Пример #1
0
def train(logpath, modeldir, batch_size=256, epochs=100):
    modelpath = modeldir + 'model.h5'
    dictpath = modeldir + 'word_dict.json'
    for filepath in [logpath, modelpath, dictpath]:
        check_validity(filepath)
    check_file(logpath)
    # load data
    train_data = get_train_dataset(logpath)

    # pre-process
    from autoencoder import AutoEncoder  # lazy load

    pre_processor = Preprocessor(filepath=dictpath)
    train_sr, time_sr = pre_processor.pre_process(train_data)
    autoencoder = AutoEncoder(shape=(train_sr.shape[1], train_sr.shape[2]),
                              filepath=modelpath)
    cluster_model = Cluster(dirpath=modeldir)

    # train
    autoencoder.fit(train_sr, batch_size=batch_size, epochs=epochs)
    train_vector = autoencoder.transfer(train_sr)
    predict_result, cluster_number, dist_tbl = cluster_model.classify(
        train_vector)
    top_index = get_topn_sql(dist_tbl, topn=1)
    topn_sql = train_data[
        top_index][:, -1]  # typical SQL template for each cluster
    cluster_model.get_cluster_info(predict_result, time_sr, cluster_number)
    print("Train complete!")
    return cluster_number, topn_sql
Пример #2
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def test_mnist():
    # Gradient check using MNIST
    (train_x, _), (_, _) = mnist.load_data()
    train_x = train_x / 255  # Normalizing images
    # plotter.plot_mnist(train_x, "original")                           # Show original mnist images

    num_img, img_dim, _ = train_x.shape  # Get number of images and # pixels per square img
    num_features = 500
    mnist_in = np.reshape(
        train_x, (img_dim * img_dim,
                  num_img))  # Reshape images to match autoencoder input
    ga = Algorithm(x=mnist_in, num_features=num_features, debug=1, pop_size=20)
    w_out, best_cost, logs = ga.run()

    print(
        f"Average time/generation (sec): {sum(logs['times']) / len(logs['times'])}"
    )
    print(f"Total time to run GA (sec): {logs['times']}")

    ae = AutoEncoder(mnist_in, num_features, random_seed=1234, use_gpu=True)
    z, _ = ae.psi(w_out)
    phi_w_img = ae.phi(w_out)  # Calculate phi(W)
    new_mnist = z @ phi_w_img  # Recreate original images using Z and phi(W)
    new_imgs = np.reshape(
        new_mnist, train_x.shape)  # Reshape new images have original shape
    plotter.plot_mnist(new_imgs,
                       f"{num_features}_features_ga")  # Show new images

    # print(loss_values)
    plotter.plot_loss(logs['min'], "MNIST_Gradient_Loss_Over_Generations")
Пример #3
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def train_ae(encoder_sizes,
             decoder_sizes,
             train_loader,
             num_epochs=1,
             bw=False):
    """
    Pre-train Autoencoder on condition sketches
    :return trained encoder nn.Module
    """
    AE = AutoEncoder(encoder_sizes, decoder_sizes, bw=bw).to(device)
    optimizer = torch.optim.Adam(AE.parameters(), lr=0.0005, weight_decay=3e-6)
    for epoch in tqdm(range(num_epochs), "Encoder pretraining"):
        epoch_loss = 0
        for batch, (sketch, real,
                    label) in enumerate(tqdm(dataloader_train, "Batch")):
            optimizer.zero_grad()
            sketch, real, label = sketch.to(device), real.to(device), label.to(
                device)
            recon = AE(sketch)
            loss = torch.mean((sketch - recon)**2)
            loss.backward()
            epoch_loss += loss.item() / len(dataloader_train)
            optimizer.step()
        print("AutoEncoder pretraining: Epoch {} Loss: {}".format(
            epoch, epoch_loss))
    del AE.decoder
    optimizer.zero_grad()
    del optimizer
    return AE.encoder
Пример #4
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    def build_interaction_and_encoded_attr_mat(
            self, interaction_and_attribute_info, attr_encoded_dim,
            interaction_and_encoded_attr_mat_info_name):

        print("start to build interaction and encode attribute matrix .... ")

        attrs_vec = interaction_and_attribute_info["attrs_vec"]
        attr_dim = len(attrs_vec[0])
        print("encode attr embedding from {} to {}".format(
            attr_dim, attr_encoded_dim))

        print(attrs_vec.shape)
        autoEncoder = AutoEncoder(attrs_vec, target_dim=attr_encoded_dim)
        autoEncoder.train()

        print("end to build interaction and encode attribute matrix .... ")

        self.encoded_attr_vec = np.asarray(autoEncoder.get_coded_val())
        print("encoded_attr_vec : {}".format(self.encoded_attr_vec.shape))
        print(self.encoded_attr_vec)

