import json
import os
import time
from abc import ABC
import numpy as np
from sklearn.model_selection import train_test_split
from astroNN.config import MULTIPROCESS_FLAG
from astroNN.config import _astroNN_MODEL_NAME
from astroNN.config import keras_import_manager
from astroNN.datasets import H5Loader
from astroNN.models.base_master_nn import NeuralNetMaster
from astroNN.nn.callbacks import VirutalCSVLogger
from astroNN.nn.losses import mean_squared_error, mean_error, mean_absolute_error
from astroNN.nn.utilities import Normalizer
from astroNN.nn.utilities.generator import GeneratorMaster
keras = keras_import_manager()
regularizers = keras.regularizers
ReduceLROnPlateau = keras.callbacks.ReduceLROnPlateau
Adam = keras.optimizers.Adam
class CVAEDataGenerator(GeneratorMaster):
"""
To generate data to NN
:param batch_size: batch size
:type batch_size: int
:param shuffle: Whether to shuffle batches or not
:type shuffle: bool
:param data: List of data to NN
:type data: list
:History:
| 2017-Dec-02 - Written - Henry Leung (University of Toronto)
| 2019-Feb-17 - Updated - Henry Leung (University of Toronto)
"""
def __init__(self, batch_size, shuffle, steps_per_epoch, data):
super().__init__(batch_size=batch_size, shuffle=shuffle, steps_per_epoch=steps_per_epoch, data=data)
self.inputs = self.data[0]
self.recon_inputs = self.data[1]
# initial idx
self.idx_list = self._get_exploration_order(range(self.inputs.shape[0]))
self.current_idx = 0
def _data_generation(self, inputs, recon_inputs, idx_list_temp):
x = self.input_d_checking(inputs, idx_list_temp)
y = self.input_d_checking(recon_inputs, idx_list_temp)
return x, y
def __getitem__(self, index):
x, y = self._data_generation(self.inputs, self.recon_inputs,
self.idx_list[self.current_idx:self.current_idx + self.batch_size])
self.current_idx += self.batch_size
return x, y
def on_epoch_end(self):
# shuffle the list when epoch ends for the next epoch
self.idx_list = self._get_exploration_order(range(self.inputs.shape[0]))
# reset counter
self.current_idx = 0
class CVAEPredDataGenerator(GeneratorMaster):
"""
To generate data to NN for prediction
:param batch_size: batch size
:type batch_size: int
:param shuffle: Whether to shuffle batches or not
:type shuffle: bool
:param data: List of data to NN
:type data: list
:History:
| 2017-Dec-02 - Written - Henry Leung (University of Toronto)
| 2019-Feb-17 - Updated - Henry Leung (University of Toronto)
"""
def __init__(self, batch_size, shuffle, steps_per_epoch, data):
super().__init__(batch_size=batch_size, shuffle=shuffle, steps_per_epoch=steps_per_epoch, data=data)
self.inputs = self.data[0]
# initial idx
self.idx_list = self._get_exploration_order(range(self.inputs.shape[0]))
self.current_idx = 0
def _data_generation(self, inputs, idx_list_temp):
# Generate data
x = self.input_d_checking(inputs, idx_list_temp)
return x
def __getitem__(self, index):
x = self._data_generation(self.inputs, self.idx_list[self.current_idx:self.current_idx + self.batch_size])
self.current_idx += self.batch_size
return x
def on_epoch_end(self):
# shuffle the list when epoch ends for the next epoch
self.idx_list = self._get_exploration_order(range(self.inputs.shape[0]))
# reset counter
self.current_idx = 0
[docs]class ConvVAEBase(NeuralNetMaster, ABC):
"""
Top-level class for a Convolutional Variational Autoencoder
:History: 2018-Jan-06 - Written - Henry Leung (University of Toronto)
"""
def __init__(self):
super().__init__()
self.name = 'Convolutional Variational Autoencoder'
self._model_type = 'CVAE'
self.initializer = None
self.activation = None
self._last_layer_activation = None
self.num_filters = None
self.filter_len = None
self.pool_length = None
self.num_hidden = None
self.reduce_lr_epsilon = None
self.reduce_lr_min = None
self.reduce_lr_patience = None
self.l1 = None
self.l2 = None
self.maxnorm = None
self.latent_dim = None
self.val_size = 0.1
self.dropout_rate = 0.0
self.keras_vae = None
self.keras_encoder = None
self.keras_decoder = None
self.loss = None
self._input_shape = None
self.input_norm_mode = 255
self.labels_norm_mode = 255
self.input_mean = None
self.input_std = None
self.labels_mean = None
self.labels_std = None
def compile(self, optimizer=None, loss=None, metrics=None, loss_weights=None, sample_weight_mode=None):
self.keras_model, self.keras_encoder, self.keras_decoder = self.model()
