def plot_3D_a_b (alphas,betas, aves_OMN):
# Plots the training and Validation score of a realization
X = np.array(alphas)
Y = np.array(betas)
X, Y = np.meshgrid(X, Y)
Z = mu.convert_to_matrix(aves_OMN).T
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
plt.tight_layout(pad=2, w_pad=0.5, h_pad=1.0)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.gray_r,
linewidth=0)
ax.set_zlim(np.min(Z.flatten()), np.max(Z.flatten()))
ax.zaxis.set_major_locator(LinearLocator(8))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
plt.xlabel(r'$\alpha$', fontsize=25)
plt.ylabel(r'$\beta$', fontsize=25)
# fig.colorbar(surf, shrink=0.5, aspect=5)
ax.set_zlabel('% accuracy rate', fontsize=15)
plt.show()
def plot_3D_a_nH (alphas,nHs, aves_OMN):
# Plots the training and Validation score of a realization
X = np.array(alphas)
Y = np.array(nHs)
X, Y = np.meshgrid(X, Y)
Z = mu.convert_to_matrix(aves_OMN).T
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
ax.set_zlim(np.min(Z.flatten()), np.max(Z.flatten()))
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
plt.xlabel('alpha')
plt.ylabel('Hidden neurons')
fig.colorbar(surf, shrink=0.5, aspect=5)
ax.set_zlabel('Z Label')
plt.show()
nL_max = 200 # Maximum number of layers
# Obtain the score of training and validation for every layer and realization
All_scoreTr_layers = []
All_scoreVal_layers = []
for i in range(N_neurons):
scoreTr_layers, scoreVal_layers = rd.get_scores_layers(All_Object_list[i])
All_scoreTr_layers.append(scoreTr_layers)
All_scoreVal_layers.append(scoreVal_layers)
# Get the average value and std for all the layers of the validation score
All_aves_rea_val = []
All_stds_rea_val = []
for i in range(N_neurons):
matrix = mu.convert_to_matrix(All_scoreVal_layers[i], nL_max)
aves_rea_val, stds_rea_val = mu.get_ave_std_unfilled_matrix(matrix)
All_aves_rea_val.append(aves_rea_val)
All_stds_rea_val.append(stds_rea_val)
# Get the average value and std for all the layers of the training score
All_aves_rea_tr = []
All_stds_rea_tr = []
for i in range(N_neurons):
matrix = mu.convert_to_matrix(All_scoreTr_layers[i], nL_max)
aves_rea_tr, stds_rea_tr = mu.get_ave_std_unfilled_matrix(matrix)
All_aves_rea_tr.append(aves_rea_tr)
All_stds_rea_tr.append(stds_rea_tr)
def PPE_1(PPE_p, main_folder, database_number,beta_i,alpha_i,Nepoch_i,nH_i, nL_max = 200): # Maximum number of layers):
PPE_p.nH_list = np.array(PPE_p.nH_list) # Esta conversion es para luego poderle pasar una lista de indices
PPE_p.N_epochs_list = np.array(PPE_p.N_epochs_list)
# Folder where we read the RAW files
folder = "../"+main_folder +"/ResultsNeoDSN"+str(database_number)
# Folder where we read and store the Preread files
base_folder_in = "../"+main_folder +"/PreRead/"
# Folder where we store the graph
base_folder_out = "../"+main_folder +"/Gold/" + str(database_number) +"/Nl/"
results = pkl.load_pickle(base_folder_in + str(database_number) + "/" + "data_" +
str(beta_i) + "_"+ str(alpha_i)+"_EVO",1) # If the result file existis coz it was previously read
if (results == []):
print "FILE NOT PREREAD"
raise ValueError
exit(0)
All_Object_list = results
nLs_list = np.array(range(nL_max)) + 1
# print Nepoch_i, nH_i
Object_list = All_Object_list[Nepoch_i][nH_i]
# Obtain the score of training and validation for every layer and realization
scoreTr_layers, scoreVal_layers = rd.get_scores_layers (Object_list)
# Get the average value and std for all the layers of the validation score
matrix = mu.convert_to_matrix(scoreVal_layers,nL_max)
aves_rea_val, stds_rea_val = mu.get_ave_std_unfilled_matrix(matrix)
# Get the average value and std for all the layers of the training score
matrix = mu.convert_to_matrix(scoreTr_layers,nL_max)
aves_rea_tr, stds_rea_tr = mu.get_ave_std_unfilled_matrix(matrix)
# Get the gammas values and their average and std
gammas = rd.get_gammas (Object_list)
N_realizations = len(gammas) # Number of realizations
matrix = mu.convert_to_matrix(gammas)
aves_gam, stds_gam = mu.get_ave_std_unfilled_matrix(matrix)
base_folder_out = base_folder_out + "a:"+ str(alpha_i)+"/" +"b:" +str(beta_i)+ "/"
mu.create_dirs(base_folder_out)
""" 1st GRAPH """
# Plot all realizations for the given number of Epoch and Neurons
mg.plot_all_realizations_EVO (scoreTr_layers,scoreVal_layers )
