Tensorflow同じトレーニングの精度が続いています

Question

TensorflowのCNNモデルに固執しています。 私のコードは以下の通りです。

ライブラリ

# -*- coding: utf-8 -*- 
import tensorflow as tf 
import time 
import json 
import numpy as np 
import matplotlib.pyplot as plt 
import random 
import multiprocessing as mp 
import glob 
import os 

モデル

def inference(images_placeholder, keep_prob): 

    def weight_variable(shape): 
     initial = tf.truncated_normal(shape, stddev=0.1) 
     return tf.Variable(initial) 

    def bias_variable(shape): 
     initial = tf.constant(0.1, shape=shape) 
     return tf.Variable(initial) 

    # convolution 
    def conv2d(x, W): 
     return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 

    # X2 pooling 
    def max_pool_2x128(x): 
     return tf.nn.max_pool(x, ksize=[1, 2, 1, 1],strides=[1, 2, 1, 1], padding='VALID') 
    # X4 pooling 
    def max_pool_4x128(x): 
     return tf.nn.max_pool(x, ksize=[1, 4, 1, 1],strides=[1, 4, 1, 1], padding='VALID') 

    x_image = tf.reshape(images_placeholder, [-1,599,1,128]) 

    #1st conv 
    with tf.name_scope('conv1') as scope: 
     W_conv1 = weight_variable([4, 1, 128, 256]) 
     b_conv1 = bias_variable([256]) 

     print "image変形後のshape" 
     print tf.Tensor.get_shape(x_image) 
     print "conv1の形" 
     print tf.Tensor.get_shape(conv2d(x_image, W_conv1)) 

     h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 

    #1st pooling X4 
    with tf.name_scope('pool1') as scope: 
     h_pool1 = max_pool_4x128(h_conv1) 
     print "h_pool1の形" 
     print tf.Tensor.get_shape(h_pool1) 

    #2nd conv 
    with tf.name_scope('conv2') as scope: 
     W_conv2 = weight_variable([4, 1, 256, 256]) 
     b_conv2 = bias_variable([256]) 
     h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 

    #2nd pooling X2 
    with tf.name_scope('pool2') as scope: 
     h_pool2 = max_pool_2x128(h_conv2) 
     print "h_pool2の形" 
     print tf.Tensor.get_shape(h_pool2) 

    #3rd conv 
    with tf.name_scope('conv3') as scope: 
     W_conv3 = weight_variable([4, 1, 256, 512]) 
     b_conv3 = bias_variable([512]) 
     h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3) 

    #3rd pooling X2 
    with tf.name_scope('pool3') as scope: 
     h_pool3 = max_pool_2x128(h_conv3) 
     print "h_pool3の形" 
     print tf.Tensor.get_shape(h_pool3) 

    #flatten + 1st fully connected 
    with tf.name_scope('fc1') as scope: 
     W_fc1 = weight_variable([37 * 1 * 512, 2048]) 
     b_fc1 = bias_variable([2048]) 
     h_pool3_flat = tf.reshape(h_pool3, [-1, 37 * 1 * 512]) 
     h_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) + b_fc1) 
     #ドロップ層の設定 
     h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 

    #2nd fully connected 
    with tf.name_scope('fc2') as scope: 
     W_fc2 = weight_variable([2048, NUM_CLASSES]) 
     b_fc2 = bias_variable([NUM_CLASSES]) 

    #softmax output 
    with tf.name_scope('softmax') as scope: 
     y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) 

    return y_conv 

損失

def loss(logits, labels): 
    # cross entropy 
    cross_entropy = -tf.reduce_sum(labels*tf.log(tf.clip_by_value(logits,1e-10,1.0))) 
    # TensorBoard 
    tf.scalar_summary("cross_entropy", cross_entropy) 
    return cross_entropy 

トレーニング

def training(loss, learning_rate): 
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss) 
    return train_step 

精度

def accuracy(logits, labels): 
    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 
    tf.scalar_summary("accuracy", accuracy) 
    return accuracy 

メイン

if __name__ == '__main__': 

    flags = tf.app.flags 
    FLAGS = flags.FLAGS 

    flags.DEFINE_string('train_dir', '/tmp/data', 'Directory to put the training data.') 
    flags.DEFINE_integer('max_steps', , 'Number of steps to run trainer.') 
    flags.DEFINE_integer('batch_size', 10, 'Batch size' 
         'Must divide evenly into the dataset sizes.') 
    flags.DEFINE_float('learning_rate', 1e-4, 'Initial learning rate.') 

