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optimizers.py
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import numpy as np
from nn import *
from metrics import *
import copy
import wandb
import pickle
def vgd(ffn, train_images, train_labels, val = None, epochs =1 , lr = 0.001, batch_size = 1):
'''
Reference :- http://www.cse.iitm.ac.in/~miteshk/CS7015/Slides/Handout/Lecture5.pdf
Implements the vanilla version of the Gradient Descent Algorithm
If batch size is set to 1, acts like the Stochiastic version.
If batch size smaller than input.shape[0] is set, acts like the Mini-batch version.
If batch size is set to input.shape[0], batch gradient descent.
'''
best_model, best_acc = 0,0
for j in range(epochs):
for i in range(0, train_images.shape[0], batch_size):
#Pick a batch of examples to train.
batch_images = train_images[i:min(train_images.shape[0],i+batch_size)]
batch_labels = train_labels[i:min(train_images.shape[0],i+batch_size)]
batch_pred = ffn.forward_propogation(batch_images)
ffn.backward_propogation(batch_labels, batch_pred)
for k in range(0, len(ffn.layers)):
ffn.layers[k].weights = ffn.layers[k].weights - (lr * ffn.layers[k].grad_w + lr * ffn.weight_decay*ffn.layers[k].weights)
ffn.layers[k].bias = ffn.layers[k].bias - (lr *ffn.layers[k].grad_b)
y_hat = ffn.get_prediction(train_images)
train_loss = eval(ffn.loss_fn + '(train_labels, y_hat)')
train_pred = ffn.get_prediction(train_images)
train_acc = accuracy(train_labels, train_pred)
if val != None:
val_images, val_labels = val
val_pred = ffn.get_prediction(val_images)
val_acc = accuracy(val_labels, val_pred)
val_loss = eval(ffn.loss_fn + '(val_labels, val_pred)')
if val_acc > best_acc:
best_acc = val_acc
best_model = copy.deepcopy(ffn)
with open('model_pkl', 'wb') as files:
pickle.dump(ffn, files)
print("Epoch {} completed, training_loss = {}, validation_loss = {}.".format(j, train_loss, val_loss))
print("Training accuracy = {}, Validation Accuracy = {}".format(train_acc, val_acc))
wandb.log({"train_acc": train_acc,"val_acc": val_acc,"train_loss": train_loss,"val_loss": val_loss})
print(best_acc)
return best_model
def mgd(ffn, train_images, train_labels, val = None, epochs =1 , lr = 0.01, batch_size = 1, gamma = 0.9):
'''
Implements Momentum based Gradient Descent.
Reference :- http://www.cse.iitm.ac.in/~miteshk/CS7015/Slides/Handout/Lecture5.pdf
'''
best_model, best_acc = 0,0
prev_W = [np.zeros_like(layer.weights) for layer in ffn.layers]
prev_B = [np.zeros_like(layer.bias) for layer in ffn.layers]
for j in range(epochs):
for i in range(0, train_images.shape[0], batch_size):
#Pick a batch of examples to train.
batch_images = train_images[i:min(train_images.shape[0],i+batch_size)]
batch_labels = train_labels[i:min(train_images.shape[0],i+batch_size)]
batch_pred = ffn.get_prediction(batch_images)
ffn.backward_propogation(batch_labels, batch_pred)
for k in range(0, len(ffn.layers)):
if j==0:
ffn.layers[k].weights = ffn.layers[k].weights - (lr * ffn.layers[k].grad_w + lr * ffn.weight_decay*ffn.layers[k].weights)
ffn.layers[k].bias = ffn.layers[k].bias - (lr *ffn.layers[k].grad_b)
prev_W[k] = lr*ffn.layers[k].grad_w + lr * ffn.weight_decay*ffn.layers[k].weights
prev_B[k] = lr *ffn.layers[k].grad_b
else:
prev_W[k] = np.multiply(gamma, prev_W[k]) + lr*ffn.layers[k].grad_w + lr * ffn.weight_decay*ffn.layers[k].weights
prev_B[k] = np.multiply(gamma, prev_B[k]) + lr *ffn.layers[k].grad_b
ffn.layers[k].weights = ffn.layers[k].weights - prev_W[k]
ffn.layers[k].bias = ffn.layers[k].bias - prev_B[k]
y_hat = ffn.get_prediction(train_images)
train_loss = eval(ffn.loss_fn + '(train_labels, y_hat)')
train_pred = ffn.get_prediction(train_images)
train_acc = accuracy(train_labels, train_pred)
if val != None:
val_images, val_labels = val
val_pred = ffn.get_prediction(val_images)
val_acc = accuracy(val_labels, val_pred)
val_loss = eval(ffn.loss_fn + '(val_labels, val_pred)')
if val_acc > best_acc:
best_acc = val_acc
best_model = copy.deepcopy(ffn)
with open('model_pkl', 'wb') as files:
pickle.dump(ffn, files)
print("Epoch {} completed, training_loss = {}, validation_loss = {}.".format(j, train_loss, val_loss))
print("Training accuracy = {}, Validation Accuracy = {}".format(train_acc, val_acc))
wandb.log({"train_acc": train_acc,"val_acc": val_acc,"train_loss": train_loss,"val_loss": val_loss})
print(best_acc)
return best_model
def nag(ffn, train_images, train_labels, val = None, epochs =1 , lr = 0.01, batch_size = 1, gamma = 0.9):
'''
Implements Nesterov Accelrated Gradient Descent.
