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'''Artificial neural network and noisy data with tensorboard logging loss from p. 45 of the book.'''

import tensorflow as tf

import numpy as np

'''Adds a layer to the artificial neural network.'''

def add_layer(inputs,in_size,out_size,activation_function=None):

with tf.name_scope("layer"):

with tf.name_scope("weights"):

Weights = tf.Variable(tf.random_normal([in_size,out_size]))

with tf.name_scope("biases"):

biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)

with tf.name_scope("Wx_plus_b"):

Wx_plus_b = tf.matmul(inputs, Weights) + biases

if activation_function is None:

outputs = Wx_plus_b

else:

outputs = activation_function(Wx_plus_b)

return outputs

'''Runs p. 41 artificial neural network code with p. 44 noisy data and tensorboard loss logging.'''

def main():

# Create training data

x_data = np.linspace(-1, 1, 300)[:, np.newaxis]

noise = np.random.normal(0, 0.05, x_data.shape)

y_data = np.square(x_data) - 0.5 + noise

#Define placeholders for network inputs

with tf.name_scope("inputs"):

xs = tf.placeholder(tf.float32, [None,1],name="x_input")

ys = tf.placeholder(tf.float32, [None,1],name="y_input")

#Hidden Layer

l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)

#Output Layer

prediction = add_layer(l1, 10, 1, activation_function=None)

#Calculating loss

with tf.name_scope("loss"):

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1]))

tf.summary.scalar('loss', loss)

with tf.name_scope("train"):

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()

merged = tf.summary.merge_all()

writer = tf.summary.FileWriter("logs/", sess.graph)

init = tf.global_variables_initializer()

sess.run(init)

#Training

for i in range(1000):

sess.run(train_step, feed_dict={xs: x_data, ys: y_data})

if i % 50 == 0:

result = sess.run(merged,feed_dict={xs: x_data, ys: y_data})

print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))

writer.add_summary(result, i)

main()