def main():
# MLP to be fit
net_mlp = None
with open("/Users/alange/programming/MNIST/store/classic_32.json",
"r") as f:
net_mlp = Network.read_from_json(f)
# Initial particle network
net = ParticleNetwork(cost="categorical_cross_entropy",
particle_input=ParticleInput(784, s=2.0))
net.append(Particle(784, 32, activation="tanh", zeta=1.0, s=2.0))
net.append(Particle(32, 10, activation="softmax", zeta=1.0, s=2.0))
compute_matrices(net)
error = compute_error(net_mlp, net)
de_db, de_dq, de_dt, de_drx, de_dry, de_drz = compute_grad_w(net_mlp, net)
fd_b, fd_q, fd_t, fd_x, fd_y, fd_z = compute_fd_grad(net_mlp, net)
for l, layer in enumerate(net.layers):
diff_b = np.mean(de_db[l] - fd_b[l])
diff_q = np.mean(de_dq[l] - fd_q[l])
diff_t = np.mean(de_dt[l] - fd_t[l])
diff_x = np.mean(de_drx[l] - fd_x[l])
print("b", diff_b)
print("q", diff_q)
print("t", diff_t)
print("x", diff_x)
def main4():
net = Network(cost="quadratic")
net.append(Dense(2, 5))
net.append(Dense(5, 3))
train_X = np.asarray([[0.2, -0.3]])
train_Y = np.asarray([[0.0, 1.0, 0.0]])
with open("/tmp/tmp.json", "w") as f:
net.write_to_json(f)
net2 = None
with open("/tmp/tmp.json", "r") as f:
net2 = Network.read_from_json(f)
print(net.cost(train_X, train_Y))
print(net2.cost(train_X, train_Y))
def main4():
net = Network(cost="quadratic")
net.append(Dense(2, 5))
net.append(Dense(5, 3))
train_X = np.asarray([[0.2, -0.3]])
train_Y = np.asarray([[0.0, 1.0, 0.0]])
with open("/tmp/tmp.json", "w") as f:
net.write_to_json(f)
net2 = None
with open("/tmp/tmp.json", "r") as f:
net2 = Network.read_from_json(f)
print(net.cost(train_X, train_Y))
print(net2.cost(train_X, train_Y))
