我在使用VGG19神经网络进行连接时遇到问题。这是整个图表：
class VGG(object):
def __init__(self, n_classes=252):
self.input_data = tf.placeholder(dtype=tf.float32, name='input_data', shape=(5, 224, 224, 3))
with tf.variable_scope("group1"):
with tf.variable_scope("conv1"):
self.weights1 = tf.get_variable('weights',[3, 3, 3, 64], initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias1 = tf.get_variable('bias', [64], initializer=tf.constant_initializer(0.0))
self.convolve1 = tf.nn.conv2d(self.input_data, self.weights1, [1, 1, 1, 1], padding="SAME")
self.conv1 = tf.nn.elu(tf.nn.bias_add(self.convolve1, self.bias1))
with tf.variable_scope("conv2"):
self.weights2= tf.get_variable('weights', [3, 3, 64, 64],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias2 = tf.get_variable('bias', [64], initializer=tf.constant_initializer(0.0))
self.convolve2 = tf.nn.conv2d(self.conv1, self.weights2, [1, 1, 1, 1], padding="SAME")
self.conv2 = tf.nn.elu(tf.nn.bias_add(self.convolve2, self.bias2))
self.pool1 = tf.nn.max_pool(self.conv2, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME', name='pool1')
with tf.variable_scope("group2"):
with tf.variable_scope("conv3"):
self.weights3 = tf.get_variable('weights', [3, 3, 64, 128],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias3 = tf.get_variable('bias', [128], initializer=tf.constant_initializer(0.0))
self.convolve3 = tf.nn.conv2d(self.pool1, self.weights3, [1, 1, 1, 1], padding="SAME")
self.conv3 = tf.nn.elu(tf.nn.bias_add(self.convolve3, self.bias3))
with tf.variable_scope("conv4"):
self.weights4 = tf.get_variable('weights', [3, 3, 128, 128],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias4 = tf.get_variable('bias', [128], initializer=tf.constant_initializer(0.0))
self.convolve4 = tf.nn.conv2d(self.conv3, self.weights4, [1, 1, 1, 1], padding="SAME")
self.conv4 = tf.nn.elu(tf.nn.bias_add(self.convolve4, self.bias4))
self.pool2 = tf.nn.max_pool(self.conv4, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME', name='pool2')
with tf.variable_scope("group3"):
with tf.variable_scope("conv5"):
self.weights5 = tf.get_variable('weights', [3, 3, 128, 256],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias5 = tf.get_variable('bias', [256], initializer=tf.constant_initializer(0.0))
self.convolve5 = tf.nn.conv2d(self.pool2, self.weights5, [1, 1, 1, 1], padding="SAME")
self.conv5 = tf.nn.elu(tf.nn.bias_add(self.convolve5, self.bias5))
with tf.variable_scope("conv6"):
self.weights6 = tf.get_variable('weights', [3, 3, 256, 256],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias6 = tf.get_variable('bias', [256], initializer=tf.constant_initializer(0.0))
self.convolve6 = tf.nn.conv2d(self.conv5, self.weights6, [1, 1, 1, 1], padding="SAME")
self.conv6 = tf.nn.elu(tf.nn.bias_add(self.convolve6, self.bias6))
with tf.variable_scope("conv7"):
self.weights7 = tf.get_variable('weights', [3, 3, 256, 256],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias7 = tf.get_variable('bias', [256], initializer=tf.constant_initializer(0.0))
self.convolve7 = tf.nn.conv2d(self.conv6, self.weights7, [1, 1, 1, 1], padding="SAME")
self.conv7 = tf.nn.elu(tf.nn.bias_add(self.convolve7, self.bias7))
with tf.variable_scope("conv8"):
self.weights8 = tf.get_variable('weights', [3, 3, 256, 256],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias8 = tf.get_variable('bias', [256], initializer=tf.constant_initializer(0.0))
self.convolve8 = tf.nn.conv2d(self.conv7, self.weights8, [1, 1, 1, 1], padding="SAME")
self.conv8 = tf.nn.elu(tf.nn.bias_add(self.convolve8, self.bias8))
self.pool3 = tf.nn.max_pool(self.conv8, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME', name='pool3')
with tf.variable_scope("group4"):
with tf.variable_scope("conv9"):
self.weights9 = tf.get_variable('weights', [3, 3, 256, 512],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias9 = tf.get_variable('bias', [512], initializer=tf.constant_initializer(0.0))
self.convolve9 = tf.nn.conv2d(self.pool3, self.weights9, [1, 1, 1, 1], padding="SAME")
self.conv9 = tf.nn.elu(tf.nn.bias_add(self.convolve9, self.bias9))
with tf.variable_scope("conv10"):
self.weights10 = tf.get_variable('weights', [3, 3, 512, 512],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias10 = tf.get_variable('bias', [512], initializer=tf.constant_initializer(0.0))
self.convolve10 = tf.nn.conv2d(self.conv9, self.weights10, [1, 1, 1, 1], padding="SAME")
self.conv10 = tf.nn.elu(tf.nn.bias_add(self.convolve10, self.bias10))
with tf.variable_scope("conv11"):
self.weights11 = tf.get_variable('weights', [3, 3, 512, 512],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias11 = tf.get_variable('bias', [512], initializer=tf.constant_initializer(0.0))
self.convolve11 = tf.nn.conv2d(self.conv10, self.weights11, [1, 1, 1, 1], padding="SAME")
self.conv11 = tf.nn.elu(tf.nn.bias_add(self.convolve11, self.bias11))
