Andrew
Reputation: 154
Issues connecting vgg19 in tensorflow
I am having an issue getting my VGG19 neural network to connect. Here is the whole graph:
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')
I have gone through and read the paper repeatedly and searched for other Tensorflow implementations. At this point, I think I am blind to the cause. I get a wrong shape error on the reshape from self.pool5 into self.fc6. self.pool5 ends up with shape (?, 7, 7, 512) when it should be (?, 8, 8, 512). Any help would be greatly appreciated.
Thanks.
Upvotes: 2
Views: 437
Answers (2)
zimmermc
Reputation: 633
I think your problem is, that you tried to reshape pool5 into the size of the fully connected layer 4096. But those 4096 are only the amount of neurons that fc layer has but not its number of inputs. I tried to change your code accordingly.
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')
Upvotes: 1
Olivier Moindrot
Reputation: 28198
This is totally normal, your implementation if fine.
VGG uses 5 max pooling with stride 2 and padding='SAME', which reduces the feature map size by 2 in height and 2 in width (if it is divisible by 2).
You specify an input image of size 224x224:
self.input_data = tf.placeholder(dtype=tf.float32, name='input_data', shape=(5, 224, 224, 3))
Therefore, the feature map size is divided by 2^5=32 and you get in the end an output of shape: [?, 7, 7, feature_size] (224/32 = 7).
If you really want to have an output size of shape [?, 8, 8, feature_size] with the VGG architecture, you should start with an image of shape 256 x 256
Upvotes: 0
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