Yann LeCun, Director of AI Research at Facebook and Professor at NYU, defined Generative Adversarial Networks (GANs) as the most interesting idea in the last 10 years in ML.
The idea is to simultaneously train two neural nets. The first one, called the Discriminator — let’s denote it D(Y) — takes an input (e.g. an image) and outputs a scalar that indicates whether the image Y looks “natural” or not. In one instance of adversarial training, D(Y) can be seem as some sort of energy function that takes a low value (e.g. close to 0) when Y is a real sample (e.g. an image from a database) and a positive value when it is not (e.g. if it’s a noisy or strange looking image). The second network is called the Generator, denoted G(Z), where Z is generally a vector randomly sampled in a simple distribution (e.g. Gaussian). The role of the generator is to produce images so as to train the D(Y) function to take the right shape (low values for real images, higher values for everything else). During training D is shown a real image, and adjusts its parameter to make its output lower. Then D is shown an image produced from G and adjusts its parameters to make its output D(G(Z)) larger (following the gradient of some objective predefined function). But G(Z) will train itself to produce images so as to fool D into thinking they are real. It does this by getting the gradient of D with respect to Y for each sample it produces. In other words, it’s trying to minimize the output of D while D is trying to maximize it. Hence the name adversarial training.
GANs are able to learn how to reproduce synthetic data that looks real. For instance, computers can learn how to paint and create realistic images. The idea was originally proposed by Ian Goodfellow.
WaveNet is a deep generative network proposed by Google DeepMind to teach computers how to reproduce human voices and musical instruments with impressive quality.
NIPS 2016 Tutorial: Generative Adversarial Networks, by I. Goodfellow, 2016
To simplify the discussion somewhat, we will focus on generative models that work via the principle of maximum likelihood. Not every generative model uses maximum likelihood. Some generative models do not use maximum likelihood by default, but can be made to do so (GANs fall into this category).
The basic idea of maximum likelihood is to define a model that provides an estimate of the probability distribution of $p(x^{(i)},\theta)$, parameterized by parameters θ with respect to a training data $\{x^{(1)},...,x^{(m)}\}$. The principle of maximum likelihood simply says to choose the parameters for the model that maximize the likelihood of the training data. This is easiest to do in log space, where we have a sum rather than a product over examples. This sum simplifies the algebraic expressions for the derivatives of the likelihood with respect to the models, and when implemented on a digital computer, is less prone to numerical problems, such as underflow resulting from multiplying together several very small probabilities.
$$\theta^*=argmax \big( \prod_{n=1}^{m}p(x^{(i)},\theta) \big) = argmax \quad log \big( \prod_{n=1}^{m}p(x^{(i)},\theta) \big) = argmax \quad \sum_{n=1}^{m} log \big( p(x^{(i)},\theta) \big)$$We can also think of maximum likelihood estimation as minimizing the KL divergence between the data generating distribution and the model:
$$\theta^*=argmin \quad D_{KL} \big( p_{data}(x) || p_{model}(x;\theta) \big)$$If we were able to do this precisely, then if $p_{data}$ lies within the family of distributions $p_{model}(x; \ theta)$, the model would recover $p_{data}$ exactly. In practice, we do not have access to $p_{data}$ itself, but only to a training set consisting of m samples from pdata. We uses these to define $p_{data}$, an empirical distribution that places mass only on exactly those m points, approximating pdata. Minimizing the KL divergence between $p_{data}$ and $p_{model}(x; \theta)$ is exactly equivalent to maximizing the log-likelihood of the training set.
If we restrict our attention to deep generative models that work by maximizing the likelihood, we can compare several models by contrasting the ways that they compute either the likelihood and its gradients, or approximations to these quantities. As mentioned earlier, many of these models are often used with principles other than maximum likelihood, but we can examine the maximum likelihood variant of each of them in order to reduce the amount of distracting differences between the methods. Following this approach, we construct the taxonomy shown in the following figure.
In the left branch of the taxonomy shown there are models that define an explicit density function $p_{model}(x;\theta)$. For these models, maxmimization of the likelihood is straightforward; we simply plug the model’s definition of the density function into the expression for the likelihood, and follow the gradient uphill.
In the leftmost leaf of the taxonomic tree of the figure are the models that define an explicit density function that is computationally tractable. There are currently two popular approaches to tractable explicit density models:
To avoid some of the disadvantages imposed by the design requirements of models with tractable density functions, other models have been developed that still provide an explicit density function but use one that is intractable, requiring the use of approximations to maximize the likelihood. These fall roughly into two categories: those using deterministic approximations, which almost always means variational methods, and those using stochastic approximations, meaning Markov chain Monte Carlo method.
Variational methods define a lower bound $L(x;\theta) \leq log(p_{model}(x;\theta))$
A learning algorithm that maximizes L is guaranteed to obtain at least as high a value of the log-likelihood as it does of L. For many families of models, it is possible to define an L that is computationally tractable even when the loglikelihood is not. Currently, the most popular approach to variational learning in deep generative models is the variational autoencoder (Kingma, 2013; Rezende et al., 2014) or VAE. The main drawback of variational methods is that, when too weak of an approximate posterior distribution or too weak of a prior distribution is used, even with a perfect optimization algorithm and infinite training data, the gap between L and the true likelihood can result in $p_{model}$ learning something other than the true $p_{data}$.
Most deep learning algorithms make use of some form of stochastic approximation, at the very least in the form of using a small number of randomly selected training examples to form a minibatch used to minimize the expected loss. Usually, sampling-based approximations work reasonably well as long as a fair sample can be generated quickly (e.g. selecting a single example from the training set is a cheap operation) and as long as the variance across samples is not too high. Some models require the generation of more expensive samples, using Markov chain techniques.Unfortunately, the convergence can be very slow, and there is no clear way to test whether the chain has converged, so in practice one often uses x too early, before it has truly converged to be a fair sample from $p_{model}$.
Some models can be trained without even needing to explicitly define a density functions. These models instead offer a way to train the model while interacting only indirectly with $p_{model}$, usually by sampling from it. These constitute the second branch, on the right side, of our taxonomy of generative models depicted in above figure. Some of these implicit models based on drawing samples from $p_{model}$ define a Markov chain transition operator that must be run several times to obtain a sample from the model. From this family, the primary example is the generative stochastic network (Bengio et al., 2014).
GANs were designed to avoid these problems. Finally, the rightmost leaf of our taxonomic tree is the family of implicit models that can generate a sample in a single step. At the time of their introduction, GANs were the only notable member of this family, but since then they have been joined by additional models based on kernelized moment matching (Li et al., 2015; Dziugaite et al., 2015).
In summary, GANs were designed to avoid many disadvantages associated with other generative models:
At the same time, GANs have taken on a new disadvantage: training them requires finding the Nash equilibrium of a game, which is a more difficult problem than optimizing an objective function.
The basic idea of GANs is to set up a game between two players. One of them is called the generator. The generator creates samples that are intended to come from the same distribution as the training data. The other player is the discriminator. The discriminator examines samples to determine whether they are real or fake.
The discriminator learns using traditional supervised learning techniques, dividing inputs into two classes (real or fake).
