Harvard的Python深度神经网络快速原型库：Kayak
jopen
9年前
Harvard的Python深度神经网络快速原型库，其特色在于足够简单和可扩展，可实现原型架构的快速开发与思路验证。
import kayak import numpy.random as npr X = ... your feature matrix ... Y = ... your label matrix ... # Create Kayak objects for features and labels. inputs = kayak.Inputs(X) targets = kayak.Targets(Y) # Create Kayak objects first-layer weights and biases. Initialize # them with random Numpy matrices. weights_1 = kayak.Parameter(npr.randn( input_dims, hidsize_1 )) biases_1 = kayak.Parameter(npr.randn( 1, hidsize_1 )) # Create Kayak objects that implement a network layer. First, # multiply the features by weights and add biases. hiddens_1a = kayak.ElemAdd(kayak.MatMult( inputs, weights_1 ), biases_1) # Then, apply a "relu" (rectified linear) nonlinearity. # Alternatively, you can apply your own favorite nonlinearity, or # add one for an idea that you want to try out. hiddens_1b = kayak.HardReLU(hiddens_1a) # Now, apply a "dropout" layer to prevent co-adaptation. Got a # new idea for dropout? It's super easy to extend Kayak with it. hiddens_1 = kayak.Dropout(hiddens_1b, drop_prob=0.5) # Okay, with that layer constructed, let's make another one the # same way: linear transformation + bias with ReLU and dropout. # First, create the second-layer parameters. weights_2 = kayak.Parameter(npr.randn(hidsize_1, hidsize_2)) biases_2 = kayak.Parameter(npr.randn(1, hidsize_2)) # This time, let's compose all the steps, just to show we can. hiddens_2 = kayak.Dropout( kayak.HardReLU( kayak.ElemAdd( \ kayak.MatMult( hiddens_1, weights_2), biases_2)), drop_prob=0.5) # Make the output layer linear. weights_out = kayak.Parameter(npr.randn(hidsize_2, 1)) biases_out = kayak.Parameter(npr.randn()) out = kayak.ElemAdd( kayak.MatMult( hiddens_2, weights_out), biases_out) # Apply a loss function. In this case, we'll just do squared loss. loss = kayak.MatSum( kayak.L2Loss( out, targets )) # Maybe roll in an L1 norm for the first layer and an L2 norm for the others? objective = kayak.ElemAdd(loss, kayak.L1Norm(weights_1, weight=100.0), kayak.L2Norm(weights_2, weight=50.0), kayak.L2Norm(weights_out, weight=3.0)) # This is the fun part and is the whole point of Kayak. You can # now get the gradient of anything in terms of anything else. # Probably, if you're doing neural networks, you want the gradient # of the parameters in terms of the overall objective. That way # you can go off and do some kind of optimization. weights_1_grad = objective.grad(weights_1) biases_1_grad = objective.grad(biases_1) weights_2_grad = objective.grad(weights_2) biases_2_grad = objective.grad(biases_2) weights_out_grad = objective.grad(weights_out) biases_out-grad = objective.grad(biases_out) ... use the gradients for learning ... ... probably this whole thing would be in a loop ... ... in practice you'd probably also use minibatches ...