Simple Feedforward Neural Network with TensorFlow won't learn

Question

I am trying to build a simple neural network with TensorFlow. The goal is to find the center of a rectangle in a 32 pixel x 32 pixel image. The rectangle is described by five vectors. The first vector is the position vector, the other four are direction vectors and make up the rectangle edges. One vector has two values (x and y).

The corresponding input for this image would be (2,5)(0,4)(6,0)(0,-4)(-6,0). The center (and therefore the desired output) is located at (5,7).

The code I came up with looks like the following:

    import tensorflow as tf 
    import numpy as np
    import Rectangle_Records

    def init_weights(shape):
        """ Weight initialization """
        weights = tf.random_normal(shape, stddev=0.1)
        return tf.Variable(weights)

    def forwardprop(x, w_1, w_2):
        """ Forward-propagation """
        h = tf.nn.sigmoid(tf.matmul(x, w_1))
        y_predict = tf.matmul(h, w_2)
        return y_predict

    def main():
        x_size = 10
        y_size = 2
        h_1_size = 256

        # Prepare input data
        input_data = Rectangle_Records.DataSet()

        x = tf.placeholder(tf.float32, shape = [None, x_size])
        y_label = tf.placeholder(tf.float32, shape = [None, y_size])

        # Weight initializations
        w_1 = init_weights((x_size, h_1_size))
        w_2 = init_weights((h_1_size, y_size))

        # Forward propagation
        y_predict = forwardprop(x, w_1, w_2)

        # Backward propagation
        cost = tf.reduce_mean(tf.square(y_predict - y_label))

        updates = tf.train.GradientDescentOptimizer(0.01).minimize(cost)

        # Run
        sess = tf.Session()
        init = tf.global_variables_initializer()
        sess.run(init)

        for i in range(200):
            batch = input_data.next_batch(10)
            sess.run(updates, feed_dict = {x: batch[0], y_label: batch[1]})

        sess.close()

    if __name__ == "__main__":
        main()

Sadly, the network won't learn properly. The result is too far off. For example, [[ 3.74561882 , 3.70766664]] when it should be arround [[ 3. , 7.]]. What am I doing wrong?

Answer 1

The main problem is your whole training is done only for one epoch, thats not enough training. Try the following changes:

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
for j in range(30):
    input_data = Rectangle_Records.DataSet()
    for i in range(200):
        batch = input_data.next_batch(10)
        loss, _ = sess.run([cost,updates], feed_dict = {x: batch[0], y_label: batch[1]})

    pred = sess.run(y_predict, feed_dict={x: batch[0]})
    print('Cost:', loss  )
    print('pred:', pred)
    print('actual:', batch[1])
sess.close()

Change your optimizer to a momentum optimizer for faster convergence: tf.train.AdamOptimizer(0.01).minimize(cost)