difficult

Author: edwardyang12

Summer20/NeuralNetwork/bigfunc.py

@@ -0,0 +1,58 @@
+from tensorflow.keras import datasets, models, layers, losses
+import tensorflow as tf
+import numpy as np
+import random
+import math
+import matplotlib.pyplot as plt
+
+def generatePoints(number, bounds):
+    inputList = []
+    outputList = []
+    while(number>0):
+        value = random.uniform(bounds[0],bounds[1])
+        inputList.append([value])
+        outputList.append([math.sin(value**2)])
+        number = number - 1
+    return inputList, outputList
+
+bounds = (-5,5) # represents full system dynamics
+inputList, outputList = generatePoints(30000, bounds)
+
+# neural network code
+model = models.Sequential()
+model.add(layers.Dense(32, activation='tanh', input_shape=(1,)))
+model.add(layers.Dense(48, activation='tanh'))
+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=300)
+#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, 0.5])
+plt.legend(loc='lower right')
+plt.show()
+
+
+bounds = (-7,7)
+# generate test data
+inputTest, outputTest = generatePoints(20, bounds)
+print(model.predict(np.array(inputTest)))
+print(outputTest)
+
+graph = plt.figure()
+ax = graph.add_subplot(111)
+
+x = np.linspace(-8,8,500)
+y = np.sin(x**2)
+
+plt.plot(x,y, label= 'y = sin(x^2)', markersize = 2, c='c')
+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()