def load():
args = argparse.ArgumentParser()
args.add_argument('-modelName')
args.add_argument('-data')
args.add_argument('-target')
args = args.parse_args()
if args.modelName:
os.mkdir(args.modelName)
model_filename = raw_input('Model File: ')
model_file = open(model_filename, 'r')
layer_arr = model_file.readlines()[:-2]
layer_arr = [x[:-1] for x in layer_arr]
model_file.close()
layer_arr = layer_arr[1:]
model = Model()
for i in layer_arr:
s = i.split()
if s[0] == 'linear':
layer = Linear(int(s[1]), int(s[2]))
model.addLayer(layer)
elif s[0] == 'relu':
layer = ReLU()
model.addLayer(layer)
print 'Model loaded successfully from file:', model_filename
return model
def test_forward(self):
expected_tensor = np.zeros([self.batch_size, self.input_size])
expected_tensor[self.half_batch_size:self.batch_size, :] = 1
layer = ReLU.ReLU()
output_tensor = layer.forward(self.input_tensor)
self.assertEqual(np.sum(np.power(output_tensor - expected_tensor, 2)), 0)
def test_gradient(self):
input_tensor = np.abs(np.random.random((self.batch_size, self.input_size)))
input_tensor *= 2.
input_tensor -= 1.
layers = list()
layers.append(ReLU.ReLU())
layers.append(L2Loss())
difference = Helpers.gradient_check(layers, input_tensor, self.label_tensor)
self.assertLessEqual(np.sum(difference), 1e-5)
def test_data_access(self):
net = NeuralNetwork.NeuralNetwork(Optimizers.Sgd(1))
categories = 3
input_size = 4
net.data_layer = Helpers.IrisData(50)
net.loss_layer = Loss.CrossEntropyLoss()
fcl_1 = FullyConnected.FullyConnected(input_size, categories)
net.append_layer(fcl_1)
net.append_layer(ReLU.ReLU())
fcl_2 = FullyConnected.FullyConnected(categories, categories)
net.append_layer(fcl_2)
net.append_layer(SoftMax.SoftMax())
out = net.forward()
out2 = net.forward()
self.assertNotEqual(out, out2)
def test_iris_data(self):
net = NeuralNetwork.NeuralNetwork(Optimizers.Sgd(1e-3))
categories = 3
input_size = 4
net.data_layer = Helpers.IrisData(50)
net.loss_layer = Loss.CrossEntropyLoss()
fcl_1 = FullyConnected.FullyConnected(input_size, categories)
net.append_layer(fcl_1)
net.append_layer(ReLU.ReLU())
fcl_2 = FullyConnected.FullyConnected(categories, categories)
net.append_layer(fcl_2)
net.append_layer(SoftMax.SoftMax())
net.train(4000)
plt.figure(
'Loss function for a Neural Net on the Iris dataset using SGD')
plt.plot(net.loss, '-x')
plt.show()
data, labels = net.data_layer.get_test_set()
results = net.test(data)
index_maximum = np.argmax(results, axis=1)
one_hot_vector = np.zeros_like(results)
for i in range(one_hot_vector.shape[0]):
one_hot_vector[i, index_maximum[i]] = 1
correct = 0.
wrong = 0.
for column_results, column_labels in zip(one_hot_vector, labels):
if column_results[column_labels > 0].all() > 0:
correct += 1
else:
wrong += 1
accuracy = correct / (correct + wrong)
print('\nOn the Iris dataset, we achieve an accuracy of: ' +
str(accuracy * 100) + '%')
self.assertGreater(accuracy, 0.8)
myModel = Model()
# remove occurences of /n in all strings
with open(parser.config) as f:
arr = f.readlines()
# print("ARR",arr)
num_layers = int(arr[0].replace('\n', ''))
i = 1
# print("NUM",num_layers)
while (num_layers > 0):
arr[i] = arr[i].replace('\n', '')
v = arr[i].split(' ')
# print("V0",v[0])
# print("LAST",v[0][-1])
if (v[0] == "relu"):
myModel.addLayer(ReLU())
elif (v[0] == "linear"):
# print("YO")
num_layers -= 1
inp = int(v[1])
out = int(v[2])
myModel.addLayer(Linear(inp, out))
i += 1
layer_weights_path = arr[i]
layer_bias_path = arr[i + 1]
layer_bias_path = layer_bias_path.replace('\n', '')
layer_weights_path = layer_weights_path.replace('\n', '')
weights = torchfile.load(layer_weights_path)
# print(weights)
# weights = torch.from_numpy(weights)
def test_trainable(self):
layer = ReLU.ReLU()
self.assertFalse(layer.trainable)