<gh_stars>10-100
# -*- coding: utf-8 -*-
from typing import Optional

from VITAE.utils import Early_Stopping, get_embedding

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.utils import Progbar


def clear_session():
    '''Clear Tensorflow sessions.
    '''
    tf.keras.backend.clear_session()
    return None

    
def warp_dataset(X_normalized, c_score, batch_size:int, X=None, scale_factor=None, seed=0):
    '''Get Tensorflow datasets.

    Parameters
    ----------
    X_normalized : np.array
        \([N, G]\) The preprocessed data.
    c_score : float, optional
        The normalizing constant.
    batch_size : int
        The batch size.
    X : np.array, optional
        \([N, G]\) The raw count data.
    scale_factor : np.array, optional
        \([N, ]\) The raw count data.
    seed : int, optional
        The random seed for data shuffling.

    Returns
    ----------
    dataset : tf.Dataset
        The Tensorflow Dataset object.
    '''
    # fake c_score
    if c_score is None:
        c_score = np.zeros((X_normalized.shape[0],1), tf.keras.backend.floatx())
        
    if X is not None:
        train_dataset = tf.data.Dataset.from_tensor_slices((X, X_normalized, c_score, scale_factor))
        train_dataset = train_dataset.shuffle(buffer_size = X.shape[0], seed=seed,
                                        reshuffle_each_iteration=True).batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
        return train_dataset
    else:
        test_dataset = tf.data.Dataset.from_tensor_slices((X_normalized, 
                                                          c_score)).batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
        return test_dataset


def pre_train(train_dataset, test_dataset, vae, learning_rate: float, L: int, alpha: float,
              num_epoch_pre: int, num_step_per_epoch: int, 
              early_stopping_patience: int, early_stopping_tolerance: int, early_stopping_warmup: int):
    '''Pretraining.

    Parameters
    ----------
    train_dataset : tf.Dataset
        The Tensorflow Dataset object.
    test_dataset : tf.Dataset
        The Tensorflow Dataset object.
    vae : VariationalAutoEncoder
        The model.
    learning_rate : float
        The initial learning rate for the Adam optimizer.
    L : int
        The number of MC samples.
    alpha : float, optional
        The value of alpha in [0,1] to encourage covariate adjustment. Not used if there is no covariates.
    num_epoch_pre : int
        The maximum number of epoches.
    num_step_per_epoch : int
        The number of step per epoch, it will be inferred from number of cells and batch size if it is None.            
    early_stopping_patience : int
        The maximum number of epoches if there is no improvement.
    early_stopping_tolerance : float
        The minimum change of loss to be considered as an improvement.
    early_stopping_warmup : int, optional
        The number of warmup epoches.

    Returns
    ----------
    vae : VariationalAutoEncoder
        The pretrained model.
    '''    
    optimizer = tf.keras.optimizers.Adam(learning_rate = learning_rate)
    loss_train = tf.keras.metrics.Mean()
    loss_test = tf.keras.metrics.Mean()
    early_stopping = Early_Stopping(patience=early_stopping_patience, tolerance=early_stopping_tolerance, warmup=early_stopping_warmup)

    for epoch in range(num_epoch_pre):
        progbar = Progbar(num_step_per_epoch)
        
        print('Pretrain - Start of epoch %d' % (epoch,))

        # Iterate over the batches of the dataset.
        for step, (x_batch, x_norm_batch, c_score, x_scale_factor) in enumerate(train_dataset):
            with tf.GradientTape() as tape:
                losses = vae(x_norm_batch, c_score, x_batch, x_scale_factor, pre_train=True, L=L, alpha=alpha)
                # Compute reconstruction loss
                loss = tf.reduce_sum(losses[0])
            grads = tape.gradient(loss, vae.trainable_weights,
                        unconnected_gradients=tf.UnconnectedGradients.ZERO)
            optimizer.apply_gradients(zip(grads, vae.trainable_weights))                                
            loss_train(loss)
            
            if (step+1)%10==0 or step+1==num_step_per_epoch:
                progbar.update(step+1, [('Reconstructed Loss', float(loss))])
                