        interaction_and_attribute_info["attrs_vec"] = self.encoded_attr_vec

        cache_var = CacheVar()
        cache_var.build(interaction_and_encoded_attr_mat_info_name,
                        interaction_and_attribute_info)
def test_random():
    # Sanity test to make sure that feature number positively impacts least squares error.
    num_points = 100
    num_data_per_point = 55
    learning_rate = 0.5
    x_in = np.random.normal(size=(num_data_per_point, num_points))
    for num_features in [1, 5, 10, 15, 20, 40, 70]:
        ae = AutoEncoder(x_in, num_features, random_seed=1234)
        w_in = np.random.normal(size=(num_data_per_point, num_features))
        z_out, least_squares_test = ae.psi(w_in)
        print(
            f"(# features : Least squares error = ({num_features} : {least_squares_test})"
        )
        print("Starting gradient decent...")
        loss_values = []  # Keep track of loss values over epochs
        for epoch in range(1000):
            z_grd, ls_grd, grd = ae.calc_g(
                w_in)  # Calculate Z, Error, and Gradient Matrix
            w_in = w_in - (learning_rate * grd
                           )  # Update W using Gradient Matrix
            loss_values.append(ls_grd)  # Log loss
            print(f"Epoch: {epoch}\t----------\tLoss: {ls_grd}")

        # print(loss_values)
        plotter.plot_loss(
            loss_values,
            f"Gradient Loss Over Epochs (test) (num_features: {num_features})")
Пример #6
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 def __init__(self):
     self.edft = pd.read_csv(path + '/ML/data/pero_dft.csv', sep='\t')
     self.comps_wdup = pd.read_csv(
         path + '/ICSD_data/ICSD_all_data_with_all_phases.csv',
         sep='\t')  # compositions with duplicates for phase prediction
     self.ae = AutoEncoder()
     self.VAE = self.ae.build_AE(vae=True)
     self.VAE.load_weights(path + '/saved_models/best_model_VAE.h5')
     self.scaler = StandardScaler()
Пример #7
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    def sgd_da(corruption_level):
        # Initialize RNGs
        rng = numpy.random.RandomState(123)
        theano_rng = RandomStreams(rng.randint(2 ** 30))

        # Build the logistic regression class
        # Images in MNIST are 28*28, there are 10 output classes
        da = AutoEncoder(
            numpy_rng=rng,
            theano_rng=theano_rng,
            input=x,
            n_visible=28 * 28,
            n_hidden=500
        )

        # Cost to minimize
        cost = da.loss(corruption_level=corruption_level)

        # Stochastic Gradient descent
        updates = simple_sgd(cost, da.params, learning_rate)

        train_da = theano.function(
            inputs=[index],
            outputs=cost,
            updates=updates,
            givens=[
                (x, train_set_x[index * batch_size: (index + 1) * batch_size]),
            ]
        )

        ################
        # TRAIN MODEL  #
        ################
        start_time = timeit.default_timer()
        print("... Training the model")
        for epoch in range(n_epochs):
            c = []
            for batch_index in range(n_train_batches):
                c.append(train_da(batch_index))

            print('Training epoch {0}, cost {1}'.format(epoch, numpy.mean(c)))

        end_time = timeit.default_timer()
        training_time = (end_time - start_time)

        print(('The {0} corruption code for file ' +
              os.path.split(__file__)[1] +
              ' ran for {1:.2f}m').format(corruption_level * 100,
                                          (training_time) / 60.))

        image = Image.fromarray(
            tile_raster_images(X=da.W.get_value(borrow=True).T,
                               img_shape=(28, 28), tile_shape=(10, 10),
                               tile_spacing=(1, 1))
        )
        image.save('filters_corrpution_' + str(int(corruption_level * 100))
                   + '.png')
Пример #8
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def main(options):

    if options.num_classes == 2:
        TRAINING_PATH = 'train_2classes.txt'
    else:
        TRAINING_PATH = 'train.txt'
    IMG_PATH = '/Users/waz/JHU/CV-ADNI/ImageNoSkull'

    dset_train = AD_3DRandomPatch(IMG_PATH, TRAINING_PATH)

    train_loader = DataLoader(dset_train,
                              batch_size=options.batch_size,
                              shuffle=True,
                              num_workers=4,
                              drop_last=True)

    sparsity = 0.05
    beta = 0.5

    mean_square_loss = nn.MSELoss()
    kl_div_loss = nn.KLDivLoss()

    use_gpu = len(options.gpuid) >= 1
    autoencoder = AutoEncoder()

    autoencoder = autoencoder.cpu()

    optimizer = torch.optim.Adam(autoencoder.parameters(),
                                 lr=options.learning_rate,
                                 weight_decay=options.weight_decay)