if optimizer is not None:
self.optimizer = optimizer
elif self.optimizer is None or self.optimizer == 'adam':
self.optimizer = Adam(lr=self.lr, beta_1=self.beta_1, beta_2=self.beta_2, epsilon=self.optimizer_epsilon,
decay=0.0)
if self.loss is None:
self.loss = mean_squared_error
if self.metrics is None:
self.metrics = [mean_absolute_error, mean_error]
self.keras_model.compile(loss=self.loss, optimizer=self.optimizer, metrics=self.metrics)
return None
def pre_training_checklist_child(self, input_data, input_recon_target):
if self.task == 'classification':
raise RuntimeError('astroNN VAE does not support classification task')
elif self.task == 'binary_classification':
raise RuntimeError('astroNN VAE does not support binary classification task')
self.pre_training_checklist_master(input_data, input_recon_target)
if isinstance(input_data, H5Loader):
self.targetname = input_data.target
input_data, input_recon_target = input_data.load()
# check if exists (exists mean fine-tuning, so we do not need calculate mean/std again)
if self.input_normalizer is None:
self.input_normalizer = Normalizer(mode=self.input_norm_mode)
self.labels_normalizer = Normalizer(mode=self.labels_norm_mode)
norm_data = self.input_normalizer.normalize(input_data)
self.input_mean, self.input_std = self.input_normalizer.mean_labels, self.input_normalizer.std_labels
norm_labels = self.labels_normalizer.normalize(input_recon_target)
self.labels_mean, self.labels_std = self.labels_normalizer.mean_labels, self.labels_normalizer.std_labels
else:
norm_data = self.input_normalizer.normalize(input_data, calc=False)
norm_labels = self.labels_normalizer.normalize(input_recon_target, calc=False)
if self.keras_model is None: # only compiler if there is no keras_model, e.g. fine-tuning does not required
self.compile()
self.train_idx, self.val_idx = train_test_split(np.arange(self.num_train + self.val_num),
test_size=self.val_size)
self.training_generator = CVAEDataGenerator(batch_size=self.batch_size,
shuffle=True,
steps_per_epoch=self.num_train // self.batch_size,
data=[norm_data[self.train_idx],
norm_labels[self.train_idx]])
self.validation_generator = CVAEDataGenerator(
batch_size=self.batch_size if len(self.val_idx) > self.batch_size else len(self.val_idx),
shuffle=True,
steps_per_epoch=max(self.val_num // self.batch_size, 1),
data=[norm_data[self.val_idx],
norm_labels[self.val_idx]])
return input_data, input_recon_target
[docs] def train(self, input_data, input_recon_target):
"""
Train a Convolutional Autoencoder
:param input_data: Data to be trained with neural network
:type input_data: ndarray
:param input_recon_target: Data to be reconstructed
:type input_recon_target: ndarray
:return: None
:rtype: NoneType
:History: 2017-Dec-06 - Written - Henry Leung (University of Toronto)
"""
# Call the checklist to create astroNN folder and save parameters
self.pre_training_checklist_child(input_data, input_recon_target)
reduce_lr = ReduceLROnPlateau(monitor='val_output_loss', factor=0.5, min_delta=self.reduce_lr_epsilon,
patience=self.reduce_lr_patience, min_lr=self.reduce_lr_min, mode='min',
verbose=2)
self.virtual_cvslogger = VirutalCSVLogger()
self.__callbacks = [reduce_lr, self.virtual_cvslogger] # default must have unchangeable callbacks
if self.callbacks is not None:
if isinstance(self.callbacks, list):
self.__callbacks.extend(self.callbacks)
else:
self.__callbacks.append(self.callbacks)
start_time = time.time()
self.keras_model.fit_generator(generator=self.training_generator,
validation_data=self.validation_generator,
epochs=self.max_epochs, verbose=self.verbose, workers=os.cpu_count(),
callbacks=self.__callbacks,
use_multiprocessing=MULTIPROCESS_FLAG)
print(f'Completed Training, {(time.time() - start_time):.{2}f}s in total')
if self.autosave is True:
# Call the post training checklist to save parameters
self.save()
return None
[docs] def train_on_batch(self, input_data, input_recon_target):
"""
Train a AutoEncoder by running a single gradient update on all of your data, suitable for fine-tuning
:param input_data: Data to be trained with neural network
:type input_data: ndarray
:param input_recon_target: Data to be reconstructed
:type input_recon_target: ndarray
:return: None
:rtype: NoneType
:History: 2018-Aug-25 - Written - Henry Leung (University of Toronto)
