Tensorflow Iris数据集永远不会收敛

Question

我试图在Iris数据集上运行标准NN。标签是一个单一的列，可以有值0,1,2，这取决于物种。我将这些特征转置到x轴上，并将这些例子转换为y。

关注领域：成本函数 - 每个人似乎都使用预编译函数，但由于我的数据不是单编码编码，因此我使用标准损失。优化器 - 我将它用作黑盒子，我不确定是否能够正确更新成本。

在此先感谢您的帮助。

import tensorflow as tf 
import numpy as np 
import pandas as pd 

import tensorflow as tf 


def create_layer(previous_layer, weight, bias, activation_function=None): 
    z = tf.add(tf.matmul(weight, previous_layer), bias) 
    if activation_function is None: 
     return z 
    a = activation_function(z) 
    return a 


def cost_compute(prediction, correct_values): 
    return tf.nn.softmax_cross_entropy_with_logits(logits = prediction, labels = correct_values) 

input_features = 4 
n_hidden_units1 = 10 
n_hidden_units2 = 14 
n_hidden_units3 = 12 
n_hidden_units4 = 1 

rate = .000001 

weights = dict(
      w1=tf.Variable(tf.random_normal([n_hidden_units1, input_features])), 
      w2=tf.Variable(tf.random_normal([n_hidden_units2, n_hidden_units1])), 
      w3=tf.Variable(tf.random_normal([n_hidden_units3, n_hidden_units2])), 
      w4=tf.Variable(tf.random_normal([n_hidden_units4, n_hidden_units3])) 
      ) 

biases = dict(
      b1=tf.Variable(tf.zeros([n_hidden_units1, 1])), 
      b2=tf.Variable(tf.zeros([n_hidden_units2, 1])), 
      b3=tf.Variable(tf.zeros([n_hidden_units3, 1])), 
      b4=tf.Variable(tf.zeros([n_hidden_units4, 1])) 
      ) 

train = pd.read_csv("/Users/yazen/Desktop/datasets/iris_training.csv") 
test = pd.read_csv("/Users/yazen/Desktop/datasets/iris_test.csv") 

train.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'species'] 
test.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'species'] 

train_labels = np.expand_dims(train['species'].as_matrix(), 1) 
test_labels = np.expand_dims(test['species'].as_matrix(), 1) 

train_features = train.drop('species', axis=1) 
test_features = test.drop('species', axis=1) 

test_labels = test_labels.transpose() 
train_labels = train_labels.transpose() 
test_features = test_features.transpose() 
train_features = train_features.transpose() 

x = tf.placeholder("float32", [4, None], name="asdfadsf") 
y = tf.placeholder("float32", [1, None], name="asdfasdf2") 

layer = create_layer(x, weights['w1'], biases['b1'], tf.nn.relu) 
layer = create_layer(layer, weights['w2'], biases['b2'], tf.nn.relu) 
layer = create_layer(layer, weights['w3'], biases['b3'], tf.nn.relu) 
Z4 = create_layer(layer, weights['w4'], biases['b4']) 
cost = cost_compute(Z4, y) 

with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    for iteration in range(1,50): 
     optimizer = tf.train.GradientDescentOptimizer(learning_rate=rate).minimize(cost) 
     _, c = sess.run([optimizer, cost], feed_dict={x: train_features, y: train_labels}) 
     print("Iteration " + str(iteration) + " cost: " + str(c)) 

    prediction = tf.equal(Z4, y) 
    accuracy = tf.reduce_mean(tf.cast(prediction, "float")) 
    print(sess.run(Z4, feed_dict={x: train_features, y: train_labels})) 
    print(accuracy.eval({x: train_features, y: train_labels})) 

Answer 1

由于您有分类问题，因此您需要将标签转换为单一形式。您可以使用tf.one_hot来实现此目的。另外，您也可以在成本上应用tf.reduce_mean，如下面的示例中所示（从here获取）。另外，你的学习率对我来说似乎太小了。

mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) 

    x = tf.placeholder(tf.float32, [None, 784]) 
    W = tf.Variable(tf.zeros([784, 10])) 
    b = tf.Variable(tf.zeros([10])) 
    y = tf.matmul(x, W) + b 

    # Define loss and optimizer 
    y_ = tf.placeholder(tf.float32, [None, 10]) 

    cross_entropy = tf.reduce_mean(
     tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) 
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) 

    sess = tf.InteractiveSession() 
    tf.global_variables_initializer().run() 
    # Train 
    for _ in range(1000): 
    batch_xs, batch_ys = mnist.train.next_batch(100) 
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) 

    # Test trained model 
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
    print(sess.run(accuracy, feed_dict={x: mnist.test.images, 
             y_: mnist.test.labels}))