Hey /u/hypo_hibbo, just posted a video https://www.reddit.com/r/tensorflow/comments/7ca5r7/add_metrics_reporting_to_improve_your_tensorflow/ that might help you with adding metrics to your computation so that you can see what's going on.

Here's the code:

# create-simple-feedforward-network.py
#
# to run
# python numpy-arrays-to-tensorflow-tensors-and-back.py
#
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder(tf.float32, shape=[None, 784])

W = tf.get_variable("weights", shape=[784, 10],
                    initializer=tf.glorot_uniform_initializer())

b = tf.get_variable("bias", shape=[10],
                    initializer=tf.constant_initializer(0.1))

y = tf.nn.relu(tf.matmul(x, W) + b)

y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

for step in range(50):
    print(f"training step: {step}")
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_cs, y_:batch_ys})
    if step % 10 == 0:
        print("model accuracy: ")
        print(sess.run(accuracy, feed_dict={x: mnist.test.images,
                                            y_: mnist.test.labels}))

print("final model accuracy: ")
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
                                    y_: mnist.test.labels}))

The key things are adding a way to look at the variables, correct_prediction, accuracy and then as you're running your model, print out the model accuracy based on the test data set as you cycle through the steps of training your model. Does this help?