fgvbrt

import tensorflow as tf
from memory_profiler import profile
import argparse


@profile
def test(device, case, soft_placement):
    print(device, case, soft_placement)

    config = tf.ConfigProto()

fgvbrt

import numpy as np
import theano
import theano.tensor as T
import lasagne
from collections import OrderedDict


def get_adam_steps_and_updates(all_grads, params, learning_rate=0.001,
                               beta1=0.9, beta2=0.999, epsilon=1e-8):
    t_prev = theano.shared(lasagne.utils.floatX(0.))

fgvbrt

import numpy as np
from keras.models import Sequential
from keras.layers import GRU, Activation

INP_DIM = 10
OUT_DIM = 3
BATCH_SIZE = 1
SEQ_LEN = 5

model1 = Sequential()

fgvbrt

from keras.layers import Merge, InputLayer, Dense, Input, merge, Permute, Layer, Lambda
from keras.models import Sequential, Model
from keras import backend as K
import numpy as np

FEATURE_SIZE = 10
FEATURE_BATCH = 10
INDICES_SIZE = FEATURE_BATCH
INDICES_BATCH = 5
HIDDEN_SIZE = 20

fgvbrt

from keras.layers import Merge, InputLayer, Dense, Input, merge
from keras.models import Sequential, Model
from keras import backend as K
import numpy as np

FEATURE_SIZE = 10
FEATURE_BATCH = 10
INDICES_SIZE = FEATURE_BATCH
INDICES_BATCH = 5