Question

我基于“ Binary classification, soerendip”构建了一个NN，以不断预测二进制数。以下代码是培训过程的简化：

import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification

X, Y = make_classification(n_samples=50000, n_features=10, n_informative=8, 
                           n_redundant=0, n_clusters_per_class=2)
Y = np.array([Y, -(Y-1)]).T  # The model currently needs one column for each class
X, X_test, Y, Y_test = train_test_split(X, Y)


# Parameters
learning_rate = 0.001
training_epochs = 12
batch_size = 1
display_step = 1


# Network Parameters
n_hidden_1 = 10 # 1st layer number of features
n_hidden_2 = 10 # 2nd layer number of features
n_input = 10 # Number of feature
n_classes = 2 # Number of classes to predict


# tf Graph input
x = tf.placeholder("float", [None, n_input], name = 'x')
y = tf.placeholder("float", [None, n_classes], name = 'y')

# Create model
def multilayer_perceptron(x, weights, biases):
# Hidden layer with RELU activation
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    # Hidden layer with RELU activation
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)
    # Output layer with linear activation
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    return out_layer

# Store layers weight & bias
weights = {
        'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
        'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
        'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
        }

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

# Construct model
pred = multilayer_perceptron(x, weights, biases)

# Define loss and optimizer
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)

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
saver = tf.train.Saver()
#export_dir="/tmp/new"
#builder = tf.saved_model.builder.SavedModelBuilder(export_dir)

#if low_level == True:
with tf.Session() as sess:
    sess.run(init)
    # Training cycle

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(len(X)/batch_size)
        X_batches = np.array_split(X, total_batch)
        Y_batches = np.array_split(Y, total_batch)
        # Loop over all batches
        for i in range(total_batch):
            batch_x, batch_y = X_batches[i], Y_batches[i]
            # Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                      y: batch_y})
            # Compute average loss
            avg_cost += c / total_batch
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
        print("Optimization Finished!")


    save_path = saver.save(sess, "/tmp/model.ckpt")
    print("Model saved in path: %s" % save_path)

为了恢复和测试，我使用以下功能：

def restoring(X_test,Y_test):

    saver = tf.train.import_meta_graph("/tmp/model.ckpt.meta")
    with tf.Session() as sess:

        #now restore the variables
        sess.run(tf.global_variables_initializer())
        saver.restore(sess, "/tmp/model.ckpt")
        #print("weightsh1",weights['h1'].eval())
        #print("weightsh2",weights['h2'].eval())        
        #print("weightsout",weights['out'].eval())        
        #print("biasb1",biases['b1'].eval())
        #print("biasb2",biases['b2'].eval())        
        #print("biasout",biases['out'].eval())  

        #np.savetxt("weightsh1_check.txt",weights['h1'].eval())
        #np.savetxt("weightsh2_check.txt",weights['h2'].eval())        
        #np.savetxt("weightsout_check.txt",weights['out'].eval())        
        #np.savetxt("biasb1_check.txt",biases['b1'].eval())
        #np.savetxt("biasb2_check.txt",biases['b2'].eval())        
        #np.savetxt("biasout_check.txt",biases['out'].eval()) 

        #coord = tf.train.Coordinator()
        #threads = tf.train.start_queue_runners(coord=coord)
        aa = []
        for ss in np.arange(len(X_test)):

            aaa = X_test[ss]
            aaa = aaa.reshape(1,10)
            aa.extend(sess.run(pred,feed_dict={x: aaa}))
        #coord.request_stop()
        #coord.join(threads)
        aa = np.array(aa)
        bb = np.argmax(aa,1)
        cc = Y_test[:,1]
        dd=np.equal(bb, cc)

        accur_val = np.sum(dd)/len(X_test)
        print('Accuracy of validation:')
        print(accur_val)

精度通常> 90％，并且变量也可以根据需要恢复。在我的真实示例中，我面临以下挑战：首先，我使用来自api的时间序列数据，该数据每分钟更新一次。不幸的是，它涉及机密数据，但是，即使make_classification()并不完美，该示例也足以理解该问题。如果还不够好，请在下面评论，我会解决的。当我使用以下流程时，恢复工作正常：

下载数据
火车模型
还原和测试模型 ->良好的测试结果

当我使用以下流程时，准确性通常会大大下降。

下载数据
火车模型
还原并测试模型（效果良好）
下载新数据，还原和测试模型（不良结果，随机出现在40％到60％之间）

测试大小通常为200分钟左右，以便第一个api下载结果为：

data_test_input [t200] = [d0，d1，... d200]效果良好（> 90％）。

首次成功训练和测试模型后，我再次下载了一些更新的数据，例如这个：

data_test_input [t203] = [d3，d4，... d203]结果不佳（40-60％）

我通常会假设我的精度至少约为前一个值，但是事实证明，每次都会随机设置。是什么原因导致此问题，或者我做错了什么？

非常感谢您！

在将新数据引入经过训练并恢复的Tensorflow模型中后，结果很差

0 个答案: