In [1]:

    

import tensorflow as tf


    

In [2]:

    

output = None
hidden_layer_weights = [
    [0.1, 0.2, 0.4],
    [0.4, 0.6, 0.6],
    [0.5, 0.9, 0.1],
    [0.8, 0.2, 0.8]]
out_weights = [
    [0.1, 0.6],
    [0.2, 0.1],
    [0.7, 0.9]]

# Weights and biases
weights = [
    tf.Variable(hidden_layer_weights),
    tf.Variable(out_weights)]
biases = [
    tf.Variable(tf.zeros(3)),
    tf.Variable(tf.zeros(2))]

# Input
features = tf.Variable([[1.0, 2.0, 3.0, 4.0], [-1.0, -2.0, -3.0, -4.0], [11.0, 12.0, 13.0, 14.0]])

# TODO: Create Model
# Hidden Layer with ReLU activation function
input_hidden = tf.add(tf.matmul(features, weights[0]), biases[0])
hidden_output = tf.nn.relu(input_hidden)

output = tf.add(tf.matmul(hidden_output, weights[1]), biases[1])

# TODO: Print session results
with tf.Session() as sess:
    init = tf.global_variables_initializer()
    sess.run(init)
    print(sess.run(output))


    

    




[[  5.10999966   8.43999958]
 [  0.           0.        ]
 [ 24.01000214  38.24000168]]

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