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我试图使用深度神经网络架构来对二进制标签值 - 0和+1进行分类.这是我在tensorflow中执行此操作的代码.此问题也从前一个问题的讨论中得出结论

import tensorflow as tf
import numpy as np
from preprocess import create_feature_sets_and_labels

train_x,train_y,test_x,test_y = create_feature_sets_and_labels()

x = tf.placeholder('float', [None, 5])
y = tf.placeholder('float')

n_nodes_hl1 = 500
n_nodes_hl2 = 500
# n_nodes_hl3 = 500

n_classes = 1
batch_size = 100

def neural_network_model(data):

    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([5, n_nodes_hl1])),
                      'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}

    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                      'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}

    # hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
    #                   'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}

    # output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
    #                   'biases':tf.Variable(tf.random_normal([n_classes]))}

    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_classes])),
                    'biases':tf.Variable(tf.random_normal([n_classes]))}


    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases']) …