Tensorflow Iris數據集永遠不會收斂

Question

我試圖在Iris數據集上運行標準NN。標籤是一個單一的列，可以有值0,1,2，這取決於物種。我將這些特徵轉置到x軸上，並將這些例子轉換爲y。

關注領域：成本函數 - 每個人似乎都使用預編譯函數，但由於我的數據不是單編碼編碼，因此我使用標準損失。優化器 - 我將它用作黑盒子，我不確定是否能夠正確更新成本。

在此先感謝您的幫助。

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

由於您有分類問題，因此您需要將標籤轉換爲單一形式。您可以使用tf.one_hot來實現此目的。另外，您也可以在成本上應用tf.reduce_mean，如下面的示例中所示（從here獲取）。另外，你的學習率對我來說似乎太小了。

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}))