    train_loss = 0.
    for epoch in range(options.epochs):
        print("At {0}-th epoch.".format(epoch))
        for i, patches in enumerate(train_loader):
            for b, batch in enumerate(patches):
                batch = Variable(batch)
                output, mean_activitaion = autoencoder(batch)
                loss1 = mean_square_loss(output, batch)
                loss2 = kl_div_loss(mean_activitaion,
                                    Variable(torch.Tensor([sparsity])))
                print("loss1", loss1)
                print("loss2", loss2)
                loss = loss1 + loss2
                train_loss += loss
                logging.info(
                    "batch {0} training loss is : {1:.5f}, {1:.5f}".format(
                        b, loss1.data[0], loss2.data[0]))
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
        train_avg_loss = train_loss / len(train_loader * 1000)
        print(
            "Average training loss is {0:.5f} at the end of epoch {1}".format(
                train_avg_loss.data[0], epoch))
    torch.save(model.state_dict(), open("autoencoder_model", 'wb'))
Пример #9
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    def sgd_da(corruption_level):
        # Initialize RNGs
        rng = numpy.random.RandomState(123)
        theano_rng = RandomStreams(rng.randint(2**30))

        # Build the logistic regression class
        # Images in MNIST are 28*28, there are 10 output classes
        da = AutoEncoder(numpy_rng=rng,
                         theano_rng=theano_rng,
                         input=x,
                         n_visible=28 * 28,
                         n_hidden=500)

        # Cost to minimize
        cost = da.loss(corruption_level=corruption_level)

        # Stochastic Gradient descent
        updates = simple_sgd(cost, da.params, learning_rate)

        train_da = theano.function(
            inputs=[index],
            outputs=cost,
            updates=updates,
            givens=[
                (x, train_set_x[index * batch_size:(index + 1) * batch_size]),
            ])

        ################
        # TRAIN MODEL  #
        ################
        start_time = timeit.default_timer()
        print("... Training the model")
        for epoch in range(n_epochs):
            c = []
            for batch_index in range(n_train_batches):
                c.append(train_da(batch_index))

            print('Training epoch {0}, cost {1}'.format(epoch, numpy.mean(c)))

        end_time = timeit.default_timer()
        training_time = (end_time - start_time)

        print(
            ('The {0} corruption code for file ' + os.path.split(__file__)[1] +
             ' ran for {1:.2f}m').format(corruption_level * 100,
                                         (training_time) / 60.))

        image = Image.fromarray(
            tile_raster_images(X=da.W.get_value(borrow=True).T,
                               img_shape=(28, 28),
                               tile_shape=(10, 10),
                               tile_spacing=(1, 1)))
        image.save('filters_corrpution_' + str(int(corruption_level * 100)) +
                   '.png')
def test_mnist(num_epochs=None):
    # Gradient check using MNIST
    (train_x, _), (_, _) = mnist.load_data()
    train_x = train_x / 255  # Normalizing images
    # plotter.plot_mnist(train_x, "original")                           # Show original mnist images

    num_img, img_dim, _ = train_x.shape  # Get number of images and # pixels per square img
    learning_rate = 0.5
    num_features = 200
    loss_values = []  # Keep track of loss values over epochs
    loss_values_less = []
    loss_diffs = []

    w_in = np.random.normal(
        size=(img_dim * img_dim,
              num_features))  # Generate random W matrix to test
    mnist_in = np.reshape(
        train_x, (img_dim * img_dim,
                  num_img))  # Reshape images to match autoencoder input
    ae = AutoEncoder(mnist_in, num_features, random_seed=1234, use_gpu=True)
    start_time = time.time()
    times = []
    if num_epochs:
        for epoch in range(num_epochs):
            w_in, z_grd = do_epoch(ae, w_in, learning_rate, loss_values, times,
                                   loss_values_less, loss_diffs, epoch,
                                   start_time)
    else:
        epoch_history_check = 5
        epoch = 0
        loss_avg = 1000
        tol = 0.03
        while loss_avg > tol:
            w_in, z_grd = do_epoch(ae, w_in, learning_rate, loss_values, times,
                                   loss_values_less, loss_diffs, epoch,
                                   start_time)
            loss_check = loss_diffs[-epoch_history_check:]
            loss_avg = sum(loss_check) / len(loss_check)
            epoch += 1

    print(
        f"Total time to run gradient decent (sec): {time.time() - start_time}")
    phi_w_img = ae.phi(w_in)  # Calculate phi(W)
    new_mnist = z_grd @ phi_w_img  # Recreate original images using Z and phi(W)
    new_imgs = np.reshape(
        new_mnist, train_x.shape)  # Reshape new images have original shape
    plotter.plot_mnist(new_imgs,
                       f"{num_features}_features_gradient")  # Show new images

