nn = net1.fit(X_train, y_train)
print "# Saving weights"
net1.save_weights_to(s.net_name)
if _test:
print "# Loading weights"
net1.load_weights_from(s.net_name)
# evaluate
print "# Evaluating"
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
predictions = net1.predict(X_test)
####################### OUTPUT to shell and to file for later
filename = "experiment_log.txt"
target = open(filename, 'a+')
target.write("-------------------------------------------------------------------------\n")
from nolearn.lasagne import PrintLayerInfo
pli = PrintLayerInfo()
net1.verbose = 3
layer_info, legend = pli._get_layer_info_conv(net1)
target.write(layer_info)
target.write(classification_report(y_test, predictions))
#target.write(accuracy_score(y_test, predictions))
target.close()
print layer_info
print classification_report(y_test, predictions)
accuracy_score(y_test, predictions)