Reference :- http://www.cse.iitm.ac.in/~miteshk/CS7015/Slides/Handout/Lecture5.pdf
'''
best_model, best_acc = 0,0
prev_W = [np.zeros_like(layer.weights) for layer in ffn.layers]
prev_B = [np.zeros_like(layer.bias) for layer in ffn.layers]
for j in range(epochs):
for i in range(0, train_images.shape[0], batch_size):
#Pick a batch of examples to train.
batch_images = train_images[i:min(train_images.shape[0],i+batch_size)]
batch_labels = train_labels[i:min(train_images.shape[0],i+batch_size)]
#Look ahead
ffn_temp = copy.deepcopy(ffn)
#Take a step based on history.
if j!=0:
for k in range(0, len(ffn_temp.layers)):
ffn_temp.layers[k].weights = ffn_temp.layers[k].weights - np.multiply(gamma, prev_W[k])
ffn_temp.layers[k].bias = ffn_temp.layers[k].bias - np.multiply(gamma, prev_B[k])
#Calculate look ahead gradients.
batch_pred = ffn_temp.get_prediction(batch_images)
ffn_temp.backward_propogation(batch_labels, batch_pred)
for k in range(0, len(ffn.layers)):
if j==0:
batch_pred = ffn.get_prediction(batch_images)
ffn.backward_propogation(batch_labels, batch_pred)
ffn.layers[k].weights = ffn.layers[k].weights - (lr * ffn.layers[k].grad_w + lr * ffn.weight_decay*ffn.layers[k].weights)
ffn.layers[k].bias = ffn.layers[k].bias - (lr *ffn.layers[k].grad_b)
prev_W[k] = lr*ffn.layers[k].grad_w + lr * ffn.weight_decay*ffn.layers[k].weights
prev_B[k] = lr *ffn.layers[k].grad_b
else:
prev_W[k] = np.multiply(gamma, prev_W[k]) + lr*ffn_temp.layers[k].grad_w + lr * ffn.weight_decay*ffn.layers[k].weights
prev_B[k] = np.multiply(gamma, prev_B[k]) + lr *ffn_temp.layers[k].grad_b
ffn.layers[k].weights = ffn.layers[k].weights - prev_W[k]
ffn.layers[k].bias = ffn.layers[k].bias - prev_B[k]
y_hat = ffn.get_prediction(train_images)
train_loss = eval(ffn.loss_fn + '(train_labels, y_hat)')
train_pred = ffn.get_prediction(train_images)
train_acc = accuracy(train_labels, train_pred)
if val != None:
val_images, val_labels = val
val_pred = ffn.get_prediction(val_images)
val_acc = accuracy(val_labels, val_pred)
val_loss = eval(ffn.loss_fn + '(val_labels, val_pred)')
if val_acc > best_acc:
best_acc = val_acc
best_model = copy.deepcopy(ffn)
with open('model_pkl', 'wb') as files:
pickle.dump(ffn, files)
print("Epoch {} completed, training_loss = {}, validation_loss = {}.".format(j, train_loss, val_loss))
print("Training accuracy = {}, Validation Accuracy = {}".format(train_acc, val_acc))
wandb.log({"train_acc": train_acc,"val_acc": val_acc,"train_loss": train_loss,"val_loss": val_loss})
print(best_acc)
return best_model
def rmsprop(ffn, train_images, train_labels, val = None, epochs =1 , lr = 0.01, batch_size = 1, beta_1 = 0.9):
'''
Implements RMSProp Gradient Descent.
