Added quadratic function

Author: edwardyang12

Summer20/NeuralNetwork/tflinear.py

@@ -7,6 +7,7 @@
 # randomPoints on the sin graph
 # number - number of points
 # bounds - tuple for range of values
+# func - function to be used
 def randomPoints(number, bounds, func):
     inputList = []
     outputList = []
@@ -17,6 +18,9 @@ def randomPoints(number, bounds, func):
         number = number - 1
     return inputList, outputList
 
+def boolfunc(x):
+    return x*0 + 1
+
 def linear(x):
     return x
 
@@ -26,7 +30,7 @@ def coolLinear(x):
 # generate training data
 bounds = (-10,10) # represents full system dynamics
 
-inputList, outputList = randomPoints(10000, bounds, coolLinear)
+inputList, outputList = randomPoints(10000, bounds, boolfunc)
 
 # neural network code
 model = models.Sequential()
@@ -47,7 +51,7 @@ def coolLinear(x):
 # plt.show()
 
 # generate test data
-inputTest, outputTest = randomPoints(10, bounds, coolLinear)
+inputTest, outputTest = randomPoints(10, bounds, boolfunc)
 print(model.predict(np.array(inputTest)))
 print(outputTest)
 
@@ -56,9 +60,9 @@ def coolLinear(x):
 ax = graph.add_subplot(111)
 
 x = np.linspace(-10,10,500)
-y = 3*x + 1
+y = boolfunc(x)
 
-plt.plot(x,y, label= 'y = x', markersize = 2, c='c')
+plt.plot(x,y, label= 'y = 1', markersize = 2, c='c')
 ax.scatter(inputTest, outputTest, label = 'training', c='b')
 ax.scatter(inputTest,model.predict(np.array(inputTest)), label = 'testing', c='r')
 plt.legend(loc='lower right')

Summer20/NeuralNetwork/tfquad.py

@@ -0,0 +1,61 @@
+from tensorflow.keras import datasets, models, layers, losses
+import tensorflow as tf
+import numpy as np
+import random
+import matplotlib.pyplot as plt
+
+# randomPoints on the sin graph
+# number - number of points
+# bounds - tuple for range of values
+# bool - either 1 or 0
+def generatePoints(number, bounds):
+    inputList = []
+    outputList = []
+    while(number>0):
+        value = random.uniform(bounds[0],bounds[1])
+        inputList.append([value])
+        outputList.append([value**2])
+        number = number - 1
+    return inputList, outputList
+
+# generate training data
+bounds = (-10,10) # represents full system dynamics
+
+inputList, outputList = generatePoints(10000, bounds)
+
+# neural network code
+model = models.Sequential()
+model.add(layers.Dense(16, activation='exponential', input_shape=(1,)))
+model.add(layers.Dense(1, activation=None))
+model.compile(optimizer='Adam',
+                loss=losses.MeanSquaredError(),
+                metrics=['mean_squared_error'])
+
+history = model.fit(np.array(inputList),np.array(outputList), epochs=200)
+print(model.get_weights())
+
+# plots out learning curve
+# plt.plot(history.history['mean_squared_error'], label='mean_squared_error')
+# plt.xlabel('Epoch')
+# plt.ylabel('MSE')
+# plt.ylim([0.0, 0.2])
+# plt.legend(loc='lower right')
+# plt.show()
+
+# generate test data
+inputTest, outputTest = generatePoints(10, bounds)
+print(model.predict(np.array(inputTest)))
+print(outputTest)
+
+# graph of the test, train points
+graph = plt.figure()
+ax = graph.add_subplot(111)
+
+x = np.linspace(-10,10,500)
+y = x**2
+
+plt.plot(x,y, label= 'y = x^2', markersize = 2, c='c')
+ax.scatter(inputTest, outputTest, label = 'training', c='b')
+ax.scatter(inputTest,model.predict(np.array(inputTest)), label = 'testing', c='r')
+plt.legend(loc='lower right')
+plt.show()