import time import math import numpy as np import tensorflow as tf import ops from config import config from mac_cell import MACCell ''' The MAC network model. It performs reasoning processes to answer a question over knowledge base (the image) by decomposing it into attention-based computational steps, each perform by a recurrent MAC cell. The network has three main components. Input unit: processes the network inputs: raw question strings and image into distributional representations. The MAC network: calls the MACcells (mac_cell.py) config.netLength number of times, to perform the reasoning process over the question and image. The output unit: a classifier that receives the question and final state of the MAC network and uses them to compute log-likelihood over the possible one-word answers. ''' class MACnet(object): '''Initialize the class. Args: embeddingsInit: initialization for word embeddings (random / glove). answerDict: answers dictionary (mapping between integer id and symbol). ''' def __init__(self, embeddingsInit, answerDict): self.embeddingsInit = embeddingsInit self.answerDict = answerDict self.build() ''' Initializes placeholders. questionsIndicesAll: integer ids of question words. [batchSize, questionLength] questionLengthsAll: length of each question. [batchSize] imagesPlaceholder: image features. [batchSize, channels, height, width] (converted internally to [batchSize, height, width, channels]) answersIndicesAll: integer ids of answer words. [batchSize] lr: learning rate (tensor scalar) train: train / evaluation (tensor boolean) dropout values dictionary (tensor scalars) ''' # change to H x W x C? def addPlaceholders(self): with tf.variable_scope("Placeholders"): ## data # questions self.questionsIndicesAll = tf.placeholder(tf.int32, shape = (None, None)) self.questionLengthsAll = tf.placeholder(tf.int32, shape = (None, )) # images # put image known dimension as last dim? self.imagesPlaceholder = tf.placeholder(tf.float32, shape = (None, None, None, None)) self.imagesAll = tf.transpose(self.imagesPlaceholder, (0, 2, 3, 1)) # self.imageH = tf.shape(self.imagesAll)[1] # self.imageW = tf.shape(self.imagesAll)[2] # answers self.answersIndicesAll = tf.placeholder(tf.int32, shape = (None, )) ## optimization self.lr = tf.placeholder(tf.float32, shape = ()) self.train = tf.placeholder(tf.bool, shape = ()) self.batchSizeAll = tf.shape(self.questionsIndicesAll)[0] ## dropouts # TODO: change dropouts to be 1 - current self.dropouts = { "encInput": tf.placeholder(tf.float32, shape = ()), "encState": tf.placeholder(tf.float32, shape = ()), "stem": tf.placeholder(tf.float32, shape = ()), "question": tf.placeholder(tf.float32, shape = ()), # self.dropouts["question"]Out = tf.placeholder(tf.float32, shape = ()) # self.dropouts["question"]MAC = tf.placeholder(tf.float32, shape = ()) "read": tf.placeholder(tf.float32, shape = ()), "write": tf.placeholder(tf.float32, shape = ()), "memory": tf.placeholder(tf.float32, shape = ()), "output": tf.placeholder(tf.float32, shape = ()) } # batch norm params self.batchNorm = {"decay": config.bnDecay, "train": self.train} # if config.parametricDropout: # self.dropouts["question"] = parametricDropout("qDropout", self.train) # self.dropouts["read"] = parametricDropout("readDropout", self.train) # else: # self.dropouts["question"] = self.dropouts["_q"] # self.dropouts["read"] = self.dropouts["_read"] # if config.tempDynamic: # self.tempAnnealRate = tf.placeholder(tf.float32, shape = ()) self.H, self.W, self.imageInDim = config.imageDims # Feeds data into placeholders. See addPlaceholders method for further details. def createFeedDict(self, data, images, train): feedDict = { self.questionsIndicesAll: np.array(data["question"]), self.questionLengthsAll: np.array(data["questionLength"]), self.imagesPlaceholder: images, # self.answersIndicesAll: [0], self.dropouts["encInput"]: config.encInputDropout if train else 1.0, self.dropouts["encState"]: config.encStateDropout if train else 1.0, self.dropouts["stem"]: config.stemDropout if train else 1.0, self.dropouts["question"]: config.qDropout if train else 1.0, #_ self.dropouts["memory"]: config.memoryDropout if train else 1.0, self.dropouts["read"]: config.readDropout if train else 1.0, #_ self.dropouts["write"]: config.writeDropout if train else 