Gradient Clipping

gradient_clipping.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

n_inputs = 784
n_hidden1 = 300
n_hidden2 = 50
n_hidden3 = 50
n_hidden4 = 50
n_hidden5 = 50
n_outputs = 10

X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")
y = tf.placeholder(tf.int64, shape=(None), name="y")

with tf.name_scope("dnn"):
    hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu, name="hidden1")
    hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, name="hidden2")
    hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, name="hidden3")
    hidden4 = tf.layers.dense(hidden3, n_hidden4, activation=tf.nn.relu, name="hidden4")
    hidden5 = tf.layers.dense(hidden4, n_hidden5, activation=tf.nn.relu, name="hidden5")
    logits = tf.layers.dense(hidden5, n_outputs, name="outputs")

with tf.name_scope("loss"):
    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
    loss = tf.reduce_mean(xentropy, name="loss")

with tf.name_scope("evaluation"):
    correct = tf.nn.in_top_k(logits, y, 1)
    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name="accuracy")

threshold = 1.0
learning_rate = 0.01

with tf.name_scope("train"):
    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
    grads_and_vars = optimizer.compute_gradients(loss)
    capped_gvs = [(tf.clip_by_value(grad, -threshold, threshold), var) for grad, var in grads_and_vars]
    training_op = optimizer.apply_gradients(capped_gvs)

init = tf.global_variables_initializer()

n_epochs = 20
batch_size = 200
mnist = input_data.read_data_sets("/tmp/data/")

with tf.Session() as sess:
    init.run()
    for epoch in range(n_epochs):
        for iteration in range(mnist.train.num_examples // batch_size):
            X_batch, y_batch = mnist.train.next_batch(batch_size)
            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
        accuracy_val = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels})
        print(epoch, "Test accuracy:", accuracy_val)