テンソル流損失が減少せず、腐食が0.00％で止まったか？

Question

コード

I "メートルUCF101単一のフレームデータとCNNを養成しようとしている。 私の知る限りは問題はどちらかであるtf.nn.softmax and tf.nn.softmax_cross_entropy_with_logitsやコストとオプティマイザ機能のいずれかでの重量の初期化や 損失を理解して。

また、初期化??

import tensorflow as tf 
import numpy as np 
import scipy as sci 
import cv2 
import input_data_conv 
import skimage.transform 
from skimage import color 


# Parameters 
learning_rate = 0.001 
training_iters = 200000 
batch_size = 64 
display_step = 20 
n_classes=101 # number of classes 

#Input data and classes 
global train_data,train_class,test_data,test_classs,train_i,test_i 
test_i, train_i = 0,0 
train_data=input_data_conv.train_single_frames 
train_class=input_data_conv.train_single_classes 
test_data=input_data_conv.test_single_frames 
test_classs=input_data_conv.test_single_classes 


# Network Parameters 
n_input = [227, 227, 3 ]# MNIST data input (img shape: 227*227*3) 
dropout = 0.5 # Dropout, probability to keep units 

# tf Graph input 
x = tf.placeholder(tf.float32, [None, 227,227,3]) 
y = tf.placeholder(tf.float32, [None, n_classes]) 
keep_prob = tf.placeholder(tf.float32) # dropout (keep probability) 

def resize_im(im, nh, nw): 
    im=np.copy(im) 
    h, w, _ = im.shape 
    im = skimage.transform.resize(im, (nh, nw), preserve_range=True) 
    return im 
def create_class_vec(val,nuoclasses): 
    x=np.zeros(nuoclasses) 
    x[val]=1 
    return x 

def init_weights(shape): 
    return tf.Variable(tf.random_normal(shape, stddev=0.01)) 

def conv2d(name, l_input, w, b,s): 
    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, s, s, 1], padding='SAME'),b), name=name) 
def conv2dpad(name, l_input, w, b,s): 
    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, s, s, 1], padding='VALID'),b), name=name) 

def max_pool(name, l_input, k,s): 
    return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, s, s, 1], padding='SAME', name=name) 

def norm(name, l_input, lsize): 
    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.0001/9.0, beta=0.75, name=name) 

def vgg_single_frame(_X, _weights, _biases, _dropout): 
    # Reshape input picture 
    _X = tf.reshape(_X, shape=[-1, 227, 227, 3]) 

    conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'],s=2) 
    pool1 = max_pool('pool1', conv1, k=3,s=2) 
    norm1 = norm('norm1', pool1, lsize=5) 

    conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'],s=2) 
    pool2 = max_pool('pool2', conv2, k=3,s=2) 
    norm2 = norm('norm2', pool2, lsize=5) 


    conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'],s=1) 
    conv4 = conv2d('conv4', conv3, _weights['wc4'], _biases['bc4'],s=1) 
    conv5 = conv2d('conv4', conv4, _weights['wc5'], _biases['bc5'],s=1) 
    pool5 = max_pool('pool5', conv5, k=3,s=2) 

    # Fully connected layer 
    dense1 = tf.reshape(pool5, [-1, _weights['wd1'].get_shape().as_list()[0]]) # Reshape conv3 output to fit dense layer input 
    dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc6') # Relu activation 
    dense1 = tf.nn.dropout(dense1, _dropout) 
    dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc7') # Relu activation 
    dense2 = tf.nn.dropout(dense2, _dropout) 

    # Output, class prediction 
    out = tf.nn.softmax(tf.matmul(dense2, _weights['out']) + _biases['out']) 
    return out 

weights = { 
    'wc1': tf.Variable(tf.random_normal([7, 7, 3, 96])), # 7x7 conv, 1 input, 96 outputs ,stride 2 
    'wc2': tf.Variable(tf.random_normal([5, 5, 96, 384])), # 5x5 conv, 32 inputs, 64 outputs 
    'wc3': tf.Variable(tf.random_normal([3, 3, 384, 512])),#s 2 ,p a 
    'wc4': tf.Variable(tf.random_normal([3, 3, 512, 512])),#s 2, p 1 
    'wc5': tf.Variable(tf.random_normal([3, 3, 512, 384])),#s 2, p 1 
    'wd1': tf.Variable(tf.random_normal([8*8*384, 4096])), # fully connected, 7*7*64 inputs, 1024 outputs 
    'wd2': tf.Variable(tf.random_normal([4096, 4096])), # fully connected, 7*7*64 inputs, 1024 outputs 
    'out': tf.Variable(tf.random_normal([4096, n_classes])) # 1024 inputs, 10 outputs (class prediction) 
} 