"""
# check if exists (exists mean fine-tuning, so we do not need calculate mean/std again)
if self.input_normalizer is None:
self.input_normalizer = Normalizer(mode=self.input_norm_mode)
self.labels_normalizer = Normalizer(mode=self.labels_norm_mode)
norm_data = self.input_normalizer.normalize(input_data)
self.input_mean, self.input_std = self.input_normalizer.mean_labels, self.input_normalizer.std_labels
norm_labels = self.labels_normalizer.normalize(input_recon_target)
self.labels_mean, self.labels_std = self.labels_normalizer.mean_labels, self.labels_normalizer.std_labels
else:
norm_data = self.input_normalizer.normalize(input_data, calc=False)
norm_labels = self.labels_normalizer.normalize(input_recon_target, calc=False)
steps = input_data.shape[0] // self.batch_size if input_data.shape[0] > self.batch_size else 1
start_time = time.time()
fit_generator = CVAEDataGenerator(batch_size=input_data.shape[0],
shuffle=False,
steps_per_epoch=1,
data=[norm_data,
norm_labels])
scores = self.keras_model.fit_generator(generator=fit_generator,
epochs=1,
verbose=self.verbose,
workers=os.cpu_count(),
use_multiprocessing=MULTIPROCESS_FLAG)
print(f'Completed Training on Batch, {(time.time() - start_time):.{2}f}s in total')
return None
def post_training_checklist_child(self):
self.keras_model.save(self.fullfilepath + _astroNN_MODEL_NAME)
print(_astroNN_MODEL_NAME + f' saved to {(self.fullfilepath + _astroNN_MODEL_NAME)}')
self.hyper_txt.write(f"Dropout Rate: {self.dropout_rate} \n")
self.hyper_txt.flush()
self.hyper_txt.close()
data = {'id': self.__class__.__name__,
'pool_length': self.pool_length,
'filterlen': self.filter_len,
'filternum': self.num_filters,
'hidden': self.num_hidden,
'input': self._input_shape,
'labels': self._labels_shape,
'task': self.task,
'activation': self.activation,
'input_mean': self.input_mean.tolist(),
'labels_mean': self.labels_mean.tolist(),
'input_std': self.input_std.tolist(),
'labels_std': self.labels_std.tolist(),
'valsize': self.val_size,
'targetname': self.targetname,
'dropout_rate': self.dropout_rate,
'l1': self.l1, 'l2': self.l2,
'maxnorm': self.maxnorm,
'input_norm_mode': self.input_norm_mode,
'labels_norm_mode': self.labels_norm_mode,
'batch_size': self.batch_size,
'latent': self.latent_dim}
with open(self.fullfilepath + '/astroNN_model_parameter.json', 'w') as f:
json.dump(data, f, indent=4, sort_keys=True)
[docs] def test(self, input_data):
"""
Use the neural network to do inference and get reconstructed data
:param input_data: Data to be inferred with neural network
:type input_data: ndarray
:return: reconstructed data
:rtype: ndarry
:History: 2017-Dec-06 - Written - Henry Leung (University of Toronto)
"""
self.pre_testing_checklist_master()
input_data = np.atleast_2d(input_data)
if self.input_normalizer is not None:
input_array = self.input_normalizer.normalize(input_data, calc=False)
else:
# Prevent shallow copy issue
input_array = np.array(input_data)
input_array -= self.input_mean
input_array /= self.input_std
total_test_num = input_data.shape[0] # Number of testing data
# for number of training data smaller than batch_size
if input_data.shape[0] < self.batch_size:
self.batch_size = input_data.shape[0]
# Due to the nature of how generator works, no overlapped prediction
data_gen_shape = (total_test_num // self.batch_size) * self.batch_size
remainder_shape = total_test_num - data_gen_shape # Remainder from generator
predictions = np.zeros((total_test_num, self._labels_shape, 1))
start_time = time.time()
print("Starting Inference")
# Data Generator for prediction
prediction_generator = CVAEPredDataGenerator(batch_size=self.batch_size,
shuffle=False,
steps_per_epoch=input_array.shape[0] // self.batch_size,
data=[input_array[:data_gen_shape]])
predictions[:data_gen_shape] = np.asarray(self.keras_model.predict_generator(
prediction_generator))
if remainder_shape != 0:
remainder_data = input_array[data_gen_shape:]
# assume its caused by mono images, so need to expand dim by 1
if len(input_array[0].shape) != len(self._input_shape):
remainder_data = np.expand_dims(remainder_data, axis=-1)
result = self.keras_model.predict(remainder_data)
predictions[data_gen_shape:] = result
if self.labels_normalizer is not None:
predictions[:, :, 0] = self.labels_normalizer.denormalize(predictions[:, :, 0])
else:
