Python cifar10自动编码器出现错误：必须为占位符张量'；x'；使用dtype float

我尝试用cifar10构建隐私自动编码器，我参考了Magnus Erik Hvass Pedersen（）为cnn编写的教程。 这是我的代码：

import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
import time
from datetime import timedelta
import math
import os
import cifar10
from cifar10 import img_size, num_channels, num_classes

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.03)
session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

cifar10.maybe_download_and_extract()
class_names = cifar10.load_class_names()

images_train, cls_train, labels_train = cifar10.load_training_data()
images_test, cls_test, labels_test = cifar10.load_test_data()
print("Size of:")
print("- Training-set:\t\t{}".format(len(images_train)))
print("- Test-set:\t\t{}".format(len(images_test)))
img_size_cropped = 24

x = tf.placeholder(tf.float32, shape=[None, img_size, img_size, num_channels], name='x')
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')
y_true_cls = tf.argmax(y_true, dimension=1)

def pre_process_image(image, training):
    # This function takes a single image as input,
    # and a boolean whether to build the training or testing graph.

    if training:
        # For training, add the following to the TensorFlow graph.

        # Randomly crop the input image.
        image = tf.random_crop(image, size=[img_size_cropped, img_size_cropped, num_channels])

        # Randomly flip the image horizontally.
        image = tf.image.random_flip_left_right(image)

        # Randomly adjust hue, contrast and saturation.
        image = tf.image.random_hue(image, max_delta=0.05)
        image = tf.image.random_contrast(image, lower=0.3, upper=1.0)
        image = tf.image.random_brightness(image, max_delta=0.2)
        image = tf.image.random_saturation(image, lower=0.0, upper=2.0)

        # Some of these functions may overflow and result in pixel
        # values beyond the [0, 1] range. It is unclear from the
        # documentation of TensorFlow 0.10.0rc0 whether this is
        # intended. A simple solution is to limit the range.

        # Limit the image pixels between [0, 1] in case of overflow.
        image = tf.minimum(image, 1.0)
        image = tf.maximum(image, 0.0)
    else:
        # For training, add the following to the TensorFlow graph.

        # Crop the input image around the centre so it is the same
        # size as images that are randomly cropped during training.
        image = tf.image.resize_image_with_crop_or_pad(image,
                                                       target_height=img_size_cropped,
                                                       target_width=img_size_cropped)
    return image

def pre_process(images, training):
    # Use TensorFlow to loop over all the input images and call
    # the function above which takes a single image as input.
    images = tf.map_fn(lambda image: pre_process_image(image, training), images)

    return images


#define the first privacy autoencoder parameters
n_input=img_size_cropped*img_size_cropped*num_channels    #24*24*3
n_hidden_1=math.floor(img_size_cropped*img_size_cropped*num_channels/2)
weights={
    'encoder_h1':tf.Variable(tf.truncated_normal([n_input,n_hidden_1], stddev=1/192.0)),
    'decoder_h1':tf.Variable(tf.truncated_normal([n_hidden_1,n_input], stddev=1/192.0)),
}
biases={
    'encoder_b1':tf.Variable(tf.random_normal([n_hidden_1])),
    'decoder_b1':tf.Variable(tf.random_normal([n_input])),
}    
epsilon = 0.0005
train_batch_size = 128
batch_size = 128                         

def inference(images):
    xs=tf.reshape(images,[tf.shape(x)[0], img_size_cropped*img_size_cropped*num_channels])
    #encoder
    h=tf.add(tf.matmul(xs, weights['encoder_h1']), biases['encoder_b1'])   
    encoder_layer_1=tf.nn.sigmoid(h)

    #decoder
    x_tilde=tf.add(tf.matmul(encoder_layer_1,weights['decoder_h1']), biases['decoder_b1'])
    decoder_layer_1=tf.nn.sigmoid(x_tilde)

    #encoder_layer_1 for next dPA use, decoder_layer_1 for decoder_output, x_tilde for objective function( loss_restruction() )
    return encoder_layer_1, decoder_layer_1, x_tilde

def loss_restruction(images,decoder_layer_1, x_tilde):     
    xs=tf.reshape(images,[tf.shape(x)[0], img_size_cropped*img_size_cropped*num_channels])
    y_pred=decoder_layer_1
    f1=tf.multiply(xs,math.log(2))  #shape=(128, 1728)
    f2=tf.multiply((tf.ones_like(xs)-xs),math.log(2))  #shape=(128,1728)
    #difine polynomial coefficients to stuck laplacian noise
    coefficient_1=tf.add(f1,f2) 
    coefficient_2=tf.add(f1,f2)/1
    coefficient_3=tf.add(f1,f2)/(2*1)

