TensorFlow Eager Execution

gistfile1.py

import tensorflow as tf
import numpy as np

tfe = tf.contrib.eager
tf.enable_eager_execution()

N, D_in, H, D_out = 64, 1000, 100, 10

x = tf.constant(np.random.rand(N, D_in))
y = tf.constant(np.random.rand(N, D_out))
w1 = tfe.Variable(np.random.rand(D_in, H))
w2 = tfe.Variable(np.random.rand(H, D_out))

learning_rate = 1e-6

for i in range(500):
  with tf.GradientTape() as tape:
    y_pred = tf.matmul(tf.nn.relu(tf.matmul(x, w1)), w2)
    loss = tf.reduce_mean(tf.reduce_sum((y_pred - y) ** 2, axis=1))

  dw1, dw2 = tape.gradient(loss, [w1, w2])
  
  w1.assign_sub(dw1 * learning_rate)
  w2.assign_sub(dw2 * learning_rate)