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I cannot understand the working of reduce_sum when the optimizer is run in a loop.

I have 30 samples in my train_x and train_y lists. I run my optimizer in a loop by feeding one sample from both at an iteration. My cost function computes the sum of the difference of predicted and actual values for all samples using the tensorflow's reduce_sum method. According to the graph the optimzer depends on the cost function and so the cost will be computed for every x and y. I need to know whether the reduce_sum will wait for all the 30 samples or take one sample (x, y) at a time. Here n_samples is 30. I also need to know whether the weights and bias will be updated for each epoch or for each x and y.

X = tf.placeholder(tf.float32)

Y = tf.placeholder(tf.float32)



W = tf.Variable(np.random.randn(), name='weights')

B = tf.Variable(np.random.randn(), name='bias')



pred = X * W + B



cost = tf.reduce_sum((pred - Y) ** 2) / (2 * n_samples)



optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)



init = tf.global_variables_initializer()



with tf.Session() as sesh:

    sesh.run(init)



    for epoch in range(epochs):

        for x, y in zip(train_x, train_y):

            sesh.run(optimizer, feed_dict={X: x, Y: y})



        if not epoch % 20:

            c = sesh.run(cost, feed_dict={X: train_x, Y: train_y})

            w = sesh.run(W)

            b = sesh.run(B)

            print(f'epoch: {epoch:04d} c={c:.4f} w={w:.4f} b={b:.4f}')

I need to know whether the reduce_sum will wait for all the 30 samples or take one sample (x, y) at a time.

tf.reduce_sum is an operation and as such it does not have any implicit mutable state. The result of tf.reduce_sum is fully defined by the model parameters (W and B) and the placeholder values explicitly provided in the feed_dict argument to the sess.run(cost, feed_dict={...}) call.

If you would like to aggregate the value of a metric across all batches check out tf.metrics:

y_pred = tf.placeholder(tf.float32)

y_true = tf.placeholder(tf.float32)

mse, update_op = tf.metrics.mean_squared_error(y_true, y_pred)

init = tf.local_variables_initializer()  # MSE state is local!

sess = tf.Session()

sess.run(init)



# Update the metric and compute the value after the update.

sess.run(update_op, feed_dict={y_pred: [0.0], y_true: [42.0]})  # => 1764.0

# Get current value.

sess.run(mse)  # => 1764.0

I also need to know whether the weights and bias will be updated for each epoch or for each x and y.

Each sess.run(optimizer, ...) call will compute the gradients of the trainable variables and apply these gradients to the variable values. See GradientDescentOptimizer.minimize.