The generator is trained to fool the discriminator. We can think of the generator as being like a counterfeiter, trying to make fake money, and the discriminator as being like police, trying to allow legitimate money and catch counterfeit money. To succeed in this game, the counterfeiter must learn to make money that is indistinguishable from genuine money, and the generator network must learn to create samples that are drawn from the same distribution as the training data.
Both players have cost functions that are defined in terms of both players’ parameters. The discriminator wishes to minimize $J^{D}(\theta^{(G)},\theta^{(D)})$. and must do so while controlling only $\theta^{(D)}$. The generator wishes to minimize $J^{G}(\theta^{(G)},\theta^{(D)})$ controlling only $\theta^{(G)}$. Because each player’s cost depends on the other player’s parameters, but each player cannot control the other player’s parameters, this scenario is most straightforward to describe as a game rather than as an optimization problem. The solution to an optimization problem is a (local) minimum, a point in parameter space where all neighboring points have greater or equal cost. The solution to a game is a Nash equilibrium. Here, we use the terminology of local differential Nash equilibria (Ratliff et al., 2013). In this context, a Nash equilibrium is a tuple $(\theta^{(G)},\theta^{(D)})$ that is a local minimum of $J^{(D)}$ with respect to $\theta^{(D)}$ and a local minimum of $J^{(G)}$ with respect to $\theta^{(G)}$.
....
We have seen that the generator learns how to forge data. This means that it learns how to create new synthetic data, which is created by the network, that looks real and like it was created by humans. Here some recent applications:
The deep convolutional generative adversarial networks (DCGAN) are introduced in the paper: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, by A. Radford, L. Metz, and S. Chintala, arXiv: 1511.06434, 2015. The generator uses a 100-dimensional, uniform distribution space, Z, which is then projected into a smaller space by a series of vis-a-vis convolution operations.
Implementation, available at: https://github.com/jacobgil/keras-dcgan.
From https://github.com/Zackory/Keras-MNIST-GAN ported to Python 3.
In [1]:
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
from keras.layers import Input
from keras.models import Model, Sequential
from keras.layers.core import Reshape, Dense, Dropout, Flatten
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import Convolution2D, UpSampling2D
from keras.datasets import mnist
from keras.optimizers import Adam
from keras import backend as K
#from keras import initializers
from keras import initializers
K.set_image_dim_ordering('th')
# Deterministic output.
# Tired of seeing the same results every time? Remove the line below.
np.random.seed(1000)
# The results are a little better when the dimensionality of the random vector is only 10.
# The dimensionality has been left at 100 for consistency with other GAN implementations.
randomDim = 100
# Load MNIST data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = (X_train.astype(np.float32) - 127.5)/127.5
X_train = X_train[:, np.newaxis, :, :]
# Function for initializing network weights
def initNormal():
return initializers.RandomNormal()
# Optimizer
adam = Adam(lr=0.0002, beta_1=0.5)
# Generator
generator = Sequential()
generator.add(Dense(128*7*7, input_dim=randomDim, init=initializers.RandomNormal(mean=0.0, stddev=0.05, seed=None)))
generator.add(LeakyReLU(0.2))
generator.add(Reshape((128, 7, 7)))
generator.add(UpSampling2D(size=(2, 2)))
generator.add(Convolution2D(64, 5, 5, border_mode='same'))
generator.add(LeakyReLU(0.2))
generator.add(UpSampling2D(size=(2, 2)))
generator.add(Convolution2D(1, 5, 5, border_mode='same', activation='tanh'))
generator.compile(loss='binary_crossentropy', optimizer=adam)
# Discriminator
discriminator = Sequential()
discriminator.add(Convolution2D(64, 5, 5, border_mode='same', subsample=(2, 2), input_shape=(1, 28, 28), init=initializers.RandomNormal(mean=0.0, stddev=0.05, seed=None)))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Convolution2D(128, 5, 5, border_mode='same', subsample=(2, 2)))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))
discriminator.add(Flatten())
discriminator.add(Dense(1, activation='sigmoid'))
discriminator.compile(loss='binary_crossentropy', optimizer=adam)
# Combined network
discriminator.trainable = False
ganInput = Input(shape=(randomDim,))
x = generator(ganInput)
ganOutput = discriminator(x)
gan = Model(input=ganInput, output=ganOutput)
gan.compile(loss='binary_crossentropy', optimizer=adam)
dLosses = []
gLosses = []
# Plot the loss from each batch
def plotLoss(epoch):
plt.figure(figsize=(10, 8))
plt.plot(dLosses, label='Discriminitive loss')
plt.plot(gLosses, label='Generative loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.savefig('images/dcgan_loss_epoch_%d.png' % epoch)
# Create a wall of generated MNIST images
def plotGeneratedImages(epoch, examples=100, dim=(10, 10), figsize=(10, 10)):
noise = np.random.normal(0, 1, size=[examples, randomDim])
generatedImages = generator.predict(noise)
plt.figure(figsize=figsize)
for i in range(generatedImages.shape[0]):
plt.subplot(dim[0], dim[1], i+1)
plt.imshow(generatedImages[i, 0], interpolation='nearest', cmap='gray_r')
plt.axis('off')
plt.tight_layout()
plt.savefig('images/dcgan_generated_image_epoch_%d.png' % epoch)
# Save the generator and discriminator networks (and weights) for later use
def saveModels(epoch):
generator.save('models/dcgan_generator_epoch_%d.h5' % epoch)
discriminator.save('models/dcgan_discriminator_epoch_%d.h5' % epoch)
def train(epochs=1, batchSize=128):
batchCount = X_train.shape[0] / batchSize
print('Epochs:', epochs)
print('Batch size:', batchSize)
print('Batches per epoch:', batchCount)
for e in range(1, epochs+1):
print('-'*15, 'Epoch %d' % e, '-'*15)
for _ in tqdm(range(int(batchCount))):
# Get a random set of input noise and images
noise = np.random.normal(0, 1, size=[batchSize, randomDim])
imageBatch = X_train[np.random.randint(0, X_train.shape[0], size=batchSize)]
# Generate fake MNIST images
generatedImages = generator.predict(noise)
X = np.concatenate([imageBatch, generatedImages])
# Labels for generated and real data
yDis = np.zeros(2*batchSize)
# One-sided label smoothing
yDis[:batchSize] = 0.9
# Train discriminator
discriminator.trainable = True
dloss = discriminator.train_on_batch(X, yDis)
# Train generator
noise = np.random.normal(0, 1, size=[batchSize, randomDim])
yGen = np.ones(batchSize)
discriminator.trainable = False
gloss = gan.train_on_batch(noise, yGen)
# Store loss of most recent batch from this epoch
dLosses.append(dloss)
gLosses.append(gloss)
if e == 1 or e % 5 == 0:
plotGeneratedImages(e)
saveModels(e)
# Plot losses from every epoch
plotLoss(e)
Using TensorFlow backend.
C:\Users\gtesei\AppData\Local\Continuum\Miniconda3\lib\site-packages\ipykernel_launcher.py:42: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(6272, kernel_initializer=<keras.ini..., input_dim=100)`
C:\Users\gtesei\AppData\Local\Continuum\Miniconda3\lib\site-packages\ipykernel_launcher.py:46: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (5, 5), padding="same")`
C:\Users\gtesei\AppData\Local\Continuum\Miniconda3\lib\site-packages\ipykernel_launcher.py:49: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1, (5, 5), activation="tanh", padding="same")`
C:\Users\gtesei\AppData\Local\Continuum\Miniconda3\lib\site-packages\ipykernel_launcher.py:54: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (5, 5), strides=(2, 2), padding="same", input_shape=(1, 28, 28..., kernel_initializer=<keras.ini...)`
C:\Users\gtesei\AppData\Local\Continuum\Miniconda3\lib\site-packages\ipykernel_launcher.py:57: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (5, 5), strides=(2, 2), padding="same")`
C:\Users\gtesei\AppData\Local\Continuum\Miniconda3\lib\site-packages\ipykernel_launcher.py:69: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor("se..., inputs=Tensor("in...)`
Let's run a couple of epochs just to show the deep need of GPUs in running this code.
In [2]:
train(2, 128)
Epochs: 2
Batch size: 128
Batches per epoch: 468.75
--------------- Epoch 1 ---------------
100%|██████████| 468/468 [1:00:15<00:00, 8.30s/it]
--------------- Epoch 2 ---------------
100%|██████████| 468/468 [1:01:01<00:00, 8.16s/it]
Running ~50 epochs you should obtain loss functions for generators and discriminator like this:
Is this a Nash equilibrium? I think John Nash would be the first one to be surpised.
From https://github.com/bstriner/keras-adversarial with some fixes, i.e.
In [19]:
###
import itertools
import numpy as np
from keras import backend as K
from keras import optimizers
from keras.models import Model
from keras_adversarial.adversarial_utils import fix_names, merge_updates
class AdversarialModel(Model):
"""
Adversarial training for multi-player games.
Given a base model with n targets and k players, create a model with n*k targets.
Each player optimizes loss on that player's targets.
"""
def __init__(self, player_params, base_model=None, player_models=None, player_names=None):
"""
Initialize adversarial model. Specify base_model or player_models, not both.
:param player_params: list of player parameters for each player (shared variables)
:param base_model: base model will be duplicated for each player to create player models
:param player_models: model for each player
:param player_names: names of each player (optional)
"""
assert (len(player_params) > 0)
self.player_params = player_params
self.player_count = len(self.player_params)
if player_names is None:
player_names = ["player_{}".format(i) for i in range(self.player_count)]
assert (len(player_names) == self.player_count)
self.player_names = player_names
self.generator_optimizer = None
self.discriminator_optimizer = None
self.loss = None
self.total_loss = None
self.optimizer = None
self._function_kwargs = None
if base_model is None and player_models is None:
raise ValueError("Please specify either base_model or player_models")
if base_model is not None and player_models is not None:
raise ValueError("Specify base_model or player_models, not both")
if base_model is not None:
self.layers = []
for i in range(self.player_count):
# duplicate base model
model = Model(base_model.inputs,
fix_names(base_model(base_model.inputs), base_model.output_names))
# add model to list
self.layers.append(model)
if player_models is not None:
assert (len(player_models) == self.player_count)
self.layers = player_models
def adversarial_compile(self, adversarial_optimizer, player_optimizers, loss, compile_kwargs={},
**kwargs):
"""
Configures the learning process.
:param adversarial_optimizer: instance of AdversarialOptimizer
:param player_optimizers: list of optimizers for each player
:param loss: loss function or function name
:param kwargs: additional arguments to function compilation
:return:
"""
self._function_kwargs = kwargs
self.adversarial_optimizer = adversarial_optimizer
assert (len(player_optimizers) == self.player_count)
self.optimizers = [optimizers.get(optimizer) for optimizer in player_optimizers]
self.loss = loss
self.optimizer = None
# Build player models
for opt, model in zip(self.optimizers, self.layers):
model.compile(opt, loss=self.loss, **compile_kwargs)
self.train_function = None
self.test_function = None
# Inputs are same for each model
def filter_inputs(inputs):
return inputs
self.internal_input_shapes = filter_inputs(self.layers[0].internal_input_shapes)
self.input_names = filter_inputs(self.layers[0].input_names)
self.inputs = filter_inputs(self.layers[0].inputs)
# Outputs are concatenated player models
models = self.layers
def collect(f):
return list(itertools.chain.from_iterable(f(m) for m in models))
self.internal_output_shapes = collect(lambda m: m.internal_output_shapes)
self.loss_functions = collect(lambda m: m.loss_functions)
self.targets = collect(lambda m: m.targets)
self.outputs = collect(lambda m: m.outputs)
self.sample_weights = collect(lambda m: m.sample_weights)
self.sample_weight_modes = collect(lambda m: m.sample_weight_modes)
# for each target, output name is {player}_{target}
self.output_names = []
for i in range(self.player_count):
for name in models[i].output_names:
self.output_names.append("{}_{}".format(self.player_names[i], name))
# for each metric, metric name is {player}_{metric}
self.metrics_names = ["loss"]
for i in range(self.player_count):
for name in models[i].metrics_names:
self.metrics_names.append("{}_{}".format(self.player_names[i], name))
# total loss is sum of losses
self.total_loss = np.float32(0)
for model in models:
self.total_loss += model.total_loss
# Keras-2
self._feed_loss_fns = self.loss_functions
self._feed_inputs = self.inputs
self._feed_input_names = self.input_names
self._feed_input_shapes = self.internal_input_shapes
self._feed_outputs = self.outputs
self._feed_output_names = self.output_names
self._feed_output_shapes = self.internal_output_shapes
self._feed_sample_weights = self.sample_weights
self._feed_sample_weight_modes = self.sample_weight_modes
@property
def constraints(self):
return list(itertools.chain.from_iterable(model.constraints for model in self.layers))
@property
def updates(self):
return merge_updates(list(itertools.chain.from_iterable(model.updates for model in self.layers)))
@property
def regularizers(self):
return list(itertools.chain.from_iterable(model.regularizers for model in self.layers))
def _make_train_function(self):
if not hasattr(self, 'train_function'):
raise Exception('You must compile your model before using it.')
if self.train_function is None:
inputs = self.inputs + self.targets + self.sample_weights
if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
inputs += [K.learning_phase()]
outputs = [self.total_loss]
outputs += list(itertools.chain.from_iterable(
[model.total_loss] + model.metrics_tensors
for model in self.layers))
# returns loss and metrics. Updates weights at each call.
self.train_function = self.adversarial_optimizer.make_train_function(inputs, outputs,
[model.total_loss for model in
self.layers],
self.player_params,
self.optimizers,
[model.constraints for model in
self.layers],
self.updates,
self._function_kwargs)
def _make_test_function(self):
if not hasattr(self, 'test_function'):
raise Exception('You must compile your model before using it.')
if self.test_function is None:
inputs = self.inputs + self.targets + self.sample_weights
if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
inputs += [K.learning_phase()]
outputs = [self.total_loss]
outputs += list(itertools.chain.from_iterable(
[model.total_loss] + model.metrics_tensors
for model in self.layers))
self.test_function = K.function(inputs,
outputs,
updates=self.state_updates,
**self._function_kwargs)
###
import matplotlib as mpl
# This line allows mpl to run with no DISPLAY defined
mpl.use('Agg')
import pandas as pd
import numpy as np
import os
from keras.layers import Reshape, Flatten, LeakyReLU, Activation
from keras.models import Sequential
from keras.optimizers import Adam
from keras.callbacks import TensorBoard
from keras_adversarial.image_grid_callback import ImageGridCallback
#from keras_adversarial import AdversarialModel, simple_gan, gan_targets
from keras_adversarial import simple_gan, gan_targets
from keras_adversarial import normal_latent_sampling, AdversarialOptimizerSimultaneous
from keras_adversarial.legacy import l1l2, Dense, fit
import keras.backend as K
from keras.models import Model
import numpy as np
from keras.datasets import mnist
def mnist_process(x):
x = x.astype(np.float32) / 255.0
return x
def mnist_data():
(xtrain, ytrain), (xtest, ytest) = mnist.load_data()
return mnist_process(xtrain), mnist_process(xtest)
def model_generator(latent_dim, input_shape, hidden_dim=1024, reg=lambda: l1l2(1e-5, 1e-5)):
return Sequential([
Dense(int(hidden_dim / 4), name="generator_h1", input_dim=latent_dim, W_regularizer=reg()),
LeakyReLU(0.2),
Dense(int(hidden_dim / 2), name="generator_h2", W_regularizer=reg()),
LeakyReLU(0.2),
Dense(int(hidden_dim), name="generator_h3", W_regularizer=reg()),
LeakyReLU(0.2),
Dense(np.prod(input_shape), name="generator_x_flat", W_regularizer=reg()),
Activation('sigmoid'),
Reshape(input_shape, name="generator_x")],
name="generator")
def model_discriminator(input_shape, hidden_dim=1024, reg=lambda: l1l2(1e-5, 1e-5), output_activation="sigmoid"):
return Sequential([
Flatten(name="discriminator_flatten", input_shape=input_shape),
Dense(hidden_dim, name="discriminator_h1", W_regularizer=reg()),
LeakyReLU(0.2),
Dense(int(hidden_dim / 2), name="discriminator_h2", W_regularizer=reg()),
LeakyReLU(0.2),
Dense(int(hidden_dim / 4), name="discriminator_h3", W_regularizer=reg()),
LeakyReLU(0.2),
Dense(1, name="discriminator_y", W_regularizer=reg()),
Activation(output_activation)],
name="discriminator")
def example_gan(adversarial_optimizer, path, opt_g, opt_d, nb_epoch, generator, discriminator, latent_dim,
targets=gan_targets, loss='binary_crossentropy'):
csvpath = os.path.join(path, "history.csv")
if os.path.exists(csvpath):
print("Already exists: {}".format(csvpath))
return
print("Training: {}".format(csvpath))
# gan (x - > yfake, yreal), z generated on GPU
gan = simple_gan(generator, discriminator, normal_latent_sampling((latent_dim,)))
# print summary of models
generator.summary()
discriminator.summary()
gan.summary()
# build adversarial model
model = AdversarialModel(base_model=gan,
player_params=[generator.trainable_weights, discriminator.trainable_weights],
player_names=["generator", "discriminator"])
model.adversarial_compile(adversarial_optimizer=adversarial_optimizer,
player_optimizers=[opt_g, opt_d],
loss=loss)
# create callback to generate images
zsamples = np.random.normal(size=(10 * 10, latent_dim))
def generator_sampler():
return generator.predict(zsamples).reshape((10, 10, 28, 28))
generator_cb = ImageGridCallback(os.path.join(path, "epoch-{:03d}.png"), generator_sampler)
# train model
xtrain, xtest = mnist_data()
y = targets(xtrain.shape[0])
ytest = targets(xtest.shape[0])
callbacks = [generator_cb]
if K.backend() == "tensorflow":
callbacks.append(
TensorBoard(log_dir=os.path.join(path, 'logs'), histogram_freq=0, write_graph=True, write_images=True))
history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=callbacks, nb_epoch=nb_epoch,
batch_size=32)
# save history to CSV
df = pd.DataFrame(history.history)
df.to_csv(csvpath)
# save models
generator.save(os.path.join(path, "generator.h5"))
discriminator.save(os.path.join(path, "discriminator.h5"))
## main
# z \in R^100
latent_dim = 100
# x \in R^{28x28}
input_shape = (28, 28)
# generator (z -> x)
generator = model_generator(latent_dim, input_shape)
# discriminator (x -> y)
discriminator = model_discriminator(input_shape)
example_gan(AdversarialOptimizerSimultaneous(), "output/gan",
opt_g=Adam(1e-4, decay=1e-4),
opt_d=Adam(1e-3, decay=1e-4),
nb_epoch=1, generator=generator, discriminator=discriminator,
latent_dim=latent_dim)
C:\Users\gtesei\AppData\Local\Continuum\Miniconda3\lib\site-packages\matplotlib\__init__.py:1401: UserWarning: This call to matplotlib.use() has no effect
because the backend has already been chosen;
matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.
warnings.warn(_use_error_msg)
Training: output/gan\history.csv
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
generator_h1 (Dense) (None, 256) 25856
_________________________________________________________________
leaky_re_lu_53 (LeakyReLU) (None, 256) 0
_________________________________________________________________
generator_h2 (Dense) (None, 512) 131584
_________________________________________________________________
leaky_re_lu_54 (LeakyReLU) (None, 512) 0
_________________________________________________________________
generator_h3 (Dense) (None, 1024) 525312
_________________________________________________________________
leaky_re_lu_55 (LeakyReLU) (None, 1024) 0
_________________________________________________________________
generator_x_flat (Dense) (None, 784) 803600
_________________________________________________________________
activation_17 (Activation) (None, 784) 0
_________________________________________________________________
generator_x (Reshape) (None, 28, 28) 0
=================================================================
Total params: 1,486,352.0
Trainable params: 1,486,352.0
Non-trainable params: 0.0
_________________________________________________________________
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
discriminator_flatten (Flatt (None, 784) 0
_________________________________________________________________
discriminator_h1 (Dense) (None, 1024) 803840
_________________________________________________________________
leaky_re_lu_56 (LeakyReLU) (None, 1024) 0
_________________________________________________________________
discriminator_h2 (Dense) (None, 512) 524800
_________________________________________________________________
leaky_re_lu_57 (LeakyReLU) (None, 512) 0
_________________________________________________________________
discriminator_h3 (Dense) (None, 256) 131328
_________________________________________________________________
leaky_re_lu_58 (LeakyReLU) (None, 256) 0
_________________________________________________________________
discriminator_y (Dense) (None, 1) 257
_________________________________________________________________
activation_18 (Activation) (None, 1) 0
=================================================================
Total params: 1,460,225.0
Trainable params: 1,460,225.0
Non-trainable params: 0.0
_________________________________________________________________
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
discriminator_flatten_input (Inp (None, 28, 28) 0
____________________________________________________________________________________________________
lambda_5 (Lambda) (None, 100) 0
____________________________________________________________________________________________________
gan (Model) [(None, 1), (None, 1) 2946577
____________________________________________________________________________________________________
yfake (Activation) (None, 1) 0
____________________________________________________________________________________________________
yreal (Activation) (None, 1) 0
====================================================================================================
Total params: 2,946,577.0
Trainable params: 2,946,577.0
Non-trainable params: 0.0
____________________________________________________________________________________________________
Train on 60000 samples, validate on 10000 samples
Epoch 1/1
1216/60000 [..............................] - ETA: 1393s - loss: 20.9093 - generator_loss: 10.5030 - generator_yfake_loss: 0.7347 - generator_yreal_loss: 0.7024 - discriminator_loss: 10.4063 - discriminator_yfake_loss: 0.6534 - discriminator_yreal_loss: 0.687 - ETA: 873s - loss: 22.7545 - generator_loss: 12.6496 - generator_yfake_loss: 2.9077 - generator_yreal_loss: 0.6862 - discriminator_loss: 10.1049 - discriminator_yfake_loss: 0.3298 - discriminator_yreal_loss: 0.719 - ETA: 687s - loss: 23.3306 - generator_loss: 13.5439 - generator_yfake_loss: 3.2751 - generator_yreal_loss: 1.2256 - discriminator_loss: 9.7867 - discriminator_yfake_loss: 0.2260 - discriminator_yreal_loss: 0.5176 - ETA: 591s - loss: 23.7990 - generator_loss: 14.1803 - generator_yfake_loss: 3.0089 - generator_yreal_loss: 2.1428 - discriminator_loss: 9.6187 - discriminator_yfake_loss: 0.1985 - discriminator_yreal_loss: 0.391 - ETA: 536s - loss: 25.2511 - generator_loss: 15.7637 - generator_yfake_loss: 3.7424 - generator_yreal_loss: 3.0076 - discriminator_loss: 9.4874 - discriminator_yfake_loss: 0.1591 - discriminator_yreal_loss: 0.314 - ETA: 505s - loss: 26.9377 - generator_loss: 17.5443 - generator_yfake_loss: 4.8452 - generator_yreal_loss: 3.7009 - discriminator_loss: 9.3934 - discriminator_yfake_loss: 0.1326 - discriminator_yreal_loss: 0.262 - ETA: 474s - loss: 28.8163 - generator_loss: 19.4958 - generator_yfake_loss: 6.0911 - generator_yreal_loss: 4.4231 - discriminator_loss: 9.3205 - discriminator_yfake_loss: 0.1136 - discriminator_yreal_loss: 0.225 - ETA: 458s - loss: 30.6860 - generator_loss: 21.4253 - generator_yfake_loss: 7.3445 - generator_yreal_loss: 5.1169 - discriminator_loss: 9.2606 - discriminator_yfake_loss: 0.0994 - discriminator_yreal_loss: 0.197 - ETA: 443s - loss: 32.3103 - generator_loss: 23.1012 - generator_yfake_loss: 8.3193 - generator_yreal_loss: 5.8365 - discriminator_loss: 9.2091 - discriminator_yfake_loss: 0.0884 - discriminator_yreal_loss: 0.175 - ETA: 431s - loss: 33.6112 - generator_loss: 24.4480 - generator_yfake_loss: 9.0992 - generator_yreal_loss: 6.4230 - discriminator_loss: 9.1632 - discriminator_yfake_loss: 0.0795 - discriminator_yreal_loss: 0.157 - ETA: 422s - loss: 34.6799 - generator_loss: 25.5587 - generator_yfake_loss: 9.7373 - generator_yreal_loss: 6.9161 - discriminator_loss: 9.1211 - discriminator_yfake_loss: 0.0723 - discriminator_yreal_loss: 0.143 - ETA: 413s - loss: 35.6271 - generator_loss: 26.5452 - generator_yfake_loss: 10.2690 - generator_yreal_loss: 7.3922 - discriminator_loss: 9.0819 - discriminator_yfake_loss: 0.0663 - discriminator_yreal_loss: 0.13 - ETA: 404s - loss: 36.4844 - generator_loss: 27.4399 - generator_yfake_loss: 10.7189 - generator_yreal_loss: 7.8590 - discriminator_loss: 9.0446 - discriminator_yfake_loss: 0.0612 - discriminator_yreal_loss: 0.12 - ETA: 400s - loss: 37.1623 - generator_loss: 28.1536 - generator_yfake_loss: 11.1046 - generator_yreal_loss: 8.2100 - discriminator_loss: 9.0087 - discriminator_yfake_loss: 0.0568 - discriminator_yreal_loss: 0.11 - ETA: 397s - loss: 37.8383 - generator_loss: 28.8645 - generator_yfake_loss: 11.4388 - generator_yreal_loss: 8.6101 - discriminator_loss: 8.9739 - discriminator_yfake_loss: 0.0530 - discriminator_yreal_loss: 0.10 - ETA: 391s - loss: 38.4368 - generator_loss: 29.4970 - generator_yfake_loss: 11.7313 - generator_yreal_loss: 8.9744 - discriminator_loss: 8.9398 - discriminator_yfake_loss: 0.0497 - discriminator_yreal_loss: 0.09 - ETA: 386s - loss: 38.9714 - generator_loss: 30.0651 - generator_yfake_loss: 11.9893 - generator_yreal_loss: 9.3091 - discriminator_loss: 8.9063 - discriminator_yfake_loss: 0.0468 - discriminator_yreal_loss: 0.09 - ETA: 382s - loss: 39.4535 - generator_loss: 30.5803 - generator_yfake_loss: 12.2187 - generator_yreal_loss: 9.6203 - discriminator_loss: 8.8732 - discriminator_yfake_loss: 0.0442 - discriminator_yreal_loss: 0.08 - ETA: 377s - loss: 39.8298 - generator_loss: 30.9893 - generator_yfake_loss: 12.4239 - generator_yreal_loss: 9.8499 - discriminator_loss: 8.8405 - discriminator_yfake_loss: 0.0419 - discriminator_yreal_loss: 0.08 - ETA: 374s - loss: 40.2044 - generator_loss: 31.3964 - generator_yfake_loss: 12.6086 - generator_yreal_loss: 10.0986 - discriminator_loss: 8.8080 - discriminator_yfake_loss: 0.0398 - discriminator_yreal_loss: 0.079 - ETA: 372s - loss: 40.5196 - generator_loss: 31.7441 - generator_yfake_loss: 12.7758 - generator_yreal_loss: 10.3059 - discriminator_loss: 8.7756 - discriminator_yfake_loss: 0.0379 - discriminator_yreal_loss: 0.075 - ETA: 369s - loss: 40.8191 - generator_loss: 32.0758 - generator_yfake_loss: 12.9277 - generator_yreal_loss: 10.5129 - discriminator_loss: 8.7433 - discriminator_yfake_loss: 0.0362 - discriminator_yreal_loss: 0.071 - ETA: 366s - loss: 41.1067 - generator_loss: 32.3957 - generator_yfake_loss: 13.0664 - generator_yreal_loss: 10.7215 - discriminator_loss: 8.7111 - discriminator_yfake_loss: 0.0346 - discriminator_yreal_loss: 0.068 - ETA: 362s - loss: 41.3485 - generator_loss: 32.6696 - generator_yfake_loss: 13.1936 - generator_yreal_loss: 10.8962 - discriminator_loss: 8.6789 - discriminator_yfake_loss: 0.0331 - discriminator_yreal_loss: 0.065 - ETA: 361s - loss: 41.5792 - generator_loss: 32.9324 - generator_yfake_loss: 13.3105 - generator_yreal_loss: 11.0702 - discriminator_loss: 8.6467 - discriminator_yfake_loss: 0.0318 - discriminator_yreal_loss: 0.063 - ETA: 359s - loss: 41.7930 - generator_loss: 33.1784 - generator_yfake_loss: 13.4185 - generator_yreal_loss: 11.2367 - discriminator_loss: 8.6146 - discriminator_yfake_loss: 0.0306 - discriminator_yreal_loss: 0.060 - ETA: 360s - loss: 41.9857 - generator_loss: 33.4032 - generator_yfake_loss: 13.5185 - generator_yreal_loss: 11.3903 - discriminator_loss: 8.5825 - discriminator_yfake_loss: 0.0295 - discriminator_yreal_loss: 0.058 - ETA: 360s - loss: 42.1345 - generator_loss: 33.5841 - generator_yfake_loss: 13.6113 - generator_yreal_loss: 11.5073 - discriminator_loss: 8.5504 - discriminator_yfake_loss: 0.0284 - discriminator_yreal_loss: 0.056 - ETA: 359s - loss: 42.2917 - generator_loss: 33.7735 - generator_yfake_loss: 13.6978 - generator_yreal_loss: 11.6395 - discriminator_loss: 8.5182 - discriminator_yfake_loss: 0.0274 - discriminator_yreal_loss: 0.054 - ETA: 359s - loss: 42.4484 - generator_loss: 33.9624 - generator_yfake_loss: 13.7785 - generator_yreal_loss: 11.7772 - discriminator_loss: 8.4861 - discriminator_yfake_loss: 0.0265 - discriminator_yreal_loss: 0.052 - ETA: 356s - loss: 42.5738 - generator_loss: 34.1199 - generator_yfake_loss: 13.8539 - generator_yreal_loss: 11.8889 - discriminator_loss: 8.4539 - discriminator_yfake_loss: 0.0257 - discriminator_yreal_loss: 0.051 - ETA: 356s - loss: 42.6914 - generator_loss: 34.2697 - generator_yfake_loss: 13.9247 - generator_yreal_loss: 11.9977 - discriminator_loss: 8.4217 - discriminator_yfake_loss: 0.0249 - discriminator_yreal_loss: 0.049 - ETA: 356s - loss: 42.7763 - generator_loss: 34.3868 - generator_yfake_loss: 13.9912 - generator_yreal_loss: 12.0783 - discriminator_loss: 8.3894 - discriminator_yfake_loss: 0.0241 - discriminator_yreal_loss: 0.048 - ETA: 354s - loss: 42.8683 - generator_loss: 34.5111 - generator_yfake_loss: 14.0537 - generator_yreal_loss: 12.1701 - discriminator_loss: 8.3572 - discriminator_yfake_loss: 0.0234 - discriminator_yreal_loss: 0.046 - ETA: 354s - loss: 42.9405 - generator_loss: 34.6154 - generator_yfake_loss: 14.1127 - generator_yreal_loss: 12.2457 - discriminator_loss: 8.3251 - discriminator_yfake_loss: 0.0227 - discriminator_yreal_loss: 0.045 - ETA: 353s - loss: 43.0162 - generator_loss: 34.7233 - generator_yfake_loss: 14.1684 - generator_yreal_loss: 12.3282 - discriminator_loss: 8.2929 - discriminator_yfake_loss: 0.0221 - discriminator_yreal_loss: 0.044 - ETA: 351s - loss: 43.1013 - generator_loss: 34.8406 - generator_yfake_loss: 14.2211 - generator_yreal_loss: 12.4232 - discriminator_loss: 8.2607 - discriminator_yfake_loss: 0.0215 - discriminator_yreal_loss: 0.042 - ETA: 350s - loss: 43.1689 - generator_loss: 34.9404 - generator_yfake_loss: 14.2710 - generator_yreal_loss: 12.5036 - discriminator_loss: 8.2285 - discriminator_yfake_loss: 0.0209 - discriminator_yreal_loss: 0.0417 2432/60000 [>.............................] - ETA: 349s - loss: 43.2218 - generator_loss: 35.0254 - generator_yfake_loss: 14.3184 - generator_yreal_loss: 12.5717 - discriminator_loss: 8.1964 - discriminator_yfake_loss: 0.0204 - discriminator_yreal_loss: 0.040 - ETA: 348s - loss: 43.2717 - generator_loss: 35.1074 - generator_yfake_loss: 14.3634 - generator_yreal_loss: 12.6394 - discriminator_loss: 8.1643 - discriminator_yfake_loss: 0.0199 - discriminator_yreal_loss: 0.039 - ETA: 346s - loss: 43.3220 - generator_loss: 35.1898 - generator_yfake_loss: 14.4062 - generator_yreal_loss: 12.7096 - discriminator_loss: 8.1322 - discriminator_yfake_loss: 0.0194 - discriminator_yreal_loss: 0.038 - ETA: 345s - loss: 43.3614 - generator_loss: 35.2613 - generator_yfake_loss: 14.4469 - generator_yreal_loss: 12.7709 - discriminator_loss: 8.1001 - discriminator_yfake_loss: 0.0189 - discriminator_yreal_loss: 0.037 - ETA: 344s - loss: 43.3876 - generator_loss: 35.3194 - generator_yfake_loss: 14.4858 - generator_yreal_loss: 12.8209 - discriminator_loss: 8.0681 - discriminator_yfake_loss: 0.0185 - discriminator_yreal_loss: 0.036 - ETA: 343s - loss: 43.4154 - generator_loss: 35.3792 - generator_yfake_loss: 14.5229 - generator_yreal_loss: 12.8743 - discriminator_loss: 8.0362 - discriminator_yfake_loss: 0.0181 - discriminator_yreal_loss: 0.036 - ETA: 342s - loss: 43.4301 - generator_loss: 35.4258 - generator_yfake_loss: 14.5583 - generator_yreal_loss: 12.9160 - discriminator_loss: 8.0043 - discriminator_yfake_loss: 0.0177 - discriminator_yreal_loss: 0.035 - ETA: 341s - loss: 43.4597 - generator_loss: 35.4872 - generator_yfake_loss: 14.5922 - generator_yreal_loss: 12.9742 - discriminator_loss: 7.9725 - discriminator_yfake_loss: 0.0173 - discriminator_yreal_loss: 0.034 - ETA: 340s - loss: 43.4820 - generator_loss: 35.5413 - generator_yfake_loss: 14.6247 - generator_yreal_loss: 13.0265 - discriminator_loss: 7.9407 - discriminator_yfake_loss: 0.0169 - discriminator_yreal_loss: 0.033 - ETA: 339s - loss: 43.4980 - generator_loss: 35.5889 - generator_yfake_loss: 14.6558 - generator_yreal_loss: 13.0737 - discriminator_loss: 7.9091 - discriminator_yfake_loss: 0.0166 - discriminator_yreal_loss: 0.033 - ETA: 338s - loss: 43.4967 - generator_loss: 35.6192 - generator_yfake_loss: 14.6857 - generator_yreal_loss: 13.1047 - discriminator_loss: 7.8775 - discriminator_yfake_loss: 0.0162 - discriminator_yreal_loss: 0.032 - ETA: 337s - loss: 43.4860 - generator_loss: 35.6400 - generator_yfake_loss: 14.7143 - generator_yreal_loss: 13.1274 - discriminator_loss: 