Book: Neural Networks and Deep Learning
I'm using this material to refresh my deep learning knowledge.
Basic Network for MNIST
http://neuralnetworksanddeeplearning.com/chap1.html#perceptrons
class Network():
def __init__(self, sizes):
self.num_layers = len(sizes)
self.sizes = sizes
self.biases = [np.random.randn(y, 1) for y in sizes[1:]]
# this assignment is kinda clever
self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]
def feedforward(self, a):
for b, w in zip(self.biases, self.weights):
a = sigmoid_vec(np.dot(w, a) + b)
return a
def SGD(self, training_data, epochs, mini_batch_size, eta, test_data=None):
"""Train the neural network using mini-batch sgd.
Parameters
----------
training_data : a list of tuples ``(x, y)``, shape = [n_samples, n_features]
The training inputs and the desired outputs.
"""
We need to use np.vectorize to define sigmoid as follows:
def sigmoid(z):
return 1.0 / (1.0 + np.exp(-z))
sigmoid_vec = np.vectorize(sigmoid)