def test(self):
net = nn.NeuralNetwork("test_net", 1)
layer = ld.hdict["fc"](10)
net.add_layer(layer)
layer = ld.hdict["fc"](1)
net.add_layer(layer)
layer = ld.odict["sigmoid"](1)
net.add_layer(layer)
net.set_l2_loss_coeff(.001)
np.random.seed(1)
learning_rate = wup.LearningRate(alpha=0.1)
params = wup.GradientDescentParams(learning_rate)
net.set_weight_update_function(params)
net.initialize_parameters()
a = 0.8
for i in range(10000):
x = (np.random.rand(1, 32) * 0.1 + 0.75)
y = x > a
net.train(x, y)
x = 0.79
# print(net.predict(x))
self.assertTrue(net.predict(x) < 0.5)
x = 0.81
# print(net.predict(x))
self.assertTrue(net.predict(x) > 0.5)
def test(self):
net = nn.NeuralNetwork("test_net", 1)
layer = ld.hdict["fc"](1)
net.add_layer(layer)
layer = ld.odict["loss"]("linear_mean_squared_loss")
net.add_layer(layer)
np.random.seed(1)
learning_rate = wup.LearningRate(alpha=0.1)
params = wup.GradientDescentParams(learning_rate)
net.set_weight_update_function(params)
net.initialize_parameters()
net.set_l2_loss_coeff(.001)
a = 4.5;
for i in range(100):
x = (np.random.rand(1,32) - 0.5) * 10
y = a * x
net.train(x,y)
x = 10
self.assertTrue((np.absolute(net.predict(x) - a*x)/a*x) < 0.1)
def test(self):
net = nn.NeuralNetwork("test_net", 4)
layer = ld.hdict["fc"](10)
net.add_layer(layer)
layer = ld.hdict["fc"](40)
net.add_layer(layer)
layer = ld.hdict["fc"](4)
net.add_layer(layer)
layer = ld.odict["loss"]("linear_mean_squared_loss")
net.add_layer(layer)
net.set_l2_loss_coeff(.001)
np.random.seed(1)
learning_rate = wup.LearningRate(alpha=0.01)
params = wup.GradientDescentParams(learning_rate)
net.set_weight_update_function(params)
net.initialize_parameters()
a = np.array([[1], [2], [3], [4]])
b = np.array([[4], [5], [6], [7]])
for i in range(1000):
x = (np.random.rand(4,32) - 0.5) * 10
y = a * x + b
net.train(x,y)
x = np.array([[10], [10], [10], [10]])
y_exp = a * x + b
self.assertTrue(((np.absolute(net.predict(x) - y_exp)/y_exp) < 0.1).all())
def test(self):
self.define_nn()
self.learning_rate = wup.LearningRate(alpha=0.1)
self.params = wup.GradientDescentParams(self.learning_rate)
self.net.set_weight_update_function(self.params)
np.random.seed(1)
self.net.initialize_parameters()
self.initialize()
self.set_training_example()
self.assertTrue(self.net.check_gradient(self.x, self.y))
self.assertTrue(self.extra_checks())
layer = ld.hdict["fc"](10) net.add_layer(layer) # Add output layer layer = ld.odict["softmax"](10) net.add_layer(layer) # Neural Network definition done net.check_arch() # Specify l2 loss net.set_l2_loss_coeff(.001) # Define weight update method learning_rate = wup.LearningRate(alpha=.3) params = wup.GradientDescentParams(learning_rate) # params = wup.MomentumParams(learning_rate) # learning_rate = wup.LearningRate(alpha=.01) # params = wup.AdamParams(learning_rate) net.set_weight_update_function(params) # For repeatability during testing # np.random.seed(1) # Initialize the network net.initialize_parameters() # Set training related parameters mini_batch_size = 32 epochs = 20 verbose = 0