沒有變量來保存Tensorflow中的錯誤

Question

我試圖保存模型，然後重新使用它來對圖像進行分類，但不幸的是，我在恢復已保存的模型時遇到了錯誤。

其中模型已經建立代碼：

# Deep Learning 
# ============= 
# 
# Assignment 4 
# ------------ 

# In[25]: 

# These are all the modules we'll be using later. Make sure you can import them 
# before proceeding further. 
from __future__ import print_function 
import numpy as np 
import tensorflow as tf 
from six.moves import cPickle as pickle 
from six.moves import range 


# In[37]: 

pickle_file = 'notMNIST.pickle' 

with open(pickle_file, 'rb') as f: 
    save = pickle.load(f) 
    train_dataset = save['train_dataset'] 
    train_labels = save['train_labels'] 
    valid_dataset = save['valid_dataset'] 
    valid_labels = save['valid_labels'] 
    test_dataset = save['test_dataset'] 
    test_labels = save['test_labels'] 
    del save # hint to help gc free up memory 
    print('Training set', train_dataset.shape, train_labels.shape) 
    print('Validation set', valid_dataset.shape, valid_labels.shape) 
    print('Test set', test_dataset.shape, test_labels.shape) 
    print(test_labels) 


# Reformat into a TensorFlow-friendly shape: 
# - convolutions need the image data formatted as a cube (width by height by #channels) 
# - labels as float 1-hot encodings. 

# In[38]: 

image_size = 28 
num_labels = 10 
num_channels = 1 # grayscale 

import numpy as np 

def reformat(dataset, labels): 
    dataset = dataset.reshape(
    (-1, image_size, image_size, num_channels)).astype(np.float32) 
    #print(np.arange(num_labels)) 
    labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32) 
    #print(labels[0,:]) 
    print(labels[0]) 
    return dataset, labels 
train_dataset, train_labels = reformat(train_dataset, train_labels) 
valid_dataset, valid_labels = reformat(valid_dataset, valid_labels) 
test_dataset, test_labels = reformat(test_dataset, test_labels) 
print('Training set', train_dataset.shape, train_labels.shape) 
print('Validation set', valid_dataset.shape, valid_labels.shape) 
print('Test set', test_dataset.shape, test_labels.shape) 
#print(labels[0]) 


# In[39]: 

def accuracy(predictions, labels): 
    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) 
     /predictions.shape[0]) 


# Let's build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we'll limit its depth and number of fully connected nodes. 

# In[47]: 

batch_size = 16 
patch_size = 5 
depth = 16 
num_hidden = 64 

graph = tf.Graph() 

with graph.as_default(): 

    # Input data. 
    tf_train_dataset = tf.placeholder(
    tf.float32, shape=(batch_size, image_size, image_size, num_channels)) 
    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels)) 
    tf_valid_dataset = tf.constant(valid_dataset) 
    tf_test_dataset = tf.constant(test_dataset) 

    # Variables. 
    layer1_weights = tf.Variable(tf.truncated_normal(
     [patch_size, patch_size, num_channels, depth], stddev=0.1),name="layer1_weights") 
    layer1_biases = tf.Variable(tf.zeros([depth]),name = "layer1_biases") 
    layer2_weights = tf.Variable(tf.truncated_normal(
     [patch_size, patch_size, depth, depth], stddev=0.1),name = "layer2_weights") 
    layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]),name ="layer2_biases") 
    layer3_weights = tf.Variable(tf.truncated_normal(
     [image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1),name="layer3_biases") 
    layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]),name = "layer3_biases") 
    layer4_weights = tf.Variable(tf.truncated_normal(
     [num_hidden, num_labels], stddev=0.1),name = "layer4_weights") 
    layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]),name = "layer4_biases") 

    # Model. 
    def model(data): 
    conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME') 
    hidden = tf.nn.relu(conv + layer1_biases) 
    conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME') 
    hidden = tf.nn.relu(conv + layer2_biases) 
    shape = hidden.get_shape().as_list() 
    reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]]) 
    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases) 
    return tf.matmul(hidden, layer4_weights) + layer4_biases 

    # Training computation. 
    logits = model(tf_train_dataset) 
    loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels)) 

    # Optimizer. 
    optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss) 

    # Predictions for the training, validation, and test data. 
    train_prediction = tf.nn.softmax(logits) 
    valid_prediction = tf.nn.softmax(model(tf_valid_dataset)) 
    test_prediction = tf.nn.softmax(model(tf_test_dataset)) 


# In[48]: 

num_steps = 1001 
#saver = tf.train.Saver() 
with tf.Session(graph=graph) as session: 
    tf.initialize_all_variables().run() 
    print('Initialized') 
    for step in range(num_steps): 
    offset = (step * batch_size) % (train_labels.shape[0] - batch_size) 
    batch_data = train_dataset[offset:(offset + batch_size), :, :, :] 
    batch_labels = train_labels[offset:(offset + batch_size), :] 
    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels} 
    _, l, predictions = session.run(
     [optimizer, loss, train_prediction], feed_dict=feed_dict) 
    if (step % 50 == 0): 
     print('Minibatch loss at step %d: %f' % (step, l)) 
     print('Minibatch accuracy: %.1f%%' % accuracy(predictions, batch_labels)) 
     print('Validation accuracy: %.1f%%' % accuracy(
     valid_prediction.eval(), valid_labels)) 
    print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels)) 
    save_path = tf.train.Saver().save(session, "/tmp/model.ckpt") 
    print("Model saved in file: %s" % save_path) 

