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denoising.py
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denoising.py
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import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
def sigmoid(x):
return 1. / (1 + np.exp(-x))
def sigmoid_der(x):
s = 1. / (1 + np.exp(-x))
return s * (1. - s)
def one_hot_encode_labels(labels):
n_unique = len(set(labels))
output = []
for label in labels:
a = np.zeros(n_unique)
a[label] = 1
output.append(a)
return np.asarray(output)
def add_noise(data_original, noise_percentage=20):
data = np.copy(data_original)
n_columns = data.shape[1]
n_noisy = int(noise_percentage * n_columns / 100)
for i in range(data.shape[0]):
noisy = np.random.choice(np.arange(n_columns), n_noisy, replace=False)
data[i][noisy] = 0
return data
def encode(x, params):
return sigmoid(np.dot(x, params["W1"].T) + params["b1"])
def decode(h, params):
return sigmoid(np.dot(h, params["W2"].T) + params["b2"])
def cost_function(x, z):
eps = 1e-10
return - np.sum((x * np.log(z + eps) + (1. - x) * np.log(1. - z + eps)))
def get_n_samples_per_class(data, labels, n):
samples = []
for i in range(10):
s = data[labels == i]
s = s[:n]
samples.append(s)
return np.asarray(samples).reshape(n * 10, 784)
def calc_gradient(x_batch, y_expected, params):
x_data = x_batch
p = encode(x_data, params)
y = decode(p, params)
cost = np.sum(cost_function(y_expected, y))
delta1 = y - y_expected
dW2 = np.sum(np.dot(delta1.T, p), axis=0)
db2 = np.sum(delta1, axis=0)
delta2 = np.sum(np.dot(params["W2"].T, delta1.T) * sigmoid_der(p).T, axis=0)
dW1 = np.sum(np.dot(delta2, x_data), axis=0)
db1 = np.sum(delta2, axis=0)
cost /= len(x_batch)
dW1 /= len(x_batch)
dW2 /= len(x_batch)
db1 /= len(x_batch)
db2 /= len(x_batch)
return cost, dW1, dW2, db1, db2
def train(X, Y, params, epochs=10, batch_size=128, alpha=0.1):
costs = []
batch_num = len(X) // batch_size
for epoch in range(epochs):
total_cost = 0.0
print("Epoch", epoch)
for i in range(batch_num):
batch = X[i * batch_size: (i + 1) * batch_size]
batch_output = Y[i * batch_size: (i + 1) * batch_size]
cost, gradW1, gradW2, gradb1, gradb2 = calc_gradient(batch, batch_output, params)
total_cost += cost
params["W1"] -= alpha * gradW1
params["W2"] -= alpha * gradW2
params["b1"] -= alpha * gradb1
params["b2"] -= alpha * gradb2
costs.append((1. / batch_num) * total_cost)
print("Epoch", epoch, "Cost", (1. / batch_num) * total_cost)
plt.plot(costs)
plt.ylabel("Cost")
plt.xlabel("Epochs")
plt.title("Training - Denoising autoencoder")
plt.show()
# Training data
print("Reading training data...")
raw_training_data = pd.read_csv("../dataset/fashion-mnist_train.csv")
training_data = np.asarray(raw_training_data.loc[:, raw_training_data.columns != 'label'], dtype=np.float)
training_labels = one_hot_encode_labels(np.asarray(raw_training_data['label']))
print("Reading testing data...")
# Testing data
raw_testing_data = pd.read_csv("../dataset/fashion-mnist_test.csv")
testing_data = np.asarray(raw_testing_data.loc[:, raw_testing_data.columns != 'label'], dtype=np.float)
testing_labels = one_hot_encode_labels(np.asarray(raw_testing_data['label']))
print("Training", training_data.shape, training_labels.shape)
print("Testing", testing_data.shape, testing_labels.shape)
training_data = training_data / 255.0
n_inputs = training_data.shape[1]
n_output = n_inputs
n_hidden = 1000
noise = 10
noise_testing = 10
noisy_training_data = add_noise(training_data, noise_percentage=noise)
params = {"W1": np.asarray(np.random.uniform(low=(-1. / n_inputs), high=(1. / n_inputs), size=(n_hidden, n_inputs))),
"W2": np.asarray(np.random.uniform(low=(-1. / n_hidden), high=(1. / n_hidden), size=(n_output, n_hidden))),
"b1": np.zeros((n_hidden)),
"b2": np.zeros((n_output))}
train(noisy_training_data, training_data, params)
samples_input = get_n_samples_per_class(testing_data, raw_testing_data['label'], 1)
noisy_sample_input = add_noise(samples_input, noise_percentage=noise_testing)
output = decode((encode(noisy_sample_input, params)), params)
for i in range(10):
f = plt.subplot(3, 10, i + 1)
plt.imshow(samples_input[i].reshape(28, 28), cmap='gray', interpolation='nearest')
plt.axis('off')
f.set_xticklabels([])
f.set_yticklabels([])
for i in range(10):
f = plt.subplot(3, 10, 10 + i + 1)
plt.imshow(noisy_sample_input[i].reshape(28, 28), cmap='gray', interpolation='nearest')
plt.axis('off')
f.set_xticklabels([])
f.set_yticklabels([])
for i in range(10):
f = plt.subplot(3, 10, 20 + i + 1)
plt.imshow(output[i].reshape(28, 28), cmap='gray', interpolation='nearest')
plt.axis('off')
f.set_xticklabels([])
f.set_yticklabels([])
plt.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9, wspace=0.0, hspace=0.0)
# plt.tight_layout()
plt.show()