# Save figure !!
plt.savefig(base_folder_out + "All_rea_"
+ str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +"_" + str(PPE_p.nH_list[PPE_p.nH_indx_list[nH_i]]) +".png")
plt.close("all")
""" 2nd GRAPH """
# Plot the Average Training and Validation score !!
# Plot the average and shit
mg.plot_tr_val_nL(nLs_list, aves_rea_val, aves_rea_tr,stds_rea_val,stds_rea_tr)
plt.savefig(base_folder_out + "Ave_Acc(nL)"
+ str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +"_" + str(PPE_p.nH_list[PPE_p.nH_indx_list[nH_i]]) +".png")
def PostProcessEvolution_nH(All_Object_list):
N_neurons = len(All_Object_list)
nL_max = 100 # Maximum number of layers
nLs_list = np.array(range(nL_max)) + 1
# Obtain the score of training and validation for every layer and realization
All_scoreTr_layers = []
All_scoreVal_layers = []
for i in range(N_neurons):
scoreTr_layers, scoreVal_layers = rd.get_scores_layers(
All_Object_list[i])
All_scoreTr_layers.append(scoreTr_layers)
All_scoreVal_layers.append(scoreVal_layers)
# Get the average value and std for all the layers of the validation score
All_aves_rea_val = []
All_stds_rea_val = []
for i in range(N_neurons):
matrix = mu.convert_to_matrix(All_scoreVal_layers[i], nL_max)
aves_rea_val, stds_rea_val = mu.get_ave_std_unfilled_matrix(matrix)
All_aves_rea_val.append(aves_rea_val)
All_stds_rea_val.append(stds_rea_val)
# Get the average value and std for all the layers of the training score
All_aves_rea_tr = []
All_stds_rea_tr = []
for i in range(N_neurons):
matrix = mu.convert_to_matrix(All_scoreTr_layers[i], nL_max)
aves_rea_tr, stds_rea_tr = mu.get_ave_std_unfilled_matrix(matrix)
All_aves_rea_tr.append(aves_rea_tr)
All_stds_rea_tr.append(stds_rea_tr)
# Get the gammas values and their average and std
All_gammas = []
N_realizations = []
All_aves_gammas = []
All_stds_gam = []
for i in range(N_neurons):
gammas = rd.get_gammas(All_Object_list[i])
All_gammas.append(gammas)
N_realizations.append(len(gammas))
matrix = mu.convert_to_matrix(gammas)
aves_gam, stds_gam = mu.get_ave_std_unfilled_matrix(matrix)
All_aves_gammas.append(aves_gam)
All_stds_gam.append(stds_gam)
""" 1rd GRAPH """
