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# Author: Juan Miguel Valverde Martinez
# Date: 19 August 2017
# Youtube tutorial link: https://www.youtube.com/watch?v=WZVcE4X976w
# Index: http://laid.delanover.com/tensorflow-tutorial/
import numpy as np
import tensorflow as tf
def sigmoid(x):
return 1/(1+np.exp(-x))
n_input = 3
n_hidden = 2
n_output = 1
W = { "h1": tf.Variable(tf.ones([n_input, n_hidden]),name="h1"),
"out": tf.Variable(tf.ones([n_hidden, n_output]))
}
b = { "b1": tf.Variable(tf.zeros([n_hidden])),
"bout": tf.Variable(tf.zeros([n_output]))
}
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_output])
l1 = tf.add(tf.matmul(x,W["h1"]),b["b1"])
l1_act = tf.sigmoid(l1)
out = tf.add(tf.matmul(l1_act,W["out"]),b["bout"])
out_act = tf.sigmoid(out)
cost = tf.reduce_mean(tf.abs(tf.subtract(out_act,y)))
train_step = tf.train.AdadeltaOptimizer(learning_rate=1.0).minimize(cost)
x_raw = np.array([[1,2,3]])
y_raw = np.array([3])
y_raw = np.reshape(y_raw,(1,1))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
pred = out_act.eval({x: x_raw})
print(pred)
print(sigmoid(2*sigmoid(6)))
for epoch in range(100):
k = sess.run(train_step,feed_dict={x: x_raw,y: y_raw})