split

Author: edwardyang12

Fall20/NeuralNetworks1/NN.py

@@ -3,42 +3,31 @@
 def linear_activation(z):
     return z
 
-def parametric_activation(a,z):
-    if z<=0:
-        return a*z
-    else:
-        return z
 
 def tanh_activation(z):
     return np.tanh(z)
-    
+
 # 2 layer NN for implementation of OR gate
-def orgate(x):
-    weights = np.array([2,2])
+def orgate(input1, input2):
     bias = -1
-    weighted_input = np.matmul(weights,x) + bias
+    weighted_input = 2*input1 + 2*input2 + bias
     y = linear_activation(weighted_input)
-    return y
-
-x = np.array([0,0])
-print(orgate(x))
+    if y<0:
+        return False
+    else:
+        return True
 
-# 4 layer NN for computing whether absolute difference is between 1 and 3
-# if between 1 and 3 outputs >0 else output <=0
-def multilayer(x):
-    w1 = np.array([1,-1])
-    b1 = 0
-    weighted_input1 = np.matmul(w1,x) + b1
-    output1 = parametric_activation(-1,weighted_input1)
-    w2 = np.array([1])
-    b2 = -2
-    weighted_input2 = np.matmul(w2,[output1]) + b2
-    output2 = parametric_activation(-1,weighted_input2)
-    w3 = np.array([-1])
-    b3 = 1
-    weighted_input3 = np.matmul(w3,[output2]) + b3
-    y = tanh_activation(weighted_input3)
-    return y
+def boolToBinary(bool1,bool2):
+    binary = []
+    if bool1:
+        binary.append(1)
+    else:
+        binary.append(0)
+    if bool2:
+        binary.append(1)
+    else:
+        binary.append(0)
+    return binary[0], binary[1]
 
-x = np.array([4,5.5])
-print(multilayer(x))
+input1, input2 = boolToBinary(True,True)
+print(orgate(input1,input2))

Fall20/NeuralNetworks1/vectorized.py

@@ -3,6 +3,15 @@
 def activation(z):
     return z
 
+def parametric_activation(a,z):
+    if z<=0:
+        return a*z
+    else:
+        return z
+
+def tanh_activation(z):
+    return np.tanh(z)
+
 # Simple 2 layer neural network that returns the average of a 3 numbers given as a 3x1 column vector
 # this function does a "forward pass" of the input x through the 2 layer network and returns the results
 def average_nn(x):
@@ -47,7 +56,27 @@ def random_nn(x):
 
     z_3 = np.matmul(w_3, a_2) + b_3
     a_3 = random_nn_activation(z_3)
-    
+
     return a_3
 
 print("On 3 layer network, input {} fed forward gives {}".format(x, random_nn(x)))
+
+# 4 layer NN for computing whether absolute difference is between 1 and 3
+# if between 1 and 3 outputs >0 else output <=0
+def multilayer(x):
+    w1 = np.array([1,-1])
+    b1 = 0
+    weighted_input1 = np.matmul(w1,x) + b1
+    output1 = parametric_activation(-1,weighted_input1)
+    w2 = np.array([1])
+    b2 = -2
+    weighted_input2 = np.matmul(w2,[output1]) + b2
+    output2 = parametric_activation(-1,weighted_input2)
+    w3 = np.array([-1])
+    b3 = 1
+    weighted_input3 = np.matmul(w3,[output2]) + b3
+    y = tanh_activation(weighted_input3)
+    return y
+
+x = np.array([4,5.5])
+print(multilayer(x))