# Plot the Average Training and Validation score !!
# Plot the average and shit
mg.plot_acc_nL_nH(nH_list, All_aves_rea_val)
plt.savefig(base_folder_out + str(database_number) + "/" +
"Ave_Accu_val(nL,nH)_" + str(N_epochs_list[Nepoch_i]) + ".png")
plt.close("all")
mg.plot_acc_nL_nH(nH_list, All_aves_rea_tr)
plt.savefig(base_folder_out + str(database_number) + "/" +
"Ave_Accu_tr(nL,nH)_" + str(N_epochs_list[Nepoch_i]) + ".png")
plt.close("all")
""" 2rd GRAPH """
# Plot the gammas evolution
# Obtain the average Acc and nL for the different neurons applying
# a common strop criteria.
ave_val = np.ones((N_neurons, 1))
std_val = np.ones((N_neurons, 1))
ave_NLs = np.ones((N_neurons, 1))
std_Nls = np.ones((N_neurons, 1))
for nh_i in range(N_neurons):
# Obtain nLs
nLs = np.ones(N_realizations[nh_i])
accuracies = np.ones((N_realizations[nh_i], 1))
for j in range(N_realizations[nh_i]): # For every realization
nLs[j] = mu.check_stop(All_gammas[nh_i][j], 5)
# Get the NLs statistics
ave_NLs[nh_i] = np.mean(nLs)
std_Nls[nh_i] = np.std(nLs)
for j in range(N_realizations[nh_i]): # For every realization
pene = All_scoreVal_layers[nh_i][j]
accuracies[j] = pene[nLs[j] - 1]
ave_val[nh_i] = np.mean(accuracies)
std_val[nh_i] = np.std(accuracies)
mg.plot_accu_nH(nH_list, ave_val, std_val)
plt.savefig(base_folder_out + str(database_number) + "/" + "Ave_Accu(nH)" +
str(N_epochs_list[Nepoch_i]) + ".png")
plt.close("all")
mg.plot_nL_nH(nH_list, ave_NLs, std_Nls)
plt.savefig(base_folder_out + str(database_number) + "/" + "Ave_nLs(nH)" +
str(N_epochs_list[Nepoch_i]) + ".png")
plt.close("all")
""" 2rd GRAPH """
# Plot the Average Training and Validation score !!
mg.plot_3D_nH_nL(nH_list, nLs_list, All_aves_rea_tr)
plt.savefig(base_folder_out + str(database_number) + "/" +
"3D_Accu_tr(nH,nL)" + str(N_epochs_list[Nepoch_i]) + ".png")
plt.close("all")
mg.plot_3D_nH_nL(nH_list, nLs_list, All_aves_rea_val)
plt.savefig(base_folder_out + str(database_number) + "/" +
"3D_Accu_val(nH,nL)" + str(N_epochs_list[Nepoch_i]) + ".png")
plt.close("all")
def PPE_nH(PPE_p,
main_folder,
database_number,
beta_i,
alpha_i,
Nepoch_i,
nL_max=300): # Maximum number of layers):
# Folder where we read the RAW files
folder = "../" + main_folder + "/ResultsNeoDSN" + str(database_number)
# Folder where we read and store the Preread files
base_folder_in = "../" + main_folder + "/PreRead/"
# Folder where we store the graph
base_folder_out = "../" + main_folder + "/Gold/" + str(
database_number) + "/Nh/"
results = pkl.load_pickle(
base_folder_in + str(database_number) + "/" + "data_" +
str(PPE_p.beta_indx_list[beta_i]) + "_" +
str(PPE_p.alpha_indx_list[alpha_i]) + "_EVO",
1) # If the result file existis coz it was previously read
if (results == []):
print "FILE NOT PREREAD"
raise ValueError
exit(0)
All_Object_list = results
All_Object_list = All_Object_list[Nepoch_i]
N_neurons = len(All_Object_list)
nLs_list = np.array(range(nL_max)) + 1
# Obtain the score of training and validation for every layer and realization
All_scoreTr_layers = []
All_scoreVal_layers = []
for i in range(N_neurons):
