#print("The number of layers were: ",myfmodel.n_layers_)
#print("The output activation function was ",myfmodel.out_activation_)
#Percentage errors in each test data sample
perror = 100.0 * (np.abs(ytest - yhat) / ytest)
#find maximum percentage error
maxperror = np.max(100.0 * (np.abs(ytest - yhat) / ytest))
print("Maximum percet error = ", maxperror)
#Plot the percentage errors
#import matplotlib.pyplot as plt
#from IPython import get_ipython
#plt.plot(perror)
#plt.show()
#Plot the true and predicted Transmittance
#get_ipython().run_line_magic('matplotlib', 'qt')
plot_true_vs_pred(ytest, yhat, ytrain, ytrain_out, norm_lims, exp_str, j,
"", "")
export_csv = performance_out.to_csv(
exp_str + "/" + r'Ynew_performances_1_2.csv', index=None,
header=True) #Don't forget to add '.csv' at the end of the path
pklfile = open(file=exp_str + "/train_learned_nh_" + str(num_hidden_neuron) +
".pkl",
mode="rb")
obj = pickle.load(pklfile)
pklfile.close()
#See the Regression metrics section of the user guide for further details.
#
#metrics.explained_variance_score(y_true, y_pred) Explained variance regression score function
#metrics.mean_absolute_error(y_true, y_pred) Mean absolute error regression loss
#metrics.mean_squared_error(y_true, y_pred[, …]) Mean squared error regression loss
#print("The number of layers were: ",myfmodel.n_layers_)
#print("The output activation function was ",myfmodel.out_activation_)
#Percentage errors in each test data sample
perror = 100.0 * (np.abs(ytest - yhat) / ytest)
#find maximum percentage error
maxperror = np.max(100.0 * (np.abs(ytest - yhat) / ytest))
print("Maximum percet error = ", maxperror)
#Plot the percentage errors
#import matplotlib.pyplot as plt
#from IPython import get_ipython
#plt.plot(perror)
#plt.show()
#Plot the true and predicted Transmittance
#get_ipython().run_line_magic('matplotlib', 'qt')
plot_true_vs_pred(ytest, yhat, ytrain, ytrain_out, norm_lims, exp_str, j,
which_conc_feature, "")
export_csv = performance_out.to_csv(
exp_str + "/" + r'Ynew_performances_1_1.csv', index=None,
header=True) #Don't forget to add '.csv' at the end of the path
pklfile = open(file=exp_str + "/train_learned_nh_" + str(num_hidden_neuron) +
which_conc_feature + ".pkl",
mode="rb")
obj = pickle.load(pklfile)
pklfile.close()
#metrics section of the user guide for further details.
#
#metrics.explained_variance_score(y_true, y_pred) Explained variance regression score function
#metrics.mean_absolute_error(y_true, y_pred) Mean absolute error regression loss
#metrics.mean_squared_error(y_true, y_pred[, …]) Mean squared error regression loss