Question

在玩了tensorflow之后，我终于能够让我的代码运行但是损失似乎是负面的，即使只有很少的时代。为什么会这样？我只用numpy做了简单的神经网络，从来没有遇到过这个问题，有人可以解释我做错了什么吗？

这是输入文件，如果有人想测试它：input file

附加信息：标签不是onehot数组，我的损失函数是正确的，因为它不是onehot数组？我遵循了使用onehot数组标签的教程。

import tensorflow as tf
import numpy as np

data = np.load("test_data.npz")
trng_input = np.array(data['Input'], dtype=np.float64)
trng_output = np.array(data['Output'], dtype=np.float64)

nhl1 = 12
nhl2 = 8
n_classes = 4


x = tf.placeholder(dtype=tf.float64, shape=[len(trng_input),24])
y = tf.placeholder(dtype=tf.float64, shape=[len(trng_output),n_classes])

def NN(data):
    hl1 = {"weights":tf.Variable(tf.random_normal([24, nhl1], dtype=tf.float64)),
           "biases":tf.Variable(tf.random_normal([nhl1], dtype=tf.float64))}

    hl2 = {"weights":tf.Variable(tf.random_normal([nhl1, nhl2], dtype=tf.float64)),
           "biases":tf.Variable(tf.random_normal([nhl2], dtype=tf.float64))}

    output_layer = {"weights":tf.Variable(tf.random_normal([nhl2, n_classes], dtype=tf.float64)),
                    "biases":tf.Variable(tf.random_normal([n_classes], dtype=tf.float64))}

    l1 = tf.add(tf.matmul(data, hl1["weights"]), hl1["biases"])
    l1 = tf.nn.relu(l1)

    l2 = tf.add(tf.matmul(l1, hl2["weights"]), hl2["biases"])
    l2 = tf.nn.relu(l2)

    output = tf.add(tf.matmul(l2, output_layer["weights"]), output_layer["biases"])
    output = tf.nn.relu(output)

    return output

def train(data, epochs):
    prediction = NN(data)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction, labels=y))
    optimizer = tf.train.AdamOptimizer().minimize(cost)

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

        for epoch in range(epochs):
            _, c = sess.run([optimizer, cost], feed_dict={x: trng_input, y: trng_output})
            if not epoch % 20:
                print(F"Epoch {epoch} completed out of {epochs}. Loss:{c}")

        correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y, 1))

        accuracy = tf.reduce_mean(tf.cast(correct,"float"))
        Eval = accuracy.eval({x:trng_input, y:trng_output})
        print(F"Accuracy:{Eval}")

train(trng_input, 200)

张量流失为负值

0 个答案: