570 lines
26 KiB
Python
570 lines
26 KiB
Python
# -*- coding: utf-8 -*-
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#
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# Copyright 2019 Jim Martens
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Training functionality for my AAE implementation.
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This module provides functions to train the Adversarial Auto Encoder.
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Attributes:
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GRACE: specifies the number of epochs that the training loss can stagnate or worsen
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before the training is stopped early
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TOTAL_LOSS_GRACE_CAP: upper limit for total loss, grace countdown only enabled if total loss higher
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Functions:
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prepare_training_data(...): prepares the mnist training data
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train(...): trains the AAE models
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Todos:
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- fix early stopping
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- fix losses reaching exactly zero
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"""
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import functools
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import os
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import time
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from typing import Callable
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from typing import Dict
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from typing import Tuple
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import math
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import tensorflow as tf
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from tensorflow.python.ops import summary_ops_v2
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from twomartens.masterthesis.aae import model
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from twomartens.masterthesis.aae import util
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from twomartens.masterthesis.aae.train import LOG_FREQUENCY
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# shortcuts for tensorflow sub packages and classes
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K = tf.keras.backend
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tfe = tf.contrib.eager
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GRACE: int = 10
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TOTAL_LOSS_GRACE_CAP: int = 6
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def train(dataset: tf.data.Dataset,
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iteration: int,
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weights_prefix: str,
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channels: int = 1,
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zsize: int = 32,
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lr: float = 0.002,
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batch_size: int = 128,
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train_epoch: int = 80,
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verbose: bool = True,
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early_stopping: bool = False) -> None:
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"""
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Trains AAE for given data set.
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This function provides early stopping and creates checkpoints after every
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epoch as well as after finishing training (or stopping early). When starting
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this function with the same ``iteration`` then the training will try to
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continue where it ended last time by restoring a saved checkpoint.
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The loss values are provided as scalar summaries. Reconstruction and sample
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images are provided as summary images.
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Args:
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dataset: train dataset
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iteration: identifier for the current training run
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weights_prefix: prefix for weights directory
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channels: number of channels in input image (default: 1)
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zsize: size of the intermediary z (default: 32)
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lr: initial learning rate (default: 0.002)
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batch_size: the size of each batch (default: 128)
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train_epoch: number of epochs to train (default: 80)
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verbose: if True prints train progress info to console (default: True)
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early_stopping: if True the early stopping mechanic is enabled (default: False)
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Notes:
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The training stops early if for ``GRACE`` number of epochs the loss is not
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decreasing. Specifically all individual losses are accounted for and any one
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of those not decreasing triggers a ``strike``. If the total loss, which is
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a sum of all individual losses, is also not decreasing and has a total
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value of more than ``TOTAL_LOSS_GRACE_CAP``, the counter for the remaining grace period is
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decreased. If in any epoch afterwards all losses are decreasing the grace
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period is reset to ``GRACE``. Lastly the training loop will be stopped early
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if the grace counter reaches ``0`` at the end of an epoch.
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"""
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# non-preserved tensors
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y_real = K.ones(batch_size)
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y_fake = K.zeros(batch_size)
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sample = K.expand_dims(K.expand_dims(K.random_normal((64, zsize)), axis=1), axis=1)
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# z generator function
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z_generator = functools.partial(_get_z_variable, batch_size=batch_size, zsize=zsize)
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# non-preserved python variables
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encoder_lowest_loss = math.inf
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decoder_lowest_loss = math.inf
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enc_dec_lowest_loss = math.inf
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zd_lowest_loss = math.inf
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xd_lowest_loss = math.inf
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total_lowest_loss = math.inf
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grace_period = GRACE
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# checkpointed tensors and variables
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checkpointables = {
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'learning_rate_var': K.variable(lr),
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}
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checkpointables.update({
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# get models
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'encoder': model.Encoder(zsize),
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'decoder': model.Decoder(channels, zsize),
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'z_discriminator': model.ZDiscriminator(),
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'x_discriminator': model.XDiscriminator(),
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# define optimizers
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'decoder_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
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beta1=0.5, beta2=0.999),
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'enc_dec_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
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beta1=0.5, beta2=0.999),
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'z_discriminator_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
