Tensorflow La stessa precisione di allenamento continua

Question

Sono bloccato sul modello CNN su Tensorflow. Il mio codice come di seguito.

Biblioteche

# -*- 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 

Girl

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 

perdita

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 

formazione

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

Precisione

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 

principale

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") 

Tuttavia, ho avuto la stessa precisione di formazione. Come risolvere questo problema?

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 

Ho fatto riferimento al seguente modello e il mio tensore è il seguente.

Answer 1

potrebbe essere che non si sta riducendo al minimo il tensore giusto? Stai minimizzando cross_entropy, ma dovrebbe essere cross_entropy_mean (precisione nel tuo codice).

sostanza con la seguente logica:

cross_entropy = tf.nn.softmax_cross_entropy_with_logits ( logit, ground_truth_placeholder)

cross_entropy_mean = tf.reduce_mean (cross_entropy)

train_step = tf.train.GradientDescentOptimizer (FLAGS.learning_rate) .minimize ( cross_entropy_mean)