predictions[:, :, 0] *= self.labels_std
predictions[:, :, 0] += self.labels_mean
print(f'Completed Inference, {(time.time() - start_time):.{2}f}s elapsed')
return predictions
[docs] def test_encoder(self, input_data):
"""
Use the neural network to do inference and get the hidden layer encoding/representation
:param input_data: Data to be inferred with neural network
:type input_data: ndarray
:return: hidden layer encoding/representation
:rtype: ndarray
:History: 2017-Dec-06 - Written - Henry Leung (University of Toronto)
"""
self.pre_testing_checklist_master()
# Prevent shallow copy issue
if self.input_normalizer is not None:
input_array = self.input_normalizer.normalize(input_data, calc=False)
else:
# Prevent shallow copy issue
input_array = np.array(input_data)
input_array -= self.input_mean
input_array /= self.input_std
total_test_num = input_data.shape[0] # Number of testing data
# for number of training data smaller than batch_size
if input_data.shape[0] < self.batch_size:
self.batch_size = input_data.shape[0]
# Due to the nature of how generator works, no overlapped prediction
data_gen_shape = (total_test_num // self.batch_size) * self.batch_size
remainder_shape = total_test_num - data_gen_shape # Remainder from generator
encoding = np.zeros((total_test_num, self.latent_dim))
start_time = time.time()
print("Starting Inference on Encoder")
# Data Generator for prediction
prediction_generator = CVAEPredDataGenerator(batch_size=self.batch_size,
shuffle=False,
steps_per_epoch=input_array.shape[0] // self.batch_size,
data=[input_array[:data_gen_shape]])
encoding[:data_gen_shape] = np.asarray(self.keras_encoder.predict_generator(
prediction_generator))
if remainder_shape != 0:
remainder_data = input_array[data_gen_shape:]
# assume its caused by mono images, so need to expand dim by 1
if len(input_array[0].shape) != len(self._input_shape):
remainder_data = np.expand_dims(remainder_data, axis=-1)
result = self.keras_encoder.predict(remainder_data)
encoding[data_gen_shape:] = result
print(f'Completed Inference on Encoder, {(time.time() - start_time):.{2}f}s elapsed')
return encoding
[docs] def evaluate(self, input_data, labels):
"""
Evaluate neural network by provided input data and labels/reconstruction target to get back a metrics score
:param input_data: Data to be inferred with neural network
:type input_data: ndarray
:param labels: labels
:type labels: ndarray
:return: metrics score
:rtype: float
:History: 2018-May-20 - Written - Henry Leung (University of Toronto)
"""
# check if exists (exists mean fine-tuning, so we do not need calculate mean/std again)
if self.input_normalizer is None:
self.input_normalizer = Normalizer(mode=self.input_norm_mode)
self.labels_normalizer = Normalizer(mode=self.labels_norm_mode)
norm_data = self.input_normalizer.normalize(input_data)
self.input_mean, self.input_std = self.input_normalizer.mean_labels, self.input_normalizer.std_labels
norm_labels = self.labels_normalizer.normalize(labels)
self.labels_mean, self.labels_std = self.labels_normalizer.mean_labels, self.labels_normalizer.std_labels
else:
norm_data = self.input_normalizer.normalize(input_data, calc=False)
norm_labels = self.labels_normalizer.normalize(labels, calc=False)
eval_batchsize = self.batch_size if input_data.shape[0] > self.batch_size else input_data.shape[0]
steps = input_data.shape[0] // self.batch_size if input_data.shape[0] > self.batch_size else 1
start_time = time.time()
print("Starting Evaluation")
evaluate_generator = CVAEDataGenerator(batch_size=eval_batchsize,
shuffle=False,
steps_per_epoch=steps,
data=[norm_data,
norm_labels])
scores = self.keras_model.evaluate_generator(evaluate_generator)
outputname = self.keras_model.output_names
funcname = []
if isinstance(self.keras_model.metrics, dict):
func_list = self.keras_model.metrics[outputname[0]]
else:
func_list = self.keras_model.metrics
for func in func_list:
if hasattr(func, __name__):
funcname.append(func.__name__)
else:
funcname.append(func.__class__.__name__)
# funcname = [func.__name__ for func in self.keras_model.metrics]
output_funcname = [outputname[0] + '_' + name for name in funcname]
list_names = ['loss', *output_funcname]
print(f'Completed Evaluation, {(time.time() - start_time):.{2}f}s elapsed')
return {name: score for name, score in zip(list_names, scores)}
