#perCsp2 = np.array([0.000022,0.0006,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001])
perCsp1 = np.linspace(0.0000006, 0.00005, iteration_steps)
perCsp2 = np.linspace(0.000022, 0.0006, iteration_steps)
perCsp = np.zeros((iteration_steps, 1))
Usparsity1 = np.zeros((iteration_steps, 1))
Usparsity2 = np.zeros((iteration_steps, 1))
for i in range(0, iteration_steps):
params1[8] = perCsp1[i]
params2[8] = perCsp2[i]
U1, obj1, tmp_sparsity1 = feta.FastNetTrim(Xtr1, Ytr1, Xval1, Yval1,
params1)
U2, obj2, tmp_sparsity2 = feta.FastNetTrim(Xtr2, Ytr2, Xval2, Yval2,
params2)
Usparsity1[i] = tmp_sparsity1[-1]
Usparsity2[i] = tmp_sparsity2[-1]
perCsp[i] = (Usparsity1[i] * weights1.shape[0] * weights1.shape[1] / 100 +
Usparsity2[i] * weights2.shape[0] * weights2.shape[1] /
100) / (weights1.shape[0] * weights1.shape[1] +
weights2.shape[0] * weights2.shape[1])
new_dense_1 = U1[1:, :]
new_dense_2 = U2[1:, :]
bias1 = U1[0, :]
params[4]=200 # batch size.
params[8] = 0.0004 #the lambda parameter controlling sparsity. Needs to be hand tuned.
#Creating the synthetic dataset
Xtr = np.random.normal(0,1,(d_input,Training_size))
Xval = np.random.normal(0,1,(d_input,Validation_size))
U_dense = np.random.normal(0,1,(d_output,d_input))
bias_dense = np.random.normal(0,1,(d_output,1))
Ytr = np.maximum(U_dense@Xtr+bias_dense,0)
Yval = np.maximum(U_dense@Xval+bias_dense,0)
# Running FeTa
U, obj, tmp_sparsity = feta.FastNetTrim(Xtr,Ytr,Xval,Yval,params)
# Plotting the results
size_font = 12
fig1, ax1 = plt.subplots()
plt.ylabel('|| ||_2',fontsize=size_font)
plt.xlabel('Iterations #',fontsize=size_font)
ax1.plot(np.arange(0,params[0]),obj,linestyle = '-',color = 'xkcd:blue')
plt.grid(linestyle=':')
plt.title('FeTa || ||_2')
fig2, ax2 = plt.subplots()
plt.ylabel('Sparsity %',fontsize=size_font)
plt.xlabel('Iterations #',fontsize=size_font)