如何在神经网络模型中使用张量流会话

Question

我正在学习一个关于编写宪法神经网络的sentdex教程，我想知道我是否可以找出我自己的池层。问题是，作为这个池化层的一部分，我必须使用会话执行张量流函数。

def customPool(x):
patches = tf.extract_image_patches(x, [1, 2, 2, 1], [1,2,2,1], [1,1,1,1], 'SAME')
tempSess = tf.Session()
bool1 = tempSess.run( tf.greater( tf.reduce_max(patches) , tf.contrib.distributions.percentile(patches, q=75.) ) )
tempSess.close()
if bool1:
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
else:
return tf.nn.avg_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

但问题是，至少我认为，因为我从占位符开始所有事情

x = tf.placeholder('float', [None, 784])

基本上我的问题是：如果传递占位符变量，我如何使用神经网络模型内的张量流会话计算某些内容？非常感谢帮助！ 完整代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
n_classes = 10
batch_size = 128
x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float')
keep_rate = 0.8
keep_prob = tf.placeholder(tf.float32)

def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def maxpool2d(x):
#                        size of window         movement of window
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
def customPool(x):
patches = tf.extract_image_patches(x, [1, 2, 2, 1], [1,2,2,1], [1,1,1,1], 'SAME')
tempSess = tf.Session()
bool1 = tempSess.run( tf.greater( tf.reduce_max(patches) , tf.contrib.distributions.percentile(patches, q=75.) ) )
tempSess.close()
if bool1:
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
else:
return tf.nn.avg_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
def convolutional_neural_network(x):
weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,1,32])),
'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
'W_fc':tf.Variable(tf.random_normal([7*7*64,1024])),
'out':tf.Variable(tf.random_normal([1024, n_classes]))}
biases = {'b_conv1':tf.Variable(tf.random_normal([32])),
'b_conv2':tf.Variable(tf.random_normal([64])),
'b_fc':tf.Variable(tf.random_normal([1024])),
'out':tf.Variable(tf.random_normal([n_classes]))}
x = tf.reshape(x, shape=[-1, 28, 28, 1])
conv1 = tf.nn.relu(conv2d(x, weights['W_conv1']) + biases['b_conv1'])
#conv1 = maxpool2d(conv1)
conv1 = customPool(conv1)
conv2 = tf.nn.relu(conv2d(conv1, weights['W_conv2']) + biases['b_conv2'])
#conv2 = maxpool2d(conv2)
conv1 = customPool(conv1)
fc = tf.reshape(conv2,[-1, 7*7*64])
fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+biases['b_fc'])
fc = tf.nn.dropout(fc, keep_rate)
output = tf.matmul(fc, weights['out'])+biases['out']
return output
def train_neural_network(x):
prediction = convolutional_neural_network(x)
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )
optimizer = tf.train.AdamOptimizer().minimize(cost)

hm_epochs = 1
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver()
for epoch in range(hm_epochs):
epoch_loss = 0
for _ in range(int(mnist.train.num_examples/batch_size)):
epoch_x, epoch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
epoch_loss += c
print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:',accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))
save_path = saver.save(sess, "/tmp/convnet_maxpool")
print("Model saved in file: %s" % save_path)

#sess = tf.Session()
train_neural_network(x)
#sess.close()

编辑：在遵循马克西姆的建议后，我运行了它，它抛出了错误

_, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
InvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[512,10] labels_size=[128,10]
[[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Reshape_2, Reshape_3)]]

它可以追溯到：

File "conv net custom test 1.py", line 89, in <module>
train_neural_network(x)
File "conv net custom test 1.py", line 59, in train_neural_network
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )

Answer 1

使用tf.cond，而不是会话：

def customPool(x):
patches = tf.extract_image_patches(x, [1, 2, 2, 1], [1,2,2,1], [1,1,1,1], 'SAME')
pred = tf.greater(tf.reduce_max(patches), 
tf.contrib.distributions.percentile(patches, q=75.))
return tf.cond(pred, 
lambda: tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME'),
lambda: tf.nn.avg_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME'))

更新：

你还有一个复制粘贴错误：它连续两次conv1 = customPool(conv1)，conv2没有下采样，因此尺寸错误。