    # print(loss_values)
    plotter.plot_loss(loss_values, "MNIST_Gradient_Loss_Over_Epochs")
    plotter.plot_loss(
        loss_values_less,
        "MNIST_Gradient_Loss_Over_Epochs_all_epochs_except_zero")
Пример #11
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def test_cifar10(num_epochs=None):
    # (train_x, _), (_, _) = cifar10.load_data()
    (_, _), (train_x, _) = cifar10.load_data()
    print(train_x.shape)
    plotter.plot_mnist(train_x, "original")
    train_x = rgb2gray(train_x)
    train_x = train_x / 255
    plotter.plot_mnist(train_x, "grayscale")
    num_img, img_h, img_w = train_x.shape
    print(train_x.shape)
    learning_rate = 0.5
    num_features = 768;
    loss_values = []
    loss_values_less = []
    loss_diffs = []

    w_in = np.random.normal(size=(img_h * img_w, num_features))
    cifar_in = np.reshape(train_x, (img_h * img_w, num_img))
    # cifar_in = np.reshape(train_x, (img_h, img_w, num_img*img_ch))
    print(cifar_in.shape)

    ae = AutoEncoder(cifar_in, num_features, random_seed=1234, use_gpu=True)
    start_time = time.time()
    times = []
    if num_epochs:
        for epoch in range(num_epochs):
            w_in, z_grd = do_epoch(ae, w_in, learning_rate, loss_values, times, loss_values_less, loss_diffs, epoch,
                                   start_time)
    else:
        epoch_history_check = 5
        epoch = 0
        loss_avg = 1000
        tol = 0.03
        while loss_avg > tol:
            w_in, z_grd = do_epoch(ae, w_in, learning_rate, loss_values, times, loss_values_less, loss_diffs, epoch,
                                   start_time)
            loss_check = loss_diffs[-epoch_history_check:]
            loss_avg = sum(loss_check) / len(loss_check)
            epoch += 1

    print(f"Total time to run gradient decent (sec): {time.time() - start_time}")
    phi_w_img = ae.phi(w_in)  # Calculate phi(W)
    new_cifar = z_grd @ phi_w_img  # Recreate original images using Z and phi(W)
    print(new_cifar.shape)
    new_imgs = np.reshape(new_cifar, train_x.shape)  # Reshape new images have original shape
    plotter.plot_mnist(new_imgs, f"{num_features}_features_gradient")  # Show new images

    # print(loss_values)
    plotter.plot_loss(loss_values, "CIFAR10_Gradient_Loss_Over_Epochs")
    plotter.plot_loss(loss_values_less, "CIFAR10_Gradient_Loss_Over_Epochs_all_epochs_except_zero")
    # return train_x
    return new_imgs
Пример #12
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    def __init__(self, name, configuration, graph=None):
        c = configuration
        self.configuration = c

        AutoEncoder.__init__(self, name, graph, configuration)

        with tf.variable_scope(name):
            self.z = c.encoder(self.x, **c.encoder_args)
            self.vz = c.embedder(self.vx, **c.embedder_args)

            self.bottleneck_size = int(self.z.get_shape()[1])
            layer = c.decoder(self.z, **c.decoder_args)
            c.decoder_args['reuse'] = True
            vlayer = c.decoder(self.vz, **c.decoder_args)
            c.decoder_args['reuse'] = False

            if c.exists_and_is_not_none('close_with_tanh'):
                layer = tf.nn.tanh(layer)
                vlayer = tf.nn.tanh(vlayer)

            self.x_reconstr = tf.reshape(
                layer, [-1, self.n_output[0], self.n_output[1]])
            self.vx_reconstr = tf.reshape(
                vlayer, [-1, self.n_output[0], self.n_output[1]])

            self.saver = tf.train.Saver(tf.global_variables(),
                                        max_to_keep=c.saver_max_to_keep)

            self._create_loss()
            self._setup_optimizer()

            # GPU configuration
            if hasattr(c, 'allow_gpu_growth'):
                growth = c.allow_gpu_growth
            else:
                growth = True

            config = tf.ConfigProto()
            config.gpu_options.allow_growth = growth

            # Summaries
            self.merged_summaries = tf.summary.merge_all()
            self.train_writer = tf.summary.FileWriter(
                osp.join(configuration.train_dir, 'summaries'), self.graph)

            # Initializing the tensor flow variables
            self.init = tf.global_variables_initializer()

            # Launch the session
            self.sess = tf.Session(config=config)
            self.sess.run(self.init)
def train_ae(dataloaders_dict, device=0, num_epochs=5):

    ae = AutoEncoder()
    ae = ae.to(device)
    distance = nn.MSELoss()
    optimizer = optim.Adam(ae.parameters(), lr=0.001)

    for epoch in range(num_epochs):
        print('\nEpoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)
        for phase in ["train", "val", "test"]:

            if phase == "train":
                ae.train()  # Set ae to training mode
            else:
                ae.eval()

            for data in dataloaders_dict[phase]:
                inputs, _ = data
                inputs = inputs.to(device)
                with torch.set_grad_enabled(phase == "train"):
                    output = ae(inputs)
                    loss = distance(output, inputs)
                    optimizer.zero_grad()
                    if phase == "train":
                        loss.backward()
                        optimizer.step()
            print("{} Loss: {:.4f}".format(phase, loss.item()))
    return ae
Пример #14
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    def _load_aes(self, path):
        autoencoders = [
            AutoEncoder(30, False, False, 0.001, name)
            for name in string.ascii_uppercase[:3]
        ]
        baseline = AutoEncoder(30, False, False, 0.001, "baseline")

