create simple neural network

Day 9 - simple neural network/neural_network.py

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+import numpy as np
+
+#sigmoid function
+def nonlin(x, deriv=False):
+	if(deriv==True):
+		return x*(1-x)
+	return 1/(1+np.exp(-x))
+
+#input dataset
+X = np.array([ [0,0,1],
+			   [0,1,1],
+			   [1,0,1],
+			   [1,1,1] ])
+
+#output dataset
+y = np.array([[0,0,1,1]]).T
+
+#seed random numbers to make calculation
+# deterministic (just a good practice)
+np.random.seed(1)
+
+# initialize weights randomly with mean 0
+syn0 = 2*np.random.random((3,1)) - 1
+
+for iter in range(10000):
+
+	#forward propagation
+	l0 = X
+	l1 = nonlin(np.dot(l0, syn0))
+
+	#how much did we miss?
+	l1_error = y - l1
+
+	#multiply how much we missed by the
+	#slope of the sigmoid at the values in l1
+	l1_delta = l1_error * nonlin(l1, True)
+
+	#update weights
+	syn0 += np.dot(l0.T, l1_delta)
+
+print("Output After Training:")
+print(l1)
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