        for step, (x_batch, x_norm_batch, c_score, x_scale_factor) in enumerate(test_dataset):
            losses = vae(x_norm_batch, c_score, x_batch, x_scale_factor, pre_train=True, L=L, alpha=alpha)
            loss = tf.reduce_sum(losses[0])
            loss_test(loss)
        print(' Training loss over epoch: %.4f. Testing loss over epoch: %.4f' % (float(loss_train.result()),
                                                                            float(loss_test.result())))
        if early_stopping(float(loss_test.result())):
            print('Early stopping.')
            break
        loss_train.reset_states()
        loss_test.reset_states()

    print('Pretrain Done.')
    return vae


def train(train_dataset, test_dataset, whole_dataset, vae,
        learning_rate: float, 
        L: int, alpha: float, beta: float,
        num_epoch: int, num_step_per_epoch: int, 
        early_stopping_patience: int, early_stopping_tolerance: float, early_stopping_warmup: int,         
        labels, plot_every_num_epoch: Optional[int] = None, dimred: str = 'umap', **kwargs):
    '''Training.

    Parameters
    ----------
    train_dataset : tf.Dataset
        The Tensorflow Dataset object.
    test_dataset : tf.Dataset
        The Tensorflow Dataset object.
    whole_dataset : tf.Dataset
        The Tensorflow Dataset object for visualizations.
    vae : VariationalAutoEncoder
        The model.
    learning_rate : float
        The initial learning rate for the Adam optimizer.
    L : int
        The number of MC samples.
    alpha : float
        The value of alpha in [0,1] to encourage covariate adjustment. Not used if there is no covariates.
    beta : float
        The value of beta in beta-VAE.
    num_epoch : int
        The maximum number of epoches.
    num_step_per_epoch : int
        The number of step per epoch, it will be inferred from number of cells and batch size if it is None.            
    early_stopping_patience : int
        The maximum number of epoches if there is no improvement.
    early_stopping_tolerance : float, optional 
        The minimum change of loss to be considered as an improvement.
    early_stopping_warmup : int
        The number of warmup epoches.
    labels: np.array
        The labels for visualizations of the intermediate results.
    plot_every_num_epoch : int, optional 
        Plot the intermediate result every few epoches, or not plotting if it is None.            
    dimred : str, optional 
        The name of dimension reduction algorithms, can be 'umap', 'pca' and 'tsne'. Only used if 'plot_every_num_epoch' is not None. 
    **kwargs : 
        Extra key-value arguments for dimension reduction algorithms.    

    Returns
    ----------
    vae : VariationalAutoEncoder
        The trained model.
    '''   
    optimizer_ = tf.keras.optimizers.Adam(learning_rate)
    optimizer = tf.keras.optimizers.Adam(learning_rate)
    loss_test = [tf.keras.metrics.Mean() for _ in range(4)]
    loss_train = [tf.keras.metrics.Mean() for _ in range(4)]
    early_stopping = Early_Stopping(patience = early_stopping_patience, tolerance = early_stopping_tolerance, warmup=early_stopping_warmup)

    print('Warmup:%d'%early_stopping_warmup)
    weight = np.array([1,beta,beta], dtype=tf.keras.backend.floatx())
    weight = tf.convert_to_tensor(weight)
    
    for epoch in range(num_epoch):
        print('Start of epoch %d' % (epoch,))
        progbar = Progbar(num_step_per_epoch)
        
        # Iterate over the batches of the dataset.
        for step, (x_batch, x_norm_batch, c_score, x_scale_factor) in enumerate(train_dataset):
            if epoch<early_stopping_warmup:
                with tf.GradientTape() as tape:
                    losses = vae(x_norm_batch, c_score, x_batch, x_scale_factor, L=L, alpha=alpha)
                    # Compute reconstruction loss
                    loss = tf.reduce_sum(losses[1:])
                grads = tape.gradient(loss, vae.latent_space.trainable_weights,
                            unconnected_gradients=tf.UnconnectedGradients.ZERO)
                optimizer_.apply_gradients(zip(grads, vae.latent_space.trainable_weights))
            else:
                with tf.GradientTape() as tape:
                    losses = vae(x_norm_batch, c_score, x_batch, x_scale_factor, L=L, alpha=alpha)
                    # Compute reconstruction loss
                    loss = tf.reduce_sum(losses*weight)
                grads = tape.gradient(loss, vae.trainable_weights,
                            unconnected_gradients=tf.UnconnectedGradients.ZERO)
                optimizer.apply_gradients(zip(grads, vae.trainable_weights))

