In [52]:

    

#Import the required packages
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import scipy
import math
import random
import string


    

In [53]:

    

epochs = 10000 #No of iterations
learning_rate = 0.01 #Rate of learning


    

In [54]:

    

random.seed(123)
# Display plots inline 
%matplotlib inline
# Define plot's default figure size
matplotlib.rcParams['figure.figsize'] = (8.0, 6.0)


    

In [55]:

    

training = pd.read_csv("intro_to_ann.csv") #Read dataset
print (training.head())
mti, mtl = np.array(training.ix[:,0:2]), np.array(training.ix[:,2:3]) #mti=image, mtl=layer
plt.scatter(mti[:,0], mti[:,1], s=40, c=mtl, cmap=plt.cm.BuGn) #Scatter plot
print(mti.shape, mtl.shape) #dimension of mti & mtl


    

    




   Feature1  Feature2  Target
0  2.067788  0.258133       1
1  0.993994 -0.609145       1
2 -0.690315  0.749921       0
3  1.023582  0.529003       0
4  0.700747 -0.496724       1

[5 rows x 3 columns]
(500, 2) (500, 1)






    




/usr/lib/python3/dist-packages/matplotlib/collections.py:549: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == 'face':






    

In [56]:

    

x = tf.placeholder(tf.float32, [None, 2])
Wh = tf.Variable(tf.random_normal([2, 6])) #Weight of hidden layer
W = tf.Variable(tf.random_normal([2,6])) #Normal weight
bh = tf.Variable(tf.random_normal([6])) #Bias of hidden layer
b = tf.Variable(tf.zeros([1])) #Bias of layer
y = tf.nn.softmax(tf.add(tf.matmul(x, W) , b)) #Output
y_= tf.placeholder(tf.float32,[None,1])


    

In [57]:

    

# Define loss and optimizer
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)


    

In [58]:

    

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


    

In [59]:

    

#Creating and running the session for accuracy
init = tf.initialize_all_variables()
errors=[]
with tf.Session() as sess:
        sess.run(init)
        for i in range(epochs):
            batch_xs = mti
            batch_ys = mtl
            _, cross_entropy_value, y_value = sess.run([optimizer, cross_entropy, y], feed_dict={x: batch_xs, y_: batch_ys})
            accuracy_value = sess.run(accuracy, feed_dict={x: mti, y_: mtl})
            errors.append(1-accuracy_value)
        print (accuracy_value)


    

    




0.688

In [60]:

    

plt.plot([np.mean(errors[i-50:i]) for i in range(len(errors))])
plt.show()


    

    




/usr/local/lib/python3.4/dist-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.
  warnings.warn("Mean of empty slice.", RuntimeWarning)






    

In [ ]: