with tf.name_scope('SGD'):
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
# Op to calculate every variable gradient
grads = tf.gradients(loss, tf.trainable_variables())
grads = list(zip(grads, tf.trainable_variables()))
# Op to update all variables according to their gradient
apply_grads = optimizer.apply_gradients(grads_and_vars=grads)
# Create summaries to visualize weights
for var in tf.trainable_variables():
tf.summary.histogram(var.name, var)
# Summarize all gradients
for grad, var in grads:
tf.summary.histogram(var.name + '/gradient', grad)
image.png
