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optimization_with_regularization.py
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optimization_with_regularization.py
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from optimization import *
from regularization import *
def gradient_descent_with_l2(train_X, train_Y, epochs, l_rate, layers, L, af, af_choices, batch_size):
s = batch_size
m = s
parameters = initialize_parameters(layers, L)
costs = []
accs = []
for e in range(epochs):
for i in range(0, train_X.shape[0], s):
X = train_X[i:s + i, :]
Y = train_Y[i:s + i, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l2(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
parameters[f'W{str(l)}'] -= l_rate * derivatives[f'dW{str(l)}']
parameters[f'b{str(l)}'] -= l_rate * derivatives[f'db{str(l)}']
if e % 1 == 0:
AA, _ = forward_prop(train_X, parameters, L, af, af_choices)
cost = crossentropy_with_l2(AA, train_Y, parameters, L)
acc = pred(train_X, train_Y, parameters, L, af, af_choices)
costs.append(cost)
accs.append(acc)
print(f'epoch: {e} - cost= {cost} - Train Acc= {acc}')
return parameters, costs, accs
def gradient_descent_with_l1(train_X, train_Y, epochs, l_rate, layers, L, af, af_choices, batch_size):
s = batch_size
m = s
parameters = initialize_parameters(layers, L)
costs = []
accs = []
for e in range(epochs):
for i in range(0, train_X.shape[0], s):
X = train_X[i:s + i, :]
Y = train_Y[i:s + i, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l1(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
parameters[f'W{str(l)}'] -= l_rate * derivatives[f'dW{str(l)}']
parameters[f'b{str(l)}'] -= l_rate * derivatives[f'db{str(l)}']
if e % 1 == 0:
AA, _ = forward_prop(train_X, parameters, L, af, af_choices)
cost = crossentropy_with_l1(AA, train_Y, parameters, L)
acc = pred(train_X, train_Y, parameters, L, af, af_choices)
costs.append(cost)
accs.append(acc)
print(f'epoch: {e} - cost= {cost} - Train Acc= {acc}')
return parameters, costs, accs
def adagrad_with_l2(train_X, train_Y, epochs, l_rate, layers, L, af, af_choices, batch_size):
s = batch_size
m = s
parameters = initialize_parameters(layers, L)
costs = []
accs = []
eps = 1e-7
r_dW = dict()
r_db = dict()
X = train_X[0:s, :]
Y = train_Y[0:s, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l2(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
r_dW[f'dW{str(l)}'] = np.zeros(derivatives[f'dW{str(l)}'].shape)
r_db[f'db{str(l)}'] = np.zeros(derivatives[f'db{str(l)}'].shape)
for e in range(epochs):
for i in range(0, train_X.shape[0], s):
X = train_X[i:s + i, :]
Y = train_Y[i:s + i, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l2(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
r_dW[f'dW{str(l)}'] += derivatives[f'dW{str(l)}'] ** 2
r_db[f'db{str(l)}'] += derivatives[f'db{str(l)}'] ** 2
parameters[f'W{str(l)}'] -= (l_rate / (eps + np.sqrt(r_dW[f'dW{str(l)}']))) * derivatives[f'dW{str(l)}']
parameters[f'b{str(l)}'] -= (l_rate / (eps + np.sqrt(r_db[f'db{str(l)}']))) * derivatives[f'db{str(l)}']
if e % 1 == 0:
AA, _ = forward_prop(train_X, parameters, L, af, af_choices)
cost = crossentropy_with_l2(AA, train_Y, parameters, L)
acc = pred(train_X, train_Y, parameters, L, af, af_choices)
accs.append(acc)
costs.append(cost)
print(f'epoch: {e} - cost= {cost} - Train Acc= {acc}')
return parameters, costs, accs
def adagrad_with_l1(train_X, train_Y, epochs, l_rate, layers, L, af, af_choices, batch_size):
s = batch_size
m = s
parameters = initialize_parameters(layers, L)
costs = []
accs = []
eps = 1e-7
r_dW = dict()
r_db = dict()
X = train_X[0:s, :]
Y = train_Y[0:s, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l1(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
r_dW[f'dW{str(l)}'] = np.zeros(derivatives[f'dW{str(l)}'].shape)
r_db[f'db{str(l)}'] = np.zeros(derivatives[f'db{str(l)}'].shape)
for e in range(epochs):
