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 normal_latent_sampling, AdversarialOptimizerSimultaneous
from keras_adversarial.legacy import l1l2, Dense, fit
import keras.backend as K
from mnist_utils import mnist_data


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(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"))


def 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=100, generator=generator, discriminator=discriminator,
                latent_dim=latent_dim)


if __name__ == "__main__":
    main()

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 normal_latent_sampling, AdversarialOptimizerSimultaneous
from keras_adversarial.legacy import l1l2, Dense, fit
import keras.backend as K
from mnist_utils import mnist_data


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(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"))


def 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=100, generator=generator, discriminator=discriminator,
                latent_dim=latent_dim)


if __name__ == "__main__":
    main()

# This line allows mpl to run with no DISPLAY defined # gan (x - > yfake, yreal), z generated on GPU # print summary of models # build adversarial model # create callback to generate images # train model # save history to CSV # save models # z \in R^100 # x \in R^{28x28} # generator (z -> x) # discriminator (x -> y)