'DataFrame'对象没有属性'train'

请帮帮我，我失踪了哪里？为什么我总是得到这个错误：

'DataFrame'对象没有属性'train'

# -*- coding: utf-8 -*-
 import tensorflow as tf
 from tensorflow.contrib import rnn
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd

 dataset = pd.read_csv("all.csv")
 x = dataset.iloc[:, 1:51].values
 y = dataset.iloc[:, 51].values

 time_steps=5
 num_units=128
 n_input=50
 learning_rate=0.001
 n_classes=2
 batch_size=5

 #weights and biases of appropriate shape to accomplish above task
 out_weights=tf.Variable(tf.random_normal([num_units,n_classes]))
 out_bias=tf.Variable(tf.random_normal([n_classes]))

 #defining placeholders
 #input image placeholder
 x=tf.placeholder("float",[None,time_steps,n_input])
 #input label placeholder
 y=tf.placeholder("float",[None,n_classes])

 #processing the input tensor from [batch_size,n_steps,n_input] to 
 "time_steps" 
 number of [batch_size,n_input] tensors
 input=tf.unstack(x ,time_steps,1)

 #defining the network
 lstm_layer=rnn.BasicLSTMCell(num_units,forget_bias=1)
 outputs,_=rnn.static_rnn(lstm_layer,input,dtype="float32")

 #converting last output of dimension [batch_size,num_units] to 
 [batch_size,n_classes] by out_weight multiplication
 prediction=tf.matmul(outputs[-1],out_weights)+out_bias

 #loss_function

loss = tf.reduce_mean（tf.nn.softmax_cross_entropy_with_logits（logits = prediction，labels = y））＃optimization opt = tf.train.AdamOptimizer（learning_rate = learning_rate）.minimize（loss）

#model evaluation
 correct_prediction=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
 accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

 #initialize variables
 init=tf.global_variables_initializer()
 with tf.Session() as sess:
     sess.run(init)
     iter=1
     while iter<800:
         batch_x,batch_y=dataset.train.next_batch(batch_size=batch_size)

         batch_x=batch_x.reshape((batch_size,time_steps,n_input))

         sess.run(opt, feed_dict={x: batch_x, y: batch_y})

         if iter %10==0:
            acc=sess.run(accuracy,feed_dict={x:batch_x,y:batch_y})
            los=sess.run(loss,feed_dict={x:batch_x,y:batch_y})
            print("For iter ",iter)
            print("Accuracy ",acc)
            print("Loss ",los)
            print("__________________")

         iter=iter+1