Let’s begin by a short introduction to variable sharing. It is a mechanism in TensorFlow that allows for sharing variables accessed in different parts of the code without passing references to the variable around. The method tf.get_variable can be used with the name of the variable as the argument to either create a new variable … Read more
In short, you use tf.Variable for trainable variables such as weights (W) and biases (B) for your model. weights = tf.Variable( tf.truncated_normal([IMAGE_PIXELS, hidden1_units], stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))), name=”weights”) biases = tf.Variable(tf.zeros([hidden1_units]), name=”biases”) tf.placeholder is used to feed actual training examples. images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, IMAGE_PIXELS)) labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size)) This is how you feed the training … Read more
Logits is an overloaded term which can mean many different things: In Math, Logit is a function that maps probabilities ([0, 1]) to R ((-inf, inf)) Probability of 0.5 corresponds to a logit of 0. Negative logit correspond to probabilities less than 0.5, positive to > 0.5. In ML, it can be the vector of … Read more