scoreTr_layers, scoreVal_layers = rd.get_scores_layers(
All_Object_list[i])
All_scoreTr_layers.append(scoreTr_layers)
All_scoreVal_layers.append(scoreVal_layers)
# Get the average value and std for all the layers of the validation score
All_aves_rea_val = []
All_stds_rea_val = []
for i in range(N_neurons):
matrix = mu.convert_to_matrix(All_scoreVal_layers[i], nL_max)
aves_rea_val, stds_rea_val = mu.get_ave_std_unfilled_matrix(matrix)
All_aves_rea_val.append(aves_rea_val)
All_stds_rea_val.append(stds_rea_val)
# Get the average value and std for all the layers of the training score
All_aves_rea_tr = []
All_stds_rea_tr = []
for i in range(N_neurons):
matrix = mu.convert_to_matrix(All_scoreTr_layers[i], nL_max)
aves_rea_tr, stds_rea_tr = mu.get_ave_std_unfilled_matrix(matrix)
All_aves_rea_tr.append(aves_rea_tr)
All_stds_rea_tr.append(stds_rea_tr)
# Get the gammas values and their average and std
All_gammas = []
N_realizations = []
All_aves_gammas = []
All_stds_gam = []
for i in range(N_neurons):
gammas = rd.get_gammas(All_Object_list[i])
All_gammas.append(gammas)
N_realizations.append(len(gammas))
matrix = mu.convert_to_matrix(gammas)
aves_gam, stds_gam = mu.get_ave_std_unfilled_matrix(matrix)
All_aves_gammas.append(aves_gam)
All_stds_gam.append(stds_gam)
base_folder_out = base_folder_out + "a:" + str(alpha_i) + "/" + "b:" + str(
beta_i) + "/"
mu.create_dirs(base_folder_out)
""" 1rd GRAPH """
# Plot the Average Training and Validation score !!
# Plot the average and shit
mg.plot_acc_nL_nH(PPE_p.nH_list[PPE_p.nH_indx_list], All_aves_rea_val)
plt.savefig(base_folder_out + "Ave_Accu_val(nL,nH)_" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
".png")
plt.close("all")
mg.plot_acc_nL_nH(PPE_p.nH_list[PPE_p.nH_indx_list], All_aves_rea_tr)
plt.savefig(base_folder_out + "Ave_Accu_tr(nL,nH)_" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
".png")
plt.close("all")
""" 2rd GRAPH """
# Plot the gammas evolution
# Obtain the average Acc and nL for the different neurons applying
# a common strop criteria.
ave_val = np.ones((N_neurons, 1))
std_val = np.ones((N_neurons, 1))
ave_NLs = np.ones((N_neurons, 1))
std_Nls = np.ones((N_neurons, 1))
for nh_i in range(N_neurons):
# Obtain nLs
nLs = np.ones(N_realizations[nh_i])
accuracies = np.ones((N_realizations[nh_i], 1))
for j in range(N_realizations[nh_i]): # For every realization
nLs[j] = mu.check_stop(All_gammas[nh_i][j], 5)
# Get the NLs statistics
ave_NLs[nh_i] = np.mean(nLs)
std_Nls[nh_i] = np.std(nLs)
for j in range(N_realizations[nh_i]): # For every realization
pene = All_scoreVal_layers[nh_i][j]
accuracies[j] = pene[nLs[j] - 1]
ave_val[nh_i] = np.mean(accuracies)
std_val[nh_i] = np.std(accuracies)
mg.plot_accu_nH(PPE_p.nH_list[PPE_p.nH_indx_list], ave_val, std_val)
plt.savefig(base_folder_out + "Ave_Accu(nH)" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
".png")
plt.close("all")
mg.plot_nL_nH(PPE_p.nH_list[PPE_p.nH_indx_list], ave_NLs, std_Nls)
plt.savefig(base_folder_out + "Ave_nLs(nH)" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
".png")
plt.close("all")
""" 2rd GRAPH """
