First commit

main.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import time
+from n_network import N_Network, plot_decision_boundary
+
+
+def load_planar_dataset(random_seed):
+    np.random.seed(random_seed)
+    m = 400 # number of examples
+    N = int(m / 2) # number of points per class
+    D = 2 # dimensionality
+    X = np.zeros((m,D)) # data matrix where each row is a single example
+    Y = np.zeros((m, 1), dtype='uint8') # labels vector (0 for red, 1 for blue)
+    a = 4 # maximum ray of the flower
+
+    for j in range(2):
+        ix = range(N * j, N * (j + 1))
+        t = np.linspace(j * 3.12, (j + 1) * 3.12, N) + np.random.randn(N) * 0.2 # theta
+        r = a * np.sin(4 * t) + np.random.randn(N) * 0.2 # radius
+        X[ix] = np.c_[r * np.sin(t), r * np.cos(t)]
+        Y[ix] = j
+        
+    X = X.T
+    Y = Y.T
+    return X, Y
+
+random_seed = 1
+Xtrain, ytrain = load_planar_dataset(random_seed)
+X = Xtrain.T
+y = ytrain.T
+print('X', X.shape, 'y', y.shape)
+
+# Visualize the data:
+plt.scatter(X[:, 0], X[:, 1], c=y.T[0], s=40, cmap=plt.cm.Spectral);
+plt.title('Dataset')
+plt.show();
+
+#Define a four layer network
+nu = [X.shape[1], 10, 7, 5, 1]
+xg = [0, N_Network.relu, N_Network.relu, N_Network.relu, N_Network.sigmoid]
+xgprime = [0, N_Network.relu_prime, N_Network.relu_prime, N_Network.relu_prime, N_Network.sigmoid_prime]
+init_params = dict(m=X.shape[0], n=X.shape[1], n_units=nu, g=xg, optim='sgd',
+                   gprime=xgprime, epochs=10000, alpha=0.075)
+nd = N_Network(init_params)
+nd.set_seed(random_seed)
+costs = nd.train(X, y)
+print("First cost: {0:.6f} final cost: {1:.6f}".format(costs[0], costs[-1]))
+print("Number of units in each layer: ", nu)
+nd.print_time()
+nd.plot_costs()
+pred = nd.valid(X, y)
+indices = nd.mislabeled(y, pred)
+# Plot decission boundary
+plot_decision_boundary(nd, X, y, True, '4 Layers N_Network')