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beta1=0.5, beta2=0.999),
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'x_discriminator_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
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beta1=0.5, beta2=0.999),
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# global step counter
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'epoch_var': K.variable(-1, dtype=tf.int64),
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'global_step': tf.train.get_or_create_global_step(),
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'global_step_decoder': K.variable(0, dtype=tf.int64),
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'global_step_enc_dec': K.variable(0, dtype=tf.int64),
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'global_step_xd': K.variable(0, dtype=tf.int64),
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'global_step_zd': K.variable(0, dtype=tf.int64),
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})
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# checkpoint
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checkpoint_dir = os.path.join(weights_prefix, str(iteration) + '/')
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os.makedirs(checkpoint_dir, exist_ok=True)
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checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')
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latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
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checkpoint = tf.train.Checkpoint(**checkpointables)
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checkpoint.restore(latest_checkpoint)
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def _get_last_epoch(epoch_var: tf.Variable, **kwargs) -> int:
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return int(epoch_var)
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last_epoch = _get_last_epoch(**checkpointables)
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previous_epochs = 0
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if last_epoch != -1:
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previous_epochs = last_epoch + 1
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with summary_ops_v2.always_record_summaries():
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summary_ops_v2.scalar(name='learning_rate', tensor=checkpointables['learning_rate_var'],
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step=checkpointables['global_step'])
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for epoch in range(train_epoch - previous_epochs):
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_epoch = epoch + previous_epochs
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outputs = _train_one_epoch(_epoch, dataset, targets_real=y_real,
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targets_fake=y_fake, z_generator=z_generator,
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verbose=verbose, batch_size=batch_size,
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**checkpointables)
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if verbose:
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print((
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f"[{_epoch + 1:d}/{train_epoch:d}] - "
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f"train time: {outputs['per_epoch_time']:.2f}, "
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f"Decoder loss: {outputs['decoder_loss']:.3f}, "
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f"X Discriminator loss: {outputs['xd_loss']:.3f}, "
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f"Z Discriminator loss: {outputs['zd_loss']:.3f}, "
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f"Encoder + Decoder loss: {outputs['enc_dec_loss']:.3f}, "
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f"Encoder loss: {outputs['encoder_loss']:.3f}"
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))
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# save sample image summary
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def _save_sample(decoder: model.Decoder, global_step: tf.Variable, **kwargs) -> None:
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resultsample = decoder(sample).cpu()
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grid = util.prepare_image(resultsample)
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summary_ops_v2.image(name='sample', tensor=K.expand_dims(grid, axis=0),
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max_images=1, step=global_step)
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with summary_ops_v2.always_record_summaries():
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_save_sample(**checkpointables)
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# save weights at end of epoch
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checkpoint.save(checkpoint_prefix)
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# check for improvements in error reduction - otherwise early stopping
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if early_stopping:
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strike = False
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total_strike = False
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total_loss = outputs['encoder_loss'] + outputs['decoder_loss'] + outputs['enc_dec_loss'] + \
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outputs['xd_loss'] + outputs['zd_loss']
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if total_loss < total_lowest_loss:
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total_lowest_loss = total_loss
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elif total_loss > TOTAL_LOSS_GRACE_CAP:
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total_strike = True
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if outputs['encoder_loss'] < encoder_lowest_loss:
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encoder_lowest_loss = outputs['encoder_loss']
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else:
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strike = True
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if outputs['decoder_loss'] < decoder_lowest_loss:
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decoder_lowest_loss = outputs['decoder_loss']
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else:
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strike = True
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if outputs['enc_dec_loss'] < enc_dec_lowest_loss:
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enc_dec_lowest_loss = outputs['enc_dec_loss']
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else:
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strike = True
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if outputs['xd_loss'] < xd_lowest_loss:
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xd_lowest_loss = outputs['xd_loss']
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else:
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strike = True
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if outputs['zd_loss'] < zd_lowest_loss:
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zd_lowest_loss = outputs['zd_loss']
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else:
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strike = True
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if strike and total_strike:
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grace_period -= 1
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elif strike:
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pass
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else:
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grace_period = GRACE
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if grace_period == 0:
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break
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if verbose:
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if grace_period > 0:
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print("Training finish!... save model weights")
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if grace_period == 0:
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print("Training stopped early!... save model weights")
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# save trained models
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checkpoint.save(checkpoint_prefix)
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def _train_one_epoch(epoch: int,
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dataset: tf.data.Dataset,
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targets_real: tf.Tensor,
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verbose: bool,
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batch_size: int,
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targets_fake: tf.Tensor,
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z_generator: Callable[[], tf.Variable],
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learning_rate_var: tf.Variable,