biases = { 
    'bc1': tf.Variable(tf.random_normal([96])), 
    'bc2': tf.Variable(tf.random_normal([384])), 
    'bc3': tf.Variable(tf.random_normal([512])), 
    'bc4': tf.Variable(tf.random_normal([512])), 
    'bc5': tf.Variable(tf.random_normal([384])), 
    'bd1': tf.Variable(tf.random_normal([4096])), 
    'bd2': tf.Variable(tf.random_normal([4096])), 
    'out': tf.Variable(tf.random_normal([n_classes])) 
} 

def train_next_batch(batch_size): 
    temp_data=np.ndarray(shape=(batch_size,227,227,3),dtype=float) 
    temp_class=np.ndarray(shape=(batch_size,n_classes),dtype=float) 
    for idx,x in enumerate(train_data[train_i:train_i+batch_size]): 
     temp_data[idx,:,:,:]=resize_im(cv2.imread(x,1),227,227) 
     temp_class[idx,:]=create_class_vec(train_class[train_i+idx],101) 
    return temp_data,temp_class 


pred = vgg_single_frame(x, weights, biases, keep_prob) 
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) 
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 
# Evaluate model 
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) 
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) 
# 
# cost = -tf.reduce_sum(y*tf.log(pred)) 
# optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 
# correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) 
# accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) 

# Initializing the variables 
init = tf.initialize_all_variables() 

with tf.Session() as sess: 
    sess.run(init) 
    step = 1 
    # Keep training until reach max iterations 
    while step * batch_size < training_iters: 
     batch_xs, batch_ys = train_next_batch(batch_size) 
     # Fit training using batch data 
     sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout}) 
     if step % display_step == 0: 
      # Calculate batch accuracy 
      acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) 
      # Calculate batch loss 
      loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) 
      print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc) 
     step += 1 
    print "Optimization Finished!" 
    # Calculate accuracy for 256 mnist test images 
    print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}) 

出力をザビエル使用する方法はあり

Total memory: 12.00GiB 
Free memory: 10.77GiB 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:717] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:03:00.0) 
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 1.0KiB 
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I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 1.00GiB 
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 2.00GiB 
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 4.00GiB 
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 8.00GiB 
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 16.00GiB 
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:73] Allocating 10.23GiB bytes. 
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:83] GPU 0 memory begins at 0xb06c80000 extends to 0xd9579bb34 
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Iter 198400, Minibatch Loss= 4.631989, Training Accuracy= 0.00000 
Iter 199680, Minibatch Loss= 4.631989, Training Accuracy= 0.00000 
Optimization Finished! 

Answer 1

変数の初期値に対してnumpy配列を作成することによって、異なる初期化スキームを使用できます。

あなたの損失はまったく変化していないので、デバッグの1つの方法は、変数の更新中に実際に変数が変化していることを確認することです。

Answer 2

Tensorflowからdocumentation！

警告：この操作では、効率のために内部でロジットを実行するため、スケールされていないロジットが必要です。 softmaxの出力でこのopを呼び出さないでください。誤った結果が生成されるためです。あなたのネットワークの定義から

out = tf.nn.softmax(tf.matmul(dense2, _weights['out']) + _biases['out']) 

を取り出し、あなたが微調整、すなわち、同様の問題に訓練された重みを移植していないされていないので

out = tf.matmul(dense2, _weights['out']) + _biases['out'] 

で置き換える必要がありますこのことを念頭に

トレーニングは遅くなる傾向があります。これは、トレーニングの多くの問題の1つになる可能性があることに留意してください。それが役に立てば幸い。

Answer 3

私は同じ問題に直面しました。最後の高密度層からのrelu活性化を落とす。私のために働いた。この後

dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc7') # Relu activation 

再び

out = tf.nn.softmax(tf.matmul(dense2, _weights['out']) + _biases['out']) 

したがって最初の "reluは、" 私には冗長であるように思われます。私も似たようなことをしました。その行を削除した後、問題を解決しました。