        all_agents: List[AutoEncoder] = autoencoders + [baseline]
        [
            agent.load_state_dict(
                torch.load(f"{path}/{agent.name}.pt", map_location=self.dev)
            )
            for agent in all_agents
        ]
        return all_agents
Пример #15
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    def train_classifier(self, agent: AutoEncoder):
        mlp = MLP(30).to(self.dev)
        agent.to(self.dev)

        for i in range(int(self.cfg.nsteps)):
            X, y = map(
                lambda x: x.to(self.dev),
                self.dataset.sample_with_label(int(self.cfg.bsize)),
            )
            latent = agent.encode(X)
            mlp.train(latent, y)
            acc = mlp.compute_acc(latent, y)
            self.tb.add_scalar("Accuracy-Post", acc, global_step=i)
            # self.writer.add((acc.item(), agent.name), step=i)
        return mlp
Пример #16
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def run_model(data, in_memory=False):
    """
    Runs the autoencoder model.

    @param data is
        if in_memory == True:
            ([[size, incoming]], [webpage_label])
        else:
            A list of paths
    """
    tf.reset_default_graph()
    tf.set_random_seed(123)

    # Only print small part of array
    np.set_printoptions(threshold=10)

    with tf.Session() as session:

        model = AutoEncoder(args.layers,
                            args.batch_size,
                            activation_func=args.activation_func,
                            learning_rate=args.learning_rate,
                            batch_norm=args.batch_norm)

        session.run(tf.global_variables_initializer())

        loss_track = train_on_copy_task(session,
                                        model,
                                        data,
                                        batch_size=args.batch_size,
                                        batches_in_epoch=100,
                                        verbose=False)
Пример #17
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    def __init__(self, args):
        super(DCN, self).__init__()
        self.args = args
        self.beta = args.beta  # coefficient of the clustering term
        self.lamda = args.lamda  # coefficient of the reconstruction term
        self.device = torch.device('cuda' if args.cuda else 'cpu')

        # Validation check
        if not self.beta > 0:
            msg = 'beta should be greater than 0 but got value = {}.'
            raise ValueError(msg.format(self.beta))

        if not self.lamda > 0:
            msg = 'lambda should be greater than 0 but got value = {}.'
            raise ValueError(msg.format(self.lamda))

        if len(self.args.hidden_dims) == 0:
            raise ValueError('No hidden layer specified.')

        self.kmeans = batch_KMeans(args)
        self.autoencoder = AutoEncoder(args).to(self.device)

        self.criterion = nn.MSELoss()
        self.optimizer = torch.optim.Adam(self.parameters(),
                                          lr=args.lr,
                                          weight_decay=args.wd)
Пример #18
0
def test_gradient():
    num_points = 30  # N
    num_data_per_point = 20  # n
    num_features = 12  # m
    const = np.random.normal(size=(num_data_per_point, num_points))  # X
    x = np.random.normal(size=(num_data_per_point, num_features))  # W
    dx = x * 1e-3
    ae = AutoEncoder(const, num_features, random_seed=1234)

    def f(input):
        return ae.psi(input)[1]

    def df(input):
        return ae.calc_g(input)[2]  # G

    # Test 1: Check norm(dx)
    check1 = f(x + dx)
    check2 = f(x - dx)
    check3 = 2 * np.tensordot(df(x), dx, axes=2)
    differror = np.linalg.norm(check1 - check2 - check3) / np.linalg.norm(dx)
    print("Test 1 of gradient check: differror should be smaller than, or close to, norm(dx):")
    print(f"differror: {differror}")
    print(f"norm(dx): {np.linalg.norm(dx)}")
    print()

    # Test 2: drop dx by factor of 10 and see if differror drops by 10-100
    new_dx = dx * 0.1
    check1 = f(x + new_dx)
    check2 = f(x - new_dx)
    check3 = 2 * np.tensordot(df(x), new_dx, axes=2)
    new_differror = np.linalg.norm(check1 - check2 - check3) / np.linalg.norm(new_dx)
    print("Test 2 of gradient check: new_differror should be 10-100 factors smaller than differror:")
    print(f"new_differror: {new_differror}")
    print(f"differror: {differror}")
Пример #19
0
def main():
    """Load data, build autoencoder, train, and log data."""
    train_loader, test_loader = get_data()
    autoencoder = AutoEncoder(train_loader, latent_dim=2)
    train_autoencoder_and_log(
        autoencoder=autoencoder,
        train_loader=train_loader,
        test_loader=test_loader,
        )
Пример #20
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def test_autoencoder():
    """
    Test that all components of the auto-encoder work correctly by executing a
    training run against generated data.
    """

    input_shape = (3, )
    epochs = 1000

    # Generate some data
    x_train = np.random.rand(100, 3)
    x_test = np.random.rand(30, 3)

    # Define encoder and decoder model
    def create_encoder_model(input_shape):
        model_input = Input(shape=input_shape)

        encoder = Dense(4)(model_input)
        encoder = BatchNormalization()(encoder)
        encoder = Activation(activation='relu')(encoder)

        return Model(model_input, encoder)

    def create_decoder_model(embedding_shape):
        embedding_a = Input(shape=embedding_shape)

        decoder = Dense(3)(embedding_a)
        decoder = BatchNormalization()(decoder)
        decoder = Activation(activation='relu')(decoder)

        return Model(embedding_a, decoder)

