GROUPWORK_minor changes

p2/test.py

@@ -6,6 +6,7 @@ import matplotlib.pyplot as plt
 
 # Internal
 import modules as n
+from optim import *
 from train import *
 from net import Network
 from helper import *
@@ -13,18 +14,13 @@ from helper import *
 # autograd globally off
 torch.set_grad_enabled(False)
 
-# generate train and test data
-train_input, train_target = generate_disc_set(1000)
-test_input, test_target = generate_disc_set(1000)
-
-# normalize train and test inputs
-# mean, std = train_input.mean(), train_input.std()
-# train_input.sub_(mean).div_(std)
-# test_input.sub_(mean).div_(std)
-
+# generate train and test data for a given center and radius of a circle
 center = (0.5, 0.5)
 radius = 1/math.sqrt(2*math.pi)
 
+train_input, train_target = generate_disc_set(1000, center, radius)
+test_input, test_target = generate_disc_set(1000, center, radius)
+
 # network parameters
 lr = 1e-4
 gamma = 0.9
@@ -65,9 +61,8 @@ networks = {
                     ],
             }
 
-# # initialize a network from the networks dictionary
-model = Network(networks[1])
-# train the network
+# initialize a network from the networks dictionary
+model = Network(networks[3])
 optimizer = SGD(model, mini_batch_size, lr, gamma)
 loss_train, acc_train = train_model(model, train_input, train_target, n.BCE(), lr, gamma, mini_batch_size, nb_epochs, optimizer=optimizer)
 # test the network
@@ -78,11 +73,15 @@ print(f' test_acc = {acc_test}')
 
 print(output.size())
 
-plot_figure(test_input, test_target, center=center, radius=radius)
-plot_figure(test_input, output, center=center, radius=radius)
+plot_figure(test_input, ~test_target, center=center, radius=radius)
+plt.title('Test input x Test target')
+plot_figure(test_input, ~output.int(), center=center, radius=radius)
+plt.title('Test input x Network output')
 
-plt.plot(range(nb_epochs), loss_train)
-plt.plot(range(nb_epochs), acc_train)
+plt.figure()
+plt.plot(range(nb_epochs), loss_train, label='Train Loss')
+plt.plot(range(nb_epochs), acc_train, label='Train Accuracy')
+plt.legend()
 
 plt.show()