neural network - how / where weights and bias get value/updates in tensorflow - 


from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("mnist_data/", one_hot=true)  import tensorflow tf  x = tf.placeholder(tf.float32, [none, 784])  w = tf.variable(tf.zeros([784, 10])) #weights!  b = tf.variable(tf.zeros([10])) # bias!  y = tf.nn.softmax(tf.matmul(x,w)+b)  y_ = tf.placeholder(tf.float32, [none,10])  cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y), reduction_indices=1)) train = tf.train.gradientdescentoptimizer(0.01).minimize(cross_entropy)  sess=  tf.interactivesession()  tf.global_variables_initializer().run() in range(10):     xs,ys = mnist.train.next_batch(100)     sess.run(train, {x: xs,y_:ys})     print(sess.run(w,{x: xs,y_:ys} ))  cross_validation = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) acu = tf.reduce_mean(tf.cast(cross_validation, tf.float32)) print(sess.run(acu, {x:mnist.test.images, y_: mnist.test.labels}))

this tensorflow tutorial code. understand completely, don't understand intuition behind weight , bias updates.

how see it:

matmul calculates x * w tf.zero , bias tf.zero well.. result first epoch forward propagation zero. 1*0+0 = 0. tensorflow adds random weights , bias value?


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