def test(self):
net = nn.NeuralNetwork("test_net", 1)
layer = ld.hdict["fc"](2)
net.add_layer(layer)
layer = ld.odict["softmax"](2)
net.add_layer(layer)
np.random.seed(1)
learning_rate = wup.LearningRate(alpha=0.1)
params = wup.AdamParams(learning_rate)
net.set_weight_update_function(params)
net.initialize_parameters()
net.set_l2_loss_coeff(.001)
for i in range(10000):
x = (np.random.rand(1, 32) * 0.1 + 0.75)
y = x < 0.8
y = np.append(y, x >= 0.8, axis=0)
y = np.array(y, dtype=float)
net.train(x, y)
x = 0.79
self.assertTrue(net.predict_classify(x) == 0)
x = 0.81
self.assertTrue(net.predict_classify(x) == 1)
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", 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):
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 define_nn(self):
self.net = nn.NeuralNetwork("test_net", 3)
self.layer = ld.hdict["fc"](4)
self.net.add_layer(self.layer)
self.layer = ld.odict["softmax"](4)
self.net.add_layer(self.layer)
def define_nn(self):
self.net = nn.NeuralNetwork("test_net", 1)
self.layer = ld.hdict["fc"](1)
self.net.add_layer(self.layer)
self.layer = ld.odict["sigmoid"](1)
self.net.add_layer(self.layer)
def define_nn(self):
self.net = nn.NeuralNetwork("test_net", 1)
self.layer = ld.hdict["fc"](1)
self.net.add_layer(self.layer)
self.layer = ld.odict["loss"]("linear_mean_squared_loss")
self.net.add_layer(self.layer)
def define_nn(self):
self.net = nn.NeuralNetwork("test_net", 1)
self.layer = ld.hdict["fc"](1)
self.net.add_layer(self.layer)
self.layer = ld.hdict["sigmoid"](1)
self.net.add_layer(self.layer)
self.layer = ld.odict["loss"]("sigmoid_cross_entropy_loss")
self.net.add_layer(self.layer)
def test(self):
net = nn.NeuralNetwork("test_net", 1)
layer = ld.hdict["fc"](30)
net.add_layer(layer)
layer = ld.hdict["fc"](10)
net.add_layer(layer)
layer = ld.odict["softmax"](10)
net.add_layer(layer)
np.random.seed(1)
learning_rate = wup.LearningRate(alpha=0.1)
params = wup.AdamParams(learning_rate)
net.set_weight_update_function(params)
net.initialize_parameters()
net.set_l2_loss_coeff(.001)
for i in range(100000):
x = np.random.rand(1, 32)
y = np.array(x < 0.1)
for j in range(1, 10):
low = j * 0.1
high = (j + 1) * 0.1
yj = (x >= low) * (x < high)
y = np.append(y, yj, axis=0)
y = np.array(y, dtype=float)
net.train(x, y)
for j in range(0, 10):
xl = j * 0.1 + .02
xh = j * 0.1 + .08
self.assertTrue(net.predict_classify(xl) == j)
self.assertTrue(net.predict_classify(xh) == j)
validation_x = np.transpose(validation_data[:, 0:784])
validation_y_class = np.transpose(validation_data[:, 794])
val_acc = lambda: net.classification_accuracy(validation_x, validation_y_class)
test_x = np.transpose(test_data[:, 0:784])
test_y_class = np.transpose(test_data[:, 794])
test_acc = lambda: net.classification_accuracy(test_x, test_y_class)
##
# Creating Neural Network
print("Creating neural network")
print()
# Step 1: Create Network - specify input layer neurons (28x28=784)
net = nn.NeuralNetwork("test_net", 784)
# Step 2: Add hidden layers in sequence
# Fully connected layer
layer = ld.hdict["fc"](800)
net.add_layer(layer)
# Relu activation layer
layer = ld.hdict["relu"](800)
net.add_layer(layer)
layer = ld.hdict["fc"](10)
net.add_layer(layer)
# Add output layer