            loss_train[0](losses[0])
            loss_train[1](losses[1])
            loss_train[2](losses[2])
            loss_train[3](loss)

            if (step+1)%10==0 or step+1==num_step_per_epoch:
                progbar.update(step+1, [
                        ('loss_neg_E_nb'    ,   float(losses[0])),
                        ('loss_neg_E_pz'    ,   float(losses[1])),
                        ('loss_E_qzx   '    ,   float(losses[2])),
                        ('loss_total'       ,   float(loss))
                        ])
                        
        for step, (x_batch, x_norm_batch, c_score, x_scale_factor) in enumerate(test_dataset):
            losses = vae(x_norm_batch, c_score, x_batch, x_scale_factor, L=L, alpha=alpha)
            loss = tf.reduce_sum(losses*weight)
            loss_test[0](losses[0])
            loss_test[1](losses[1])
            loss_test[2](losses[2])
            loss_test[3](loss)
            
        if early_stopping(float(loss_test[3].result())):
            print('Early stopping.')
            break
        
        print(' Training loss over epoch: %.4f (%.4f, %.4f, %.4f) Testing loss over epoch: %.4f (%.4f, %.4f, %.4f)' % (
            float(loss_train[3].result()),
            float(loss_train[0].result()),
            float(loss_train[1].result()),
            float(loss_train[2].result()),
            float(loss_test[3].result()),
            float(loss_test[0].result()),
            float(loss_test[1].result()),
            float(loss_test[2].result())))
        [l.reset_states() for l in loss_train]
        [l.reset_states() for l in loss_test]


        if plot_every_num_epoch is not None and (epoch%plot_every_num_epoch==0 or epoch==num_epoch-1):
            _, mu, _, w_tilde, _, z_mean = vae.inference(whole_dataset, 1)
            c = np.argmax(w_tilde, axis=-1)
            
            concate_z = np.concatenate((z_mean, mu.T), axis=0)
            u = get_embedding(concate_z, dimred, **kwargs)
            uz = u[:len(z_mean),:]
            um = u[len(z_mean):,:]            
            
            if labels is None:
                fig, ax1 = plt.subplots(1, figsize=(7, 6))
            else:
                fig, (ax1,ax2) = plt.subplots(1,2, figsize=(16, 6))
            
                ax2.scatter(uz[:,0], uz[:,1], c = labels, s = 2)
                ax2.set_title('Ground Truth')
            
            ax1.scatter(uz[:,0], uz[:,1], c = c, s = 2, alpha = 0.5)
            ax1.set_title('Prediction')
            cluster_center = [(len(um)+(1-i)/2)*i for i in range(len(um))]
            ax1.scatter(um[:,0], um[:,1], c=cluster_center, s=100, marker='s')
            plt.show()

    print('Training Done!')

    return vae

# -*- coding: utf-8 -*-
from typing import Optional

from VITAE.utils import Early_Stopping, get_embedding

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.utils import Progbar


def clear_session():
    '''Clear Tensorflow sessions.
    '''
    tf.keras.backend.clear_session()
    return None

    
def warp_dataset(X_normalized, c_score, batch_size:int, X=None, scale_factor=None, seed=0):
    '''Get Tensorflow datasets.

    Parameters
    ----------
    X_normalized : np.array
        \([N, G]\) The preprocessed data.
    c_score : float, optional
        The normalizing constant.
    batch_size : int
        The batch size.
    X : np.array, optional
        \([N, G]\) The raw count data.
    scale_factor : np.array, optional
        \([N, ]\) The raw count data.
    seed : int, optional
        The random seed for data shuffling.

    Returns
    ----------
    dataset : tf.Dataset
        The Tensorflow Dataset object.
    '''
    # fake c_score
    if c_score is None:
        c_score = np.zeros((X_normalized.shape[0],1), tf.keras.backend.floatx())
        
    if X is not None:
        train_dataset = tf.data.Dataset.from_tensor_slices((X, X_normalized, c_score, scale_factor))
        train_dataset = train_dataset.shuffle(buffer_size = X.shape[0], seed=seed,
                                        reshuffle_each_iteration=True).batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
        return train_dataset
    else:
        test_dataset = tf.data.Dataset.from_tensor_slices((X_normalized, 
                                                          c_score)).batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
        return test_dataset


def pre_train(train_dataset, test_dataset, vae, learning_rate: float, L: int, alpha: float,
              num_epoch_pre: int, num_step_per_epoch: int, 
              early_stopping_patience: int, early_stopping_tolerance: int, early_stopping_warmup: int):
    '''Pretraining.

