def main():
# train_X = np.asarray([[0.2, -0.3]])
# train_Y = np.asarray([[0.0, 1.0, 0.0]])
train_X = np.asarray([[0.2, -0.3], [0.1, -0.9]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
net = RelativeNetwork(cost="mse", relative_input=RelativeInput(2))
net.append(Relative(2, 5, activation="sigmoid"))
net.append(Relative(5, 3, activation="sigmoid"))
print(net.predict(train_X))
print(net.cost(train_X, train_Y))
def main():
# train_X = np.asarray([[0.2, -0.3]])
# train_Y = np.asarray([[0.0, 1.0, 0.0]])
train_X = np.asarray([[0.2, -0.3], [0.1, -0.9]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
net = RelativeNetwork(cost="mse", relative_input=RelativeInput(2))
net.append(Relative(2, 5, activation="sigmoid"))
net.append(Relative(5, 3, activation="sigmoid"))
print(net.predict(train_X))
print(net.cost(train_X, train_Y))
def fd():
# train_X = np.asarray([[0.2, -0.3]])
# train_Y = np.asarray([[0.0, 1.0, 0.0]])
train_X = np.asarray([[0.2, -0.3], [0.1, -0.9]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
# net = ParticleNetwork(cost="categorical_cross_entropy", particle_input=ParticleInput(2))
net = RelativeNetwork(cost="categorical_cross_entropy",
relative_input=RelativeInput(2))
net.append(Relative(2, 5, activation="sigmoid"))
net.append(Relative(5, 4, activation="sigmoid"))
net.append(Relative(4, 3, activation="softmax"))
# Finite difference checking
net.cost(train_X, train_Y)
db, dq, dx = net.cost_gradient(train_X, train_Y)
h = 0.001
print("analytic b")
print(db)
fd_b = []
for l in range(len(net.layers)):
lb = []
for c in range(len(net.layers[l].b)):
for b in range(len(net.layers[l].b[c])):
orig = net.layers[l].b[c][b]
net.layers[l].b[c][b] += h
fp = net.cost(train_X, train_Y)
net.layers[l].b[c][b] -= 2 * h
fm = net.cost(train_X, train_Y)
lb.append((fp - fm) / (2 * h))
net.layers[l].b[c][b] = orig
fd_b.append(lb)
print("numerical b")
print(fd_b)
print("analytic q")
for x in dq:
print(x)
fd_q = []
for l in range(len(net.layers)):
lq = []
for i in range(len(net.layers[l].q)):
orig = net.layers[l].q[i]
net.layers[l].q[i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].q[i] -= 2 * h
fm = net.cost(train_X, train_Y)
lq.append((fp - fm) / (2 * h))
net.layers[l].q[i] = orig
fd_q.append(lq)
print("numerical q")
for x in fd_q:
print(x)
print("analytic x")
for x in dx:
print(x)
# input layer
fd_x = []
layer = net.relative_input
lt = []
for i in range(len(layer.x)):
orig = layer.x[i]
layer.x[i] += h
fp = net.cost(train_X, train_Y)
layer.x[i] -= 2 * h
fm = net.cost(train_X, train_Y)
lt.append((fp - fm) / (2 * h))
layer.x[i] = orig
fd_x.append(lt)
# layers
for l in range(len(net.layers)):
lt = []
for i in range(len(net.layers[l].x)):
orig = net.layers[l].x[i]
net.layers[l].x[i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].x[i] -= 2 * h
fm = net.cost(train_X, train_Y)
lt.append((fp - fm) / (2 * h))
net.layers[l].x[i] = orig
fd_x.append(lt)
print("numerical x")
for x in fd_x:
print(x)
def fd():
# train_X = np.asarray([[0.2, -0.3]])
# train_Y = np.asarray([[0.0, 1.0, 0.0]])
train_X = np.asarray([[0.2, -0.3], [0.1, -0.9]])
train_Y = np.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
# net = ParticleNetwork(cost="categorical_cross_entropy", particle_input=ParticleInput(2))
net = RelativeNetwork(cost="categorical_cross_entropy", relative_input=RelativeInput(2))
net.append(Relative(2, 5, activation="sigmoid"))
net.append(Relative(5, 4, activation="sigmoid"))
net.append(Relative(4, 3, activation="softmax"))
# Finite difference checking
net.cost(train_X, train_Y)
db, dq, dx = net.cost_gradient(train_X, train_Y)
h = 0.001
print("analytic b")
print(db)
fd_b = []
for l in range(len(net.layers)):
lb = []
for c in range(len(net.layers[l].b)):
for b in range(len(net.layers[l].b[c])):
orig = net.layers[l].b[c][b]
net.layers[l].b[c][b] += h
fp = net.cost(train_X, train_Y)
net.layers[l].b[c][b] -= 2*h
fm = net.cost(train_X, train_Y)
lb.append((fp - fm) / (2*h))
net.layers[l].b[c][b] = orig
fd_b.append(lb)
print("numerical b")
print(fd_b)
print("analytic q")
for x in dq:
print(x)
fd_q = []
for l in range(len(net.layers)):
lq = []
for i in range(len(net.layers[l].q)):
orig = net.layers[l].q[i]
net.layers[l].q[i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].q[i] -= 2*h
fm = net.cost(train_X, train_Y)
lq.append((fp - fm) / (2*h))
net.layers[l].q[i] = orig
fd_q.append(lq)
print("numerical q")
for x in fd_q:
print(x)
print("analytic x")
for x in dx:
print(x)
# input layer
fd_x = []
layer = net.relative_input
lt = []
for i in range(len(layer.x)):
orig = layer.x[i]
layer.x[i] += h
fp = net.cost(train_X, train_Y)
layer.x[i] -= 2*h
fm = net.cost(train_X, train_Y)
lt.append((fp - fm) / (2*h))
layer.x[i] = orig
fd_x.append(lt)
# layers
for l in range(len(net.layers)):
lt = []
for i in range(len(net.layers[l].x)):
orig = net.layers[l].x[i]
net.layers[l].x[i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].x[i] -= 2*h
fm = net.cost(train_X, train_Y)
lt.append((fp - fm) / (2*h))
net.layers[l].x[i] = orig
fd_x.append(lt)
print("numerical x")
for x in fd_x:
print(x)