    #compute global sensitivity to generate laplacian noise
    global_sensitivity=2.0*tf.reduce_max(
        tf.reduce_sum(tf.add(tf.add(tf.abs(coefficient_1), tf.abs(coefficient_2)),tf.abs(coefficient_3)),axis=1, keep_dims=True),reduction_indices=0)  #[1].*2   
    sen=session.run(global_sensitivity)
    lap_noise1=np.random.laplace(loc=0.0, scale=(sen[0]/epsilon), size=(tf.shape(x)[0], img_size_cropped*img_size_cropped*num_channels))
    lap_noise2=np.random.laplace(loc=0.0, scale=(sen[0]/epsilon), size=(tf.shape(x)[0], img_size_cropped*img_size_cropped*num_channels))
    lap_noise3=np.random.laplace(loc=0.0, scale=(sen[0]/epsilon), size=(tf.shape(x)[0], img_size_cropped*img_size_cropped*num_channels))

    #restruction function with laplacian noise as a loss function
    loss=tf.reduce_mean(tf.reduce_sum(tf.add(
        tf.add(tf.add(coefficient_1,lap_noise1), tf.multiply(tf.add(coefficient_2,lap_noise2), x_tilde)),
        tf.multiply(tf.add(coefficient_3,lap_noise3), tf.square(x_tilde)) ),1))

    return y_pred, loss

'''
    Helper-function for creating Main Processing
    The following helper-function creates the main part of the privacy autoencoder. 
    '''      
def main_network(images, training):
    encoder_layer_1, decoder_layer_1, x_tilde=inference(images)

    if training:
        y_pred, loss=loss_restruction(images, decoder_layer_1, x_tilde)
    else:
        y_pred=decoder_layer_1
        loss=tf.constant([0])

    return y_pred, loss  


def create_network(training):
    with tf.variable_scope('network', reuse=not training):
        # Just rename the input placeholder variable for convenience.
        images = x

        # Create TensorFlow graph for pre-processing.
        images = pre_process(images=images, training=training)

        # Create TensorFlow graph for the main processing.
        y_pred, loss = main_network(images=images, training=training)
    return y_pred, loss


global_step = tf.Variable(initial_value=0, name='global_step', trainable=False)    
_, loss = create_network(training=True)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss, global_step=global_step)
y_pred, _ = create_network(training=False)

saver = tf.train.Saver()
save_dir = 'checkpoints/'
if not os.path.exists(save_dir):
    os.makedirs(save_dir)
save_path = os.path.join(save_dir, 'cifar10_privacy_autoencoder')
try:
    print("Trying to restore last checkpoint ...")
    last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=save_dir)
    saver.restore(session, save_path=last_chk_path)
    print("Restored checkpoint from:", last_chk_path)
except:
    print("Failed to restore checkpoint. Initializing variables instead.")
    session.run(tf.global_variables_initializer())


'''
    Function for selecting a random batch of images from the training-set.
    '''
def random_batch():
    # Number of images in the training-set.
    num_images = len(images_train)

    # Create a random index.
    idx = np.random.choice(num_images,
                           size=train_batch_size,
                           replace=False)

    # Use the random index to select random images and labels.
    x_batch = images_train[idx, :, :, :]
    y_batch = labels_train[idx, :]

    return x_batch, y_batch


def optimize(num_iterations):
    # Start-time used for printing time-usage below.
    start_time = time.time()

    for i in range(num_iterations):

        x_batch, y_true_batch = random_batch()
        feed_dict_train = {x: x_batch,
                           y_true: y_true_batch}

        i_global, _, cost = session.run([global_step, optimizer, loss],
                                  feed_dict=feed_dict_train)

        # Print status to screen every 100 iterations (and last).
        if (i_global % 100 == 0) or (i == num_iterations - 1):
            # Print status.
            msg = "Global Step: {0:>6}, Training Batch Cost: {1:>6.1%}"
            print(msg.format(i_global, cost))

        # Save a checkpoint to disk every 1000 iterations (and last).
        if (i_global % 1000 == 0) or (i == num_iterations - 1):
            saver.save(session,
                       save_path=save_path,
                       global_step=global_step)

            print("Saved checkpoint.")