    Parameters
    ----------
    train_dataset : tf.Dataset
        The Tensorflow Dataset object.
    test_dataset : tf.Dataset
        The Tensorflow Dataset object.
    vae : VariationalAutoEncoder
        The model.
    learning_rate : float
        The initial learning rate for the Adam optimizer.
    L : int
        The number of MC samples.
    alpha : float, optional
        The value of alpha in [0,1] to encourage covariate adjustment. Not used if there is no covariates.
    num_epoch_pre : int
        The maximum number of epoches.
    num_step_per_epoch : int
        The number of step per epoch, it will be inferred from number of cells and batch size if it is None.            
    early_stopping_patience : int
        The maximum number of epoches if there is no improvement.
    early_stopping_tolerance : float
        The minimum change of loss to be considered as an improvement.
    early_stopping_warmup : int, optional
        The number of warmup epoches.

    Returns
    ----------
    vae : VariationalAutoEncoder
        The pretrained model.
    '''    
    optimizer = tf.keras.optimizers.Adam(learning_rate = learning_rate)
    loss_train = tf.keras.metrics.Mean()
    loss_test = tf.keras.metrics.Mean()
    early_stopping = Early_Stopping(patience=early_stopping_patience, tolerance=early_stopping_tolerance, warmup=early_stopping_warmup)

    for epoch in range(num_epoch_pre):
        progbar = Progbar(num_step_per_epoch)
        
        print('Pretrain - Start of epoch %d' % (epoch,))

        # Iterate over the batches of the dataset.
        for step, (x_batch, x_norm_batch, c_score, x_scale_factor) in enumerate(train_dataset):
            with tf.GradientTape() as tape:
                losses = vae(x_norm_batch, c_score, x_batch, x_scale_factor, pre_train=True, L=L, alpha=alpha)
                # Compute reconstruction loss
                loss = tf.reduce_sum(losses[0])
            grads = tape.gradient(loss, vae.trainable_weights,
                        unconnected_gradients=tf.UnconnectedGradients.ZERO)
            optimizer.apply_gradients(zip(grads, vae.trainable_weights))                                
            loss_train(loss)
            
            if (step+1)%10==0 or step+1==num_step_per_epoch:
                progbar.update(step+1, [('Reconstructed Loss', float(loss))])
                
        for step, (x_batch, x_norm_batch, c_score, x_scale_factor) in enumerate(test_dataset):
            losses = vae(x_norm_batch, c_score, x_batch, x_scale_factor, pre_train=True, L=L, alpha=alpha)
            loss = tf.reduce_sum(losses[0])
            loss_test(loss)
        print(' Training loss over epoch: %.4f. Testing loss over epoch: %.4f' % (float(loss_train.result()),
                                                                            float(loss_test.result())))
        if early_stopping(float(loss_test.result())):
            print('Early stopping.')
            break
        loss_train.reset_states()
        loss_test.reset_states()

    print('Pretrain Done.')
    return vae


def train(train_dataset, test_dataset, whole_dataset, vae,
        learning_rate: float, 
        L: int, alpha: float, beta: float,
        num_epoch: int, num_step_per_epoch: int, 
        early_stopping_patience: int, early_stopping_tolerance: float, early_stopping_warmup: int,         
        labels, plot_every_num_epoch: Optional[int] = None, dimred: str = 'umap', **kwargs):
    '''Training.