    # Create auto-encoder network
    encoder_model = create_encoder_model(input_shape)
    decoder_model = create_decoder_model(encoder_model.output_shape)
    autoencoder = AutoEncoder(encoder_model, decoder_model)

    # Prepare auto-encoder for training
    autoencoder.compile(loss='binary_crossentropy', optimizer='adam')

    # Evaluate network before training to establish a baseline
    score_before = autoencoder.evaluate(x_train, x_train)

    # Train network
    autoencoder.fit(x_train,
                    x_train,
                    validation_data=(x_test, x_test),
                    epochs=epochs)

    # Evaluate network
    score_after = autoencoder.evaluate(x_train, x_train)

    # Ensure that the training loss score improved as a result of the training
    assert (score_before > score_after)
def plot_img_reconstructions(
    root_path: str,
    name_of_best_exp: str,
    path_to_plot: str,
    baseline: bool = False,
    epoch: int = 49999,
):
    dataset = MNISTDataset()
    ae = AutoEncoder(30, False, False, 0.001, "test")
    ae.load_state_dict(
        torch.load(
            os.path.join(
                root_path,
                name_of_best_exp,
                f"params/step_{epoch}/rank_0/{'A' if not baseline else 'baseline'}.pt",
            ),
            map_location=torch.device("cpu"),
        )
    )
    digits: torch.Tensor = dataset.sample(50)

    _, axes = plt.subplots(
        nrows=10,
        ncols=10,
        figsize=(10, 8),
        gridspec_kw=dict(
            wspace=0.0, hspace=0.0, top=0.95, bottom=0.05, left=0.17, right=0.845
        ),
    )
    axes = axes.reshape(50, 2)

    for digit, ax_column in zip(digits, axes):
        ax_column[0].imshow(digit.squeeze().detach())
        ax_column[0].set_axis_off()
        rec = ae(digit.reshape(1, 1, 28, 28))
        ax_column[1].imshow(rec.squeeze().detach())
        ax_column[1].set_axis_off()
    plt.show()
    exit(1)
    plt_path = f"plots/{path_to_plot}/reconstructions_baseline_{baseline}"
    plt.savefig(plt_path + ".pdf")
    plt.savefig(plt_path + ".svg")
    plt.close()
Пример #22
0
def work(in_files, out_files, device_id, total_device, device_idx):
    """
        Stylized the images
        This function supports multi-GPU transformation

        Arg:    in_files        - The path list of input images
                out_files       - The path list of output images
                device_id       - The name of device which is following the rule that tensorflow makes
                total_device    - The total number of devices
                devices_idx     - The index of the current device
    """
    global adopt_revision

    with tf.Graph().as_default():
        tf_config = tf.ConfigProto(allow_soft_placement=True)
        tf_config.gpu_options.allow_growth = True

        # Construct graph
        img_ph = tf.placeholder(tf.float32, shape=image_shape)
        if adopt_revision == True:
            logits = SmallAutoEncoder(img_ph)
        else:
            logits = AutoEncoder(img_ph)

        # Run
        with tf.Session(config=tf_config) as sess:
            with tf.device(device_id):  # Adopt multi-GPU to transfer the image
                sess.run(tf.global_variables_initializer())
                saver = tf.train.Saver()
                saver.restore(sess, model_path + model_name)

                if total_device <= 1:
                    start = 0
                    end = int(len(in_files) // 1)
                else:
                    start = device_idx * int(len(in_files) // total_device)
                    end = device_idx * int(
                        len(in_files) // total_device) + int(
                            len(in_files) // total_device)
                conduct_time = time.time()
                for i in range(start, end, 1):
                    # print("progress: ", i, ' / ', end - start, '\t proc: ', device_idx)
                    img_batch = np.ndarray(image_shape)
                    for j, img_path in enumerate(in_files[i:i + 1]):
                        img = get_img(img_path)
                        img_batch[j] = img
                    _style_result = sess.run(
                        [
                            logits,
                        ], feed_dict={img_ph: img_batch / 255.0})
                    for j, img_path in enumerate(out_files[i:i + 1]):
                        save_img(img_path, _style_result[0][j])
                conduct_time = time.time() - conduct_time
                print("Conduct time: ", conduct_time)
Пример #23
0
def main():
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = AutoEncoder(channels_list, latent_dim).to(device)
    optimizer = optim.Adam(model.parameters(), lr=lr)
    train_loader = torch.utils.data.DataLoader(datasets.MNIST(
        './data', train=True, download=True, transform=transforms.ToTensor()),
                                               batch_size=batch_size,
                                               shuffle=True)
    test_loader = torch.utils.data.DataLoader(datasets.MNIST(
        './data', train=False, transform=transforms.ToTensor()),
                                              batch_size=batch_size,
                                              shuffle=True)
    runner = AERunner(model, optimizer, train_loader, test_loader, device)
    for epoch in range(1, epochs + 1):
        runner.train(epoch)
        runner.test(epoch)
        with torch.no_grad():
            sample = model.sample(80, device)
            save_image(
                sample, './results_ae_' + str(latent_dim) + '/sample_' +
                str(epoch) + '.png')
Пример #24
0
def main(options):

