farizrahman4u

• On the Java end, exposing an op from libnd4j involves adding classes and methods to 3 different places. We should consider having a universal function/op factory.

• Blur the lines between Nd4j/samediff. All the ops from the universal function factory mentioned above should be available under Nd4j namespace. In other words, Nd4j.some_op should work on both INDArray as well as SDVariable inputs.

• More concrete shape inference. Shape inference in Samediff seems to be flaky, and dependent on op execution. Shape inference should be greedy with provision for unknown dimensions (this would be more involved and require changes in libnd4j). Should also consider symbolic shapes, i.e, SDVariable.shape() would return a 1-d SDVariable.

farizrahman4u

[INFO] Scanning for projects...
[INFO] ------------------------------------------------------------------------
[INFO] Reactor Build Order:
[INFO] 
[INFO] nd4j
[INFO] nd4j-shade
[INFO] jackson
[INFO] nd4j-common
[INFO] nd4j-context
[INFO] nd4j-buffer

farizrahman4u

import jumpy
import numpy as np
jumpy.init()


def numpy(jp_array):
    # convert back to numpy array
    array = jp_array.array  # INDArray instance
    get = array.getDouble
    shape = array.shape()

farizrahman4u

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>dl4j</artifactId>
    <packaging>jar</packaging>
    <version>1.0-SNAPSHOT</version>
    <name>dl4j</name>
    <url>http://maven.apache.org</url>

farizrahman4u

import numpy as np
from simple_classifier import SimpleClassifier

def vectorize(x):
    # vectorize a string
    if len(x) > 1:
        return np.sum([vectorize(c) for c in x], axis=0)
    if x == '.':
        i = 27
    elif x == ' ':

farizrahman4u

import numpy as np

def vectorize(x):
    # vectorize a string
    if len(x) > 1:
        return np.sum([vectorize(c) for c in x], axis=0)
    if x == '.':
        i = 27
    elif x == ' ':
        i = 26

farizrahman4u

from keras.layers import Recurrent
from keras.models import Sequential
from keras import backend as K



def _isRNN(layer):
	return issubclass(layer.__class__, Recurrent)

def _zeros(shape):

farizrahman4u

import numpy as np

__author__ = 'Fariz Rahman'


def eq(x, y):
	return x.lower().replace(" ", "") == y.lower().replace(" ", "")

def get_words(x):
	x = x.replace("  ", " ")