    Parameters
    ----------
    train_dataset : tf.Dataset
        The Tensorflow Dataset object.
    test_dataset : tf.Dataset
        The Tensorflow Dataset object.
    whole_dataset : tf.Dataset
        The Tensorflow Dataset object for visualizations.
    vae : VariationalAutoEncoder
        The model.
    learning_rate : float
        The initial learning rate for the Adam optimizer.
    L : int
        The number of MC samples.
    alpha : float
        The value of alpha in [0,1] to encourage covariate adjustment. Not used if there is no covariates.
    beta : float
        The value of beta in beta-VAE.
    num_epoch : int
        The maximum number of epoches.
    num_step_per_epoch : int
        The number of step per epoch, it will be inferred from number of cells and batch size if it is None.            
    early_stopping_patience : int
        The maximum number of epoches if there is no improvement.
    early_stopping_tolerance : float, optional 
        The minimum change of loss to be considered as an improvement.
    early_stopping_warmup : int
        The number of warmup epoches.
    labels: np.array
        The labels for visualizations of the intermediate results.
    plot_every_num_epoch : int, optional 
        Plot the intermediate result every few epoches, or not plotting if it is None.            
    dimred : str, optional 
        The name of dimension reduction algorithms, can be 'umap', 'pca' and 'tsne'. Only used if 'plot_every_num_epoch' is not None. 
    **kwargs : 
        Extra key-value arguments for dimension reduction algorithms.    

    Returns
    ----------
    vae : VariationalAutoEncoder
        The trained model.
    '''   
    optimizer_ = tf.keras.optimizers.Adam(learning_rate)
    optimizer = tf.keras.optimizers.Adam(learning_rate)
    loss_test = [tf.keras.metrics.Mean() for _ in range(4)]
    loss_train = [tf.keras.metrics.Mean() for _ in range(4)]
    early_stopping = Early_Stopping(patience = early_stopping_patience, tolerance = early_stopping_tolerance, warmup=early_stopping_warmup)

    print('Warmup:%d'%early_stopping_warmup)
    weight = np.array([1,beta,beta], dtype=tf.keras.backend.floatx())
    weight = tf.convert_to_tensor(weight)
    
    for epoch in range(num_epoch):
        print('Start of epoch %d' % (epoch,))
        progbar = Progbar(num_step_per_epoch)
        
        # Iterate over the batches of the dataset.
        for step, (x_batch, x_norm_batch, c_score, x_scale_factor) in enumerate(train_dataset):
            if epoch<early_stopping_warmup:
                with tf.GradientTape() as tape:
                    losses = vae(x_norm_batch, c_score, x_batch, x_scale_factor, L=L, alpha=alpha)
                    # Compute reconstruction loss
                    loss = tf.reduce_sum(losses[1:])
                grads = tape.gradient(loss, vae.latent_space.trainable_weights,
                            unconnected_gradients=tf.UnconnectedGradients.ZERO)
                optimizer_.apply_gradients(zip(grads, vae.latent_space.trainable_weights))
            else:
                with tf.GradientTape() as tape:
                    losses = vae(x_norm_batch, c_score, x_batch, x_scale_factor, L=L, alpha=alpha)
                    # Compute reconstruction loss
                    loss = tf.reduce_sum(losses*weight)
                grads = tape.gradient(loss, vae.trainable_weights,
                            unconnected_gradients=tf.UnconnectedGradients.ZERO)
                optimizer.apply_gradients(zip(grads, vae.trainable_weights))

            loss_train[0](losses[0])
            loss_train[1](losses[1])
            loss_train[2](losses[2])
            loss_train[3](loss)

            if (step+1)%10==0 or step+1==num_step_per_epoch:
                progbar.update(step+1, [
                        ('loss_neg_E_nb'    ,   float(losses[0])),
                        ('loss_neg_E_pz'    ,   float(losses[1])),
                        ('loss_E_qzx   '    ,   float(losses[2])),
                        ('loss_total'       ,   float(loss))
                        ])
                        
        for step, (x_batch, x_norm_batch, c_score, x_scale_factor) in enumerate(test_dataset):
            losses = vae(x_norm_batch, c_score, x_batch, x_scale_factor, L=L, alpha=alpha)
            loss = tf.reduce_sum(losses*weight)
            loss_test[0](losses[0])
            loss_test[1](losses[1])
            loss_test[2](losses[2])
            loss_test[3](loss)
            
        if early_stopping(float(loss_test[3].result())):
            print('Early stopping.')
            break
        
        print(' Training loss over epoch: %.4f (%.4f, %.4f, %.4f) Testing loss over epoch: %.4f (%.4f, %.4f, %.4f)' % (
            float(loss_train[3].result()),
            float(loss_train[0].result()),
            float(loss_train[1].result()),
            float(loss_train[2].result()),
            float(loss_test[3].result()),
            float(loss_test[0].result()),
            float(loss_test[1].result()),
            float(loss_test[2].result())))
        [l.reset_states() for l in loss_train]
        [l.reset_states() for l in loss_test]


