3d graph

Author: edwardyang12

Summer20/NeuralNetwork/tf3d.py

@@ -0,0 +1,92 @@
+from tensorflow.keras import datasets, models, layers, losses
+import tensorflow as tf
+from mpl_toolkits import mplot3d
+import numpy as np
+import matplotlib.pyplot as plt
+import random
+
+def cone(x,y):
+    return np.sqrt(x**2 + y**2)
+
+def ripple(x,y):
+    return np.sin(10 * (x**2 + y**2)) / 10
+
+def makeTuple(X,Y):
+    inputList = []
+    for index, value in enumerate(X):
+        for index1, value1 in enumerate(value):
+            inputList.append([value1, Y[index][index1]])
+    return inputList
+
+def unpackTuple(A):
+    X = []
+    Y = []
+    for item in A:
+        X.append([item[0]])
+        Y.append([item[1]])
+    return X, Y
+
+def makeArray(Z):
+    zList = []
+    for subList in Z:
+        for value in subList:
+            zList.append([value])
+    return zList
+
+def randomPoints(number, bounds):
+    inputList = []
+    outputList = []
+    while(number > 0):
+        value1 = random.uniform(bounds[0],bounds[1])
+        value2 = random.uniform(bounds[0],bounds[1])
+        inputList.append([value1, value2])
+        outputList.append([ripple(value1, value2)])
+        number = number -1
+    return inputList, outputList
+
+bounds = (-1,1)
+inputList, outputList = randomPoints(50000, bounds)
+X_Train, Y_Train = unpackTuple(inputList)
+
+model = models.Sequential()
+model.add(layers.Dense(32, activation='exponential', input_shape=(2,)))
+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, 0.2])
+# plt.legend(loc='lower right')
+# plt.show()
+
+# generate test data
+inputTest, outputTest = randomPoints(10, bounds)
+X_Test, Y_Test = unpackTuple(inputTest)
+print(model.predict(np.array(inputTest)))
+print(outputTest)
+
+x = np.linspace(-1, 1, 800)
+y = np.linspace(-1, 1, 800)
+
+X, Y = np.meshgrid(x, y)
+Z = ripple(X, Y)
+
+fig = plt.figure()
+ax = plt.axes(projection="3d")
+
+ax.plot_wireframe(X, Y, Z, color='c')
+ax.scatter3D(X_Test, Y_Test, model.predict(np.array(inputTest)), c='r')
+ax.set_xlabel('x')
+ax.set_ylabel('y')
+ax.set_zlabel('z')
+
+plt.show()