我以前从未在Python中编程，我必须为类编写推理函数。大部分代码都是为学生编写的，我们只需要进行推理功能。我在网上找到了一些例子，但是我们必须使用不同于在线示例的图层。我对塑造感到困惑，但我对任何代码都不太确定。
这就是我所拥有的：
import tensorflow as tf
def _variable_with_weight_decay(name, shape, wd):
with tf.device('/cpu:0'):
var = tf.get_variable(name, shape,
initializer=tf.contrib.layers.xavier_initializer())
if wd is not None:
weight_decay = tf.multiply(tf.nn.l2_loss(var), wd,
name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var
def inference(X,phase=False,dropout_rate=0.8,n_classes=10,weight_decay=1e-4):
# logits should be of dimension (batch_size, n_classes)
# X is tensor with dimension (NONE, 32, 32,3)
# conv layer 1
batchSize = tf.shape(X)[0] # is this batch_size?
with tf.variable_scope('conv1') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64], wd=weight_decay)
conv = tf.nn.conv2d(X, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.get_variable('biases', [64], initializer=tf.constant_initializer(0.0))
pre_activation = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(pre_activation, name=scope.name)
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')
# Batch Normalization
#Batch variables
epsilon = 1e-3
batch_mean1, batch_var1 = tf.nn.moments(pool1,[0])
scale1 = tf.Variable(tf.ones([64]))
beta1 = tf.Variable(tf.zeros([64]))
batch1 = tf.nn.batch_normalization(pool1,batch_mean1,batch_var1,scale1,beta1,epsilon)
with tf.variable_scope('conv2') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 64, 64], wd=weight_decay)
conv = tf.nn.conv2d(batch1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.get_variable('biases', [64], initializer=tf.constant_initializer(0.0))
pre_activation = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(pre_activation, name=scope.name)
pool2 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')
#Batch Normalization 2
#Batch variables
batch_mean2, batch_var2 = tf.nn.moments(pool2,[0])
keep_prob = tf.placeholder(tf.float32)
scale2 = tf.Variable(tf.ones([64]))
beta2 = tf.Variable(tf.zeros([64]))
batch2 = tf.nn.batch_normalization(pool2,batch_mean2,batch_var2,scale2,beta2,epsilon)
#dropout 2
drop2 = tf.nn.dropout(batch2, keep_prob)
with tf.variable_scope('local1') as scope:
reshape = tf.reshape(drop2, [batchSize, -1])
#dim = reshape.get_shape()[1].value
dim = reshape[-1].value
weights = _variable_with_weight_decay('weights', shape=[dim, 384], wd=0.004)
biases = tf.get_variable('biases', [384], initializer=tf.constant_initializer(0.1))
local1 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
# #_activation_summary(local1)
# # local2
with tf.variable_scope('local2') as scope:
weights = _variable_with_weight_decay('weights', shape=[384, 192], wd=0.004)
biases = tf.get_variable('biases', [192], initializer=tf.constant_initializer(0.1))
local2 = tf.nn.relu(tf.matmul(local1, weights) + biases, name=scope.name)
with tf.variable_scope('softmax_linear') as scope:
weights = _variable_with_weight_decay('weights', [192, n_classes], wd=None)
biases = tf.get_variable('biases', [n_classes], initializer=tf.constant_initializer(0.1))
logits = tf.add(tf.matmul(local2, weights), biases, name=scope.name)
return logits
由于
EDIT ---------------------------------------------- ----------
好的，所以我修了一些，但现在给我一个问题的是行 dim = reshape.get_shape()[1].value
我的批量大小是32，直到结束时它变为16.如果总共有336个，那么将有10个完整批次的32个和1批16个。上面的行给出错误，＆＃34;必须完全定义新变量的形状，而是（？，256）。＆＃34;
这是我的全部新代码。我继续尝试做4个卷积层。
import tensorflow as tf
def _variable_with_weight_decay(name, shape, wd):
with tf.device('/cpu:0'):
var = tf.get_variable(name, shape, initializer=tf.contrib.layers.xavier_initializer())
if wd is not None:
weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var
def inference(X,phase=False,dropout_rate=0.8,n_classes=10,weight_decay=1e-4):
# logits should be of dimension (batch_size, n_classes)
# conv layer 1
#batchSize = tf.shape(X)[0]
batchSize = X.get_shape().as_list()[0]
if batchSize is None:
batchSize = 32
with tf.variable_scope('conv1') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64], wd=weight_decay)
conv = tf.nn.conv2d(X, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.get_variable('biases', [64], initializer=tf.constant_initializer(0.0))