# Plot the Average Training and Validation score !!
mg.plot_3D_nH_nL(PPE_p.nH_list[PPE_p.nH_indx_list], nLs_list,
All_aves_rea_tr)
plt.savefig(base_folder_out + "3D_Accu_tr(nH,nL)" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
".png")
plt.close("all")
mg.plot_3D_nH_nL(PPE_p.nH_list[PPE_p.nH_indx_list], nLs_list,
All_aves_rea_val)
plt.savefig(base_folder_out + "3D_Accu_val(nH,nL)" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
".png")
plt.close("all")
def PPE_1Tr(PPE_p,
main_folder,
database_number,
beta_i,
alpha_i,
Ninit_i,
Roh_i,
Nepoch_i,
nH_i,
nL_max=200): # Maximum number of layers):
# Folder where we read the RAW files
folder = "../" + main_folder + "/ResultsNeoDSN" + str(database_number)
# Folder where we read and store the Preread files
base_folder_in = "../" + main_folder + "/PreRead/"
# Folder where we store the graph
base_folder_out = "../" + main_folder + "/Gold/" + str(
database_number) + "/Nl/"
results = pkl.load_pickle(
base_folder_in + str(database_number) + "/" + "data_" + str(beta_i) +
"_" + str(alpha_i) + "_EVO",
1) # If the result file existis coz it was previously read
if (results == []):
print "FILE NOT PREREAD"
raise ValueError
exit(0)
All_Object_list = results
nLs_list = np.array(range(nL_max)) + 1
Object_list = All_Object_list[Ninit_i][Roh_i][Nepoch_i][nH_i]
# Obtain the score of training and validation for every layer and realization
scoreTr_layers, scoreVal_layers = rd.get_scores_layers(Object_list)
# Get the average value and std for all the layers of the validation score
matrix = mu.convert_to_matrix(scoreVal_layers, nL_max)
aves_rea_val, stds_rea_val = mu.get_ave_std_unfilled_matrix(matrix)
# Get the average value and std for all the layers of the training score
matrix = mu.convert_to_matrix(scoreTr_layers, nL_max)
aves_rea_tr, stds_rea_tr = mu.get_ave_std_unfilled_matrix(matrix)
# Get the gammas values and their average and std
gammas = rd.get_gammas(Object_list)
N_realizations = len(gammas) # Number of realizations
matrix = mu.convert_to_matrix(gammas)
aves_gam, stds_gam = mu.get_ave_std_unfilled_matrix(matrix)
base_folder_out = base_folder_out + "a:" + str(alpha_i) + "/" + "b:" + str(
beta_i) + "/"
base_folder_out = base_folder_out + "Ninit:" + str(
Ninit_i) + "/" + "Roh:" + str(Roh_i) + "/"
mu.create_dirs(base_folder_out)
""" 1st GRAPH """
# Plot all realizations for the given number of Epoch and Neurons
mg.plot_all_realizations_EVO(scoreTr_layers, scoreVal_layers)
# Save figure !!
plt.savefig(base_folder_out + "All_rea_" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
"_" + str(PPE_p.nH_list[PPE_p.nH_indx_list[nH_i]]) + ".png")
plt.close("all")
""" 2nd GRAPH """
# Plot the Average Training and Validation score !!
# Plot the average and shit
mg.plot_tr_val_nL(nLs_list, aves_rea_val, aves_rea_tr, stds_rea_val,
stds_rea_tr)
plt.savefig(base_folder_out + "Ave_Acc(nL)" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
"_" + str(PPE_p.nH_list[PPE_p.nH_indx_list[nH_i]]) + ".png")
plt.close("all")
""" 3rd GRAPH """