    if options.num_classes == 2:
        TRAINING_PATH = 'train_2classes.txt'
    else:
        TRAINING_PATH = 'train.txt'
    IMG_PATH = './Image'

    dset_train = AD_3DRandomPatch(IMG_PATH, TRAINING_PATH)

    train_loader = DataLoader(dset_train,
                              batch_size=options.batch_size,
                              shuffle=True,
                              num_workers=4,
                              drop_last=True)

    sparsity = 0.05
    beta = 0.5

    mean_square_loss = nn.MSELoss()
    kl_div_loss = nn.KLDivLoss(reduce=False)

    use_gpu = len(options.gpuid) >= 1
    autoencoder = AutoEncoder()

    if (use_gpu):
        autoencoder = autoencoder.cuda()
    else:
        autoencoder = autoencoder.cpu()

    optimizer = torch.optim.Adam(autoencoder.parameters(),
                                 lr=options.learning_rate,
                                 weight_decay=options.weight_decay)

    train_loss = 0.
    for epoch in range(options.epochs):
        print("At {0}-th epoch.".format(epoch))
        for i, patches in enumerate(train_loader):
            print(i)
            print(len(patches))
Пример #25
0
def run_model(data, in_memory=False):
    """
    Runs a autoencoder model.

    @param data is
        if in_memory == True:
            ([[size, incoming]], [webpage_label])
        else:
            A list of paths
    """
    tf.reset_default_graph()
    tf.set_random_seed(123)

    # Only print small part of array
    np.set_printoptions(threshold=10)

    with tf.Session() as session:

        # with bidirectional encoder, decoder state size should be
        # 2x encoder state size
        model = AutoEncoder(args.layers,
                            args.batch_size,
                            activation_func=args.activation_func,
                            learning_rate=args.learning_rate,
                            saved_graph=args.graph_file,
                            sess=session,
                            batch_norm=args.batch_norm)

        model.set_is_training(False)

        # session.run(tf.global_variables_initializer())

        get_vector_representations(session,
                                   model,
                                   data,
                                   DATA_DIR + '/../ae_cells',
                                   batch_size=args.batch_size,
                                   max_batches=None,
                                   batches_in_epoch=100,
                                   extension=args.extension)
Пример #26
0
    def test_get_variables(self):
        with self.test_session() as sess:
            ae_shape = [10, 20, 30, 2]
            ae = AutoEncoder(ae_shape, sess)

            with self.assertRaises(AssertionError):
                ae.get_variables_to_init(0)
            with self.assertRaises(AssertionError):
                ae.get_variables_to_init(4)

            v1 = ae.get_variables_to_init(1)
            self.assertEqual(len(v1), 3)

            v2 = ae.get_variables_to_init(2)
            self.assertEqual(len(v2), 5)

            v3 = ae.get_variables_to_init(3)
            self.assertEqual(len(v3), 2)
    def __init__(self, numpy_rng=None, input=None, n_visible=8, n_hidden=4,
            corrupt_level=0.0,
            W=None, bhid=None, bvis=None, theano_rng=None,
            sparsity=0.05, beta=0.001):

        AutoEncoder.__init__(self, 
            numpy_rng=numpy_rng,
            input = input, 
            n_visible = n_visible,
            n_hidden = n_hidden,
            sparsity = sparsity,
            beta = beta,
            W = W,
            bhid = bhid,
            bvis = bvis
            )

        if not theano_rng:
            theano_rng = RandomStreams(self.numpy_rng.randint(2 ** 3))
        self.theano_rng = theano_rng

        self.corrupt_level = corrupt_level
Пример #28
0
def predict(querypath, modeldir, ratio):
    modelpath = modeldir + 'model.h5'
    dictpath = modeldir + 'word_dict.json'
    clusterpath = modeldir + 'Kmeans_model.pkl'
    infopath = modeldir + 'cluster_info.json'
    for filepath in [querypath, modelpath, dictpath, clusterpath, infopath]:
        check_validity(filepath)
        check_file(filepath)
    # load data
    predict_data = get_test_dataset(querypath)

