Tensorflow 用张量流计算多元回归
我试图在tensorflow中实现一个多元回归,我有192个例子,其中有6个特征和一个输出变量。从我的模型中,我得到一个矩阵(192,6),而它应该是(192,1)。有人知道我的代码有什么问题吗?我在下面提供了我的代码Tensorflow 用张量流计算多元回归,tensorflow,regression,shape,tensor,Tensorflow,Regression,Shape,Tensor,我试图在tensorflow中实现一个多元回归,我有192个例子,其中有6个特征和一个输出变量。从我的模型中,我得到一个矩阵(192,6),而它应该是(192,1)。有人知道我的代码有什么问题吗?我在下面提供了我的代码 # Parameters learning_rate = 0.0001 training_epochs = 50 display_step = 5 train_X = Data_ABX3[0:192, 0:6] train_Y = Data_ABX3[0:192, [24]]
# Parameters
learning_rate = 0.0001
training_epochs = 50
display_step = 5
train_X = Data_ABX3[0:192, 0:6]
train_Y = Data_ABX3[0:192, [24]]
# placeholders for a tensor that will be always fed.
X = tf.placeholder('float', shape = [None, 6])
Y = tf.placeholder('float', shape = [None, 1])
# Training Data
n_samples = train_Y.shape[0]
# Set model weights
W = tf.cast(tf.Variable(rng.randn(1, 6), name="weight"), tf.float32)
b = tf.Variable(rng.randn(), 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)
# Accuracy
# #accuracy = tf.contrib.metrics.streaming_accuracy(Y, pred)
# 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: train_X, Y: train_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 = 0
#for (x, y) in zip(train_X, train_Y):
# tr_cost = sess.run(cost, feed_dict={X: x, Y: y})
# training_cost += tr_cost
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_Y, train_X * sess.run(W) + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
请在
pred
等式中使用tf.matmul
而不是tf.multiply
tf.multiply
执行元素级乘法,因此,它将生成与train_X
相同维度的矩阵,而tf.matmul
将执行矩阵乘法,并根据实际矩阵乘法规则生成结果矩阵
我不确定你的数据是什么。添加随机数据,然后更改代码以满足所有维度要求。如果你能帮助我了解你的意图,这将有助于更好地了解这个问题
编辑
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
# Parameters
learning_rate = 0.0001
training_epochs = 50
display_step = 5
Data_ABX3 = np.random.random((193, 8)).astype('f')
train_X = Data_ABX3[0:192, 0:6]
train_Y = Data_ABX3[0:192, [7]]
# placeholders for a tensor that will be always fed.
X = tf.placeholder('float32', shape = [None, 6])
Y = tf.placeholder('float32', shape = [None, 1])
# Training Data
n_samples = train_Y.shape[0]
# Set model weights
W = tf.cast(tf.Variable(np.random.randn(6, 1), name="weight"), tf.float32)
b = tf.Variable(np.random.randn(), name="bias")
mult_node = tf.matmul(X, W)
print(mult_node.shape)
# Construct a linear model
pred = tf.add(tf.matmul(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)
# Accuracy
# #accuracy = tf.contrib.metrics.streaming_accuracy(Y, pred)
# 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: train_X, Y: train_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 = 0
#for (x, y) in zip(train_X, train_Y):
# tr_cost = sess.run(cost, feed_dict={X: x, Y: y})
# training_cost += tr_cost
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')
line = sess.run(tf.add(tf.matmul(train_X, W), b))
# Graphic display
plt.plot(train_Y, line, label='Fitted line')
plt.legend()
plt.show()`
谢谢你,瑞秋。我使用了它,但不起作用,我得到了这样的信息:ValueError:维度必须相等,但对于输入形状为[?,6],[1,6]的“MatMul”(op:“MatMul”)来说是6和1。@Hamid不确定您的输入数据,但我在您的代码中使用了随机数据。非常感谢您的有用评论。我更改了代码的这些部分:“Data_ABX3=numpy.loadtxt(file,dtype='float32',…”和“W=tf.cast(tf.Variable(tf.zeros([6,1]),…”。现在它可以工作了,但随着培训时间的增加,我的成本越来越高(培训成本=4.81842e+28)。