tensorflow_example.py

import numpy as np
import tensorflow as tf

# Batch size = 32, Input Dimension = 500, Hidden Dimension = 50

# Define computational graph
# Create placeholders
x = tf.placeholder(tf.float32, shape=(32, 500))
y = tf.placeholder(tf.float32, shape=(32, 500))
w1 = tf.placeholer(tf.float32, shape=(500, 50))
w2 = tf.placeholder(tf.float32, shape=(50, 500))

# Forward pass
h = tf.maximum(tf.matmul(x, w1), 0)
y_pred = tf.matmul(h, w2)
diff = y_pred - y

# Use predefined common losses
loss = tf.losses.mean_squared_error(y_pred, y)

# Compute loss of gradient
grad_w1, grad_w2 = tf.gradients(loss, [w1, w2])

# Run the graph multiple times
with tf.Session() as sess:
  # Create numpy arrays
  values = {x: np.random.randn(32, 500),
            w1: np.random.randn(500, 50),
            w2: np.random.randn(50, 500),
            y: np.random.randn(32, 500),}
  
  # Train the network
  learning_rate = 1e-5
  # Run the graph in a loop
  for t in range(20):
    out = sess.run([loss, grad_w1, grad_w2], feed_dict=values)
    loss_val, grad_w1_val, grad_w2_val = out
    values[w1] -= learning_rate * grad_w1_val
    values[w2] -= learning_rate * grad_w2_val