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from keras.optimizers import Adam
from keras import backend as K
from keras.datasets import mnist
from keras.utils.np_utils import to_categorical
from keras.metrics import categorical_accuracy
from keras.initializations import glorot_uniform, zero
import numpy as np

# inputs and targets are placeholders
input_dim = 28*28

bstriner

2017-01-25 03:23:43,801 - root - INFO - Checking rate limit for user github-bot-bot
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bstriner

import glob
import os
import json


class WikiDoc(object):
    def __init__(self, url, text, id, title):
        self.url = url
        self.text = text
        self.id = id

bstriner

import keras.backend as K
from keras.callbacks import CSVLogger
from keras.datasets import mnist
from keras.layers import Input, Lambda, Dense, Flatten, BatchNormalization, Activation
from keras.models import Model


def main():
    # Both inputs and targets are `Input` tensors
    input_x = Input((28, 28), name='input_x', dtype='uint8')  # uint8 [0-255]

bstriner

"""
A series of speed tests on pytorch LSTMs.
- LSTM is fastest (no surprise)
- When you have to go timestep-by-timestep, LSTMCell is faster than LSTM
- Iterating using chunks is slightly faster than __iter__ or indexing depending on setup

**Results**

My Ubuntu server:
    OS: posix, pytorch version: 0.4.0a0+67bbf58

bstriner

--- Eigen/src/Core/arch/CUDA/Half.h	2018-06-22 18:09:44.000000000 -0400
+++ Eigen/src/Core/arch/CUDA/Half.h	2018-07-25 01:19:55.462313100 -0400
@@ -209,7 +209,7 @@
 // conversion steps back and forth.
 
 EIGEN_STRONG_INLINE __device__ half operator + (const half& a, const half& b) {
-  return __hadd(a, b);
+  return __hadd(::__half(a), ::__half(b));
 }
 EIGEN_STRONG_INLINE __device__ half operator * (const half& a, const half& b) {

bstriner

diff --git a/src/google/protobuf/generated_message_reflection.h b/src/google/protobuf/generated_message_reflection.h
index 177312cf..9a2e8e26 100644
--- a/src/google/protobuf/generated_message_reflection.h
+++ b/src/google/protobuf/generated_message_reflection.h
@@ -58,6 +58,8 @@
 #error "You cannot SWIG proto headers"
 #endif
 
+#undef GetMessage
+

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--- eigen_archive/Eigen/src/Core/arch/CUDA/Half.h	2018-06-22 18:09:44.000000000 -0400
+++ eigen_archive/Eigen/src/Core/arch/CUDA/Half.h	2018-07-25 01:19:55.462313100 -0400
@@ -209,7 +209,7 @@
 // conversion steps back and forth.
 
 EIGEN_STRONG_INLINE __device__ half operator + (const half& a, const half& b) {
-  return __hadd(a, b);
+  return __hadd(::__half(a), ::__half(b));
 }
 EIGEN_STRONG_INLINE __device__ half operator * (const half& a, const half& b) {