你好,我正在尝试使用 TensorFlow 运行线性回归,所以我 took this code并希望适合我自己的数据集 X_train (43, 5) 和 y_train (43,)。这是我的代码:
from __future__ import print_function
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
rng = numpy.random
# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50
# Data
train_X = X_train
train_Y = y_train
test_X = X_test
test_Y = y_test
n_samples = train_X.shape[0]
row = train_X.shape[0]
column = train_X.shape[1]
print(row, column)
# tf Graph Input
X = tf.placeholder("float", [row, column])
Y = tf.placeholder("float")
# Set model weights
#W = tf.Variable(rng.randn(), name="weight")
#b = tf.Variable(rng.randn(), name="bias")
W = tf.Variable(tf.zeros([column, 1]), name="weight")
b = tf.Variable(tf.zeros([1]), name="bias")
# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)
# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
# Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# Start training
with tf.Session() as sess:
# Run the initializer
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
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: train_X, Y:train_Y})
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: train_X, Y: train_Y})
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) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
print("Testing... (Mean square loss Comparison)")
testing_cost = sess.run(
tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),
feed_dict={X: test_X, Y: test_Y}) # same function as cost above
print("Testing cost=", testing_cost)
print("Absolute mean square loss difference:", abs(
training_cost - testing_cost))
plt.plot(test_X, test_Y, 'bo', label='Testing data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
我尝试follow this以匹配尺寸
但我不断收到此错误:ValueError:尺寸必须相等,但“Mul_18”(操作:“Mul”)的尺寸为 43 和 5,输入形状为:[43,5]、[5,1]。将占位符设置为随机可以解决此问题,但会在任何行中触发另一个尺寸错误:sess.run(cost, feed_dict={X: train_X, Y:train_Y})。请有人帮忙!?
最佳答案
您正在尝试进行矩阵乘法。所以你应该使用 tf.matmul
.
tf.multiply
的操作进行元素乘法,两个张量的形状必须相同。
关于python - 对于具有多个特征的 TensorFlow 回归,我的占位符应该是什么?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/47646183/