Пример #1
0
def train():   

    train_loader = DataLoader(dataset=dataset,
                                batch_size=config.batch,
                                shuffle=True,
                                collate_fn=collate_fn,
                                num_workers=0)

    model = SVM(config.embedding, config.strmaxlen, dataset.get_vocab_size(), config.output_size)
    optimizer = optim.SGD(model.parameters(), lr=config.lr)
    model.train()
    for epoch in range(config.epoch):

        sum_loss = 0
        for i, (data, labels) in enumerate(train_loader):


            optimizer.zero_grad()
            output = model(data).squeeze()
            weight = model.weight.squeeze()
            weight = weight.reshape((weight.shape[0],1))
            
            loss = model.loss(output, labels)
            tmp = weight.t() @ weight
            loss += config.c * tmp[0][0] / 2.0

            loss.backward()
            optimizer.step()

            sum_loss += float(loss)

        print("Epoch: {:4d}\tloss: {}".format(epoch, sum_loss /len(dataset)))
Пример #2
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def svm():
    # *********************    load the dataset and divide to X&y   ***********************
    from sklearn.datasets import make_blobs
    X, Y = make_blobs(cluster_std=0.9,
                      random_state=20,
                      n_samples=1000,
                      centers=10,
                      n_features=10)

    from Algorithms.ML_.helper.data_helper import split_train_val_test
    X, Xv, y, Yv, Xt, Yt = split_train_val_test(X, Y)
    print(X.shape, y.shape, Xv.shape, Yv.shape, Xt.shape, Yt.shape)

    # *********************   build model    ***********************
    from model import SVM
    from activation import Activation, Softmax, Hinge
    from regularization import Regularization, L1, L2, L12
    from optimizer import Vanilla
    model = SVM()
    learning_rate, reg_rate = 1e-3, 5e-1
    model.compile(alpha=learning_rate,
                  lambda_=reg_rate,
                  activation=Softmax(),
                  reg=L2(),
                  opt=Vanilla())
    model.describe()
    # *********************    train   ***********************
    loss_train, loss_val = model.train(X,
                                       y,
                                       val=(Xv, Yv),
                                       iter_=1000,
                                       return_loss=True,
                                       verbose=True,
                                       eps=1e-3)
    import matplotlib.pyplot as plt
    plt.plot(range(len(loss_train)), loss_train)
    plt.plot(range(len(loss_val)), loss_val)
    plt.legend(['train', 'val'])
    plt.xlabel('Iteration')
    plt.ylabel('Training loss')
    plt.title('Training Loss history')
    plt.show()
    # *********************    predict   ***********************
    pred_train = model.predict(X)
    pred_val = model.predict(Xv)
    pred_test = model.predict(Xt)

    import metrics

    print('train accuracy=', metrics.accuracy(y, pred_train))
    print('val accuracy=', metrics.accuracy(Yv, pred_val))
    print('test accuracy=', metrics.accuracy(Yt, pred_test))
    print('null accuracy=', metrics.null_accuracy(y))
    import metrics
    metrics.print_metrics(Yt, pred_test)
Пример #3
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def test_svm(train_data,
             test_data,
             kernel_func=linear_kernel,
             lambda_param=.1):
    """
    Create an SVM classifier with a specificied kernel_func, train it with
    train_data and print the accuracy of model on test_data

    :param train_data: a namedtuple including training inputs and training labels
    :param test_data: a namedtuple including test inputs and test labels
    :param kernel_func: kernel function to use in the SVM
    :return: None
    """
    svm_model = SVM(kernel_func=kernel_func, lambda_param=lambda_param)
    svm_model.train(train_data.inputs, train_data.labels)
    train_accuracy = svm_model.accuracy(train_data.inputs, train_data.labels)
    test_accuracy = svm_model.accuracy(test_data.inputs, test_data.labels)
    if not (train_accuracy is None):
        print('Train accuracy: ', round(train_accuracy * 100, 2), '%')
    if not (test_accuracy is None):
        print('Test accuracy:', round(test_accuracy * 100, 2), '%')
Пример #4
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	# load sentiment dictionary
	bag = utils.load_dictionary()

	# load model if exist
	try:
		with open("../Resources/models/model", "rb") as model_file:
			model = pickle.load(model_file)
	except IOError as err:
		# load training reviews from file	
		train_review = utils.load_reviews("../Resources/samples/train_data")
		# get feature from train data
		train_data, train_label = feature_data(tagger, exp, bag, train_review)
		# initalize classifer class
		model = SVM()
		# train model
		model.train(train_data, train_label)
		#save model
		with open("../Resources/models/model", "wb") as model_file:
			pickle.dump(model, model_file)
	else:
		print("use saved model..")
	
	# load test reviews from file
	test_review  = utils.load_reviews("../Resources/samples/test_data")
	# get feature from test data
	test_data, test_label = feature_data(tagger, exp, bag, test_review)
	
	# predict model
	result = model.predict(test_data)

	# evaluate accuracy
Пример #5
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# *********************    train   ***********************
# model = SVM()
# model.compile(lambda_=2.5e4, alpha=1e-7)  # 1e-7, reg=2.5e4,
# loss_history = model.train(X, y, eps=0.001, batch=200, iter_=1500)
#
# plt.plot(range(len(loss_history)), loss_history)
# plt.xlabel('Iteration number')
# plt.ylabel('Loss value')
# plt.show()
# print(loss_history[::100])
# lr, rg = SVM.ff(X, y, Xv, Yv, [1e-7, 1e-6],[2e4, 2.5e4, 3e4, 3.5e4, 4e4, 4.5e4, 5e4, 6e4])
# print(lr, rg)
model = SVM()
model.compile(alpha=1e-7, lambda_=2, activation=Softmax, reg=L2)
# model.compile(alpha=0, lambda_=0, activation=Hinge, Reg=L2, dReg=dL2)
history = model.train(Xd, Yd, iter_=0, eps=0.0001)
print(model.loss(model.X, model.y, add_ones=False),
      np.sum(model.grad(model.X, model.y, False)))
L, dW = model.grad(model.X, model.y, True)
print(L, np.sum(dW))
# print(np.sum(model.W))

# print(np.sum(model.grad(model.X, model.y, loss_=False)))
# print(np.sum(model.grad1(model.X, model.y)))
# L, dW = model.activation.loss_grad_loop(model.X, model.W, model.y)
# print(L, np.sum(dW))

loss_history = model.train(X, y, eps=0.0001, batch=200, iter_=1500)
plt.plot(range(len(loss_history)), loss_history)
plt.xlabel('Iteration number')
plt.ylabel('Loss value')
Пример #6
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    # load sentiment dictionary
    bag = utils.load_dictionary()

    # load model if exist
    try:
        with open("../Resources/models/model", "rb") as model_file:
            model = pickle.load(model_file)
    except IOError as err:
        # load training reviews from file
        train_review = utils.load_reviews("../Resources/samples/train_data")
        # get feature from train data
        train_data, train_label = feature_data(tagger, exp, bag, train_review)
        # initalize classifer class
        model = SVM()
        # train model
        model.train(train_data, train_label)
        #save model
        with open("../Resources/models/model", "wb") as model_file:
            pickle.dump(model, model_file)
    else:
        print("use saved model..")

    # load test reviews from file
    test_review = utils.load_reviews("../Resources/samples/test_data")
    # get feature from test data
    test_data, test_label = feature_data(tagger, exp, bag, test_review)

    # predict model
    result = model.predict(test_data)

    # evaluate accuracy