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MNIST_hierarchical_rnn.ipynb
README.md
getting_started_with_keras.ipynb
reuters_mlp.ipynb

README.md

Week 2

meeting date: 09-06-2016

Covered

Nielsen Chapter 2

  • backpropagation
    • fast alogrithm for computing gradients
    • introduced in 1970s
    • This 1986 paper recognized the usefulness of its application in neural nets

Warm up: a fast matrix-based approach to computing output of neural net

  • 3 neuron components
    • w: weight
    • b: bias
    • a: activation
  • a_l_j = σ(∑_k w_l_jk * a_l-1_k + b_l_j)
  • Weight matrix entry W_l_jk
    • lth layer
    • jth neruon in layer l
    • kth neuron in layer l - 1
  • a_l = σ(w_l*a_l-1 + b_l)
  • z_l = w_l*a_l-1 + b_l
    • Weighted input of the neurons in layer l

The two assumptions we need about the cost function

  • goal of backpropagation is to compute the partial derivatives of the cost function C with respect to any weight w aor bias b
  • 2 assumptions about cost function
    1. can be written as an average C = (1/n)∑_x C_x
      • allows us to get partial derivatives by averaging partial derivatives of individual training samples
    2. can be written as a function of the outputs from the neural network

The Hadamard product

  • s⊙t denotes elementwise product of two vectors s and t
    • (s⊙t)_j = s_j*t_j
    • called Hadamard or Schur product

The four fundamental equations behind backpropagation

  • backpropagation is about understanding how changing the weights and biases in a network changes the cost function.

  • δ_l_j represents the error in the jth neuron in the lth layer.

    • with backpropagation we compute this error and then relate it to the partial derivatives
    • δ_l_j ≡ ∂C/∂z_l_j
      • error for jth neuron layer l
  • 4 fundamental equations of backpropagation

    1. δ_L_j = ∂C/∂a_L_j * σ′(z_L_j)
      • δ_L = ∇_aC⊙σ′(z_L) in matrix form
    2. δ_l=((w_l+1)^T*δ_l+1) ⊙ σ′(z_l)
      • expresses the errors in layer l in terms of error in the next layer l+1
      • combining equation 1 and 2 allows us to compute the error for any layer in the net
    3. ∂C/∂b_l_j = δ_l_j
      • rate of change of the cost with respect to any bias in the network
    4. ∂C/∂w_l_jk = a_l−1_k*δ_l_j
      • rate of change of the cost with respect to any wight in the network
      • when activation is small, the gradient term with respect to w will tend to be small
      • weights output from low-activation neurons learn slowly
  • sigmoid function is very flat around 0 or 1, so σ′(z_L_j) ≈ 0

    • From equation 1 output neuron in final layer will learn slowly if the output neuron is either low or high activation (0 or 1).
    • From equation 2, error is likely to get small if neuron is near saturation
  • These 4 equations hold for any activation function, not just the sigmoid function

    • proofs don't use an special properties of σ
    • so we could pick an activation function whose derivative that is never close to 0 to prevent the slow-down of learning that occurs with saturated Sigmoid functions

Proof of the four fundamental equations (optional)

  • All four equations are consequences of the chain rule from multivariable calculus
  • because a_L_j = σ(z_L_j), ∂a_L_j/∂z_L_j = σ′(z_L_j)
  • We can think of backpropagation as a way of computing the gradient of the cost function by systematically applying chain rule from multi-variable calculus

The backpropagation algorithm

  • High-level steps

    1. Input x: set corresponding activation a_1 for the input layer
    2. Feedforward: for each l = 2, 3, ..., L compute z_l = w_l*a_l-1 + b_l and a_l = σ(z_l)
    3. Output error δ_L: compute the vector δ_L = ∇_a*C ⊙ σ′(z_L)
    4. Backpropagate the error: for each l = L-1, L-2, ..., 2 compute δ_l = ((w_l+1)T*δ_l+1) ⊙ σ′(z_l)
    5. Output: The gradient of the cost function is given by ∂C/∂w_l_jk = a_l−1_k * δl_j and ∂C/∂b_l_j = δ_l_j
  • Error vectors are computed backward starting with final layer.

    • cost is a function of outputs from the network
    • to understand how cost varies with earlier weights and biases, we need to apply the chain rule backwards through layers
  • Backpropagation algo computs gradient of cost function for a single training sample

    • C = C_x
  • Common to combine backpropagation with a learning algo such as stochastic gradient descent (SGD) to compute gradient for many training samples

  • Example learning step of gradient descent with mini-batch of m training samples

    1. Input a set of training examples
    2. For each training example x: set the corresponding input activation a_x,1 and perform the following steps a. Feedforward b. Output error c. Backpropagate the error
    3. Gradient descent: Fore each l = L, L-1, ..., 2 update the weights and biases based on learning rules for mini-batch

The code for backpropagation

In what sense is backpropagation a fast algorithm?

  • Example calculation without backpropagation:
    • approximate deriviative of cost: ∂C/∂wj ≈ (C(w+ϵej)−C(w))/ϵ
    • this is easy but very slow
      • if we have 1M weights in a network, to compute gradient we must compute the cost function 1M times, each requiring an forward pass through the network per training sample.
  • backpropagation allows us to simultaneously comput all partial derivatives using just one forward pass through the network, followed by a backward pass per training sample.
    • this is MUCH faster

Backpropagation: the big picture

  • 2 mysteries
    1. Building deeper intuition around what's going on during all these matrix and vector multiplications
    2. How could someone ever discover backpropagation in the first place?
  • Tracking how a change in weight or bias at a particular layer propagates through he network and results in a change in Cost leads to a complex sum over a product of partial derivatives of activations between layers
    • this expression expressed and manipulated with some calculus and linear algebra will lead to the 4 equations of backpropagation