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decoder: model.Decoder,
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encoder: model.Encoder,
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x_discriminator: model.XDiscriminator,
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z_discriminator: model.ZDiscriminator,
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decoder_optimizer: tf.train.Optimizer,
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x_discriminator_optimizer: tf.train.Optimizer,
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z_discriminator_optimizer: tf.train.Optimizer,
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enc_dec_optimizer: tf.train.Optimizer,
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global_step: tf.Variable,
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global_step_xd: tf.Variable,
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global_step_zd: tf.Variable,
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global_step_decoder: tf.Variable,
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global_step_enc_dec: tf.Variable,
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epoch_var: tf.Variable) -> Dict[str, float]:
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with summary_ops_v2.always_record_summaries():
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epoch_var.assign(epoch)
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epoch_start_time = time.time()
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# define loss variables
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encoder_loss_avg = tfe.metrics.Mean(name='encoder_loss', dtype=tf.float32)
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decoder_loss_avg = tfe.metrics.Mean(name='decoder_loss', dtype=tf.float32)
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enc_dec_loss_avg = tfe.metrics.Mean(name='encoder_decoder_loss', dtype=tf.float32)
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zd_loss_avg = tfe.metrics.Mean(name='z_discriminator_loss', dtype=tf.float32)
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xd_loss_avg = tfe.metrics.Mean(name='x_discriminator_loss', dtype=tf.float32)
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# update learning rate
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if (epoch + 1) % 30 == 0:
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learning_rate_var.assign(learning_rate_var.value() / 4)
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summary_ops_v2.scalar(name='learning_rate', tensor=learning_rate_var,
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step=global_step)
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if verbose:
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print("learning rate change!")
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for x, _ in dataset:
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# x discriminator
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_xd_train_loss = _train_xdiscriminator_step(x_discriminator=x_discriminator,
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decoder=decoder,
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optimizer=x_discriminator_optimizer,
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inputs=x,
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targets_real=targets_real,
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targets_fake=targets_fake,
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global_step_xd=global_step_xd,
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global_step=global_step,
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z_generator=z_generator)
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xd_loss_avg(_xd_train_loss)
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# --------
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# decoder
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_decoder_train_loss = _train_decoder_step(decoder=decoder,
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x_discriminator=x_discriminator,
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optimizer=decoder_optimizer,
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targets=targets_real,
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global_step_decoder=global_step_decoder,
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global_step=global_step,
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z_generator=z_generator)
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decoder_loss_avg(_decoder_train_loss)
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# ---------
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# z discriminator
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_zd_train_loss = _train_zdiscriminator_step(z_discriminator=z_discriminator,
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encoder=encoder,
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optimizer=z_discriminator_optimizer,
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inputs=x,
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targets_real=targets_real,
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targets_fake=targets_fake,
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global_step_zd=global_step_zd,
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global_step=global_step,
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z_generator=z_generator)
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zd_loss_avg(_zd_train_loss)
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# -----------
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# encoder + decoder
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encoder_loss, reconstruction_loss, x_decoded = _train_enc_dec_step(encoder=encoder,
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decoder=decoder,
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z_discriminator=z_discriminator,
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optimizer=enc_dec_optimizer,
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inputs=x,
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targets=targets_real,
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global_step_enc_dec=global_step_enc_dec,
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global_step=global_step)
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enc_dec_loss_avg(reconstruction_loss)
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encoder_loss_avg(encoder_loss)
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if int(global_step % LOG_FREQUENCY) == 0:
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comparison = K.concatenate([x[:batch_size/2], x_decoded[:batch_size/2]], axis=0)
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grid = util.prepare_image(comparison.cpu(), nrow=int(batch_size/2))
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summary_ops_v2.image(name='reconstruction',
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tensor=K.expand_dims(grid, axis=0), max_images=1,
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step=global_step)
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global_step.assign_add(1)
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epoch_end_time = time.time()
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per_epoch_time = epoch_end_time - epoch_start_time
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# final losses of epoch
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outputs = {
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'decoder_loss': decoder_loss_avg.result(False),
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'encoder_loss': encoder_loss_avg.result(False),
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'enc_dec_loss': enc_dec_loss_avg.result(False),
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'xd_loss': xd_loss_avg.result(False),
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'zd_loss': zd_loss_avg.result(False),
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'per_epoch_time': per_epoch_time,
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}
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return outputs
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def _train_xdiscriminator_step(x_discriminator: model.XDiscriminator,
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decoder: model.Decoder,
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optimizer: tf.train.Optimizer,
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inputs: tf.Tensor,
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targets_real: tf.Tensor,
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targets_fake: tf.Tensor,
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global_step: tf.Variable,
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global_step_xd: tf.Variable,
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z_generator: Callable[[], tf.Variable]) -> tf.Tensor:
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"""
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Trains the x discriminator model for one step (one batch).
