karpathy

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

RadhikaG

def parent(i):
    return i/2

def left(i):
    return 2*i

def right(i):
    return (2*i + 1)

class Heap:

danijar

# Example for my blog post at:
# https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf


def lazy_property(function):
    attribute = '_' + function.__name__

pranv

import numpy as np

from utils import orthogonal, tanh, sigmoid, dtanh, dsigmoid


class LSTM(object):
	"""Long Short Term Memory Unit
	
	Parameters
	----------

j314erre

import tensorflow as tf
import numpy as np


class TextCNN(object):
    """
    A CNN for text classification.
    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
    """
    def __init__(

gyglim

"""Simple example on how to log scalars and images to tensorboard without tensor ops.

License: BSD License 2.0
"""
__author__ = "Michael Gygli"

import tensorflow as tf
from StringIO import StringIO
import matplotlib.pyplot as plt
import numpy as np

fasiha

from numpy import linalg as la
import numpy as np


def nearestPD(A):
    """Find the nearest positive-definite matrix to input

    A Python/Numpy port of John D'Errico's `nearestSPD` MATLAB code [1], which
    credits [2].

joelthchao

import numpy as np
import tensorflow as tf
from keras.callbacks import TensorBoard
from keras.layers import Input, Dense
from keras.models import Model


def write_log(callback, names, logs, batch_no):
    for name, value in zip(names, logs):
        summary = tf.Summary()

marekgalovic

import tensorflow as tf
import numpy as np

class ConvolutionalAttentionNLI(object):
    
    def __init__(self, embeddings_shape, target_classes=2, conv_filter_size=3, conv_projection_size=300, attention_output_size=200, comparison_output_size=100, learning_rate=0.05):
        self._embeddings_shape = embeddings_shape
        self._target_classes = target_classes
        self._conv_filter_size = conv_filter_size
        self._conv_projection_size = conv_projection_size

rspeer