for i in range(0, train_X.shape[0], s):
X = train_X[i:s + i, :]
Y = train_Y[i:s + i, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l1(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
r_dW[f'dW{str(l)}'] += derivatives[f'dW{str(l)}'] ** 2
r_db[f'db{str(l)}'] += derivatives[f'db{str(l)}'] ** 2
parameters[f'W{str(l)}'] -= (l_rate / (eps + np.sqrt(r_dW[f'dW{str(l)}']))) * derivatives[f'dW{str(l)}']
parameters[f'b{str(l)}'] -= (l_rate / (eps + np.sqrt(r_db[f'db{str(l)}']))) * derivatives[f'db{str(l)}']
if e % 1 == 0:
AA, _ = forward_prop(train_X, parameters, L, af, af_choices)
cost = crossentropy_with_l1(AA, train_Y, parameters, L)
acc = pred(train_X, train_Y, parameters, L, af, af_choices)
accs.append(acc)
costs.append(cost)
print(f'epoch: {e} - cost= {cost} - Train Acc= {acc}')
return parameters, costs, accs
def rmsprop_with_l2(train_X, train_Y, epochs, l_rate, layers, L, af, af_choices, batch_size):
s = batch_size
m = s
parameters = initialize_parameters(layers, L)
costs = []
accs = []
eps = 1e-8
rho = 0.9
r_dW = dict()
r_db = dict()
X = train_X[0:s, :]
Y = train_Y[0:s, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l2(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
r_dW[f'dW{str(l)}'] = np.zeros(derivatives[f'dW{str(l)}'].shape)
r_db[f'db{str(l)}'] = np.zeros(derivatives[f'db{str(l)}'].shape)
for e in range(epochs):
for i in range(0, train_X.shape[0], s):
X = train_X[i:s + i, :]
Y = train_Y[i:s + i, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l2(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
r_dW[f'dW{str(l)}'] = rho * r_dW[f'dW{str(l)}'] + (1 - rho) * derivatives[f'dW{str(l)}'] ** 2
r_db[f'db{str(l)}'] = rho * r_db[f'db{str(l)}'] + (1 - rho) * derivatives[f'db{str(l)}'] ** 2
parameters[f'W{str(l)}'] -= (l_rate / np.sqrt(eps + r_dW[f'dW{str(l)}'])) * derivatives[f'dW{str(l)}']
parameters[f'b{str(l)}'] -= (l_rate / np.sqrt(eps + r_db[f'db{str(l)}'])) * derivatives[f'db{str(l)}']
if e % 1 == 0:
AA, _ = forward_prop(train_X, parameters, L, af, af_choices)
cost = crossentropy_with_l2(AA, train_Y, parameters, L)
acc = pred(train_X, train_Y, parameters, L, af, af_choices)
accs.append(acc)
costs.append(cost)
print(f'epoch: {e} - cost= {cost} - Train Acc= {acc}')
return parameters, costs, accs
def rmsprop_with_l1(train_X, train_Y, epochs, l_rate, layers, L, af, af_choices, batch_size):
s = batch_size
m = s
parameters = initialize_parameters(layers, L)
costs = []
accs = []
eps = 1e-8
rho = 0.9
r_dW = dict()
r_db = dict()
X = train_X[0:s, :]
Y = train_Y[0:s, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l1(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
r_dW[f'dW{str(l)}'] = np.zeros(derivatives[f'dW{str(l)}'].shape)
r_db[f'db{str(l)}'] = np.zeros(derivatives[f'db{str(l)}'].shape)
for e in range(epochs):
for i in range(0, train_X.shape[0], s):
X = train_X[i:s + i, :]
Y = train_Y[i:s + i, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l1(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
r_dW[f'dW{str(l)}'] = rho * r_dW[f'dW{str(l)}'] + (1 - rho) * derivatives[f'dW{str(l)}'] ** 2
r_db[f'db{str(l)}'] = rho * r_db[f'db{str(l)}'] + (1 - rho) * derivatives[f'db{str(l)}'] ** 2
parameters[f'W{str(l)}'] -= (l_rate / np.sqrt(eps + r_dW[f'dW{str(l)}'])) * derivatives[f'dW{str(l)}']
parameters[f'b{str(l)}'] -= (l_rate / np.sqrt(eps + r_db[f'db{str(l)}'])) * derivatives[f'db{str(l)}']
if e % 1 == 0:
AA, _ = forward_prop(train_X, parameters, L, af, af_choices)
cost = crossentropy_with_l1(AA, train_Y, parameters, L)
acc = pred(train_X, train_Y, parameters, L, af, af_choices)
accs.append(acc)
costs.append(cost)
print(f'epoch: {e} - cost= {cost} - Train Acc= {acc}')
return parameters, costs, accs
def adam_with_l2(train_X, train_Y, epochs, l_rate, layers, L, af, af_choices, batch_size):
s = batch_size
m = s
parameters = initialize_parameters(layers, L)
