Tensorflowニューラルネットワークは本当に簡単なデータセットであっても高い誤差を持っています

Question

回帰問題のために1つの隠れ層NNを実装しようとしています。損失関数は、非常に簡単なデータであっても、実際には高い誤差に陥るよりも、数回の反復で改善されます。誰かが私にこのバグを見つけるのを助けることができますか？ここに私のコードです：

import tensorflow as tf 
import scipy.io as sio 
import numpy as np 
reuse_weights = 1 
n_nodes_hl1 = 10 
batch_size = 200 
hm_epochs = 20 

# load input from matlab 
input_training = sio.loadmat('xMat.mat') 
input_training = input_training['xMat'] 
input_test = sio.loadmat('xMat.mat') 
input_test = input_test['xMat'] 

# find number of measurements and input length 
n_measurements = input_training.shape[0] 
input_length = input_training.shape[1] 

# current input 
data_y = input_training[:, input_length - 1].astype(float) 
data_x = input_training[:, 0 : input_length - 1].astype(float) 
test_data_y = input_test[:, input_length - 1].astype(float) 
test_data_x = input_test[:, 0 : input_length - 1].astype(float) 

x = tf.placeholder('float32',[None, input_length - 1]) 
y = tf.placeholder('float32') 

# place holder for Dropout algorithm drop probability 
keep_prob = tf.placeholder('float32') 

def next_batch(data): 
    """ 
    Return a total of `batch_size` samples from the array `data`. 
    """ 
    if len(data.shape) == 2: 
     idx = np.arange(0, len(data[:,0])) # get all possible indexes 
    else: 
     idx = np.arange(0, len(data)) # get all possible indexes 

    np.random.shuffle(idx) # shuffle indexes 
    idx = idx[0:batch_size] # use only `batch_size` random indexes 

    if len(data.shape) == 2: 
     data_shuffle = [data[i,:] for i in idx] # get list of `batch_size` random samples 
    else: 
     data_shuffle = [data[i] for i in idx] # get list of `batch_size` random samples 

    data_shuffle = np.asarray(data_shuffle) # get back numpy array 
    return data_shuffle 

def neural_network_model(data, weights, biases, keep_prob): 
    layer1 = tf.add(tf.matmul(data, weights['h1']), biases['b1']) 
    layer1 = tf.nn.sigmoid(layer1) 
    output = tf.add(tf.matmul(layer1, weights['out']), biases['out']) 

    return output 

if reuse_weights: 
    weights = { 
    'h1': tf.Variable(sio.loadmat('weights_h1.mat')['weights_h1'], name="weights_h1"), 
    'out': tf.Variable(sio.loadmat('weights_out.mat')['weights_out'], name="weights_out") 
    } 
    biases = { 
    'b1': tf.Variable(sio.loadmat('biases_b1.mat')['biases_b1'], name="biases_b1"), 
    'out': tf.Variable(sio.loadmat('biases_out.mat')['biases_out'], name="biases_out") 
    } 
else: # initialize weights 
    weights = { 
    'h1': tf.Variable(tf.random_normal([input_length - 1, n_nodes_hl1]), name="weights_h1"), 
    'out': tf.Variable(tf.random_normal([n_nodes_hl1, 1]), name="weights_out") 
    } 
    biases = { 
    'b1': tf.Variable(tf.random_normal([n_nodes_hl1]), name="biases_b1"), 
    'out': tf.Variable(tf.random_normal([1]), name="biases_out") 
    } 

def train_neural_network(x): 
    prediction = neural_network_model(x, weights, biases, keep_prob)[:,0] 
    cost = tf.reduce_mean(tf.abs(prediction - y)) 
    optimizer = tf.train.AdamOptimizer() 
    opt = optimizer.minimize(cost) 
    with tf.Session() as sess: 
     sess.run(tf.global_variables_initializer()) 
     print(weights['h1']) 

     for epoch in range(hm_epochs): #training 
      epoch_loss = 0 
      for _ in range(int(n_measurements/batch_size)): 
       _, c, p = sess.run([opt, cost, prediction], feed_dict = {x:next_batch(data_x),\ 
       y:next_batch(data_y) , keep_prob : 1.0}) 
       epoch_loss += c 
      print('Epoch', epoch, 'completed out of', hm_epochs, 'Average loss:', epoch_loss/int(n_measurements/batch_size)) 

     # prediction 
     accuracy = tf.reduce_mean(tf.abs(prediction - y)) 

     # Feed 1.0 for keep prob during testing 
     print("Training data accuracy:", accuracy.eval({x: data_x, y: data_y, keep_prob : 1.0})) 
     print("Training data predictions:", prediction.eval({x: data_x[0:5,:], keep_prob : 1.0})) 
     print("Training data:",data_y[0:5]) 
     #print("Test data accuracy:", accuracy.eval({x: test_data_x, y: test_data_y, keep_prob : 1.0})) 


     # save numpy arrays 
     sio.savemat('weights_h1.mat', {'weights_h1': weights['h1'].eval()}) 
     sio.savemat('biases_b1.mat', {'biases_b1': biases['b1'].eval()}) 
     sio.savemat('weights_out.mat', {'weights_out': weights['out'].eval()}) 
     sio.savemat('biases_out.mat', {'biases_out': biases['out'].eval()}) 

train_neural_network(x) 

Answer 1

それは、データのシャッフルに問題がありました。入力と応答は別々にシャッフルされ（各エポックごとに2回のランダムシャッフル）、各エポックの入力データは応答データに対応しませんでした。