Dref360

"""
Simple alarm clock to be awaken to the sound of your choice
"""
import datetime as dt
import time
import webbrowser
import argparse
import sys
from dateutil.relativedelta import relativedelta

Dref360

import keras.backend as K
import tensorflow as tf
class Model:
  def __init__(self,batch_size):
    self.batch_size = batch_size
    
    def loss_DSSIS_tf11(self, y_true, y_pred):
        """Need tf0.11rc to work"""
        y_true = tf.reshape(y_true, [self.batch_size] + get_shape(y_pred)[1:])
        y_pred = tf.reshape(y_pred, [self.batch_size] + get_shape(y_pred)[1:])

Dref360

import tensorflow as tf
import numpy as np
import keras
import keras.backend as K
from keras.models import Model
import keras.callbacks as cbks
import threading
from keras.layers import Layer, Input, InputLayer, Dense

Dref360

import operator
import threading
from functools import reduce

import keras
import keras.backend as K
from keras.engine import Model
import numpy as np
import tensorflow as tf
import time

Dref360

import os
import time
from concurrent.futures import ProcessPoolExecutor
from itertools import cycle
from queue import Queue
from threading import Thread, Event

from keras.engine.training import GeneratorEnqueuer

Dref360

import numpy as np
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable


class Layer(nn.Module):
    def __init__(self, in_, out, kernel, init_kernel='uniform', padding=1):
        super(Layer, self).__init__()

Dref360

class weighted_categorical_crossentropy:
    def __init__(self, weights):
        self.weights = weights
        self.__name__ = 'wcentroid_loss'

    def __call__(self, y_true, y_pred):
        class0 = K.ones_like(y_pred)[:, :, :, 0] * self.weights[0]
        class1 = K.ones_like(y_pred)[:, :, :, 0] * self.weights[1]
        x = K.tf.where(y_true[:, :, :, 0] > 0, class0, class1)
        result = x * K.categorical_crossentropy(y_pred, y_true)

Dref360

import multiprocessing
import threading
import time
from Queue import Queue


class Sequence():
    def __init__(self, my_list):
        self.my_list = my_list

Dref360

import numpy as np

from keras.layers import Input, Lambda
from keras.models import Model
from keras.utils import Sequence


class SSEquence(Sequence):
    def __init__(self, value=1):
        self.value = value

Dref360

import multiprocessing
import time

import numpy as np

import keras.backend as K
import keras.layers as KL
from keras import Model
from keras.callbacks import Callback
from keras.utils import Sequence