对于具有多种功能的TensorFlow回归，我的占位符应该是什么？

Question

嗨，我试图运行线性回归使用TensorFlow，所以我采取了这个代码，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()

我试着按照这个来匹配尺寸

但我一直得到这样的错误：ValueError：维度必须相等，但是对于输入形状为[43,5]，[5,1]的'Mul_18'（op:'Mul'），维度是43和5。将占位符设置为“随机”可以解决此问题，但随后会在任意行触发另一个标注错误：sess。运行（cost，feed_dict={X:train_X，Y:train_Y}）。谁来帮忙！？

Answer 1

你试图做矩阵乘法。所以你应该利用tf.matmul。

tf的操作。乘法执行元素级乘法，其中两个张量的形状必须相同。