pre_activation = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(pre_activation, name=scope.name)
#_activation_summary(conv1)
# pool1
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')
# Batch Normalization
#Batch variables
epsilon = 1e-3
batch_mean1, batch_var1 = tf.nn.moments(pool1,[0])
scale1 = tf.Variable(tf.ones([64]))
beta1 = tf.Variable(tf.zeros([64]))
batch1 = tf.nn.batch_normalization(pool1,batch_mean1,batch_var1,scale1,beta1,epsilon)
#dropout 1
#keep_prob = tf.placeholder(tf.float32,[batchSize,32,32,3])
#drop1 = tf.nn.dropout(batch1, keep_prob)
#conv layer 2
with tf.variable_scope('conv2') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 64, 128], wd=weight_decay)
conv = tf.nn.conv2d(batch1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.get_variable('biases', [128], initializer=tf.constant_initializer(0.0))
pre_activation = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(pre_activation, name=scope.name)
#_activation_summary(conv2)
#pool 2
pool2 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')
#Batch Normalization 2
#Batch variables
batch_mean2, batch_var2 = tf.nn.moments(pool2,[0])
scale2 = tf.Variable(tf.ones([128]))
beta2 = tf.Variable(tf.zeros([128]))
batch2 = tf.nn.batch_normalization(pool2,batch_mean2,batch_var2,scale2,beta2,epsilon)
#dropout 2
#drop2 = tf.nn.dropout(batch2, keep_prob)
#conv layer 3
with tf.variable_scope('conv3') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 128, 256], wd=weight_decay)
conv = tf.nn.conv2d(batch2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.get_variable('biases', [256], initializer=tf.constant_initializer(0.0))
pre_activation = tf.nn.bias_add(conv, biases)
conv3 = tf.nn.relu(pre_activation, name=scope.name)
#_activation_summary(conv3)
#pool 3
pool3 = tf.nn.max_pool(conv3, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool3')
#Batch Normalization 3
#Batch variables
batch_mean3, batch_var3 = tf.nn.moments(pool3,[0])
scale3 = tf.Variable(tf.ones([256]))
beta3 = tf.Variable(tf.zeros([256]))
batch3 = tf.nn.batch_normalization(pool3,batch_mean3,batch_var3,scale3,beta3,epsilon)
#dropout 3
#drop3 = tf.nn.dropout(batch3, keep_prob)
# conv layer 3
with tf.variable_scope('conv4') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 256, 256], wd=weight_decay)
conv = tf.nn.conv2d(batch3, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.get_variable('biases', [256], initializer=tf.constant_initializer(0.0))
pre_activation = tf.nn.bias_add(conv, biases)
conv4 = tf.nn.relu(pre_activation, name=scope.name)
#_activation_summary(conv4)
#pool 4
pool4 = tf.nn.max_pool(conv4, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool4')
#Batch Normalization 4
#Batch variables
batch_mean4, batch_var4 = tf.nn.moments(pool4,[0])
scale4 = tf.Variable(tf.ones([256]))
beta4 = tf.Variable(tf.zeros([256]))
batch4 = tf.nn.batch_normalization(pool4,batch_mean4,batch_var4,scale4,beta4,epsilon)
#dropout 4
#drop4 = tf.nn.dropout(batch4, keep_prob)
# local layer
with tf.variable_scope('local1') as scope:
reshape = tf.reshape(batch4, [batchSize, -1])
dim = reshape.get_shape()[1].value
#dim = batchSize*32
weights = _variable_with_weight_decay('weights', shape=[dim, 256], wd=0.004)
biases = tf.get_variable('biases', [256], initializer=tf.constant_initializer(0.1))
local1 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
# #_activation_summary(local1)
# # local2
with tf.variable_scope('local2') as scope:
weights = _variable_with_weight_decay('weights', shape=[256, batchSize], wd=0.004)
biases = tf.get_variable('biases', [batchSize], initializer=tf.constant_initializer(0.1))
local2 = tf.nn.relu(tf.matmul(local1, weights) + biases, name=scope.name)
# #_activation_summary(local2)
with tf.variable_scope('softmax_linear') as scope:
#reshape = tf.reshape(batch4, [batchSize, n_classes])
weights = _variable_with_weight_decay('weights', [batchSize, n_classes], wd=None)
biases = tf.get_variable('biases', [n_classes], initializer=tf.constant_initializer(0.1))
logits = tf.add(tf.matmul(local2, weights), biases, name=scope.name)
#_activation_summary(logits)
return logits