逻辑模型总是为训练和测试示例的所有输入提供相同的输出

我正在训练一个逻辑回归模型 Iris dataset 它有三类输出,使用TensorFlow。

我把我的训练集分成75%的训练和25%的测试集。

在训练模型时, Y_hat 是对训练集的预测。在打印输出(Y_-Hat)时,我看到模型正在预测 1 对于所有输入(形状(无,4))。

此外,在再次测试模型时 伊哈特 作为模型预测,它仍然显示 一 对于所有的测试集输入,这是不可避免的,因为模型在列车组上做得不好。

代码如下:

import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn import preprocessing
from sklearn.model_selection import train_test_split

iris=datasets.load_iris()
type(iris)

X=iris.data[:]
X=X.astype(np.float32)
Y=iris.target[:]
Y=Y.astype(np.float32)
print("x=",type(X)," Y=",type(Y))
print("X.shape=",X.shape,"Y.shape=",Y.shape)
Y=Y.reshape(Y.shape[0],1)
print("Y.shape=",Y.shape)

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=23, test_size=0.25, stratify=Y)
print("Size")
print("X_train=",X_train.shape)
print("Y_train=",Y_train.shape)
print("X_test=",X_test.shape)
print("Y_test=",Y_test.shape)

#Variables
W=tf.Variable(tf.random_normal(shape=[4,1]),dtype=tf.float32)
b=tf.Variable(tf.random_normal(shape=[1,1]),dtype=tf.float32)

#Placeholder
input_=tf.placeholder(tf.float32,shape=(None,iris.data[:].shape[1]))
output_=tf.placeholder(tf.float32,shape=(None,Y.shape[1]))

Y_hat=tf.nn.softmax(tf.matmul(input_,W)+b)

c=tf.nn.sigmoid_cross_entropy_with_logits(labels=Y_hat,logits=output_)
cost=tf.reduce_mean(c)

optimizer=tf.train.GradientDescentOptimizer(0.01)

train=optimizer.minimize(cost)
init=tf.global_variables_initializer()


with tf.Session() as sess:
    epochs=6
    for i in range(epochs):
        sess.run(init)
        sess.run(Y_hat,feed_dict={input_:X_train})#,output_:Y_train})

        sess.run(train,feed_dict={input_:X_train,output_:Y_train})
        print(i,"Cost=",sess.run(cost,feed_dict={input_:X_train,output_:Y_train}))


    ytest=tf.placeholder(dtype=tf.float32,shape=(None,1))


    print(sess.run(Y_hat,feed_dict={input_:X_test}))

    correct_prediction = tf.equal(tf.argmax(Y_hat,1), tf.argmax(ytest,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print(Y_hat.shape)
    print("accuracy=",sess.run(accuracy,feed_dict={input_:X_test,ytest:Y_test}))

    print("test_cost",sess.run(cost,feed_dict={input_:X_test,output_:Y_test}))

以下是所有测试集预测的输出:

x= <class 'numpy.ndarray'>  Y= <class 'numpy.ndarray'>
X.shape= (150, 4) Y.shape= (150,)
Y.shape= (150, 1)
Size
X_train= (112, 4)
Y_train= (112, 1)
X_test= (38, 4)
Y_test= (38, 1)
0 Cost= 0.377203
1 Cost= 0.377203
2 Cost= 0.377203
3 Cost= 0.377203
4 Cost= 0.377203
5 Cost= 0.377203
[[ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]]
(?, 1)
accuracy= 1.0
test_cost 0.379477

请解释模型预测背后的原因 一 在所有情况下。是因为激活功能还是其他原因?