def plot_per_experiment():
for filename in glob.glob('*all_model*'):
with open(filename, 'rb') as f:
results = cPickle.load(f)
results.pop(0) # Remove params
dnn_relu_models = results[0:10]
dnn_sigmoid_models = results[10:20]
dnn_tanh_models = results[20:30]
gdbn_models = results[30:40]
dbn_models = results[40:50]
plt.figure(filename)
plt.ylabel('Accuracy (m)', fontsize=40)
plt.xlabel('Layers', fontsize=40)
plt.grid(True)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.xticks(range(0, 15, 1))
plt.xlim([0, 10])
plt.ylim([0, 9])
plt.plot(get_metrics(results=dnn_relu_models), label='DNN Relu', linewidth=5.0)
plt.plot(get_metrics(results=dnn_sigmoid_models), label='DNN Sigmoid', linewidth=5.0)
plt.plot(get_metrics(results=dnn_tanh_models), label='DNN Tanh', linewidth=5.0)
plt.plot(get_metrics(results=gdbn_models), label='GR-DBN', linewidth=5.0)
plt.plot(get_metrics(results=dbn_models), label='DBN', linewidth=5.0)
plt.legend(fontsize='x-large')
plt.show()
def get_latex_table():
with open('traditional_algorithms_sklearn_seed_5', 'rb') as f:
results = cPickle.load(f)
results.pop(0) # Remove params
with open('all_models_with_out_noise_neither_dropout_theano_seed_5', 'rb') as f:
deep_results = cPickle.load(f)
results.append(deep_results[8])
results.append(deep_results[18])
results.append(deep_results[28])
results.append(deep_results[38])
results.append(deep_results[48])
get_metrics(results=results, latex=True)
def plot_all_experiments():
plt.figure("Depth experiement")
plt.ylabel('Accuracy (m)', fontsize=30)
plt.xlabel('Layers', fontsize=30)
plt.ylim([0, 10])
plt.yticks(range(0, 11))
plt.grid(True)
plt.tick_params(axis='both', which='major', labelsize=20)
for filename in glob.glob('*seed*'):
with open(filename, 'rb') as f:
results = cPickle.load(f)
results.pop(0) # Remove params
lst_error = get_metrics(
results=results
)
plt.plot(lst_error, label=filename, linewidth=5.0)
plt.legend(fontsize='large')
plt.show()