for correct labels
Y_ = tf.placeholder(tf.float32, [None, 1])

# loss function
import time
start=time.time()

cross_entropy = -tf.reduce_sum(Y_ * tf.log(Y))

# % of correct answers found in batch
is_correct = tf.equal(tf.argmax(Y,1), tf.argmax(Y_,1))
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))

optimizer = tf.train.GradientDescentOptimizer(0.003)
train_step = optimizer.minimize(cross_entropy)


sess = tf.Session()
sess.run(init)
for i in range(1000):
    # load batch of images and correct answers
    batch_X, batch_Y = [x[:3] for x in _data[:2000]],[x[-1] for x in _data[:2000]]
    train_data={X: batch_X, Y_: batch_Y}

    # train
    sess.run(train_step, feed_dict=train_data)

# success ?
a,c = sess.run([accuracy, cross_entropy], feed_dict=train_data)

运行代码后，我收到以下错误消息：

ValueError: Cannot feed value of shape (2000,) for Tensor 'Placeholder_1:0', which has shape '(?, 1)'

我期望的输出应该是使用交叉熵的模型的性能；下面代码行中的精度值：

a,c = sess.run([accuracy, cross_entropy], feed_dict=train_data)

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如果您能就如何改进模型或更适合我的数据的模型提出任何建议，我将不胜感激。

占位符1:0的形状与错误消息指定的输入数据

批次Y

不匹配。请注意1-D与2-D阵列

因此，您应该定义一维占位符：

Y_ = tf.placeholder(tf.float32, [None])

或准备二维数据：

    batch_X, batch_Y = [x[:3] for x in _data[:2000]],[x[-1:] for x in _data[:2000]]

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不客气。你可以标记这个问题的答案吗？