    #num output 
    NUM_CLASSES = 5 
    #num frame 
    IMAGE_SIZE = 599 
    #tensor shape 
    IMAGE_PIXELS = IMAGE_SIZE*1*128 

    ################## 
    #modify the data # 
    ################## 

    #number of training data 
    train_num = 70 
    #loading data limit 
    data_limit = 100 

    flatten_data = [] 
    flatten_label = [] 

    # データの整形 
    filenames = glob.glob(os.path.join('/Users/kosukefukui/Qosmo/WASABEAT/song_features/*.json')) 
    filenames = filenames[0:data_limit] 
    print "----loading data---" 
    for file_path in filenames: 
     data = json.load(open(file_path)) 
     data = np.array(data) 

     for_flat = np.array(data) 
     assert for_flat.flatten().shape == (IMAGE_PIXELS,) 
     flatten_data.append(for_flat.flatten().tolist()) 

    # ラベルの整形 
    f2 = open("id_information.txt") 
    print "---loading labels----" 

    for line in f2: 
     line = line.rstrip() 
     l = line.split(",") 
     tmp = np.zeros(NUM_CLASSES) 
     tmp[int(l[4])] = 1 
     flatten_label.append(tmp) 

    flatten_label = flatten_label[0:data_limit] 

    print "データ数 %s" % len(flatten_data) 
    print "ラベルデータ数 %s" % len(flatten_label) 

    #train data 
    train_image = np.asarray(flatten_data[0:train_num], dtype=np.float32) 
    train_label = np.asarray(flatten_label[0:train_num],dtype=np.float32) 

    print "訓練データ数 %s" % len(train_image) 

    #test data 
    test_image = np.asarray(flatten_data[train_num:data_limit], dtype=np.float32) 
    test_label = np.asarray(flatten_label[train_num:data_limit],dtype=np.float32) 

    print "テストデータ数 %s" % len(test_image) 

    print "599×128 = " 
    print len(train_image[0]) 

    f2.close() 

    if 1==1: 
     # Image Tensor 
     images_placeholder = tf.placeholder("float", shape=(None, IMAGE_PIXELS)) 
     # Label Tensor 
     labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES)) 
     # dropout Tensor 
     keep_prob = tf.placeholder("float") 

     # construct model 
     logits = inference(images_placeholder, keep_prob) 
     # calculate loss 
     loss_value = loss(logits, labels_placeholder) 
     # training 
     train_op = training(loss_value, FLAGS.learning_rate) 
     # accuracy 
     acc = accuracy(logits, labels_placeholder) 

     saver = tf.train.Saver() 
     sess = tf.Session() 
     sess.run(tf.initialize_all_variables()) 
     # for TensorBoard 
     summary_op = tf.merge_all_summaries() 
     summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph_def) 

     # Training 
     for step in range(FLAGS.max_steps): 
      for i in range(len(train_image)/FLAGS.batch_size): 
       # train for batch_size 
       batch = FLAGS.batch_size*i 
       sess.run(train_op, feed_dict={ 
        images_placeholder: train_image[batch:batch+FLAGS.batch_size], 
        labels_placeholder: train_label[batch:batch+FLAGS.batch_size], 
        keep_prob: 0.5}) 

      # calculate accuracy at each step 
      train_accuracy = sess.run(acc, feed_dict={ 
       images_placeholder: train_image, 
       labels_placeholder: train_label, 
       keep_prob: 1.0}) 
      print "step %d, training accuracy %g"%(step, train_accuracy) 

      # add value for Tensorboard at each step 
      summary_str = sess.run(summary_op, feed_dict={ 
       images_placeholder: train_image, 
       labels_placeholder: train_label, 
       keep_prob:1.0}) 
      summary_writer.add_summary(summary_str, step) 

    # show accuracy for test data 
    print "test accuracy %g"%sess.run(acc, feed_dict={ 
     images_placeholder: test_image, 
     labels_placeholder: test_label, 
     keep_prob: 1.0}) 
    # save the last model 
    save_path = saver.save(sess, "model.ckpt") 

しかし、私は、同じトレーニング精度を得ました。この問題を解決するには？

step 0, training accuracy 0.142857 
step 1, training accuracy 0.142857 
step 2, training accuracy 0.142857 
step 3, training accuracy 0.142857 
step 4, training accuracy 0.142857 
step 5, training accuracy 0.142857 
step 6, training accuracy 0.142857 
step 7, training accuracy 0.142857 
step 8, training accuracy 0.142857 
step 9, training accuracy 0.142857 
test accuracy 0.133333 

以下のモデルを参考にして、テンソルボードは以下のとおりです。

Answer 1

それはあなたが右のテンソルを最小限にしていないということだろうか？ cross_entropyを最小化していますが、cross_entropy_mean（コードの精度）でなければなりません。

cross_entropy = tf.nn.softmax_cross_entropy_with_logits（ logits、ground_truth_placeholder）

cross_entropy_mean = tf.reduce_mean（cross_entropy）

train_step = tf.train.GradientDescentOptimizer：以下のロジックと基本的に

（FLAGS.learning_rate）。minimize（ cross_entropy_mean）