我是张量流的极端初学者,我的任务是使用我的csv数据进行简单的线性回归,该数据包含2列,高度和充电状态(SoC),其中两个值都是浮点数 . 在CSV文件中,Height是第一个col,而SoC是第二个col .
使用高度我想预测SoC
我完全迷失了我必须在代码的“适合所有训练数据”部分中添加的内容 . 我看过其他线性回归模型,它们的代码令人难以置信,例如:
with tf.Session() as sess:
sess.run(init)
for epoch in range(training_epochs):
sess.run(training_step,feed_dict={X:train_x,Y:train_y})
cost_history = np.append(cost_history,sess.run(cost,feed_dict={X: train_x,Y: train_y}))
#calculate mean square error
pred_y = sess.run(y_, feed_dict={X: test_x})
mse = tf.reduce_mean(tf.square(pred_y - test_y))
print("MSE: %.4f" % sess.run(mse))
#plot cost
plt.plot(range(len(cost_history)),cost_history)
plt.axis([0,training_epochs,0,np.max(cost_history)])
plt.show()
fig, ax = plt.subplots()
ax.scatter(test_y, pred_y)
ax.plot([test_y.min(), test_y.max()], [test_y.min(), test_y.max()], 'k--', lw=3)
ax.set_xlabel('Measured')
ax.set_ylabel('Predicted')
plt.show()
我刚刚能够使用本指南从我的CSV文件中获取数据而不会出错:
TensorFlow:从CSV文件中读取和使用数据
完整代码:
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
rng = np.random
from numpy import genfromtxt
from sklearn.datasets import load_boston
# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50
n_samples = 221
X = tf.placeholder("float") # create symbolic variables
Y = tf.placeholder("float")
filename_queue = tf.train.string_input_producer(["battdata.csv"],shuffle=False)
reader = tf.TextLineReader(skip_header_lines=1)
key, value = reader.read(filename_queue)
# Default values, in case of empty columns. Also specifies the type of the
# decoded result.
record_defaults = [[1.], [1.]]
col1, col2= tf.decode_csv(
value, record_defaults=record_defaults)
features = tf.stack([col1])
# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")
# Construct a linear model
pred = tf.add(tf.multiply(col1, W), b) # XW + b <- y = mx + b where W is gradient, b is intercept
# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-col2, 2))/(2*n_samples)
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Initializing the variables
init = tf.global_variables_initializer()
with tf.Session() as sess:
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
_, cost_value = sess.run([optimizer,cost])
for (x, y) in zip(col2, col1):
sess.run(optimizer, feed_dict={X: x, Y: y})
#Display logs per epoch step
if (epoch+1) % display_step == 0:
c = sess.run(cost, feed_dict={X: col2, Y:col1})
print( "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
"W=", sess.run(W), "b=", sess.run(b))
print("Optimization Finished!")
training_cost = sess.run(cost, feed_dict={X: col2, Y: col1})
print ("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
#Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * col2 + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
coord.request_stop()
coord.join(threads)
错误:
INFO:tensorflow:向协调员报告错误:,尝试使用已关闭的会话 . -------------------------------------------------- ------------------------- TypeError(training_epochs)中的纪元(training_epochs)中的TypeError Traceback(最近一次调用last)()8:9 _,cost_value = sess.run([optimizer,cost])---> 10(x,y)in zip(* col1,col2):11 sess.run(optimizer,feed_dict = {X:x,Y:y})12 C:\ Users \ Shiina \ Anaconda3 \ envs \ tensorflow \ lib \ site-packages \ tensorflow \ python \ framework \ ops.py in iter(self)514 TypeError:调用时 . 515“”“ - > 516引发TypeError(”'Tensor'对象不可迭代 . “)517 518 def bool(self):TypeError:'Tensor'对象不可迭代 .
1 回答
该错误是因为您试图在
for (x, y) in zip(col2, col1)
中迭代张量,这是不允许的 . 代码的其他问题是您已经设置了输入管道队列,然后您还尝试通过feed_dict {}输入,这是错误的 . 你的训练部分应该是这样的: