autoencoders.py

from keras.layers import Input, Dense, Lambda
from keras.models import Model
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint, TensorBoard
import keras.optimizers
import multivac as m
import pandas as pd

import numpy as np
import tensorflow as tf
np.random.seed(0)
tf.set_random_seed(0)

from custom import Sampler, CustomVariationalLayer

import plotting

class VariationalAutoencoder:
    ''' Variational autoencoder implemented in Keras'''
    def __init__(self, **kwargs):
        self.batch_size = kwargs.get('batch_size')
        self.original_dim = kwargs.get('original_dim')
        self.latent_dim = kwargs.get('latent_dim')
        self.intermediate_dim = kwargs.get('intermediate_dim')
        self.epsilon_std = kwargs.get('epsilon_std')
    
        x = Input(batch_shape=(self.batch_size, self.original_dim))
        h = Dense(self.intermediate_dim, activation='relu')(x)
        z_mean = Dense(self.latent_dim)(h)
        z_log_var = Dense(self.latent_dim)(h)

        sampler = Sampler(batch_size=self.batch_size, latent_dim=self.latent_dim, epsilon_std=self.epsilon_std)
        z = Lambda(sampler.sampling, output_shape=(self.latent_dim,))([z_mean, z_log_var])

        decoder_h = Dense(self.intermediate_dim, activation='relu')
        decoder_mean = Dense(self.original_dim, activation='sigmoid')
        h_decoded = decoder_h(z)
        x_decoded_mean = decoder_mean(h_decoded)

        y = CustomVariationalLayer(self.original_dim, z_mean, z_log_var)([x, x_decoded_mean])
        self.vae = Model(x, y)
        rmsprop = keras.optimizers.RMSprop(lr=0.00001, rho=0.9, epsilon=1e-08, decay=0.0)
        self.vae.compile(optimizer=rmsprop, loss=None)
        
        self.encoder = Model(x, z_mean)

        decoder_input = Input(shape=(self.latent_dim,))
        _h_decoded = decoder_h(decoder_input)
        _x_decoded_mean = decoder_mean(_h_decoded)
        self.generator = Model(decoder_input, _x_decoded_mean)

    def train(self, x_train, epochs):
        early_stopping = EarlyStopping(monitor='loss',
                                           min_delta=0.001,
                                           patience=50,
                                           verbose=1,
                                           mode='auto')
        callbacks = [early_stopping]
        self.vae.fit(x_train,
                shuffle=True,
                epochs=epochs,
                batch_size=self.batch_size,
                callbacks=callbacks
                #validation_data=(x_test, x_test)
                )
    
    def encode(self, x_test):
        x_test_encoded = self.encoder.predict(x_test, batch_size=self.batch_size)
        return x_test_encoded

    def generate(self, z_sample):
        x_decoded = self.generator.predict(z_sample)
        return x_decoded

    def report(self, x_test, y_test):
        plotting.plot_scatter(self.encode(x_test), y_test)
        plotting.plot_manifold(self.generator)

    def persist_encoded(self, proj, dname, x_test):
        encoded = self.encode(x_test)
        df = pd.DataFrame({'l1': encoded[:,0], 'l2': encoded[:,1]})
        m.persist.core.write_data(proj, dname, df, verbose=False)
    
    def scatter_latent(self, proj, gname, x_test):
        plotting.plot_scatter(self.encode(x_test), proj=proj, gname=gname)
        
def xavier_init(fan_in, fan_out, constant=1): 
    """ Xavier initialization of network weights"""
    # https://stackoverflow.com/questions/33640581/how-to-do-xavier-initialization-on-tensorflow
    low = -constant*np.sqrt(6.0/(fan_in + fan_out)) 
    high = constant*np.sqrt(6.0/(fan_in + fan_out))
    return tf.random_uniform((fan_in, fan_out), 
                             minval=low, maxval=high, 
                             dtype=tf.float32)

class TFVariationalAutoencoder(object):
    """ Variation Autoencoder (VAE) with an sklearn-like interface implemented using TensorFlow.
    
    This implementation uses probabilistic encoders and decoders using Gaussian 
    distributions and  realized by multi-layer perceptrons. The VAE can be learned
    end-to-end.
    