costs = []
accs = []
eps = 1e-8
rho1 = 0.9
rho2 = 0.999
s_dW = dict()
s_db = dict()
r_dW = dict()
r_db = dict()
X = train_X[0:s, :]
Y = train_Y[0:s, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l2(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
s_dW[f'dW{str(l)}'] = np.zeros(derivatives[f'dW{str(l)}'].shape)
s_db[f'db{str(l)}'] = np.zeros(derivatives[f'db{str(l)}'].shape)
r_dW[f'dW{str(l)}'] = np.zeros(derivatives[f'dW{str(l)}'].shape)
r_db[f'db{str(l)}'] = np.zeros(derivatives[f'db{str(l)}'].shape)
t = 0
for e in range(epochs):
for i in range(0, train_X.shape[0], s):
X = train_X[i:s + i, :]
Y = train_Y[i:s + i, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l2(X, Y, store, m, L, af, af_choices, parameters)
t += 1
for l in range(1, L + 1):
s_dW[f'dW{str(l)}'] = rho1 * s_dW[f'dW{str(l)}'] + (1 - rho1) * derivatives[f'dW{str(l)}']
s_db[f'db{str(l)}'] = rho1 * s_db[f'db{str(l)}'] + (1 - rho1) * derivatives[f'db{str(l)}']
r_dW[f'dW{str(l)}'] = rho2 * r_dW[f'dW{str(l)}'] + (1 - rho2) * (derivatives[f'dW{str(l)}'] ** 2)
r_db[f'db{str(l)}'] = rho2 * r_db[f'db{str(l)}'] + (1 - rho2) * (derivatives[f'db{str(l)}'] ** 2)
s_dW_hat = s_dW[f'dW{str(l)}'] / (1 - (rho1 ** t))
s_db_hat = s_db[f'db{str(l)}'] / (1 - (rho1 ** t))
r_dW_hat = r_dW[f'dW{str(l)}'] / (1 - (rho2 ** t))
r_db_hat = r_db[f'db{str(l)}'] / (1 - (rho2 ** t))
parameters[f'W{str(l)}'] -= (l_rate * s_dW_hat) / (eps + np.sqrt(r_dW_hat))
parameters[f'b{str(l)}'] -= (l_rate * s_db_hat) / (eps + np.sqrt(r_db_hat))
if e % 1 == 0:
AA, _ = forward_prop(train_X, parameters, L, af, af_choices)
cost = crossentropy_with_l2(AA, train_Y, parameters, L)
acc = pred(train_X, train_Y, parameters, L, af, af_choices)
accs.append(acc)
costs.append(cost)
print(f'epoch: {e} - cost= {cost} - Train Acc= {acc}')
return parameters, costs, accs
def adam_with_l1(train_X, train_Y, epochs, l_rate, layers, L, af, af_choices, batch_size):
s = batch_size
m = s
parameters = initialize_parameters(layers, L)
costs = []
accs = []
eps = 1e-8
rho1 = 0.9
rho2 = 0.999
s_dW = dict()
s_db = dict()
r_dW = dict()
r_db = dict()
X = train_X[0:s, :]
Y = train_Y[0:s, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l1(X, Y, store, m, L, af, af_choices, parameters)
for l in range(1, L + 1):
s_dW[f'dW{str(l)}'] = np.zeros(derivatives[f'dW{str(l)}'].shape)
s_db[f'db{str(l)}'] = np.zeros(derivatives[f'db{str(l)}'].shape)
r_dW[f'dW{str(l)}'] = np.zeros(derivatives[f'dW{str(l)}'].shape)
r_db[f'db{str(l)}'] = np.zeros(derivatives[f'db{str(l)}'].shape)
t = 0
for e in range(epochs):
for i in range(0, train_X.shape[0], s):
X = train_X[i:s + i, :]
Y = train_Y[i:s + i, :]
A, store = forward_prop(X, parameters, L, af, af_choices)
derivatives = backward_prop_with_l1(X, Y, store, m, L, af, af_choices, parameters)
t += 1
for l in range(1, L + 1):
s_dW[f'dW{str(l)}'] = rho1 * s_dW[f'dW{str(l)}'] + (1 - rho1) * derivatives[f'dW{str(l)}']
s_db[f'db{str(l)}'] = rho1 * s_db[f'db{str(l)}'] + (1 - rho1) * derivatives[f'db{str(l)}']
r_dW[f'dW{str(l)}'] = rho2 * r_dW[f'dW{str(l)}'] + (1 - rho2) * (derivatives[f'dW{str(l)}'] ** 2)
r_db[f'db{str(l)}'] = rho2 * r_db[f'db{str(l)}'] + (1 - rho2) * (derivatives[f'db{str(l)}'] ** 2)
s_dW_hat = s_dW[f'dW{str(l)}'] / (1 - (rho1 ** t))
s_db_hat = s_db[f'db{str(l)}'] / (1 - (rho1 ** t))
r_dW_hat = r_dW[f'dW{str(l)}'] / (1 - (rho2 ** t))
r_db_hat = r_db[f'db{str(l)}'] / (1 - (rho2 ** t))
parameters[f'W{str(l)}'] -= (l_rate * s_dW_hat) / (eps + np.sqrt(r_dW_hat))
parameters[f'b{str(l)}'] -= (l_rate * s_db_hat) / (eps + np.sqrt(r_db_hat))
if e % 1 == 0:
AA, _ = forward_prop(train_X, parameters, L, af, af_choices)
cost = crossentropy_with_l1(AA, train_Y, parameters, L)
acc = pred(train_X, train_Y, parameters, L, af, af_choices)
accs.append(acc)
costs.append(cost)
print(f'epoch: {e} - cost= {cost} - Train Acc= {acc}')
return parameters, costs, accs