training and testing bounds different

Author: edwardyang12

Summer20/NeuralNetwork/tflinear.py

@@ -50,6 +50,8 @@ def coolLinear(x):
 # plt.legend(loc='lower right')
 # plt.show()
 
+bounds = (-15,15) # represents full system dynamics
+
 # generate test data
 inputTest, outputTest = randomPoints(10, bounds, boolfunc)
 print(model.predict(np.array(inputTest)))
@@ -63,7 +65,7 @@ def coolLinear(x):
 y = boolfunc(x)
 
 plt.plot(x,y, label= 'y = 1', markersize = 2, c='c')
-ax.scatter(inputTest, outputTest, label = 'training', c='b')
+ax.scatter(inputList, outputList, label = 'training', c='b')
 ax.scatter(inputTest,model.predict(np.array(inputTest)), label = 'testing', c='r')
 plt.legend(loc='lower right')
 plt.show()

Summer20/NeuralNetwork/tfquad.py

@@ -42,20 +42,21 @@ def generatePoints(number, bounds):
 # plt.legend(loc='lower right')
 # plt.show()
 
+bounds = (-15,15)
 # generate test data
-inputTest, outputTest = generatePoints(10, bounds)
+inputTest, outputTest = generatePoints(20, 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)
+x = np.linspace(-15,15,500)
 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(inputList, outputList, label = 'training', c='b')
 ax.scatter(inputTest,model.predict(np.array(inputTest)), label = 'testing', c='r')
 plt.legend(loc='lower right')
 plt.show()

Summer20/NeuralNetwork/tfsin.py

@@ -37,7 +37,7 @@ def selectPoints(number, angle=90):
 
 
 # generate training data
-angles = (-360,360) # represents full system dynamics
+angles = (-720,720) # represents full system dynamics
 #angles = (0,90) # doesn't represent full system dynamics
 
 angleList, sinList = randomPoints(10000, angles) # good number of points and random points
@@ -48,7 +48,7 @@ def selectPoints(number, angle=90):
 
 # neural network code
 model = models.Sequential()
-model.add(layers.Dense(12, activation='tanh', input_shape=(1,)))
+model.add(layers.Dense(16, activation='tanh', input_shape=(1,)))
 model.add(layers.Dense(1, activation=None))
 model.compile(optimizer='Adam',
                 loss=losses.MeanSquaredError(),
@@ -66,7 +66,7 @@ def selectPoints(number, angle=90):
 # plt.show()
 
 # generate test data
-angleTest, sinTest = randomPoints(10)
+angleTest, sinTest = randomPoints(20, (-1080,1080))
 print(model.predict(np.array(angleTest)))
 print(sinTest)
 
@@ -75,7 +75,7 @@ def selectPoints(number, angle=90):
 graph = plt.figure()
 ax = graph.add_subplot(111)
 
-x = np.linspace(-math.pi*2,math.pi*2,300)
+x = np.linspace(-math.pi*6,math.pi*6,300)
 y = np.sin(x)
 
 plt.plot(x,y, label= 'y = sin(x)', markersize = 2, c='c')