7.8460 - discriminator_yfake_loss: 0.0159 - discriminator_yreal_loss: 0.031 - ETA: 336s - loss: 43.4764 - generator_loss: 35.6618 - generator_yfake_loss: 14.7418 - generator_yreal_loss: 13.1521 - discriminator_loss: 7.8146 - discriminator_yfake_loss: 0.0156 - discriminator_yreal_loss: 0.031 - ETA: 335s - loss: 43.4714 - generator_loss: 35.6881 - generator_yfake_loss: 14.7683 - generator_yreal_loss: 13.1823 - discriminator_loss: 7.7833 - discriminator_yfake_loss: 0.0153 - discriminator_yreal_loss: 0.030 - ETA: 335s - loss: 43.4696 - generator_loss: 35.7175 - generator_yfake_loss: 14.7938 - generator_yreal_loss: 13.2166 - discriminator_loss: 7.7521 - discriminator_yfake_loss: 0.0150 - discriminator_yreal_loss: 0.030 - ETA: 334s - loss: 43.4514 - generator_loss: 35.7304 - generator_yfake_loss: 14.8183 - generator_yreal_loss: 13.2352 - discriminator_loss: 7.7210 - discriminator_yfake_loss: 0.0147 - discriminator_yreal_loss: 0.029 - ETA: 335s - loss: 43.4403 - generator_loss: 35.7503 - generator_yfake_loss: 14.8419 - generator_yreal_loss: 13.2617 - discriminator_loss: 7.6900 - discriminator_yfake_loss: 0.0145 - discriminator_yreal_loss: 0.028 - ETA: 334s - loss: 43.4095 - generator_loss: 35.7503 - generator_yfake_loss: 14.8647 - generator_yreal_loss: 13.2690 - discriminator_loss: 7.6592 - discriminator_yfake_loss: 0.0142 - discriminator_yreal_loss: 0.028 - ETA: 333s - loss: 43.3978 - generator_loss: 35.7693 - generator_yfake_loss: 14.8867 - generator_yreal_loss: 13.2960 - discriminator_loss: 7.6284 - discriminator_yfake_loss: 0.0140 - discriminator_yreal_loss: 0.027 - ETA: 333s - loss: 43.3791 - generator_loss: 35.7813 - generator_yfake_loss: 14.9079 - generator_yreal_loss: 13.3167 - discriminator_loss: 7.5978 - discriminator_yfake_loss: 0.0137 - discriminator_yreal_loss: 0.027 - ETA: 333s - loss: 43.3635 - generator_loss: 35.7962 - generator_yfake_loss: 14.9285 - generator_yreal_loss: 13.3408 - discriminator_loss: 7.5673 - discriminator_yfake_loss: 0.0135 - discriminator_yreal_loss: 0.026 - ETA: 333s - loss: 43.3290 - generator_loss: 35.7920 - generator_yfake_loss: 14.9483 - generator_yreal_loss: 13.3464 - discriminator_loss: 7.5370 - discriminator_yfake_loss: 0.0133 - discriminator_yreal_loss: 0.026 - ETA: 333s - loss: 43.2890 - generator_loss: 35.7822 - generator_yfake_loss: 14.9675 - generator_yreal_loss: 13.3470 - discriminator_loss: 7.5068 - discriminator_yfake_loss: 0.0130 - discriminator_yreal_loss: 0.026 - ETA: 334s - loss: 43.2518 - generator_loss: 35.7750 - generator_yfake_loss: 14.9860 - generator_yreal_loss: 13.3506 - discriminator_loss: 7.4768 - discriminator_yfake_loss: 0.0128 - discriminator_yreal_loss: 0.025 - ETA: 335s - loss: 43.2097 - generator_loss: 35.7628 - generator_yfake_loss: 15.0040 - generator_yreal_loss: 13.3498 - discriminator_loss: 7.4469 - discriminator_yfake_loss: 0.0126 - discriminator_yreal_loss: 0.025 - ETA: 334s - loss: 43.1726 - generator_loss: 35.7554 - generator_yfake_loss: 15.0214 - generator_yreal_loss: 13.3542 - discriminator_loss: 7.4171 - discriminator_yfake_loss: 0.0124 - discriminator_yreal_loss: 0.024 - ETA: 334s - loss: 43.1263 - generator_loss: 35.7387 - generator_yfake_loss: 15.0383 - generator_yreal_loss: 13.3496 - discriminator_loss: 7.3876 - discriminator_yfake_loss: 0.0122 - discriminator_yreal_loss: 0.024 - ETA: 333s - loss: 43.0891 - generator_loss: 35.7310 - generator_yfake_loss: 15.0546 - generator_yreal_loss: 13.3543 - discriminator_loss: 7.3581 - discriminator_yfake_loss: 0.0121 - discriminator_yreal_loss: 0.024 - ETA: 334s - loss: 43.0385 - generator_loss: 35.7097 - generator_yfake_loss: 15.0705 - generator_yreal_loss: 13.3459 - discriminator_loss: 7.3289 - discriminator_yfake_loss: 0.0119 - discriminator_yreal_loss: 0.023 - ETA: 334s - loss: 42.9887 - generator_loss: 35.6889 - generator_yfake_loss: 15.0859 - generator_yreal_loss: 13.3383 - discriminator_loss: 7.2998 - discriminator_yfake_loss: 0.0117 - discriminator_yreal_loss: 0.023 - ETA: 334s - loss: 42.9395 - generator_loss: 35.6686 - generator_yfake_loss: 15.1008 - generator_yreal_loss: 13.3315 - discriminator_loss: 7.2709 - discriminator_yfake_loss: 0.0115 - discriminator_yreal_loss: 0.023 - ETA: 334s - loss: 42.8814 - generator_loss: 35.6393 - generator_yfake_loss: 15.1148 - generator_yreal_loss: 13.3164 - discriminator_loss: 7.2421 - discriminator_yfake_loss: 0.0114 - discriminator_yreal_loss: 0.022 - ETA: 334s - loss: 42.8306 - generator_loss: 35.6171 - 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discriminator_yreal_loss: 0.052 - ETA: 1s - loss: 25.2511 - generator_loss: 21.9258 - generator_yfake_loss: 9.4309 - generator_yreal_loss: 9.2400 - discriminator_loss: 3.3253 - discriminator_yfake_loss: 0.0177 - discriminator_yreal_loss: 0.052 - ETA: 1s - loss: 25.2498 - generator_loss: 21.9249 - generator_yfake_loss: 9.4299 - generator_yreal_loss: 9.2404 - discriminator_loss: 3.3249 - discriminator_yfake_loss: 0.0177 - discriminator_yreal_loss: 0.052 - ETA: 1s - loss: 25.2479 - generator_loss: 21.9234 - generator_yfake_loss: 9.4287 - generator_yreal_loss: 9.2404 - discriminator_loss: 3.3245 - discriminator_yfake_loss: 0.0177 - discriminator_yreal_loss: 0.052 - ETA: 0s - loss: 25.2460 - generator_loss: 21.9220 - generator_yfake_loss: 9.4272 - generator_yreal_loss: 9.2409 - discriminator_loss: 3.3241 - discriminator_yfake_loss: 0.0177 - discriminator_yreal_loss: 0.052 - ETA: 0s - loss: 25.2437 - generator_loss: 21.9200 - generator_yfake_loss: 9.4256 - generator_yreal_loss: 9.2408 - discriminator_loss: 3.3237 - discriminator_yfake_loss: 0.0177 - discriminator_yreal_loss: 0.052 - ETA: 0s - loss: 25.2416 - generator_loss: 21.9183 - generator_yfake_loss: 9.4237 - generator_yreal_loss: 9.2414 - discriminator_loss: 3.3233 - discriminator_yfake_loss: 0.0177 - discriminator_yreal_loss: 0.052 - ETA: 0s - loss: 25.2392 - generator_loss: 21.9163 - generator_yfake_loss: 9.4214 - generator_yreal_loss: 9.2421 - discriminator_loss: 3.3229 - discriminator_yfake_loss: 0.0177 - discriminator_yreal_loss: 0.052 - ETA: 0s - loss: 25.2368 - generator_loss: 21.9143 - generator_yfake_loss: 9.4188 - generator_yreal_loss: 9.2430 - discriminator_loss: 3.3225 - discriminator_yfake_loss: 0.0177 - discriminator_yreal_loss: 0.052 - 371s - loss: 25.2341 - generator_loss: 21.9120 - generator_yfake_loss: 9.4161 - generator_yreal_loss: 9.2439 - discriminator_loss: 3.3221 - discriminator_yfake_loss: 0.0177 - discriminator_yreal_loss: 0.0524 - val_loss: 21.4125 - val_generator_loss: 18.8426 - val_generator_yfake_loss: 5.9609 - val_generator_yreal_loss: 10.3167 - val_discriminator_loss: 2.5699 - val_discriminator_yfake_loss: 0.0045 - val_discriminator_yreal_loss: 3.7174e-04
