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我正在玩 TensorFlow，我正在查看以下教程：

https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11/examples/3_NeuralNetworks

因为我不想做 MINST 数据库，所以我用我用 8000 个训练样本创建的一些数据更改了脚本。使用 300 个测试样本进行评估。输出是二进制分类。请记住，我刚刚涉足机器学习，而我的知识目前非常有限。

该脚本工作正常，但是我的成本停留在一个非常高的值并且不会收敛到 0。首先，这是否正常？我该如何改进呢？我做错什么了吗？其次准确率也不是很好，是不是收敛不好？也许 8000 不足以训练模型？或者值太分散，实际上无法获得更好的准确性。

我在这里发现了一个类似的问题：

用于回归的 tensorflow 深度神经网络总是在一批中预测相同的结果

但我不明白这个问题为什么或如何适用于我。

有人可以帮我吗？

这是输出：

Starting 1st session...
Epoch: 0001 cost= 39926820.730

最后我得到：

Epoch: 0671 cost= 64.798
Epoch: 0681 cost= 64.794
Epoch: 0691 cost= 64.791
Optimization Finished!
Accuracy: 0.716621

代码如下：

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

inputData = pd.read_csv('./myInputDataNS.csv', header=None)
runData = pd.read_csv('./myTestDataNS.csv', header=None)

trX, trY = inputData.iloc[:, :7].values, inputData.iloc[:,7].values
temp = trY.shape
trY = trY.reshape(temp[0], 1)
trY = np.concatenate((1-trY, trY), axis=1)

teX, teY = runData.iloc[:, :7].values, runData.iloc[:, 7].values
temp = teY.shape
teY = teY.reshape(temp[0], 1)
teY = np.concatenate((1-teY, teY), axis=1)


# Parameters
learning_rate = 0.001
training_epochs = 700
batch_size = 100
display_step = 10

# Network Parameters
n_hidden_1 =  320 
n_hidden_2 =  320
n_hidden_3 =  320 
n_input = 7 
n_classes = 2 # (0 or 1)

x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])


def multilayer_perceptron(x, weights, biases):
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)

    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)

    layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])
    layer_3 = tf.nn.relu(layer_3)

    out_layer = tf.matmul(layer_3, weights['out']) + biases['out']
    return out_layer

weights = {
'h1': tf.Variable(tf.random_normal([len(trX[0]), n_hidden_1])),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
'h3': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_3])),
'out': tf.Variable(tf.random_normal([n_hidden_3, n_classes]))
}

biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'b2': tf.Variable(tf.random_normal([n_hidden_2])),
'b3': tf.Variable(tf.random_normal([n_hidden_3])),
'out': tf.Variable(tf.random_normal([n_classes]))
}

pred = multilayer_perceptron(x, weights, biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

init = tf.global_variables_initializer()


print("Starting 1st session...")

with tf.Session() as sess:
    sess.run(init)

    for epoch in range(training_epochs):
        epoch_loss = 0
        i = 0
        while i < len(trX):
            start = i
            end = i + batch_size
            batch_x = np.array(trX[start:end])
            batch_y = np.array(trY[start:end])
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
            epoch_loss += c
            i += batch_size

            epoch_loss += c / len(trX[0])
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.3f}".format(epoch_loss))
    print("Optimization Finished!")

    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print("Accuracy:", accuracy.eval({x: teX, y: teY}))
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