tf.variable_scopeの再利用オプションはどのように機能しますか？

Question

私は以下の問題を抱えています.tensorflowの仕組みを学ぶための簡単なコードを書いており、tf.variable_scopeの助けを借りてコンボリューションの変数を定義しています。しかし、このスクリプトを実行しようとするたびに、Reuse = NoneまたはReuse = Trueを設定するValueErrorが表示されます。

誰かがなぜこのオプションを定義せずに関数を実行するだけでは解決できないのでしょうか？

私のコードは次のとおりです。

import re 
import tensorflow as tf 
import numpy as np 
data = np.load('/home/joanna/tensorflow-master/tensorflow/models/image/cifar10/konsensop/data.npy') 
labels = np.load('/home/joanna/tensorflow-master/tensorflow/models/image/cifar10/konsensop/labels.npy') 
labels = np.zeros((16400,)) 
labels[10001:16400]=1 
labels = labels.astype(int) 
data = data.astype(np.float32) 
#labels = tf.cast(labels,tf.int64) 

MOVING_AVERAGE_DECAY = 0.9999  # The decay to use for the moving average. 
NUM_EPOCHS_PER_DECAY = 350.0  # Epochs after which learning rate decays. 
LEARNING_RATE_DECAY_FACTOR = 0.1 # Learning rate decay factor. 
INITIAL_LEARNING_RATE = 0.1  # Initial learning rate. 
NUM_CLASSES=2 
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN= 1000 
batch_size=300 

def _variable_on_cpu(name, shape, initializer): 
    dtype = tf.float32 
    var = tf.get_variable(name, shape, initializer = initializer, dtype = dtype) 
    return var 

def _add_loss_summaries(loss): 
    """Add summaries for losses in CIFAR-10 model. 

    Generates moving average for all losses and associated summaries for 
    visualizing the performance of the network. 

    Args: 
    total_loss: Total loss from loss(). 
    Returns: 
    loss_averages_op: op for generating moving averages of losses. 
    """ 
    # Compute the moving average of all individual losses and the total loss. 
    loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') 
    losses = tf.get_collection('losses') 
    loss_averages_op = loss_averages.apply(losses + [loss]) 

    # Attach a scalar summary to all individual losses and the total loss; do the 
    # same for the averaged version of the losses. 
    for l in losses + [loss]: 
    # Name each loss as '(raw)' and name the moving average version of the loss 
    # as the original loss name. 
    tf.scalar_summary(l.op.name +' (raw)', l) 
    tf.scalar_summary(l.op.name, loss_averages.average(l)) 
    return loss_averages_op 

def _variable_with_weight_decay(name, shape, stddev, wd): 
    dtype = tf.float32 
    var = _variable_on_cpu(
     name, 
     shape, 
     tf.truncated_normal_initializer(stddev=stddev, dtype=dtype)) 
    if wd is not None: 
    weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss') 
    tf.add_to_collection('losses', weight_decay) 
    return var 

def _activation_summary(x): 
    tensor_name = re.sub('_[0-9]*/','', x.op.name) 
    tf.histogram_summary(tensor_name + '/activations', x) 
    tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) 

def iterate_batches(data, labels, batch_size, num_epochs): 
    N = int(labels.shape[0]) 
    batches_per_epoch = int(N/batch_size) 
    for i in range(num_epochs): 
    for j in range(batches_per_epoch): 
     start, stop = j*batch_size, (j+1)*batch_size 
     yield data[start:stop,:,:,:], labels[start:stop] 

def train(): 
    with tf.Graph().as_default(): 
     global_step = tf.Variable(0) 
     x_tensor = tf.placeholder(tf.float32, shape=(batch_size, 3000,1,1)) 
     y_tensor = tf.placeholder(tf.int64, shape=(batch_size,)) 
     for x,y in iterate_batches(data,labels, 300,1): 
     print('yey!') 
     with tf.variable_scope('conv1',reuse=True) as scope: 
      kernel = _variable_with_weight_decay('weights', 
              shape=[100,1,1,64], 
              stddev=5e-2, 
              wd=0.0) 
      conv = tf.nn.conv2d(x_tensor, kernel, [1,3,1,1], padding = 'SAME') 
      biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0)) 
      bias = tf.nn.bias_add(conv, biases) 
      conv1 = tf.nn.relu(bias, name=scope.name) 
      _activation_summary(conv1) 
      pool1 = tf.nn.max_pool(conv1, ksize=[1,20,1,1], strides=[1,2,1,1], padding='SAME', name='pool1') 
      norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm1') 