# Plot the average gamma value in function of the number of layers
# Also plot where the nL would be stopped applying the rule.
nLs = np.ones(N_realizations)
for i in range(N_realizations): # For every realization
nLs[i] = mu.check_stop(gammas[i])
mg.plot_gamma_nL(aves_gam, stds_gam)
plt.scatter(nLs, np.mean(aves_gam) * np.ones(N_realizations))
plt.savefig(base_folder_out + "Ave_gamma(nLs)" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
"_" + str(PPE_p.nH_list[PPE_p.nH_indx_list[nH_i]]) + ".png")
plt.close("all")
""" 4th GRAPH """
# Plot the average Accuracy and Number of layers depending on the
# stopping condition given by the ngamma
ngammas_list = range(1, 20)
accuracies = np.ones((N_realizations, 1))
ave_val = np.ones((len(ngammas_list), 1))
std_val = np.ones((len(ngammas_list), 1))
ave_NLs = np.ones((len(ngammas_list), 1))
std_Nls = np.ones((len(ngammas_list), 1))
for i in range(len(ngammas_list)):
# Obtain nLs
nLs = np.ones(N_realizations)
for j in range(N_realizations): # For every realization
nLs[j] = mu.check_stop(gammas[j], ngammas_list[i])
# Get the NLs statistics
ave_NLs[i] = np.mean(nLs)
std_Nls[i] = np.std(nLs)
for j in range(N_realizations): # For every realization
accuracies[j] = scoreVal_layers[j][nLs[j] - 1]
ave_val[i] = np.mean(accuracies)
std_val[i] = np.std(accuracies)
mg.plot_accu_ngamma(ngammas_list, ave_val, std_val)
plt.savefig(base_folder_out + "/" + "Ave_Accu(ngamma)_" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
"_" + str(PPE_p.nH_list[PPE_p.nH_indx_list[nH_i]]) + ".png")
plt.close("all")
mg.plot_nL_ngamma(ngammas_list, ave_NLs, std_Nls)
plt.savefig(base_folder_out + "Ave_nL(ngamma)_" +
str(PPE_p.N_epochs_list[PPE_p.N_epochs_indx_list[Nepoch_i]]) +
"_" + str(PPE_p.nH_list[PPE_p.nH_indx_list[nH_i]]) + ".png")
plt.close("all")
def plot_all_tr_val_nL_surface_XXXXXXXXX (nHs,nLs, aves_OMN, aves_OMN_tr):
# Plots the training and Validation score of a realization
N_neurons = len(aves_OMN)
X = np.array(nHs)
Y = np.array(nLs)
X, Y = np.meshgrid(X, Y)
# print X
# print Y
Z = mu.convert_to_matrix(aves_OMN).T
# Z2 = mu.convert_to_matrix(aves_OMN_tr).T
# Z = Z.flatten()
# print X.shape, Y.shape
# print Z.shape
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
""" VAL """
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
ax.set_zlim(np.min(Z.flatten()), np.max(Z.flatten()))
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
plt.xlabel('Hidden neurons')
plt.ylabel('Number of Layers')
fig.colorbar(surf, shrink=0.5, aspect=5)
""" TR """
# surf = ax.plot_surface(X, Y, Z2, rstride=1, cstride=1, cmap=cm.coolwarm,
# linewidth=0, antialiased=False)
#
# ax.set_zlim(np.min(Z2.flatten()), np.max(Z2.flatten()))
# ax.zaxis.set_major_locator(LinearLocator(10))
# ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
#
# plt.xlabel('Hidden neurons')
# plt.ylabel('Number of Layers')
# fig.colorbar(surf, shrink=0.5, aspect=5)
#
# plt.show()
#==============================================================================
# X = np.array(nHs)
# Y = np.array(nLs)
# # X, Y = np.meshgrid(nHs, nLs)
#
# print X
# print Y
# Z = convert_to_matrix(aves_OMN)
# # Z = Z.flatten()
#
# print X.shape, Y.shape
# print Z.shape
#
# from mpl_toolkits.mplot3d import Axes3D
# import matplotlib.pyplot as plt
#
#
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
#
# for x in range(X.size):
# for y in range(Y.size):
# ax.scatter(X[x], Y[y], Z[x,y])
#
# ax.set_xlabel('X Label')
# ax.set_ylabel('Y Label')
#==============================================================================
ax.set_zlabel('Z Label')
plt.show()