CODEWITHSUNDEEP
tensorflow
Ujjwal
Ujjwal

Reputation: 1859

Trouble with variable initialization

I am building a graph via a function and am trying to extract the value of a variable to add further operations. A part of the function I have written is shown below :

def build(self, save_path=None, save_name=None):
    g = tf.Graph()
    with g.as_default():
        init_op = tf.initialize_all_variables()
        images = tf.placeholder(tf.float32, shape=[None, 300, 300, 3], name='input')
        with tf.variable_scope('conv1_'):
            conv11 = self.conv_relu(images, kernel_shape=[3, 3, 3, 64], bias_shape=64, name='c1')
            conv12 = self.conv_relu(conv11, kernel_shape=[3, 3, 64, 64], bias_shape=64, name='c2')

        pool1 = tf.nn.max_pool(conv12, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')


        with tf.variable_scope('conv2_'):
            conv21 = self.conv_relu(pool1, kernel_shape=[3, 3, 64, 128], bias_shape=128, name='c1')
            conv22 = self.conv_relu(conv21, kernel_shape=[3, 3, 128, 128], bias_shape=128, name='c2')

        pool2 = tf.nn.max_pool(conv22, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2')


        with tf.variable_scope('conv3_'):
            conv31 = self.conv_relu(pool2, kernel_shape=[3, 3, 128, 256], bias_shape=256, name='c1')
            conv32 = self.conv_relu(conv31, kernel_shape=[3, 3, 256, 256], bias_shape=256, name='c2')
            conv33 = self.conv_relu(conv32, kernel_shape=[3, 3, 256, 256], bias_shape=256, name='c3')

        pool3 = tf.nn.max_pool(conv33, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool3')

        with tf.variable_scope('conv4_'):
            conv41 = self.conv_relu(pool3, kernel_shape=[3, 3, 256, 512], bias_shape=512, name='c1')
            conv42 = self.conv_relu(conv41, kernel_shape=[3, 3, 512, 512], bias_shape=512, name='c2')
            conv43 = self.conv_relu(conv42, kernel_shape=[3, 3, 512, 512], bias_shape=512, name='c3')

        pool4 = tf.nn.max_pool(conv43, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool4')

        with tf.variable_scope('conv5_'):
            conv51 = self.conv_relu(pool4, kernel_shape=[3, 3, 512, 512], bias_shape=512, name='c1')
            conv52 = self.conv_relu(conv51, kernel_shape=[3, 3, 512, 512], bias_shape=512, name='c2')
            conv53 = self.conv_relu(conv52, kernel_shape=[3, 3, 512, 512], bias_shape=512, name='c3')

        pool5 = tf.nn.max_pool(conv53, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool5')

        pool5_shape = tf.shape(pool5)

        pool5_reshaped = tf.reshape(pool5, shape=[pool5_shape[0], -1], name='pool5_reshaped')

        weight_rows = pool5_shape[1] * pool5_shape[2] * pool5_shape[3]
    sess = tf.Session(graph=g)
    inp = np.zeros(shape=(2, 300, 300, 3))
    print(inp.shape)
    sess.run(init_op)
    print(sess.run(weight_rows, feed_dict={images:inp}))
    sess.close()

At the line print(sess.run(weight_rows, feed_dict={images:inp})) i get the following error :

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value conv5_/biasesc3
     [[Node: conv5_/biasesc3/read = Identity[T=DT_FLOAT, _class=["loc:@conv5_/biasesc3"], _device="/job:localhost/replica:0/task:0/cpu:0"](conv5_/biasesc3)]]

What is the reason for this error when I have run the init_op operation in the session before ? Exactly how does this work and what am I doing wrong over here ?

Upvotes: 1

Views: 41

Answers (1)

standy
standy

Reputation: 1065

You need to define your init_op (i.e. call tf.initialize_all_variables()) after you declared all variables. Creating a variable via tf.get_variable or tf.Variable places it in GLOBAL_VARIABLES collection (unless otherwise specified with collections kwarg). tf.initialize_all_variables() takes a look at this collection and creates an op that initializes variables listed.

To see GLOBAL_VARIABLES collection, you can use tf.get_collection with tf.GraphKeys.GLOBAL_VARIABLES as argument.

TL;DR Place init_op = tf.initialize_all_variables() after the graph was created.

Upvotes: 1

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