def predictAndDisplay(
): #This is just a test function that predicts a random handwritten number from the dataset.
X_train, Y_train, X_val, Y_val = utils.load_full_mnist()
X_train = pre_process_images(X_train)
index = random.randint(0, X_train.shape[0])
image = np.array([X_train[index]])
printable = X_train[index]
model = pickle.load(open('model3a.sav', 'rb'))
a = model.forward(image)
predicted = (np.argmax(a))
image_2d = printable[:-1].reshape(28, 28)
label = "predicted :" + str(predicted)
plt.imshow(image_2d)
plt.title(label)
plt.show()
def pre_process_images(X: np.ndarray):
"""
Args:
X: images of shape [batch size, 784] in the range (0, 255)
Returns:
X: images of shape [batch size, 785] normalized as described in task2a
"""
assert X.shape[1] == 784,\
f"X.shape[1]: {X.shape[1]}, should be 784"
# Computing mean and std from training set
new_X = np.zeros((X.shape[0], X.shape[1] + 1))
X_train, _, _, _ = utils.load_full_mnist()
mean = np.mean(X_train)
std = np.sqrt(np.cov(X_train.flatten()))
# Normalizing
new_X[:, :-1] = (X - mean) / std
new_X[:, -1] = 1.0 # Bias Trick
return new_X
accuracy_train = calculate_accuracy(X_train, Y_train, self.model)
accuracy_val = calculate_accuracy(X_val, Y_val, self.model)
return loss, accuracy_train, accuracy_val
if __name__ == "__main__":
# hyperparameters DO NOT CHANGE IF NOT SPECIFIED IN ASSIGNMENT TEXT
num_epochs = 50
learning_rate = 0.01
batch_size = 128
l2_reg_lambda = 0
shuffle_dataset = True
# Load dataset
X_train, Y_train, X_val, Y_val = utils.load_full_mnist()
X_train = pre_process_images(X_train)
X_val = pre_process_images(X_val)
Y_train = one_hot_encode(Y_train, 10)
Y_val = one_hot_encode(Y_val, 10)
# ANY PARTS OF THE CODE BELOW THIS CAN BE CHANGED.
# Intialize model
model = SoftmaxModel(l2_reg_lambda)
# Train model
trainer = SoftmaxTrainer(
model,
learning_rate,
batch_size,
shuffle_dataset,
import numpy as np
import utils
from task2a import one_hot_encode, pre_process_images, SoftmaxModel, gradient_approximation_test
if __name__ == "__main__":
# Simple test on one-hot encoding
Y = np.zeros((1, 1), dtype=int)
Y[0, 0] = 3
Y = one_hot_encode(Y, 10)
assert Y[0, 3] == 1 and Y.sum() == 1, \
f"Expected the vector to be [0,0,0,1,0,0,0,0,0,0], but got {Y}"
X_train, Y_train, *_ = utils.load_full_mnist(0.1)
mean = np.mean(X_train)
std = np.std(X_train)
X_train = pre_process_images(X_train, mean, std)
Y_train = one_hot_encode(Y_train, 10)
assert X_train.shape[1] == 785,\
f"Expected X_train to have 785 elements per image. Shape was: {X_train.shape}"
# Modify your network here
neurons_per_layer = [64, 64, 10]
use_improved_sigmoid = True
use_improved_weight_init = True
model = SoftmaxModel(neurons_per_layer, use_improved_sigmoid,
use_improved_weight_init)
# Gradient approximation check for 100 images
X_train = X_train[:100]
Y_train = Y_train[:100]
for layer_idx, w in enumerate(model.ws):
if global_step % num_steps_per_val == 0:
_val_loss = 0
val_loss[global_step] = _val_loss
train_accuracy[global_step] = calculate_accuracy(
X_train, Y_train, model)
val_accuracy[global_step] = calculate_accuracy(
X_val, Y_val, model)
global_step += 1
return model, train_loss, val_loss, train_accuracy, val_accuracy
# Load dataset
validation_percentage = 0.1
X_train, Y_train, X_val, Y_val, X_test, Y_test = utils.load_full_mnist(
validation_percentage)
# Hyperparameters
num_epochs = 50
learning_rate = .3
batch_size = 128
l2_reg_lambda = 0.001
model, train_loss, val_loss, train_accuracy, val_accuracy = train(
num_epochs=num_epochs,
learning_rate=learning_rate,
batch_size=batch_size,
l2_reg_lambda=l2_reg_lambda)
print("Final Train Cross Entropy Loss:",
