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classifier.py
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classifier.py
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__author__ = "Shivchander Sudalairaj"
__license__ = "MIT"
'''
Autoencoder of MNIST Dataset using Multi Layer Feed Forward Neural Net implemented from scratch
'''
import numpy as np
import math
import matplotlib.pyplot as plt
np.random.seed(1)
class ClassifierNN(object):
def __init__(self):
self.n_x = 784
self.n_h = 100
self.n_l = 1
self.n_y = 784
self.layer_dims = []
self.parameters = {}
self.X = None
self.y = None
def initialize_parameters(self, n_x, n_h, n_l, n_y):
self.n_x = n_x
self.n_h = n_h
self.n_l = n_l
self.n_y = n_y
self.layer_dims = [n_x] + [n_h] * n_l + [n_y]
for l in range(1, len(self.layer_dims)):
self.parameters['W' + str(l)] = np.random.randn(self.layer_dims[l], self.layer_dims[l - 1]) * 0.01
self.parameters['b' + str(l)] = np.zeros((self.layer_dims[l], 1))
return self.parameters
def sigmoid(self, Z):
A = 1 / (1 + np.exp(-Z))
cache = Z
return A, cache
def relu(self, Z):
A = np.maximum(0, Z)
cache = Z
return A, cache
def activation_forward(self, A_prev, W, b, activation):
def linear_forward(A, W, b):
Z = np.dot(W, A) + b
cache = (A, W, b)
return Z, cache
if activation == "sigmoid":
Z, linear_cache = linear_forward(A_prev, W, b)
A, activation_cache = self.sigmoid(Z)
elif activation == "relu":
Z, linear_cache = linear_forward(A_prev, W, b)
A, activation_cache = self.relu(Z)
cache = (linear_cache, activation_cache)
return A, cache
def forward_propagation(self, X, parameters):
caches = []
A = X
L = len(parameters) // 2
for l in range(1, L):
A_prev = A
A, cache = self.activation_forward(A_prev, parameters['W' + str(l)],
parameters['b' + str(l)], activation='relu')
caches.append(cache)
AL, cache = self.activation_forward(A, parameters['W' + str(L)],
parameters['b' + str(L)], activation='sigmoid')
caches.append(cache)
return AL, caches
def compute_cost(self, AL, Y):
m = Y.shape[1]
cost = 0
for yt, yp in zip(Y.T, AL.T):
cost += np.square(yt-yp).mean()
return cost / m
def linear_backward(self, dZ, cache):
A_prev, W, b = cache
m = A_prev.shape[1]
dW = np.dot(dZ, cache[0].T) / m
db = np.squeeze(np.sum(dZ, axis=1, keepdims=True)) / m
dA_prev = np.dot(cache[1].T, dZ)
return dA_prev, dW, db
def relu_backward(self, dA, cache):
Z = cache
dZ = np.array(dA, copy=True)
dZ[Z <= 0] = 0
return dZ
def sigmoid_backward(self, dA, cache):
Z = cache
s = 1 / (1 + np.exp(-Z))
dZ = dA * s * (1 - s)
return dZ
def linear_activation_backward(self, dA, cache, activation):
linear_cache, activation_cache = cache
if activation == "relu":
dZ = self.relu_backward(dA, activation_cache)
dA_prev, dW, db = self.linear_backward(dZ, linear_cache)
db = db.reshape(len(db), 1)
elif activation == "sigmoid":
dZ = self.sigmoid_backward(dA, activation_cache)
dA_prev, dW, db = self.linear_backward(dZ, linear_cache)
db = db.reshape(len(db), 1)
return dA_prev, dW, db
def backward_propagation(self, AL, Y, caches):
grads = {}
L = len(caches)
m = AL.shape[1]
Y = Y.reshape(AL.shape)
# dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
dAL = 2*(AL - Y)
current_cache = caches[L - 1]
grads["dA" + str(L)], grads["dW" + str(L)], grads["db" + str(L)] = self.linear_activation_backward(dAL,
current_cache,
"sigmoid")
# for l in reversed(range(L - 1)):
# current_cache = caches[l]
# dA_prev_temp, dW_temp, db_temp = self.linear_activation_backward(grads["dA" + str(l + 2)], current_cache,
# "relu")
# grads["dA" + str(l + 1)] = dA_prev_temp
# grads["dW" + str(l + 1)] = dW_temp
# grads["db" + str(l + 1)] = db_temp
return grads