Reference :- http://www.cse.iitm.ac.in/~miteshk/CS7015/Slides/Handout/Lecture5.pdf
'''
best_model, best_acc = 0,0
eps = 1e-8
v_W = [np.zeros_like(layer.weights) for layer in ffn.layers]
v_B = [np.zeros_like(layer.bias) for layer in ffn.layers]
for j in range(epochs):
for i in range(0, train_images.shape[0], batch_size):
#Pick a batch of examples to train.
batch_images = train_images[i:min(train_images.shape[0],i+batch_size)]
batch_labels = train_labels[i:min(train_images.shape[0],i+batch_size)]
batch_pred = ffn.get_prediction(batch_images)
ffn.backward_propogation(batch_labels, batch_pred)
for k in range(0, len(ffn.layers)):
v_W[k] = np.multiply(beta_1, v_W[k]) + np.multiply(1 - beta_1, ffn.layers[k].grad_w**2)
v_B[k] = np.multiply(beta_1, v_B[k]) + np.multiply(1 - beta_1, ffn.layers[k].grad_b**2)
ffn.layers[k].weights = ffn.layers[k].weights - (lr / np.sqrt(v_W[k] + eps)) * ffn.layers[k].grad_w
ffn.layers[k].bias = ffn.layers[k].bias - (lr / np.sqrt(v_B[k] + eps)) * ffn.layers[k].grad_b
y_hat = ffn.get_prediction(train_images)
train_loss = eval(ffn.loss_fn + '(train_labels, y_hat)')
train_pred = ffn.get_prediction(train_images)
train_acc = accuracy(train_labels, train_pred)
if val != None:
val_images, val_labels = val
val_pred = ffn.get_prediction(val_images)
val_acc = accuracy(val_labels, val_pred)
val_loss = eval(ffn.loss_fn + '(val_labels, val_pred)')
if val_acc > best_acc:
best_acc = val_acc
best_model = copy.deepcopy(ffn)
with open('model_pkl', 'wb') as files:
pickle.dump(ffn, files)
print("Epoch {} completed, training_loss = {}, validation_loss = {}.".format(j, train_loss, val_loss))
print("Training accuracy = {}, Validation Accuracy = {}".format(train_acc, val_acc))
wandb.log({"train_acc": train_acc,"val_acc": val_acc,"train_loss": train_loss,"val_loss": val_loss})
print(best_acc)
return best_model
def adam(ffn, train_images, train_labels, val = None, epochs =1 , lr = 0.01, batch_size = 1, beta_1 = 0.9, beta_2 = 0.999):
'''
Implements Adam Gradient Descent.
Reference :- http://www.cse.iitm.ac.in/~miteshk/CS7015/Slides/Handout/Lecture5.pdf
'''
best_model, best_acc = 0,0
eps = 1e-8
v_W = [np.zeros_like(layer.weights) for layer in ffn.layers]
v_B = [np.zeros_like(layer.bias) for layer in ffn.layers]
v_W_hat = [np.zeros_like(layer.weights) for layer in ffn.layers]
v_B_hat = [np.zeros_like(layer.bias) for layer in ffn.layers]
m_W = [np.zeros_like(layer.weights) for layer in ffn.layers]
m_B = [np.zeros_like(layer.bias) for layer in ffn.layers]
m_W_hat = [np.zeros_like(layer.weights) for layer in ffn.layers]
m_B_hat = [np.zeros_like(layer.bias) for layer in ffn.layers]
for j in range(epochs):
for i in range(0, train_images.shape[0], batch_size):
#Pick a batch of examples to train.
batch_images = train_images[i:min(train_images.shape[0],i+batch_size)]
batch_labels = train_labels[i:min(train_images.shape[0],i+batch_size)]
batch_pred = ffn.get_prediction(batch_images)
ffn.backward_propogation(batch_labels, batch_pred)
for k in range(0, len(ffn.layers)):
m_W[k] = np.multiply(beta_1, m_W[k]) + np.multiply(1 - beta_1, ffn.layers[k].grad_w)
m_B[k] = np.multiply(beta_1, m_B[k]) + np.multiply(1 - beta_1, ffn.layers[k].grad_b)
v_W[k] = np.multiply(beta_2, v_W[k]) + np.multiply(1 - beta_2, ffn.layers[k].grad_w**2)
v_B[k] = np.multiply(beta_2, v_B[k]) + np.multiply(1 - beta_2, ffn.layers[k].grad_b**2)
m_W_hat[k] = m_W[k] / (1 - math.pow(beta_1, i+1))
m_B_hat[k] = m_B[k] / (1 - math.pow(beta_1, i+1))
v_W_hat[k] = v_W[k] / (1 - math.pow(beta_2, i+1))
v_B_hat[k] = v_B[k] / (1 - math.pow(beta_2, i+1))
ffn.layers[k].weights = ffn.layers[k].weights - (lr / np.sqrt(v_W_hat[k] + eps)) * m_W_hat[k]
ffn.layers[k].bias = ffn.layers[k].bias - (lr / np.sqrt(v_B_hat[k] + eps)) * m_B_hat[k]
y_hat = ffn.get_prediction(train_images)
train_loss = eval(ffn.loss_fn + '(train_labels, y_hat)')
train_pred = ffn.get_prediction(train_images)
train_acc = accuracy(train_labels, train_pred)
if val != None:
val_images, val_labels = val
val_pred = ffn.get_prediction(val_images)
val_acc = accuracy(val_labels, val_pred)
val_loss = eval(ffn.loss_fn + '(val_labels, val_pred)')
if val_acc > best_acc:
best_acc = val_acc
best_model = copy.deepcopy(ffn)
with open('model_pkl', 'wb') as files:
pickle.dump(ffn, files)
print("Epoch {} completed, training_loss = {}, validation_loss = {}.".format(j, train_loss, val_loss))
print("Training accuracy = {}, Validation Accuracy = {}".format(train_acc, val_acc))
wandb.log({"train_acc": train_acc,"val_acc": val_acc,"train_loss": train_loss,"val_loss": val_loss})
print(best_acc)
return best_model
def nadam(ffn, train_images, train_labels, val = None, epochs =1 , lr = 0.01, batch_size = 1, beta_1 = 0.9, beta_2 = 0.999):
'''
Implements Nestrov Accelrated Adam Gradient Descent.
