嗨，我是神经网络的新手，目前正在研究Tensoflow。 首先，我做了MNIST教程，效果很好。现在我想通过在Google Colab中为Cifar10建立自己的网络来深化整体。为此，我编写了以下代码：
def conv2d(input, size, inputDim, outputCount):
with tf.variable_scope("conv2d"):
## -> This area causes problems <- ##
##########variant1
weight = tf.Variable(tf.truncated_normal([size, size, inputDim, outputCount], stddev=0.1),name="weight")
bias = tf.Variable( tf.constant(0.1, shape=[outputCount]),name="bias")
##########variant2
weight = tf.get_variable("weight", tf.truncated_normal([size, size, inputDim, outputCount], stddev=0.1))
bias = tf.get_variable("bias", tf.constant(0.1, shape=[outputCount]))
##################
conv = tf.nn.relu(tf.nn.conv2d(input, weight, strides=[1, 1, 1, 1], padding='SAME') + bias)
return conv
def maxPool(conv2d):....
def fullyConnect(input, inputSize, outputCount, relu):
with tf.variable_scope("fullyConnect"):
## -> This area causes problems <- ##
##########variant1
weight = tf.Variable( tf.truncated_normal([inputSize, outputCount], stddev=0.1),name="weight")
bias = tf.Variable( tf.constant(0.1, shape=[outputCount]),name="bias")
##########variant2
weight = tf.get_variable("weight", tf.truncated_normal([inputSize, outputCount], stddev=0.1))
bias = tf.get_variable("bias", tf.constant(0.1, shape=[outputCount]))
##################
fullyIn = tf.reshape(input, [-1, inputSize])
fullyCon = fullyIn
if relu:
fullyCon = tf.nn.relu(tf.matmul(fullyIn, weight) + bias)
return fullyCon
#Model Def.
def getVGG16A(grafic,width,height,dim):
with tf.name_scope("VGG16A"):
img = tf.reshape(grafic, [-1,width,height,dim])
with tf.name_scope("Layer1"):
with tf.variable_scope("Layer1"):
with tf.variable_scope("conv1"):
l1_c = conv2d(img,3, dim, 64)
with tf.variable_scope("mp1"):
l1_mp = maxPool(l1_c) #32 > 16
with tf.name_scope("Layer2"):
with tf.variable_scope("Layer2"):
with tf.variable_scope("conv1"):
l2_c = conv2d(l1_mp,3, 64, 128)
with tf.variable_scope("mp1"):
l2_mp = maxPool(l2_c) #16 > 8
with tf.name_scope("Layer6"):
with tf.variable_scope("Layer6"):
with tf.variable_scope("fully1"):
L6_fc1 = fullyConnect(l2_mp, 8*8*128 , 1024, True)
with tf.variable_scope("fully2"):
L6_fc2 = fullyConnect(L6_fc1, 1024, 1024, True)
keep_prob = tf.placeholder(tf.float32)
drop = tf.nn.dropout(L6_fc2, keep_prob)
with tf.variable_scope("fully3"):
L6_fc3 = fullyConnect(drop,1024, 3, False)
return L6_fc3, keep_prob
x = tf.placeholder(tf.float32, [None, 3072]) #input
y_ = tf.placeholder(tf.float32, [None, 3]) #output
# Build the graph for the deep net
y_conv, keep_prob = getVGG16A(x,32,32,3) #create Model
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-3).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for batch in getBatchData(prep_filter_dataBatch1,2): #a self-written method for custom batch return
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.8})
print('test accuracy %g' % accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
对于张量流变量的定义，我首先使用了variant1(tf.variable(。 这导致重复执行后图形内存溢出。 然后我使用了变体2(tf.get_variable(。如果我正确理解了文档，这应该使用已经存在的变量(如果存在(。
但是一旦我这样做，我就会收到以下错误消息：
TypeError: Tensor objects are not iterable when eager execution is not enabled. To iterate over this tensor use tf.map_fn.
我一直在寻找洞日，但我还没有找到解释。
现在我希望这里有人可以向我解释为什么这是不可能的，或者我可以在哪里找到更多信息。错误消息让我无处可去。我不想要一个解决方案，因为我想并且必须理解这一点，因为我想在CNN领域写我的学士学位论文。
为什么我可以使用 tf.variable 而不是tf.get_variable应该这样做？
感谢您的帮助， 最好的问候，帕斯卡:)
我发现了我的错误。 我忘记了关键字initializer.
正确的行如下所示：
weight = tf.get_variable("weight",initializer=tf.truncated_normal([size, size, inputDim, outputCount], stddev=anpassung))