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#!/usr/bin/python
#
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
  Task: to implment layer y = relu(x W + b).
"""
import tensorflow as tf   # Import tensorflow library.

# Variable(<initial_value>, dtype=<tf.float32|tf.int32|...>)
W = tf.Variable([[1.0, 2.0], [3.0, 4.0]], dtype=tf.float32, name='weight')
x = tf.placeholder(tf.float32, shape=[None, 2])
b = tf.Variable([1.0, 1.0], dtype=tf.float32, name='bias')

# Matrix multiply and add bias.
y = tf.nn.bias_add(tf.matmul(x, W), b)
y = tf.nn.relu(y)

with tf.Session() as sess:
    # Initialize all variables.
    sess.run(tf.global_variables_initializer())  

    # Givne x, batch of a single example
    #  [[1.0, 1.0]]
    # M: 
    #   [1.0, 2.0]
    #   [3.0, 4.0]
    # b:
    #   [1.0, 1.0]
    print(sess.run(y, feed_dict={x: [[1.0, 1.0]]}))    # <- Expect: [[5.0, 7.0]]