    # Ending time.
    end_time = time.time()

    # Difference between start and end-times.
    time_dif = end_time - start_time

    # Print the time-usage.
    print("Time usage: " + str(timedelta(seconds=int(round(time_dif)))))
    print("Optimization Finished!")

optimize(num_iterations=2000)

不幸的是，我在计算损失函数的全局灵敏度时收到一条错误消息：

lap_noise1=np.random.laplace(loc=0.0, scale=(sen[0]/epsilon), size=(tf.shape(x)[0], img_size_cropped*img_size_cropped*num_channels))

错误消息：

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'x' with dtype float

详细的错误消息如下所示：

Loading data: /tmp/cifar10_data\cifar-10-batches-py/data_batch_2
Loading data: /tmp/cifar10_data\cifar-10-batches-py/data_batch_3
Loading data: /tmp/cifar10_data\cifar-10-batches-py/data_batch_4
Loading data: /tmp/cifar10_data\cifar-10-batches-py/data_batch_5
Loading data: /tmp/cifar10_data\cifar-10-batches-py/test_batch
Size of:
- Training-set:     50000
- Test-set:     10000
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "BestSplits" device_type: "CPU"') for unknown op: BestSplits
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "CountExtremelyRandomStats" device_type: "CPU"') for unknown op: CountExtremelyRandomStats
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "FinishedNodes" device_type: "CPU"') for unknown op: FinishedNodes
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "GrowTree" device_type: "CPU"') for unknown op: GrowTree
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ReinterpretStringToFloat" device_type: "CPU"') for unknown op: ReinterpretStringToFloat
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "SampleInputs" device_type: "CPU"') for unknown op: SampleInputs
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ScatterAddNdim" device_type: "CPU"') for unknown op: ScatterAddNdim
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNInsert" device_type: "CPU"') for unknown op: TopNInsert
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNRemove" device_type: "CPU"') for unknown op: TopNRemove
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TreePredictions" device_type: "CPU"') for unknown op: TreePredictions
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "UpdateFertileSlots" device_type: "CPU"') for unknown op: UpdateFertileSlots
Traceback (most recent call last):
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1022, in _do_call
    return fn(*args)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1004, in _run_fn
    status, run_metadata)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\contextlib.py", line 66, in __exit__
    next(self.gen)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 466, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'x' with dtype float
     [[Node: x = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "autoencoder.py", line 158, in <module>
    _, loss = create_network(training=True)
  File "autoencoder.py", line 153, in create_network
    y_pred, loss = main_network(images=images, training=training)
  File "autoencoder.py", line 136, in main_network
    y_pred, loss=loss_restruction(images, decoder_layer_1, x_tilde)
  File "autoencoder.py", line 120, in loss_restruction
    sen=session.run(global_sensitivity)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 767, in run
    run_metadata_ptr)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 965, in _run
    feed_dict_string, options, run_metadata)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1015, in _do_run
    target_list, options, run_metadata)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1035, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'x' with dtype float
     [[Node: x = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Caused by op 'x', defined at:
  File "autoencoder.py", line 25, in <module>
    x = tf.placeholder(tf.float32, shape=[None, img_size, img_size, num_channels], name='x')
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1502, in placeholder
    name=name)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 2149, in _placeholder
    name=name)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 763, in apply_op
    op_def=op_def)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 2327, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1226, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'x' with dtype float
     [[Node: x = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