        if plot_every_num_epoch is not None and (epoch%plot_every_num_epoch==0 or epoch==num_epoch-1):
            _, mu, _, w_tilde, _, z_mean = vae.inference(whole_dataset, 1)
            c = np.argmax(w_tilde, axis=-1)
            
            concate_z = np.concatenate((z_mean, mu.T), axis=0)
            u = get_embedding(concate_z, dimred, **kwargs)
            uz = u[:len(z_mean),:]
            um = u[len(z_mean):,:]            
            
            if labels is None:
                fig, ax1 = plt.subplots(1, figsize=(7, 6))
            else:
                fig, (ax1,ax2) = plt.subplots(1,2, figsize=(16, 6))
            
                ax2.scatter(uz[:,0], uz[:,1], c = labels, s = 2)
                ax2.set_title('Ground Truth')
            
            ax1.scatter(uz[:,0], uz[:,1], c = c, s = 2, alpha = 0.5)
            ax1.set_title('Prediction')
            cluster_center = [(len(um)+(1-i)/2)*i for i in range(len(um))]
            ax1.scatter(um[:,0], um[:,1], c=cluster_center, s=100, marker='s')
            plt.show()

    print('Training Done!')

    return vae
# -*- coding: utf-8 -*- Clear Tensorflow sessions. Get Tensorflow datasets.      Parameters     ----------     X_normalized : np.array         \([N, G]\) The preprocessed data.     c_score : float, optional         The normalizing constant.     batch_size : int         The batch size.     X : np.array, optional         \([N, G]\) The raw count data.     scale_factor : np.array, optional         \([N, ]\) The raw count data.     seed : int, optional         The random seed for data shuffling.      Returns     ----------     dataset : tf.Dataset         The Tensorflow Dataset object. # fake c_score Pretraining.      Parameters     ----------     train_dataset : tf.Dataset         The Tensorflow Dataset object.     test_dataset : tf.Dataset         The Tensorflow Dataset object.     vae : VariationalAutoEncoder         The model.     learning_rate : float         The initial learning rate for the Adam optimizer.     L : int         The number of MC samples.     alpha : float, optional         The value of alpha in [0,1] to encourage covariate adjustment. Not used if there is no covariates.     num_epoch_pre : int         The maximum number of epoches.     num_step_per_epoch : int         The number of step per epoch, it will be inferred from number of cells and batch size if it is None.                 early_stopping_patience : int         The maximum number of epoches if there is no improvement.     early_stopping_tolerance : float         The minimum change of loss to be considered as an improvement.     early_stopping_warmup : int, optional         The number of warmup epoches.      Returns     ----------     vae : VariationalAutoEncoder         The pretrained model. # Iterate over the batches of the dataset. # Compute reconstruction loss Training.      Parameters     ----------     train_dataset : tf.Dataset         The Tensorflow Dataset object.     test_dataset : tf.Dataset         The Tensorflow Dataset object.     whole_dataset : tf.Dataset         The Tensorflow Dataset object for visualizations.     vae : VariationalAutoEncoder         The model.     learning_rate : float         The initial learning rate for the Adam optimizer.     L : int         The number of MC samples.     alpha : float         The value of alpha in [0,1] to encourage covariate adjustment. Not used if there is no covariates.     beta : float         The value of beta in beta-VAE.     num_epoch : int         The maximum number of epoches.     num_step_per_epoch : int         The number of step per epoch, it will be inferred from number of cells and batch size if it is None.                 early_stopping_patience : int         The maximum number of epoches if there is no improvement.     early_stopping_tolerance : float, optional          The minimum change of loss to be considered as an improvement.     early_stopping_warmup : int         The number of warmup epoches.     labels: np.array         The labels for visualizations of the intermediate results.     plot_every_num_epoch : int, optional          Plot the intermediate result every few epoches, or not plotting if it is None.                 dimred : str, optional          The name of dimension reduction algorithms, can be 'umap', 'pca' and 'tsne'. Only used if 'plot_every_num_epoch' is not None.      **kwargs :          Extra key-value arguments for dimension reduction algorithms.          Returns     ----------     vae : VariationalAutoEncoder         The trained model. # Iterate over the batches of the dataset. # Compute reconstruction loss # Compute reconstruction loss