eval_sick.py 文件源码

def train_model(lrmodel, X, Y, devX, devY, devscores):
    """
    Train model, using pearsonr on dev for early stopping
    """
    done = False
    best = -1.0
    r = np.arange(1,6)

    while not done:
        # Every 100 epochs, check Pearson on development set
        lrmodel.fit(X, Y, verbose=2, shuffle=False, validation_data=(devX, devY))
        yhat = np.dot(lrmodel.predict_proba(devX, verbose=2), r)
        score = pearsonr(yhat, devscores)[0]
        if score > best:
            print score
            best = score
            bestlrmodel = copy.deepcopy(lrmodel)
        else:
            done = True

    yhat = np.dot(bestlrmodel.predict_proba(devX, verbose=2), r)
    score = pearsonr(yhat, devscores)[0]
    print 'Dev Pearson: ' + str(score)
    return bestlrmodel