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:param x_discriminator: instance of x discriminator model
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:param decoder: instance of decoder model
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:param optimizer: instance of chosen optimizer
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:param inputs: inputs from dataset
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:param targets_real: target tensor for real loss calculation
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:param targets_fake: target tensor for fake loss calculation
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:param global_step: the global step variable
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:param global_step_xd: global step variable for xd
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:param z_generator: callable function that returns a z variable
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:return: the calculated loss
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"""
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with tf.GradientTape() as tape:
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xd_result_1 = tf.squeeze(x_discriminator(inputs))
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xd_real_loss = tf.losses.log_loss(targets_real, xd_result_1)
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z = z_generator()
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x_fake = decoder(z)
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xd_result_2 = tf.squeeze(x_discriminator(x_fake))
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xd_fake_loss = tf.losses.log_loss(targets_fake, xd_result_2)
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_xd_train_loss = xd_real_loss + xd_fake_loss
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xd_grads = tape.gradient(_xd_train_loss, x_discriminator.trainable_variables)
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if int(global_step % LOG_FREQUENCY) == 0:
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summary_ops_v2.scalar(name='x_discriminator_real_loss', tensor=xd_real_loss,
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step=global_step)
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summary_ops_v2.scalar(name='x_discriminator_fake_loss', tensor=xd_fake_loss,
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step=global_step)
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summary_ops_v2.scalar(name='x_discriminator_loss', tensor=_xd_train_loss,
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step=global_step)
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for grad, variable in zip(xd_grads, x_discriminator.trainable_variables):
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summary_ops_v2.histogram(name='gradients/' + variable.name, tensor=tf.math.l2_normalize(grad),
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step=global_step)
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summary_ops_v2.histogram(name='variables/' + variable.name, tensor=tf.math.l2_normalize(variable),
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step=global_step)
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optimizer.apply_gradients(zip(xd_grads, x_discriminator.trainable_variables),
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global_step=global_step_xd)
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return _xd_train_loss
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def _train_decoder_step(decoder: model.Decoder,
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x_discriminator: model.XDiscriminator,
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optimizer: tf.train.Optimizer,
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targets: tf.Tensor,
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global_step: tf.Variable,
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global_step_decoder: tf.Variable,
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z_generator: Callable[[], tf.Variable]) -> tf.Tensor:
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"""
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Trains the decoder model for one step (one batch).
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:param decoder: instance of decoder model
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:param x_discriminator: instance of the x discriminator model
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:param optimizer: instance of chosen optimizer
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:param targets: target tensor for loss calculation
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:param global_step: the global step variable
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:param global_step_decoder: global step variable for decoder
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:param z_generator: callable function that returns a z variable
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:return: the calculated loss
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"""
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with tf.GradientTape() as tape:
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z = z_generator()
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x_fake = decoder(z)
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xd_result = tf.squeeze(x_discriminator(x_fake))
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_decoder_train_loss = tf.losses.log_loss(targets, xd_result)
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grads = tape.gradient(_decoder_train_loss, decoder.trainable_variables)
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if int(global_step % LOG_FREQUENCY) == 0:
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summary_ops_v2.scalar(name='decoder_loss', tensor=_decoder_train_loss,
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step=global_step)
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for grad, variable in zip(grads, decoder.trainable_variables):
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summary_ops_v2.histogram(name='gradients/' + variable.name, tensor=tf.math.l2_normalize(grad),
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step=global_step)
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summary_ops_v2.histogram(name='variables/' + variable.name, tensor=tf.math.l2_normalize(variable),
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step=global_step)
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optimizer.apply_gradients(zip(grads, decoder.trainable_variables),
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global_step=global_step_decoder)
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return _decoder_train_loss
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def _train_zdiscriminator_step(z_discriminator: model.ZDiscriminator,
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encoder: model.Encoder,
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optimizer: tf.train.Optimizer,
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inputs: tf.Tensor,
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targets_real: tf.Tensor,
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targets_fake: tf.Tensor,
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global_step: tf.Variable,
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global_step_zd: tf.Variable,
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z_generator: Callable[[], tf.Variable]) -> tf.Tensor:
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"""
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Trains the z discriminator one step (one batch).
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:param z_discriminator: instance of z discriminator model
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:param encoder: instance of encoder model
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:param optimizer: instance of chosen optimizer
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:param inputs: inputs from dataset
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:param targets_real: target tensor for real loss calculation
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:param targets_fake: target tensor for fake loss calculation
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:param global_step: the global step variable
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:param global_step_zd: global step variable for zd
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:param z_generator: callable function that returns a z variable
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:return: the calculated loss
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"""
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with tf.GradientTape() as tape:
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z = z_generator()
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zd_result = tf.squeeze(z_discriminator(z))
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zd_real_loss = tf.losses.log_loss(targets_real, zd_result)
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z = tf.squeeze(encoder(inputs))
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zd_result = tf.squeeze(z_discriminator(z))
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zd_fake_loss = tf.losses.log_loss(targets_fake, zd_result)
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_zd_train_loss = zd_real_loss + zd_fake_loss
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zd_grads = tape.gradient(_zd_train_loss, z_discriminator.trainable_variables)
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if int(global_step % LOG_FREQUENCY) == 0:
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summary_ops_v2.scalar(name='z_discriminator_real_loss', tensor=zd_real_loss,
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step=global_step)
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summary_ops_v2.scalar(name='z_discriminator_fake_loss', tensor=zd_fake_loss,
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step=global_step)
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summary_ops_v2.scalar(name='z_discriminator_loss', tensor=_zd_train_loss,
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step=global_step)
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for grad, variable in zip(zd_grads, z_discriminator.trainable_variables):
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summary_ops_v2.histogram(name='gradients/' + variable.name, tensor=tf.math.l2_normalize(grad),
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step=global_step)
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summary_ops_v2.histogram(name='variables/' + variable.name, tensor=tf.math.l2_normalize(variable),
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step=global_step)
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optimizer.apply_gradients(zip(zd_grads, z_discriminator.trainable_variables),
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global_step=global_step_zd)
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return _zd_train_loss
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def _train_enc_dec_step(encoder: model.Encoder, decoder: model.Decoder,
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z_discriminator: model.ZDiscriminator,
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optimizer: tf.train.Optimizer,
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inputs: tf.Tensor,
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targets: tf.Tensor,
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global_step: tf.Variable,
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global_step_enc_dec: tf.Variable) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
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"""
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Trains the encoder and decoder jointly for one step (one batch).
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:param encoder: instance of encoder model
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:param decoder: instance of decoder model
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:param z_discriminator: instance of z discriminator model
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:param optimizer: instance of chosen optimizer
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:param inputs: inputs from dataset
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:param targets: target tensor for loss calculation
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:param global_step: the global step variable
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:param global_step_enc_dec: global step variable for enc_dec
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:return: tuple of encoder loss, reconstruction loss, reconstructed input
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"""
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with tf.GradientTape() as tape:
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z = encoder(inputs)
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x_decoded = decoder(z)
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zd_result = tf.squeeze(z_discriminator(tf.squeeze(z)))
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encoder_loss = tf.losses.log_loss(targets, zd_result) * 2.0
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reconstruction_loss = tf.losses.log_loss(inputs, x_decoded)
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_enc_dec_train_loss = encoder_loss + reconstruction_loss
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enc_dec_grads = tape.gradient(_enc_dec_train_loss,
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encoder.trainable_variables + decoder.trainable_variables)
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if int(global_step % LOG_FREQUENCY) == 0:
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summary_ops_v2.scalar(name='encoder_loss', tensor=encoder_loss,
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step=global_step)
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summary_ops_v2.scalar(name='reconstruction_loss', tensor=reconstruction_loss,
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step=global_step)
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summary_ops_v2.scalar(name='encoder_decoder_loss', tensor=_enc_dec_train_loss,
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step=global_step)
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for grad, variable in zip(enc_dec_grads, encoder.trainable_variables + decoder.trainable_variables):
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summary_ops_v2.histogram(name='gradients/' + variable.name, tensor=tf.math.l2_normalize(grad),
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step=global_step)
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summary_ops_v2.histogram(name='variables/' + variable.name, tensor=tf.math.l2_normalize(variable),
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step=global_step)
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optimizer.apply_gradients(zip(enc_dec_grads,
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encoder.trainable_variables + decoder.trainable_variables),
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global_step=global_step_enc_dec)
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return encoder_loss, reconstruction_loss, x_decoded
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def _get_z_variable(batch_size: int, zsize: int) -> tf.Variable:
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"""
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Creates and returns a z variable taken from a normal distribution.
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:param batch_size: size of the batch
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:param zsize: size of the z latent space
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:return: created variable
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"""
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z = K.reshape(K.random_normal((batch_size, zsize)), (-1, 1, 1, zsize))
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return K.variable(z)
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