一切工作正常和模型存儲在相應的文件夾中。

我已經創建了那裏我試圖恢復模式，但收到的錯誤有

# In[1]: 
from __future__ import print_function 
import numpy as np 
import tensorflow as tf 
from six.moves import cPickle as pickle 
from six.moves import range 


# In[3]: 

image_size = 28 
num_labels = 10 
num_channels = 1 # grayscale 
import numpy as np 


# In[4]: 

def accuracy(predictions, labels): 
    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) 
     /predictions.shape[0]) 


# In[8]: 

batch_size = 16 
patch_size = 5 
depth = 16 
num_hidden = 64 

graph = tf.Graph() 

with graph.as_default(): 

    '''# Input data. 
    tf_train_dataset = tf.placeholder(
    tf.float32, shape=(batch_size, image_size, image_size, num_channels)) 
    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels)) 
    tf_valid_dataset = tf.constant(valid_dataset) 
    tf_test_dataset = tf.constant(test_dataset)''' 

    # Variables. 
    layer1_weights = tf.Variable(tf.truncated_normal(
     [patch_size, patch_size, num_channels, depth], stddev=0.1),name="layer1_weights") 
    layer1_biases = tf.Variable(tf.zeros([depth]),name = "layer1_biases") 
    layer2_weights = tf.Variable(tf.truncated_normal(
     [patch_size, patch_size, depth, depth], stddev=0.1),name = "layer2_weights") 
    layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]),name ="layer2_biases") 
    layer3_weights = tf.Variable(tf.truncated_normal(
     [image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1),name="layer3_biases") 
    layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]),name = "layer3_biases") 
    layer4_weights = tf.Variable(tf.truncated_normal(
     [num_hidden, num_labels], stddev=0.1),name = "layer4_weights") 
    layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]),name = "layer4_biases") 

    # Model. 
    def model(data): 
    conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME') 
    hidden = tf.nn.relu(conv + layer1_biases) 
    conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME') 
    hidden = tf.nn.relu(conv + layer2_biases) 
    shape = hidden.get_shape().as_list() 
    reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]]) 
    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases) 
    return tf.matmul(hidden, layer4_weights) + layer4_biases 

    '''# Training computation. 
    logits = model(tf_train_dataset) 
    loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels)) 

    # Optimizer. 
    optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)''' 

    # Predictions for the training, validation, and test data. 
    #train_prediction = tf.nn.softmax(logits) 
    #valid_prediction = tf.nn.softmax(model(tf_valid_dataset)) 
    #test_prediction = tf.nn.softmax(model(tf_test_dataset)) 

# In[17]: 

#saver = tf.train.Saver() 
with tf.Session() as sess: 
    # Restore variables from disk. 
    tf.train.Saver().restore(sess, "/tmp/model.ckpt") 
    print("Model restored.") 
    # Do some work with the model 

錯誤我得到一個更蟒蛇文件：

不變量，以節省

任何幫助將不勝感激

Answer 1

這裏的錯誤是相當微妙。在In[8]中，創建一個名爲的tf.Graph，並將其設置爲with graph.as_default():塊的默認值。這意味着所有變量都在中創建，如果您打印graph.all_variables()，則應該會看到您的變量列表。

然而，則創建（i）所述tf.Session之前退出with塊，和（ii）所述tf.train.Saver。這意味着會話和保護程序創建在不同的圖（全局默認值tf.Graph用於未明確創建並將其設置爲默認值時使用），其中不包含任何變量—或任何節點所有。

至少有兩種解決方案：

1. 由於Yaroslav suggests，你可以編寫程序，而無需使用with graph.as_default():塊，這避免了與多個圖形的混亂。但是，這會導致IPython筆記本中不同單元格之間的名稱衝突，這在使用tf.train.Saver時很尷尬，因爲它使用tf.Variable的name屬性作爲檢查點文件中的鍵。

2. 您可以創建保護內的with graph.as_default():塊，並創建tf.Session有一個明確的圖表，如下：

   with graph.as_default(): 
       # [Variable and model creation goes here.] 
   
       saver = tf.train.Saver() # Gets all variables in `graph`. 
   
   with tf.Session(graph=graph) as sess: 
       saver.restore(sess) 
       # Do some work with the model.... 
   

   或者，你可以創建tf.Session內的with graph.as_default():塊，在這種情況下，其所有操作都將使用。

Answer 2

您正在創建一個In[17]的新會話，它會清除變量。此外，您不需要使用with塊，如果你只有一個默認的圖形和一個默認的會話，可以改爲做這樣的事情

sess = tf.InteractiveSession() 
layer1_weights = tf.Variable(tf.truncated_normal(
    [patch_size, patch_size, num_channels, depth], stddev=0.1),name="layer1_weights") 
tf.train.Saver().restore(sess, "/tmp/model.ckpt")