with tf.variable_scope("conv12"):
self.weights12 = tf.get_variable('weights', [3, 3, 512, 512],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias12 = tf.get_variable('bias', [512], initializer=tf.constant_initializer(0.0))
self.convolve12 = tf.nn.conv2d(self.conv11, self.weights12, [1, 1, 1, 1], padding="SAME")
self.conv12 = tf.nn.elu(tf.nn.bias_add(self.convolve12, self.bias12))
self.pool4 = tf.nn.max_pool(self.conv12, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME', name="pool4")
with tf.variable_scope("group5"):
with tf.variable_scope("conv13"):
self.weights13 = tf.get_variable('weights', [3, 3, 512, 512],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias13 = tf.get_variable('bias', [512], initializer=tf.constant_initializer(0.0))
self.convolve13 = tf.nn.conv2d(self.pool4, self.weights13, [1, 1, 1, 1], padding="SAME")
self.conv13 = tf.nn.elu(tf.nn.bias_add(self.convolve13, self.bias13))
with tf.variable_scope("conv14"):
self.weights14 = tf.get_variable('weights', [3, 3, 512, 512],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias14 = tf.get_variable('bias', [512], initializer=tf.constant_initializer(0.0))
self.convolve14 = tf.nn.conv2d(self.conv13, self.weights14, [1, 1, 1, 1], padding="SAME")
self.conv14 = tf.nn.elu(tf.nn.bias_add(self.convolve14, self.bias14))
with tf.variable_scope("conv15"):
self.weights15 = tf.get_variable('weights', [3, 3, 512, 512],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias15 = tf.get_variable('bias', [512], initializer=tf.constant_initializer(0.0))
self.convolve15 = tf.nn.conv2d(self.conv14, self.weights15, [1, 1, 1, 1], padding="SAME")
self.conv15 = tf.nn.elu(tf.nn.bias_add(self.convolve15, self.bias15))
with tf.variable_scope("conv16"):
self.weights16 = tf.get_variable('weights', [3, 3, 512, 512],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias16 = tf.get_variable('bias', [512], initializer=tf.constant_initializer(0.0))
self.convolve16 = tf.nn.conv2d(self.conv15, self.weights16, [1, 1, 1, 1], padding="SAME")
self.conv16 = tf.nn.elu(tf.nn.bias_add(self.convolve16, self.bias16))
self.pool5 = tf.nn.max_pool(self.conv16, [1, 2, 2, 1], [1, 2, 2, 1], "SAME", name="pool5")
with tf.variable_scope("group6"):
self.fc6 = tf.reshape(self.pool5, [-1, 4096], 'fc6')
with tf.variable_scope("fc7"):
self.weights17 = tf.get_variable('weights', [4096, 4096],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias17 = tf.get_variable('bias', [4096], initializer=tf.constant_initializer(0.0))
self.fc7 = tf.nn.elu(tf.nn.bias_add(tf.matmul(self.fc6, self.weights17), self.bias17))
with tf.variable_scope("logits"):
self.weights17 = tf.get_variable('weights', [4096, n_classes],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias17 = tf.get_variable('bias', [n_classes], initializer=tf.constant_initializer(0.0))
self.logits = tf.nn.elu(tf.nn.bias_add(tf.matmul(self.fc6, self.weights17), self.bias17))
self.softmax = tf.nn.softmax(self.logits, 'softmax')
我经历过并反复阅读该论文并搜索了其他Tensorflow实现。在这一点上，我认为我对事业视而不见。我从self.pool5重塑为self.fc6时出现错误的形状错误。 self.pool5最终应该是形状（？，7,7,512），应该是（？，8,8,512）。任何帮助将不胜感激。
感谢。
答案 0 :(得分：1)
我认为你的问题是，你试图将pool5重塑为完全连接的层4096的大小。但是那些4096只是fc层具有的神经元数量而不是其输入数量。我试着相应地更改你的代码。
with tf.variable_scope("conv16"):
self.weights16 = tf.get_variable('weights', [3, 3, 512, 512],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias16 = tf.get_variable('bias', [512], initializer=tf.constant_initializer(0.0))
self.convolve16 = tf.nn.conv2d(self.conv15, self.weights16, [1, 1, 1, 1], padding="SAME")
self.conv16 = tf.nn.elu(tf.nn.bias_add(self.convolve16, self.bias16))
self.pool5 = tf.nn.max_pool(self.conv16, [1, 2, 2, 1], [1, 2, 2, 1], "SAME", name="pool5")
self.pool5_flatten = tf.reshape(self.pool5, [-1, 25088])
with tf.variable_scope("group6"):
with tf.variable_scope("fc7"):
self.weights17 = tf.get_variable('weights', [25088, 4096],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias17 = tf.get_variable('bias', [4096], initializer=tf.constant_initializer(0.0))
self.fc7 = tf.nn.elu(tf.nn.bias_add(tf.matmul(self.pool5_flatten, self.weights17), self.bias17))
with tf.variable_scope("logits"):
self.weights17 = tf.get_variable('weights', [4096, n_classes],
initializer=tf.random_normal_initializer(stddev=1e-2))
self.bias17 = tf.get_variable('bias', [n_classes], initializer=tf.constant_initializer(0.0))
self.logits = tf.nn.elu(tf.nn.bias_add(tf.matmul(self.fc7, self.weights17), self.bias17))
self.softmax = tf.nn.softmax(self.logits, 'softmax')
答案 1 :(得分：0)
这是完全正常的，你的实施如果正常。
VGG使用5个最大池与步幅2和padding='SAME'，它将特征图大小减少2个高度和2个宽度（如果它可以被2整除）。
指定大小为224x224的输入图像：
self.input_data = tf.placeholder(dtype=tf.float32, name='input_data', shape=(5, 224, 224, 3))
因此，要素地图大小除以2^5=32，最后得到形状输出：[?, 7, 7, feature_size]（224/32 = 7）。
如果你真的想要使用VGG架构的形状[?, 8, 8, feature_size]的输出大小，你应该从形状256 x 256