In [22]:
df = pd.read_csv('output/gan/history.csv')
df.shape
Out[22]:
(100, 15)
In [21]:
df.head()
Out[21]:
Unnamed: 0
discriminator_loss
discriminator_yfake_loss
discriminator_yreal_loss
generator_loss
generator_yfake_loss
generator_yreal_loss
loss
val_discriminator_loss
val_discriminator_yfake_loss
val_discriminator_yreal_loss
val_generator_loss
val_generator_yfake_loss
val_generator_yreal_loss
val_loss
0
0
3.453599
0.017599
0.046017
22.398084
9.886845
9.121255
25.851683
2.837423
0.007878
0.023491
17.631302
5.865684
8.959589
20.468726
1
1
2.685198
0.013137
0.023119
19.110583
6.527748
9.933892
21.795780
2.655671
0.002365
0.055328
19.403337
6.486853
10.318492
22.059007
2
2
2.733424
0.034775
0.056316
17.596399
6.061786
8.892281
20.329823
2.782662
0.095978
0.039664
15.591951
3.680485
9.264438
18.374613
3
3
2.711415
0.038075
0.059557
17.034416
5.234308
9.186324
19.745831
2.672150
0.107613
0.018004
19.407632
5.702570
11.158514
22.079783
4
4
2.580407
0.035539
0.056427
19.009651
5.461642
11.059568
21.590058
2.509613
0.036477
0.043890
18.703876
4.805115
11.469529
21.213489
In [34]:
import matplotlib.pyplot as plt
plt.figure(figsize=(15, 10))
plt.plot(df['discriminator_loss'], label='discriminator_loss')
plt.plot(df['discriminator_yfake_loss'], label='discriminator_yfake_loss')
plt.plot(df['discriminator_yreal_loss'], label='discriminator_yreal_loss')
plt.plot(df['generator_loss'], label='generator_loss')
plt.plot(df['generator_yfake_loss'], label='generator_yfake_loss')
plt.plot(df['generator_yreal_loss'], label='generator_yreal_loss')
plt.plot(df['loss'], label='loss')
plt.plot(df['val_discriminator_loss'], label='val_discriminator_loss')
plt.plot(df['val_discriminator_yfake_loss'], label='val_discriminator_yfake_loss')
plt.plot(df['val_discriminator_yreal_loss'], label='val_discriminator_yreal_loss')
plt.plot(df['val_generator_loss'], label='val_generator_loss')
plt.plot(df['val_generator_yfake_loss'], label='val_generator_yfake_loss')
plt.plot(df['val_generator_yreal_loss'], label='val_generator_yreal_loss')
plt.plot(df['val_loss'], label='val_loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend(loc=1)
plt.show()
WaveNet is a deep generative model for producing raw audio waveforms. This breakthrough technology was introduced (https://deepmind.com/blog/wavenet-generative-model-raw-audio/) by Google DeepMind (https://deepmind.com/) for teaching users how to speak to computers. The results are truly impressive, and you can find online examples of synthetic voices where the computer learns how to talk with the voices of celebrities such as Matt Damon. So, you might wonder why learning to synthesize audio is so difficult. Well, each digital sound we hear is based on 16,000 samples per second (sometimes, 48,000 or more), and building a predictive model where we learn to reproduce a sample based on all the previous ones is a very difficult challenge. Nevertheless, there are experiments showing that WaveNet has improved current state-of-the-art text-to-speech (TTS) systems, reducing the difference with human voices by 50% for both US English and Mandarin Chinese. What is even cooler is that DeepMind proved that WaveNet can also be used to teach computers how to generate the sound of musical instruments such as piano music.
Now it's time for some definitions. TTS systems are typically divided into two different classes:
In principle, WaveNet can be seen as a stack of 1D convolutional layers with a constant stride of one and with no pooling layers. Note that the input and the output have by construction the same dimension, so ConvNet is well-suited to model sequential data such as audio. However, it has been shown that in order to reach a large size for the receptive field (remember that the receptive field of a neuron in a layer is the cross section of the previous layer from which neurons provide inputs) in the output neuron it is necessary to either use a massive number of large filters or prohibitively increase the the depth of the network. For this reason, pure ConvNets are not so effective in learning how to synthesize audio. The key intuition beyond WaveNet is the dilated causal convolutions (for more information refer to the article: Multi-Scale Context Aggregation by Dilated Convolutions, by Fisher Yu, Vladlen Koltun, 2016) or sometime atrous convolution (atrous is the bastardization of the French expression à trous, meaning with holes, so an atrous convolution is a convolution with holes), which simply means that some input values are skipped when the filter of a convolutional layer is applied.
Thanks to this simple idea of introducing holes, it is possible to stack multiple dilated convolutional layers with exponentially increasing filters, and learn long range input dependencies without having an excessively deep network. A WaveNet is therefore a ConvNet where the convolutional layers have various dilation factors, allowing the receptive field to grow exponentially with depth and therefore efficiently cover thousands of audio time-steps. When we train, the input are sounds recorded from human speakers. The waveforms are quantized to a fixed integer range. A WaveNet defines an initial convolutional layer accessing only the current and previous input. Then, there is a stack of dilated ConvNet layers, still accessing only current and previous inputs. At the end, there is a series of dense layers that combine previous results, followed by a softmax activation function for categorical outputs. At each step, a value is predicted from the network and fed back into the input. At the same time, a new prediction for the next step is computed. The loss function is the cross-entropy between the output for the current step and the input at the next step .
One Keras implementation developed by Bas Veeling is available at: https://github.com/basveeling/wavenet and can be easily installed via git and here is a sample
Content source: gtesei/DeepExperiments
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