def initialize_velocity(self, parameters):
L = len(parameters) // 2
v = {}
for l in range(1, L):
v["dW" + str(l + 1)] = np.zeros_like(parameters["W" + str(l + 1)])
v["db" + str(l + 1)] = np.zeros_like(parameters["b" + str(l + 1)])
return v
def update_parameters_with_momentum(self, parameters, grads, v, learning_rate):
L = len(parameters) // 2
beta = 0.9
for l in range(1, L):
# compute velocities
v["dW" + str(l + 1)] = beta * v["dW" + str(l + 1)] + (1 - beta) * grads['dW' + str(l + 1)]
v["db" + str(l + 1)] = beta * v["db" + str(l + 1)] + (1 - beta) * grads['db' + str(l + 1)]
# update parameters
parameters["W" + str(l + 1)] = parameters["W" + str(l + 1)] - learning_rate * v["dW" + str(l + 1)]
parameters["b" + str(l + 1)] = parameters["b" + str(l + 1)] - learning_rate * v["db" + str(l + 1)]
return parameters, v
def random_mini_batches(self, X, Y, mini_batch_size=64, seed=0):
m = X.shape[1]
mini_batches = []
permutation = list(np.random.permutation(m))
shuffled_X = X[:, permutation]
shuffled_Y = Y[:, permutation]
num_complete_minibatches = math.floor(m / mini_batch_size)
for k in range(0, num_complete_minibatches):
mini_batch_X = shuffled_X[:, k * mini_batch_size:(k + 1) * mini_batch_size]
mini_batch_Y = shuffled_Y[:, k * mini_batch_size:(k + 1) * mini_batch_size]
mini_batch = (mini_batch_X, mini_batch_Y)
mini_batches.append(mini_batch)
if m % mini_batch_size != 0:
end = m - mini_batch_size * math.floor(m / mini_batch_size)
mini_batch_X = shuffled_X[:, num_complete_minibatches * mini_batch_size:]
mini_batch_Y = shuffled_Y[:, num_complete_minibatches * mini_batch_size:]
mini_batch = (mini_batch_X, mini_batch_Y)
mini_batches.append(mini_batch)
return mini_batches
def fit(self, X, y, n_x, n_h, n_l, n_y, pre_trained_weights={},
learning_rate=0.001, batch_size=64, num_epochs=1000, plot_error=True):
# parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations=2500, print_cost=True)
self.X = X.T
self.y = y.T
self.initialize_parameters(n_x, n_h, n_l, n_y)
if pre_trained_weights:
self.parameters['W1'] = pre_trained_weights['W1']
self.parameters['b1'] = pre_trained_weights['b1']
errors = []
v = self.initialize_velocity(self.parameters)
# Optimization loop
for i in range(num_epochs):
minibatches = self.random_mini_batches(self.X, self.y, batch_size)
for minibatch in minibatches:
(minibatch_X, minibatch_Y) = minibatch
# Forward propagation
al, caches = self.forward_propagation(minibatch_X, self.parameters)
# Compute cost
cost = self.compute_cost(al, minibatch_Y)
# Backward propagation
grads = self.backward_propagation(al, minibatch_Y, caches)
# update parameters
self.parameters, v = self.update_parameters_with_momentum(self.parameters, v, grads, learning_rate)
# Print the cost every 10 epoch
if plot_error and i % 10 == 0:
print("Error after epoch %i: %f" % (i, cost))
errors.append(cost)
if plot_error:
plt.plot(list(range(0, len(errors) * 10, 10)), errors)
plt.ylabel('Mean Squared Error')
plt.xlabel('epochs')
plt.title('Training Error')
plt.savefig('figs/autoencoder_error.pdf')
plt.clf()
return self.parameters
def threshold_function(self, y_preds):
rows, cols = y_preds.shape
for row in range(rows):
for col in range(cols):
if y_preds[row, col] >= 0.75:
y_preds[row, col] = 1
if y_preds[row, col] <= 0.25:
y_preds[row, col] = 0
return y_preds
def predict(self, X):
X = X.T
# Forward propagation
a, caches = self.forward_propagation(X, self.parameters)
return self.threshold_function(a.T)