Update rule Reference : https://ruder.io/optimizing-gradient-descent/index.html#nadam
'''
best_model, best_acc = 0,0
eps = 1e-8
v_W = [np.zeros_like(layer.weights) for layer in ffn.layers]
v_B = [np.zeros_like(layer.bias) for layer in ffn.layers]
v_W_hat = [np.zeros_like(layer.weights) for layer in ffn.layers]
v_B_hat = [np.zeros_like(layer.bias) for layer in ffn.layers]
m_W = [np.zeros_like(layer.weights) for layer in ffn.layers]
m_B = [np.zeros_like(layer.bias) for layer in ffn.layers]
m_W_hat = [np.zeros_like(layer.weights) for layer in ffn.layers]
m_B_hat = [np.zeros_like(layer.bias) for layer in ffn.layers]
for j in range(epochs):
for i in range(0, train_images.shape[0], batch_size):
#Pick a batch of examples to train.
batch_images = train_images[i:min(train_images.shape[0],i+batch_size)]
batch_labels = train_labels[i:min(train_images.shape[0],i+batch_size)]
batch_pred = ffn.get_prediction(batch_images)
ffn.backward_propogation(batch_labels, batch_pred)
for k in range(0, len(ffn.layers)):
m_W[k] = np.multiply(beta_1, m_W[k]) + np.multiply(1 - beta_1, ffn.layers[k].grad_w)
m_B[k] = np.multiply(beta_1, m_B[k]) + np.multiply(1 - beta_1, ffn.layers[k].grad_b)
v_W[k] = np.multiply(beta_2, v_W[k]) + np.multiply(1 - beta_2, ffn.layers[k].grad_w**2)
v_B[k] = np.multiply(beta_2, v_B[k]) + np.multiply(1 - beta_2, ffn.layers[k].grad_b**2)
m_W_hat[k] = m_W[k] / (1 - math.pow(beta_1, i+1))
m_B_hat[k] = m_B[k] / (1 - math.pow(beta_1, i+1))
v_W_hat[k] = v_W[k] / (1 - math.pow(beta_2, i+1))
v_B_hat[k] = v_B[k] / (1 - math.pow(beta_2, i+1))
b = (1-beta_1)/(1-math.pow(beta_1,i+1))
ffn.layers[k].weights = ffn.layers[k].weights - (lr / np.sqrt(v_W_hat[k] + eps)) * (beta_1 * m_W_hat[k] + b*ffn.layers[k].grad_w)
ffn.layers[k].bias = ffn.layers[k].bias - (lr / np.sqrt(v_B_hat[k] + eps)) * (beta_1 * m_B_hat[k] + b*ffn.layers[k].grad_b)
y_hat = ffn.get_prediction(train_images)
train_loss = eval(ffn.loss_fn + '(train_labels, y_hat)')
train_pred = ffn.get_prediction(train_images)
train_acc = accuracy(train_labels, train_pred)
if val != None:
val_images, val_labels = val
val_pred = ffn.get_prediction(val_images)
val_acc = accuracy(val_labels, val_pred)
val_loss = eval(ffn.loss_fn + '(val_labels, val_pred)')
if val_acc > best_acc:
best_acc = val_acc
best_model = copy.deepcopy(ffn)
with open('model_pkl', 'wb') as files:
pickle.dump(ffn, files)
print("Epoch {} completed, training_loss = {}, validation_loss = {}.".format(j, train_loss, val_loss))
print("Training accuracy = {}, Validation Accuracy = {}".format(train_acc, val_acc))
wandb.log({"train_acc": train_acc,"val_acc": val_acc,"train_loss": train_loss,"val_loss": val_loss})
print(best_acc)
return best_model