import random, sys, os sys.path.append(".") import tensorflow as tf from TFLibraries.Layer import Layers from TFLibraries.Train import Training from TFLibraries.Sparse import SparseFiles from TFLibraries.Embeddings import Embedding Layer = Layers() random.seed(20160408) indices = [] def generate_batch(size, data, labels): global indices if len(indices) < size: indices.extend(range(data.shape[0])) r = random.sample(indices, size) indices = filter(lambda a: a not in r, indices) # Randomly reorder the data return data[r], labels[r] ## Read Training/Dev/Test data os.chdir('/home/ybisk/GroundedLanguage') print("Running from ", os.getcwd()) maxlength = 80 offset = 3 labelspace = 9
import os,random,sys sys.path.append(".") import tensorflow as tf import numpy as np np.set_printoptions(threshold=np.nan) from TFLibraries.Layer import Layers from TFLibraries.Train import Training from TFLibraries.Sparse import SparseFiles from TFLibraries.Embeddings import Embedding Layer = Layers() random.seed(20160408) indices = [] def generate_batch(size, data, labels, lengths): global indices if len(indices) < size: indices.extend(range(data.shape[0])) # Random indices r = random.sample(indices, size) indices = filter(lambda a: a not in r, indices) return data[r], labels[r], lengths[r] ## Read Training/Dev/Test data os.chdir('/home/ybisk/GroundedLanguage') print("Running from ", os.getcwd()) maxlength = 80 offset = 3 labelspace = 9 Sparse = SparseFiles(maxlength, offset, labelspace=labelspace, prediction=2)
import os,random,sys sys.path.append(".") ## Model Imports import tensorflow as tf tf.set_random_seed(20160905) import numpy as np np.set_printoptions(threshold=np.nan) from TFLibraries.Layer import Layers from TFLibraries.Train import Training from TFLibraries.Sparse import SparseFiles from TFLibraries.Embeddings import Embedding Layer = Layers() ## Server Code import json import time from flask import Flask from flask import request from flask import jsonify random.seed(20160408) indices = [] def generate_batch(size, data, labels, lengths): global indices if len(indices) < size: indices.extend(range(data.shape[0])) # Random indices r = random.sample(indices, size) indices = filter(lambda a: a not in r, indices)
import os,random,sys,gzip sys.path.append(".") from TFLibraries.Embeddings import Embedding from TFLibraries.Layer import Layers from TFLibraries.Ops import * from Priors.Evaluation import Eval import tensorflow as tf import numpy as np np.set_printoptions(threshold=np.nan) Layer = Layers() """ Parameters """ random.seed(20160408) batch_size = 512 maxlength = 40 filters = int(sys.argv[1]) hiddendim = 100 num_epochs = 12 rep_dim = 32 offset = rep_dim/2 -1 block_size = 0.1528 space_size = 3.0 unit_size = space_size / rep_dim Directory = '/home/ybisk/GroundedLanguage' TrainData = 'Priors/Train.%d.L1.LangAndBlank.20.npz' % rep_dim EvalData = 'Priors/Dev.%d.L1.Lang.20.npz' % rep_dim RawEval = 'Priors/WithText/Dev.mat.gz' #EvalData = 'Priors/Test.Lang.20.npz'
import os, random, sys sys.path.append(".") ## Model Imports import tensorflow as tf tf.set_random_seed(20160905) import numpy as np np.set_printoptions(threshold=np.nan) from TFLibraries.Layer import Layers from TFLibraries.Train import Training from TFLibraries.Sparse import SparseFiles from TFLibraries.Embeddings import Embedding Layer = Layers() ## Server Code import json import time from flask import Flask from flask import request from flask import jsonify random.seed(20160408) indices = [] def generate_batch(size, data, labels, lengths): global indices if len(indices) < size: indices.extend(range(data.shape[0])) # Random indices
import random,sys,os sys.path.append(".") import tensorflow as tf from TFLibraries.Layer import Layers from TFLibraries.Train import Training from TFLibraries.Sparse import SparseFiles from TFLibraries.Embeddings import Embedding Layer = Layers() random.seed(20160408) indices = [] def generate_batch(size, data, labels): global indices if len(indices) < size: indices.extend(range(data.shape[0])) r = random.sample(indices, size) indices = filter(lambda a: a not in r, indices) # Randomly reorder the data return data[r], labels[r] ## Read Training/Dev/Test data os.chdir('/home/ybisk/GroundedLanguage') print("Running from ", os.getcwd()) maxlength = 80 offset = 3 labelspace = 9 Sparse = SparseFiles(maxlength, offset, labelspace=labelspace, prediction=2) train, _, vocabsize = Sparse.read("JSONReader/data/2016-NAACL/SRD/Train.mat") dev, _, _ = Sparse.read("JSONReader/data/2016-NAACL/SRD/Dev.mat")
import os, random, sys, gzip sys.path.append(".") from TFLibraries.Embeddings import Embedding from TFLibraries.Layer import Layers from TFLibraries.Ops import * from Priors.Evaluation import Eval import tensorflow as tf import numpy as np np.set_printoptions(threshold=np.nan) Layer = Layers() """ Parameters """ random.seed(20160408) batch_size = 512 maxlength = 40 filters = int(sys.argv[1]) hiddendim = 100 num_epochs = 12 rep_dim = 32 offset = rep_dim / 2 - 1 block_size = 0.1528 space_size = 3.0 unit_size = space_size / rep_dim Directory = '/home/ybisk/GroundedLanguage' TrainData = 'Priors/Train.%d.L1.LangAndBlank.20.npz' % rep_dim EvalData = 'Priors/Dev.%d.L1.Lang.20.npz' % rep_dim RawEval = 'Priors/WithText/Dev.mat.gz' #EvalData = 'Priors/Test.Lang.20.npz' #RawEval = 'Priors/WithText/Test.mat.gz'