In [84]:
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import string
import tensorflow as tf
import scipy
import math
import random
In [85]:
random.seed(123)
# Display plots inline
%matplotlib inline
# Define plot's default figure size
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
In [86]:
learningrate=0.1
iterations=1000
train = pd.read_csv("intro_to_ann.csv")
X_data, Y_data = np.array(train.ix[:,0:2]), np.array(train.ix[:,2])
print (train.head())
plt.scatter(X_data[:,0], X_data[:,1], s=40, c=Y_data, cmap=plt.cm.BuGn)
#print(X_data)
#print(Y_data)
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]
Out[86]:
<matplotlib.collections.PathCollection at 0x7fb5eff17438>
/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 [87]:
hotvec = (np.arange(2) == Y_data[:, None]).astype(np.float32)
print(Y_data[:, None])
print(np.arange(2)==Y_data[:,None])
print(hotvec)
print(hotvec.shape)
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(500, 2)
In [88]:
X = tf.placeholder("float", [None, 2])
Y = tf.placeholder("float", [None, 2])
W1 = tf.Variable(tf.zeros([2, 4]))
b1 = tf.Variable(tf.zeros([4]))
y1 = tf.nn.sigmoid(tf.matmul(X, W1) + b1)
W2 = tf.Variable(tf.zeros([4, 2]))
b2 = tf.Variable(tf.zeros([2]))
y2 = tf.nn.sigmoid(tf.matmul(y1, W2) + b2)
cost = tf.reduce_mean(tf.pow(Y - y2, 2))
optimizer = tf.train.GradientDescentOptimizer(learningrate).minimize(cost)
In [89]:
init = tf.initialize_all_variables()
errors=[]
with tf.Session() as sess:
sess.run(init)
correctval=tf.equal(tf.argmax(y2,1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correctval, tf.float32))
for i in range(iterations):
_,loss,predictedvalue=sess.run([optimizer,cost,y2],feed_dict={X:X_data,Y:hotvec})
accuracyeval=accuracy.eval(feed_dict={X:X_data, Y:hotvec})
errors.append(1 - accuracyeval)
print(sess.run(W2), "\n ", sess.run(b2))
print(errors[-1])
[[ 3.76251243e-08 -4.65656846e-08]
[ 3.76251243e-08 -4.65656846e-08]
[ 3.76251243e-08 -4.65656846e-08]
[ 3.76251243e-08 -4.65656846e-08]]
[ 1.42026693e-08 -2.91038305e-08]
0.5
In [ ]:
In [ ]:
Content source: ml6973/Course
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