    See "Auto-Encoding Variational Bayes" by Kingma and Welling for more details.
    """
    def __init__(self, network_architecture, transfer_fct=tf.nn.softplus, 
                 learning_rate=0.001, batch_size=100):
        self.network_architecture = network_architecture
        self.transfer_fct = transfer_fct
        self.learning_rate = learning_rate
        self.batch_size = batch_size
        
        # tf Graph input
        self.x = tf.placeholder(tf.float32, [None, network_architecture["n_input"]])
        
        # Create autoencoder network
        self._create_network()
        # Define loss function based variational upper-bound and 
        # corresponding optimizer
        self._create_loss_optimizer()
        
        # Initializing the tensor flow variables
        init = tf.global_variables_initializer()

        # Launch the session
        self.sess = tf.InteractiveSession()
        self.sess.run(init)
    
    def _create_network(self):
        # Initialize autoencode network weights and biases
        network_weights = self._initialize_weights(**self.network_architecture)

        # Use recognition network to determine mean and 
        # (log) variance of Gaussian distribution in latent
        # space
        self.z_mean, self.z_log_sigma_sq = \
            self._recognition_network(network_weights["weights_recog"], 
                                      network_weights["biases_recog"])

        # Draw one sample z from Gaussian distribution
        n_z = self.network_architecture["n_z"]
        eps = tf.random_normal((self.batch_size, n_z), 0, 1, 
                               dtype=tf.float32)
        # z = mu + sigma*epsilon
        self.z = tf.add(self.z_mean, 
                        tf.multiply(tf.sqrt(tf.exp(self.z_log_sigma_sq)), eps))

        # Use generator to determine mean of
        # Bernoulli distribution of reconstructed input
        self.x_reconstr_mean = \
            self._generator_network(network_weights["weights_gener"],
                                    network_weights["biases_gener"])
            
    def _initialize_weights(self, n_hidden_recog_1, n_hidden_recog_2, 
                            n_hidden_gener_1,  n_hidden_gener_2, 
                            n_input, n_z):
        all_weights = dict()
        all_weights['weights_recog'] = {
            'h1': tf.Variable(xavier_init(n_input, n_hidden_recog_1)),
            'h2': tf.Variable(xavier_init(n_hidden_recog_1, n_hidden_recog_2)),
            'out_mean': tf.Variable(xavier_init(n_hidden_recog_2, n_z)),
            'out_log_sigma': tf.Variable(xavier_init(n_hidden_recog_2, n_z))}
        all_weights['biases_recog'] = {
            'b1': tf.Variable(tf.zeros([n_hidden_recog_1], dtype=tf.float32)),
            'b2': tf.Variable(tf.zeros([n_hidden_recog_2], dtype=tf.float32)),
            'out_mean': tf.Variable(tf.zeros([n_z], dtype=tf.float32)),
            'out_log_sigma': tf.Variable(tf.zeros([n_z], dtype=tf.float32))}
        all_weights['weights_gener'] = {
            'h1': tf.Variable(xavier_init(n_z, n_hidden_gener_1)),
            'h2': tf.Variable(xavier_init(n_hidden_gener_1, n_hidden_gener_2)),
            'out_mean': tf.Variable(xavier_init(n_hidden_gener_2, n_input)),
            'out_log_sigma': tf.Variable(xavier_init(n_hidden_gener_2, n_input))}
        all_weights['biases_gener'] = {
            'b1': tf.Variable(tf.zeros([n_hidden_gener_1], dtype=tf.float32)),
            'b2': tf.Variable(tf.zeros([n_hidden_gener_2], dtype=tf.float32)),
            'out_mean': tf.Variable(tf.zeros([n_input], dtype=tf.float32)),
            'out_log_sigma': tf.Variable(tf.zeros([n_input], dtype=tf.float32))}
        return all_weights
            
    def _recognition_network(self, weights, biases):
        # Generate probabilistic encoder (recognition network), which
        # maps inputs onto a normal distribution in latent space.
        # The transformation is parametrized and can be learned.
        layer_1 = self.transfer_fct(tf.add(tf.matmul(self.x, weights['h1']), 
                                           biases['b1'])) 
        layer_2 = self.transfer_fct(tf.add(tf.matmul(layer_1, weights['h2']), 
                                           biases['b2'])) 
        z_mean = tf.add(tf.matmul(layer_2, weights['out_mean']),
                        biases['out_mean'])
        z_log_sigma_sq = \
            tf.add(tf.matmul(layer_2, weights['out_log_sigma']), 
                   biases['out_log_sigma'])
        return (z_mean, z_log_sigma_sq)

