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build/lib.linux-x86_64-2.7/musicnet/callbacks.py
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# -*- coding: utf-8 -*-
#
# Authors: Dmitriy Serdyuk
import os
import numpy as np
import h5py
from time import time
from os import path
from sklearn.metrics import (
precision_recall_curve, average_precision_score, log_loss)
import keras
from keras.callbacks import Callback, ModelCheckpoint, LearningRateScheduler
class SaveLastModel(Callback):
def __init__(self, workdir, period=10, name=''):
self.name = name
self.workdir = workdir
self.chkptsdir = path.join(self.workdir, "chkpts")
if not path.isdir(self.chkptsdir):
os.mkdir(self.chkptsdir)
self.period_of_epochs = period
self.link_filename = path.join(self.chkptsdir,
"model_checkpoint.hdf5")
def on_epoch_end(self, epoch, logs=None):
if (epoch + 1) % self.period_of_epochs == 0:
# Filenames
base_hdf5_filename = "model{}_checkpoint{:06d}.hdf5".format(
self.name, epoch + 1)
base_yaml_filename = "model{}_checkpoint{:06d}.yaml".format(
self.name, epoch + 1)
hdf5_filename = path.join(self.chkptsdir, base_hdf5_filename)
yaml_filename = path.join(self.chkptsdir, base_yaml_filename)
# YAML
yaml_model = self.model.to_yaml()
with open(yaml_filename, "w") as yaml_file:
yaml_file.write(yaml_model)
# HDF5
keras.models.save_model(self.model, hdf5_filename)
with h5py.File(hdf5_filename, "r+") as f:
f.require_dataset("initialEpoch", (), "uint64", True)[...] = int(epoch+1)
f.flush()
# Symlink to new HDF5 file, then atomically rename and replace.
os.symlink(base_hdf5_filename, self.link_filename + ".rename")
os.rename (self.link_filename + ".rename",
self.link_filename)
class Performance(Callback):
def __init__(self, logger):
self.logger = logger
def on_epoch_begin(self, epoch, logs=None):
self.timestamps = []
def on_epoch_end(self, epoch, logs=None):
t = np.asarray(self.timestamps, dtype=np.float64)
train_function_time = float(np.mean(t[ :,1] - t[:,0]))
load_data_time = float(np.mean(t[1:,0] - t[:-1, 1]))
self.logger.log(
{'epoch': epoch, 'train_function_time': train_function_time})
self.logger.log({'epoch': epoch, 'load_data_time': load_data_time})
def on_batch_begin(self, epoch, logs=None):
self.timestamps += [[time(), time()]]
def on_batch_end(self, epoch, logs=None):
self.timestamps[-1][-1] = time()
class Validation(Callback):
def __init__(self, x, y, name, logger):
self.x = x
self.y = y
self.name = name
self.logger = logger
def evaluate(self):
pr = self.model.predict(self.x)
average_precision = average_precision_score(
self.y.flatten(), pr.flatten())
loss = log_loss(self.y.flatten(), pr.flatten())
return average_precision, loss
def on_train_begin(self, logs=None):
average_precision, loss = self.evaluate()
self.logger.log(
{'epoch': 0, self.name + "_avg_precision": average_precision,
self.name + "_loss": loss})
def on_epoch_end(self, epoch, logs=None):
average_precision, loss = self.evaluate()
self.logger.log(
{'epoch': epoch + 1,
self.name + "_avg_precision": average_precision,
self.name + "_loss": loss})
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