加载数据：/tmp/cifar10\u data\cifar-10-batches-py/data\u batch\u 2
加载数据：/tmp/cifar10\u data\cifar-10-batches-py/data\u batch\u 3
加载数据：/tmp/cifar10\u data\cifar-10-batches-py/data\u batch\u 4
加载数据：/tmp/cifar10\u data\cifar-10-batches-py/data\u batch\u 5
加载数据：/tmp/cifar10\u data\cifar-10-batches-py/test\u batch
尺寸：
-训练集：50000
-测试集：10000
E c:\tf_jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943]OpKernel（'op:'BestSplits'设备类型：'cpu'），用于未知op:BestSplits
E c:\tf_jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943]OpKernel（'op:'CountExtremelyRandomStats“device_type:'cpu'），用于未知op:CountExtremelyRandomStats
E c:\tf_jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943]用于未知op:FinishedNodes的OpKernel（'op:'FinishedNodes'设备类型：'cpu'）
E c:\tf_jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943]用于未知op:GrowTree的OpKernel（'op:'GrowTree'设备类型：'cpu'）
E c:\tf\u jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op\kernel.cc:943]OpKernel（'op:“ReinterpretStringToFloat”设备类型：“cpu”）用于未知op:ReinterpretStringToFloat
E c:\tf_jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943]OpKernel（'op:“SampleInputs”device\u type:'cpu'），用于未知op:SampleInputs
E c:\tf_jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943]OpKernel（'op:“ScatterAddNdim”device_type:'cpu'），用于未知op:ScatterAddNdim
E c:\tf\u jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op\u kernel.cc:943]OpKernel（'op:“TopNInsert”device\u type:'cpu'），用于未知op:TopNInsert
E c:\tf\u jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op\u kernel.cc:943]OpKernel（'op:TopNRemove“device\u type:'cpu'），用于未知op:TopNRemove
E c:\tf_jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943]OpKernel（'op:“TreePredictions”device\u type:'cpu'），用于未知op:TreePredictions
E c:\tf_jenkins\home\workspace\release win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943]OpKernel（“op:“UpdateFertileSlots”设备类型：“cpu”）用于未知op:UpdateFertileSlots
回溯（最近一次呼叫最后一次）：
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\client\session.py”，第1022行，在调用中
返回fn（*args）
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\client\session.py”，第1004行，在\u run\u fn中
状态，运行（元数据）
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\contextlib.py”，第66行，在__
下一个（self.gen）
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\framework\errors\u impl.py”，第466行，处于raise\u exception\u on\u not\u ok\u状态
pywrap_tensorflow.TF_GetCode（状态））
tensorflow.python.framework.errors\u impl.InvalidArgumentError：必须为带有dtype float的占位符tensor“x”提供一个值
[[Node:x=Placeholder[dtype=DT_FLOAT，shape=[]，_device=“/job:localhost/replica:0/task:0/cpu:0”]（）]
在处理上述异常期间，发生了另一个异常：
回溯（最近一次呼叫最后一次）：
文件“autoencoder.py”，第158行，在
_，损失=创建网络（培训=真）
文件“autoencoder.py”，第153行，在创建_网络中
y_pred，损耗=主网络（图像=图像，训练=训练）
主网络中第136行的文件“autoencoder.py”
y_pred，损耗=损耗重构（图像，解码器层1，x_瓷砖）
文件“autoencoder.py”，第120行，丢失重建
sen=会话运行（全局_敏感度）
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\client\session.py”，第767行，正在运行
运行_元数据_ptr）
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\client\session.py”，第965行，正在运行
提要（dict字符串、选项、运行元数据）
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\client\session.py”，第1015行，在运行中
目标\u列表、选项、运行\u元数据）
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\client\session.py”，第1035行，在调用中
提升类型（e）（节点定义、操作、消息）
tensorflow.python.framework.errors\u impl.InvalidArgumentError：必须为带有dtype float的占位符tensor“x”提供一个值
[[Node:x=Placeholder[dtype=DT_FLOAT，shape=[]，_device=“/job:localhost/replica:0/task:0/cpu:0”]（）]
由op“x”引起，定义为：
文件“autoencoder.py”，第25行，在
x=tf.placeholder（tf.float32，shape=[None，img\u size，img\u size，num\u channels]，name='x'）
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\ops\array\u ops.py”，第1502行，占位符
名称=名称）
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\ops\gen\u array\u ops.py”，第2149行，在占位符中
名称=名称）
文件“C:\Users\Lee Janice\AppData\Local\Programs\Python\Python35\lib\site packages\tensorflow\Python\framework\op\u def
sen=session.run(global_sensitivity)

lap_noise1=np.random.laplace(loc=0.0, scale=(sen[0]/epsilon), size=(tf.shape(x)[0], img_size_cropped*img_size_cropped*num_channels))
lap_noise2=np.random.laplace(loc=0.0, scale=(sen[0]/epsilon), size=(tf.shape(x)[0], img_size_cropped*img_size_cropped*num_channels))
lap_noise3=np.random.laplace(loc=0.0, scale=(sen[0]/epsilon), size=(tf.shape(x)[0], img_size_cropped*img_size_cropped*num_channels))