     with tf.variable_scope('conv2',reuse=True) as scope: 
      kernel = _variable_with_weight_decay('weights', [50,1,64,64], stddev = 5e-2, wd=0.0) 
      conv = tf.nn.conv2d(norm1, kernel, [1,3,1,1], padding='SAME') 
      biases = _variable_on_cpu('biases',[64], tf.constant_initializer(0.1)) 
      bias = tf.nn.bias_add(conv,biases) 
      conv2 = tf.nn.relu(bias, name=scope.name) 
      _activation_summary(conv2) 
      norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9.0, beta = 0.75, name='norm2') 
      pool2 = tf.nn.max_pool(norm2, ksize=[1,10,1,1], strides=[1,2,1,1], padding='SAME', name='pool2') 

     with tf.variable_scope('conv3',reuse=True) as scope: 
      kernel = _variable_with_weight_decay('weights', [30,1,64,64], stddev = 5e-2, wd=0.0) 
      conv = tf.nn.conv2d(pool2, kernel, [1,10,1,1], padding='SAME') 
      biases = _variable_on_cpu('biases',[64], tf.constant_initializer(0.1)) 
      bias = tf.nn.bias_add(conv,biases) 
      conv3 = tf.nn.relu(bias, name=scope.name) 
      _activation_summary(conv3) 
      norm3 = tf.nn.lrn(conv3, 4, bias=1.0, alpha=0.001/9.0, beta = 0.75, name='norm3') 
      pool3 = tf.nn.max_pool(norm3, ksize=[1,9,1,1], strides=[1,9,1,1], padding='SAME', name='pool3') 

     with tf.variable_scope('fc4',reuse=True) as scope: 
    # Move everything into depth so we can perform a single matrix multiply. 
      reshape = tf.reshape(pool3, [batch_size, -1]) 
      dim = reshape.get_shape()[1].value 
      weights = _variable_with_weight_decay('weights', shape=[dim, 64], stddev=0.04, wd=0.004) 
      biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1)) 
      fc4 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) 
      _activation_summary(fc4) 

     with tf.variable_scope('fc5',reuse=True) as scope: 
      weights = _variable_with_weight_decay('weights', shape=[64, 64], 
              stddev=0.04, wd=0.004) 
      biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1)) 
      fc5 = tf.nn.relu(tf.matmul(fc4, weights) + biases, name=scope.name) 
      _activation_summary(fc5) 

     with tf.variable_scope('softmax_linear',) as scope: 
      weights = _variable_with_weight_decay('weights', [64, NUM_CLASSES], 
              stddev=1/64.0, wd=0.0) 
      biases = _variable_on_cpu('biases', [NUM_CLASSES], 
           tf.constant_initializer(0.0)) 
      softmax_linear = tf.add(tf.matmul(fc5, weights), biases, name=scope.name) 
      _activation_summary(softmax_linear) 

     cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(softmax_linear, y_tensor, name='cross_entropy_per_example') 
     cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') 
     kupa = tf.add_to_collection('losses', cross_entropy_mean) 
     loss = tf.add_n(tf.get_collection('losses'), name='total_loss') 

     #neu 
     num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /batch_size 
     decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY) 

     lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE, global_step, decay_steps, LEARNING_RATE_DECAY_FACTOR, staircase=True) 
     loss_averages_op = _add_loss_summaries(loss) 
     summary_op = tf.merge_all_summaries() 

    #neu 
     init = tf.initialize_all_variables() 
     sess = tf.Session(config = tf.ConfigProto(log_device_placement=False)) 
     sess.run(init) 
     sess.run([conv, bias, conv1, pool1, norm1, conv2,norm2, pool2, conv3, norm3, pool3,fc4,fc5], feed_dict={x_tensor:x, y_tensor:y}) 
     sess.run([softmax_linear,loss], feed_dict={x_tensor:x, y_tensor:y}) 
     sess.run([lr, loss_averages_op, summary_op], feed_dict={x_tensor:x, y_tensor:y}) 

Answer 1

問題はここに、この行を次のとおりです。

for x,y in iterate_batches(data,labels, 300,1): 

これはグラフにそれを取るでしょうとして行うには悪いことですが、何度も再作成します毎回メモリを増やしてください（これは必ずしもそうではありませんが、起こる可能性があります）。

グラフを定義するとき、reuse = Trueは次の例のようになります。

# First call creates one set of variables. 
result1 = my_image_filter(image1) 
# Another set of variables is created in the second call. 
result2 = my_image_filter(image2) 

Tensorflowはあなたのような変数は、彼らが同じパラメータを共有したりすべきではない「再利用」するかどうか分かりません。

具体的には、毎回パラメータを再作成し、単に変数を再利用するようにテンソルフローを伝えることで可能です。

グラフ作成が既に発生した後にforループを移動して、どこでもreuse = Trueを取り除くことができればよいでしょう。