    # predict
    from autoencoder import AutoEncoder  # lazy load

    pre_processor = Preprocessor(alpha=ratio, filepath=dictpath)
    predict_sr = pre_processor.transform(predict_data)
    autoencoder = AutoEncoder(shape=(predict_sr.shape[1], predict_sr.shape[2]),
                              filepath=modelpath)
    cluster_model = Cluster(dirpath=modeldir)
    predict_vector = autoencoder.transfer(predict_sr)
    result = cluster_model.predict(predict_vector)
    print('Predict result is: ')
    print(result)
    return result
Пример #29
0
def _load_aes(path):
    autoencoders = [
        AutoEncoder(30, bnorm=False, affine=False, name=name, lr=0.001)
        for name in string.ascii_uppercase[:2]
    ]
    baseline1 = AutoEncoder(30,
                            bnorm=False,
                            affine=False,
                            name="Base1",
                            lr=0.001)
    baseline2 = AutoEncoder(30,
                            bnorm=False,
                            affine=False,
                            name="Base2",
                            lr=0.001)

    all_agents: List[AutoEncoder] = autoencoders + [baseline1, baseline2]
    [
        agent.load_state_dict(
            torch.load(f"{path}/{agent.name}.pt",
                       map_location=torch.device("cpu")))
        for agent in all_agents
    ]
    return all_agents
Пример #30
0
    def _load_aes(self, path):
        autoencoders = [
            AutoEncoder(30, False, False, 0.001, name)
            for name in string.ascii_uppercase[:3]
        ]
        baseline1 = AutoEncoder(30, False, False, 0.001,
                                "baseline1").to(self.dev)
        baseline2 = AutoEncoder(30, False, False, 0.001,
                                "baseline2").to(self.dev)
        baselines = [baseline1, baseline2]

        for agent in autoencoders:
            agent.load_state_dict(
                torch.load(
                    f"{path}/rank_{int(self.rank) % 3}/{agent.name}.pt",
                    map_location=self.dev,
                ))
        for i, agent in enumerate(baselines):
            agent.load_state_dict(
                torch.load(
                    f"{path}/rank_{(int(self.rank) + i) % 3}/{'baseline'}.pt",
                    map_location=self.dev,
                ))
        return autoencoders + baselines
Пример #31
0
    def test_nets(self):
        with self.test_session() as sess:
            ae_shape = [10, 20, 30, 2]
            ae = AutoEncoder(ae_shape, sess)

            input_pl = tf.placeholder(tf.float32, shape=(100, 10))
            with self.assertRaises(AssertionError):
                ae.pretrain_net(input_pl, 0)
            with self.assertRaises(AssertionError):
                ae.pretrain_net(input_pl, 3)

            net1 = ae.pretrain_net(input_pl, 1)
            net2 = ae.pretrain_net(input_pl, 2)

            self.assertEqual(net1.get_shape().dims[1].value, 10)
            self.assertEqual(net2.get_shape().dims[1].value, 20)

            net1_target = ae.pretrain_net(input_pl, 1, is_target=True)
            self.assertEqual(net1_target.get_shape().dims[1].value, 10)
            net2_target = ae.pretrain_net(input_pl, 2, is_target=True)
            self.assertEqual(net2_target.get_shape().dims[1].value, 20)

            sup_net = ae.supervised_net(input_pl)
            self.assertEqual(sup_net.get_shape().dims[1].value, 2)
  def test_get_variables(self):
    with self.test_session() as sess:
      ae_shape = [10, 20, 30, 2]
      ae = AutoEncoder(ae_shape, sess)

      with self.assertRaises(AssertionError):
        ae.get_variables_to_init(0)
      with self.assertRaises(AssertionError):
        ae.get_variables_to_init(4)

      v1 = ae.get_variables_to_init(1)
      self.assertEqual(len(v1), 3)

      v2 = ae.get_variables_to_init(2)
      self.assertEqual(len(v2), 5)

      v3 = ae.get_variables_to_init(3)
      self.assertEqual(len(v3), 2)
  def test_nets(self):
    with self.test_session() as sess:
      ae_shape = [10, 20, 30, 2]
      ae = AutoEncoder(ae_shape, sess)

      input_pl = tf.placeholder(tf.float32, shape=(100, 10))
      with self.assertRaises(AssertionError):
        ae.pretrain_net(input_pl, 0)
      with self.assertRaises(AssertionError):
        ae.pretrain_net(input_pl, 3)

      net1 = ae.pretrain_net(input_pl, 1)
      net2 = ae.pretrain_net(input_pl, 2)

      self.assertEqual(net1.get_shape().dims[1].value, 10)
      self.assertEqual(net2.get_shape().dims[1].value, 20)

      net1_target = ae.pretrain_net(input_pl, 1, is_target=True)
      self.assertEqual(net1_target.get_shape().dims[1].value, 10)
      net2_target = ae.pretrain_net(input_pl, 2, is_target=True)
      self.assertEqual(net2_target.get_shape().dims[1].value, 20)

      sup_net = ae.supervised_net(input_pl)
      self.assertEqual(sup_net.get_shape().dims[1].value, 2)