    def _generator_network(self, weights, biases):
        # Generate probabilistic decoder (decoder network), which
        # maps points in latent space onto a Bernoulli distribution in data space.
        # The transformation is parametrized and can be learned.
        layer_1 = self.transfer_fct(tf.add(tf.matmul(self.z, weights['h1']), 
                                           biases['b1'])) 
        layer_2 = self.transfer_fct(tf.add(tf.matmul(layer_1, weights['h2']), 
                                           biases['b2'])) 
        x_reconstr_mean = \
            tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['out_mean']), 
                                 biases['out_mean']))
        return x_reconstr_mean
            
    def _create_loss_optimizer(self):
        # The loss is composed of two terms:
        # 1.) The reconstruction loss (the negative log probability
        #     of the input under the reconstructed Bernoulli distribution 
        #     induced by the decoder in the data space).
        #     This can be interpreted as the number of "nats" required
        #     for reconstructing the input when the activation in latent
        #     is given.
        # Adding 1e-10 to avoid evaluation of log(0.0)
        reconstr_loss = \
            -tf.reduce_sum(self.x * tf.log(1e-10 + self.x_reconstr_mean)
                           + (1-self.x) * tf.log(1e-10 + 1 - self.x_reconstr_mean),
                           1)
        # 2.) The latent loss, which is defined as the Kullback Leibler divergence 
        ##    between the distribution in latent space induced by the encoder on 
        #     the data and some prior. This acts as a kind of regularizer.
        #     This can be interpreted as the number of "nats" required
        #     for transmitting the the latent space distribution given
        #     the prior.
        latent_loss = -0.5 * tf.reduce_sum(1 + self.z_log_sigma_sq 
                                           - tf.square(self.z_mean) 
                                           - tf.exp(self.z_log_sigma_sq), 1)
        self.cost = tf.reduce_mean(reconstr_loss + latent_loss)   # average over batch
        # Use ADAM optimizer
        self.optimizer = \
            tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.cost)
        
    def partial_fit(self, X):
        """Train model based on mini-batch of input data.
        
        Return cost of mini-batch.
        """
        opt, cost = self.sess.run((self.optimizer, self.cost), 
                                  feed_dict={self.x: X})
        return cost
    
    def transform(self, X):
        """Transform data by mapping it into the latent space."""
        # Note: This maps to mean of distribution, we could alternatively
        # sample from Gaussian distribution
        return self.sess.run(self.z_mean, feed_dict={self.x: X})
    
    def generate(self, z_mu=None):
        """ Generate data by sampling from latent space.
        
        If z_mu is not None, data for this point in latent space is
        generated. Otherwise, z_mu is drawn from prior in latent 
        space.        
        """
        if z_mu is None:
            z_mu = np.random.normal(size=self.network_architecture["n_z"])
        # Note: This maps to mean of distribution, we could alternatively
        # sample from Gaussian distribution
        return self.sess.run(self.x_reconstr_mean, 
                             feed_dict={self.z: z_mu})
    
    def reconstruct(self, X):
        """ Use VAE to reconstruct given data. """
        return self.sess.run(self.x_reconstr_mean, 
                             feed_dict={self.x: X})
    
    def train(self, mnist, training_epochs=10, display_step=5):
        """ Train VAE ina loop"""
        n_samples = mnist.train.num_examples
        for epoch in range(training_epochs):
            avg_cost = 0
            total_batch = int(n_samples / self.batch_size)
            # Loop over all batches
            for i in range(total_batch):
                batch_xs, _ = mnist.train.next_batch(self.batch_size)
                # Fit training using batch data
                cost = self.partial_fit(batch_xs)
                # Compute average loss
                avg_cost += cost / n_samples * self.batch_size
            # Display logs per epoch step
            if epoch % display_step == 0:
                print(f'Epoch: {epoch+1:.4f} Cost= {avg_cost:.9f}')
        return self