docs = du.load_dataset('data/ner/dev')
X_dev, y_dev = du.docs_to_windows(docs, word_to_num, tag_to_num, wsize=windowsize)
# Load the test set (dummy labels only)
docs = du.load_dataset('data/ner/test.masked')
X_test, y_test = du.docs_to_windows(docs, word_to_num, tag_to_num, wsize=windowsize)
# To avoid re-inventing the wheel, we provide a base class that handles a lot of the drudgery of
# managing parameters and running gradient descent. It's based on the classifier API used by
#[`scikit-learn`](http://scikit-learn.org/stable/), so if you're familiar with that library it should be easy to use.
# We'll be using this class for the rest of this assignment, so it helps to get acquainted with a simple
# example that should be familiar from Assignment 1.
# To keep this notebook uncluttered, we've put the code in the `softmax_example.py`; take a look at it there, then run the cell below.
sr = SoftmaxRegression(wv=zeros((10,100)), dims=(100,5))
# Automatic gradient checker!
# this checks anything you add to self.grads or self.sgrads
# using the method of Assignment 1
sr.grad_check(x=5, y=4)
# In order to implement a model, you need to subclass `NNBase`, then implement the following methods:
#
# - `__init__()` (initialize parameters and hyperparameters)
# - `_acc_grads()` (compute and accumulate gradients)
# - `compute_loss()` (compute loss for a training example)
# - `predict()`, `predict_proba()`, or other prediction method (for evaluation)
#
# `NNBase` provides you with a few others that will be helpful:
# Load the dev set (for tuning hyperparameters)
docs = du.load_dataset('data/ner/dev')
X_dev, y_dev = du.docs_to_windows(docs,
word_to_num,
tag_to_num,
wsize=windowsize)
# Load the test set (dummy labels only)
docs = du.load_dataset('data/ner/test.masked')
X_test, y_test = du.docs_to_windows(docs,
word_to_num,
tag_to_num,
wsize=windowsize)
from softmax_example import SoftmaxRegression
sr = SoftmaxRegression(wv=zeros((10, 100)), dims=(100, 5))
##
# Automatic gradient checker!
# this checks anything you add to self.grads or self.sgrads
# using the method of Assignment 1
sr.grad_check(x=5, y=4)
#from nerwindow import WindowMLP
from nerwindow_msushkov import WindowMLP
clf = WindowMLP(wv,
windowsize=windowsize,
dims=[None, 100, 5],
reg=0.001,
alpha=0.01)
clf.grad_check(X_train[0], y_train[0]) # gradient check on single point
print wv.shape print len(word_to_num) print type(word_to_num) print docs[0][:] print X_train[0] print y_train[0] # To avoid re-inventing the wheel, we provide a base class that handles a lot of the drudgery of managing parameters and running gradient descent. It's based on the classifier API used by [`scikit-learn`](http://scikit-learn.org/stable/), so if you're familiar with that library it should be easy to use. # # We'll be using this class for the rest of this assignment, so it helps to get acquainted with a simple example that should be familiar from Assignment 1. To keep this notebook uncluttered, we've put the code in the `softmax_example.py`; take a look at it there, then run the cell below. # In[71]: from softmax_example import SoftmaxRegression sr = SoftmaxRegression(wv=zeros((10,100)), dims=(100,5)) ## # Automatic gradient checker! # this checks anything you add to self.grads or self.sgrads # using the method of Assignment 1 sr.grad_check(x=5, y=0) # sr._acc_grads(idx=5,label=4) # print sr.params.W.shape # print sr.params.b.shape # print sr.sparams.L.shape # # print sr.sgrads.L[1].shape # sr.compute_loss(idx=5,label=4)
