def evaluating(eval_data):
# Loading instances
classes = 10
hidden_layers = 50
instance_nn = ClassNN(model_dir='/tmp/model', classes=classes, hidden_number=hidden_layers)
print('Evaluating...')
data = instance_nn.predict_model(eval_data[0])
print(data)
data = instance_nn.predict_model(eval_data[1])
print(data)
start = time.time()
data = instance_nn.predict_model(eval_data[2])
end = time.time()
print(data)
print('Time elapsed: {0}'.format(end - start))
print('Done evaluating')
def main():
print('Initializing main function')
print('Loading datasets')
train_data, train_labels = ClassImageDataSet.load_train_mnist()
eval_data, eval_labels = ClassImageDataSet.load_eval_mnist()
print('PCA with training data')
n_features = 18
pca = PCA(n_components=n_features, svd_solver='randomized').fit(train_data)
train_pca = pca.transform(train_data)
n_classes = 10
hidden_neurons = 100
eval_pca = pca.transform(eval_data)
print('Printing shapes')
print(train_data.shape)
print(train_pca.shape)
model_dir = '/tmp/model_example_pca'
classifier = ClassNN(model_dir, n_classes, hidden_neurons)
var = input('Set 1 to train, 2 to predict. Otherwise to eval ')
if var == '1':
print('Training model')
classifier.train_model(train_pca, train_labels)
elif var == '2':
print('Predict model')
print('Total elements: ' + str(eval_pca.shape[0]))
index = 1100
eval_item = eval_pca[index]
print(eval_item.shape)
result = classifier.predict_model(eval_item)
print('Result obtained: ' + str(result['classes']))
print('Print probabilities')
print(result['probabilities'])
print('Real result: ' + str(eval_labels[index]))
else:
print('Evaluating model')
classifier.eval_model(eval_pca, eval_labels)
print('Done!')
def classify_images(list_folder_data: list, type_desc: EnumDesc):
classes_number = 0
cont = True
while cont:
cont = False
for folder_data in list_folder_data:
if folder_data[2] == classes_number:
classes_number += 1
cont = True
break
hidden_number = 60
learning_rate = 0.005
steps = 20000
# Initialize classifier instance
nn_classifier = ClassNN(model_dir=ClassNN.model_dir_pose,
classes=classes_number,
hidden_number=hidden_number,
learning_rate=learning_rate)
results = ClassLoadDescriptors.load_pose_descriptors(type_desc)
training_data_np = results['trainingData']
training_labels_np = results['trainingLabels']
eval_data_np = results['evalData']
eval_labels_np = results['evalLabels']
training_files_np = results['trainingFiles']
eval_files_np = results['evalFiles']
label_names = results['labelNames']
# Prompt for user input
selection = input('Training selected {0}. '
'Press 1 to train, 2 to evaluate, 3 to predict, 4 to save csv, '
'5 to get confusion matrix: '.format(type_desc))
if selection == '1':
# Training
nn_classifier.train_model(train_data=training_data_np,
train_labels=training_labels_np,
label_names=label_names,
steps=steps)
# Evaluate after training
nn_classifier.eval_model(eval_data_np, eval_labels_np)
elif selection == '2':
# Evaluate
nn_classifier.eval_model(eval_data_np, eval_labels_np)
elif selection == '3':
# Predict
# Select data to eval
data_eval = eval_data_np[0]
label_eval = eval_labels_np[0]
results = nn_classifier.predict_model(data_eval)
print('Predict data np: {0}'.format(results))
print('Expected data np: {0}'.format(label_eval))
elif selection == '4':
# Saving file in csv
total_data = np.concatenate((training_data_np, eval_data_np), axis=0)
total_labels = np.concatenate((training_labels_np, eval_labels_np), axis=0)
total_files = np.concatenate((training_files_np, eval_files_np))
# Add new axis to allow concatenation
total_labels = total_labels[:, np.newaxis]
total_files = total_files[:, np.newaxis]
total_np = np.concatenate((total_data, total_labels), axis=1)
print('Saving data to CSV in file {0}'.format(csv_dir))
np.savetxt(csv_dir, total_np, delimiter=',', fmt='%10.10f')
np.savetxt(csv_dir_files, total_files, delimiter=',', fmt='%s')
print('Saving training data')
# Concatenate with new axis
total_np_train = np.concatenate((training_data_np, training_labels_np[:, np.newaxis]), axis=1)
total_np_eval = np.concatenate((eval_data_np, eval_labels_np[:, np.newaxis]), axis=1)
# Saving
np.savetxt(csv_train, total_np_train, delimiter=',', fmt='%10.10f')
np.savetxt(csv_eval, total_np_eval, delimiter=',', fmt='%10.10f')
print('Done writing file in CSV')
elif selection == '5':
print('Getting confusion matrix')
confusion_np = np.zeros((classes_number, classes_number))
for i in range(eval_data_np.shape[0]):
data = eval_data_np[i]
expected = eval_labels_np[i]
obtained = nn_classifier.predict_model_fast(data)
class_prediction = obtained['classes']
print('Class: {0}'.format(class_prediction))
confusion_np[expected, class_prediction] += 1
print('Confusion matrix')
print(confusion_np)
print('Labels: {0}'.format(label_names))
else:
raise Exception('Option not supported')