def train():
"""
Performs training and evaluation of MLP model.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
########################
# PUT YOUR CODE HERE #
#######################
# set up the data
cifar10 = cifar10_utils.get_cifar10(FLAGS.data_dir)
test_images, test_labels = cifar10['test'].images, cifar10['test'].labels
test_vectors = reshape_images(test_images)
# set up the model
mlp_model = MLP(3072, dnn_hidden_units, 10)
loss_module = CrossEntropyModule()
accuracies = []
losses = []
for i in range(FLAGS.max_steps):
images, labels = cifar10['train'].next_batch(FLAGS.batch_size)
image_vectors = reshape_images(images)
# forward pass
model_pred = mlp_model.forward(image_vectors)
# backward pass
loss = loss_module.forward(model_pred, labels)
loss_grad = loss_module.backward(model_pred, labels)
mlp_model.backward(loss_grad)
# update all weights and biases
mlp_model.update(FLAGS.learning_rate)
# evaluate the model on the data set every eval_freq steps
if i % FLAGS.eval_freq == 0:
test_pred = mlp_model.forward(test_vectors)
test_accuracy = accuracy(test_pred, test_labels)
accuracies.append(test_accuracy)
losses.append(loss)
plot_curve(accuracies, 'Accuracy')
plot_curve(losses, 'Loss')
def train():
"""
Performs training and evaluation of MLP model.
"""
### DO NOT CHANGE SEEDS!
# Set the random seeds for reproducibility
np.random.seed(42)
## Prepare all functions
# Get number of units in each hidden layer specified in the string such as 100,100
if FLAGS.dnn_hidden_units:
dnn_hidden_units = FLAGS.dnn_hidden_units.split(",")
dnn_hidden_units = [
int(dnn_hidden_unit_) for dnn_hidden_unit_ in dnn_hidden_units
]
else:
dnn_hidden_units = []
data = cifar10_utils.get_cifar10(data_dir=FLAGS.data_dir)
train = data['train']
test = data['test']
n_inputs = train.images[0].flatten().shape[0]
n_classes = train.labels[0].shape[0]
mlp = MLP(n_inputs, dnn_hidden_units, n_classes)
loss_mod = CrossEntropyModule()
loss_history = []
acc_history = []
for step in range(FLAGS.max_steps): #FLAGS.max_steps
x, y = train.next_batch(FLAGS.batch_size)
x = x.reshape(x.shape[0], n_inputs)
out = mlp.forward(x)
loss = loss_mod.forward(out, y)
loss_history.append(loss)
dout = loss_mod.backward(out, y)
mlp.backward(dout)
mlp.update(FLAGS.learning_rate)
if step == 0 or (step + 1) % FLAGS.eval_freq == 0:
x, y = test.images, test.labels
x = x.reshape(x.shape[0], n_inputs)
test_out = mlp.forward(x)
acc = accuracy(test_out, y)
print('Accuracy:', acc)
acc_history.append(acc)
print('Final loss:', loss_history[-1])
print('Final acc:', acc_history[-1])
print(len(acc_history))
plt.plot(loss_history)
plt.step(range(0, FLAGS.max_steps + 1, FLAGS.eval_freq), acc_history)
plt.legend(['loss', 'accuracy'])
plt.show()