1.0, self.dropouts["output"]: config.outputDropout if train else 1.0, # self.dropouts["question"]Out: config.qDropoutOut if train else 1.0, # self.dropouts["question"]MAC: config.qDropoutMAC if train else 1.0, self.lr: config.lr, self.train: train } # if config.tempDynamic: # feedDict[self.tempAnnealRate] = tempAnnealRate return feedDict # Splits data to a specific GPU (tower) for parallelization def initTowerBatch(self, towerI, towersNum, dataSize): towerBatchSize = tf.floordiv(dataSize, towersNum) start = towerI * towerBatchSize end = (towerI + 1) * towerBatchSize if towerI < towersNum - 1 else dataSize self.questionsIndices = self.questionsIndicesAll[start:end] self.questionLengths = self.questionLengthsAll[start:end] self.images = self.imagesAll[start:end] self.answersIndices = self.answersIndicesAll[start:end] self.batchSize = end - start ''' The Image Input Unit (stem). Passes the image features through a CNN-network Optionally adds position encoding (doesn't in the default behavior). Flatten the image into Height * Width "Knowledge base" array. Args: images: image input. [batchSize, height, width, inDim] inDim: input image dimension outDim: image out dimension addLoc: if not None, adds positional encoding to the image Returns preprocessed images. [batchSize, height * width, outDim] ''' def stem(self, images, inDim, outDim, addLoc = None): with tf.variable_scope("stem"): if addLoc is None: addLoc = config.locationAware if config.stemLinear: features = ops.linear(images, inDim, outDim) else: dims = [inDim] + ([config.stemDim] * (config.stemNumLayers - 1)) + [outDim] if addLoc: images, inDim = ops.addLocation(images, inDim, config.locationDim, h = self.H, w = self.W, locType = config.locationType) dims[0] = inDim # if config.locationType == "PE": # dims[-1] /= 4 # dims[-1] *= 3 # else: # dims[-1] -= 2 features = ops.CNNLayer(images, dims, batchNorm = self.batchNorm if config.stemBN else None, dropout = self.dropouts["stem"], kernelSizes = config.stemKernelSizes, strides = config.stemStrideSizes) # if addLoc: # lDim = outDim / 4 # lDim /= 4 # features, _ = addLocation(features, dims[-1], lDim, h = H, w = W, # locType = config.locationType) if config.stemGridRnn: features = ops.multigridRNNLayer(features, H, W, outDim) # flatten the 2d images into a 1d KB features = tf.reshape(features, (self.batchSize, -1, outDim)) return features # Embed question using parametrized word embeddings. # The embedding are initialized to the values supported to the class initialization def qEmbeddingsOp(self, qIndices, embInit): with tf.variable_scope("qEmbeddings"): # if config.useCPU: # with tf.device('/cpu:0'): # embeddingsVar = tf.Variable(self.embeddingsInit, name = "embeddings", dtype = tf.float32) # else: # embeddingsVar = tf.Variable(self.embeddingsInit, name = "embeddings", dtype = tf.float32) embeddingsVar = tf.get_variable("emb", initializer = tf.to_float(embInit), dtype = tf.float32, trainable = (not config.wrdEmbFixed)) embeddings = tf.concat([tf.zeros((1, config.wrdEmbDim)), embeddingsVar], axis = 0) questions = tf.nn.embedding_lookup(embeddings, qIndices) return questions, embeddings # Embed answer words def aEmbeddingsOp(self, embInit): with tf.variable_scope("aEmbeddings"): if embInit is None: return None answerEmbeddings = tf.get_variable("emb", initializer = tf.to_float(embInit), dtype = tf.float32) return answerEmbeddings # Embed question and answer words with tied embeddings def qaEmbeddingsOp(self, qIndices, embInit): questions, qaEmbeddings = self.qEmbeddingsOp(qIndices, embInit["qa"]) aEmbeddings = tf.nn.embedding_lookup(qaEmbeddings, embInit["ansMap"]) return questions, qaEmbeddings, aEmbeddings ''' Embed question (and optionally answer) using parametrized word embeddings. The embedding are initialized to the values supported to the class initialization ''' def embeddingsOp(self, qIndices, embInit): if config.ansEmbMod == "SHARED": questions, qEmb, aEmb = self.qaEmbeddingsOp(qIndices, embInit) else: questions, qEmb = self.qEmbeddingsOp(qIndices, embInit["q"]) aEmb = self.aEmbeddingsOp(embInit["a"]) return questions, qEmb, aEmb ''' The Question Input Unit embeds the questions to randomly-initialized word vectors, and runs a recurrent bidirectional encoder (RNN/LSTM etc.) that gives back vector representations for each question (the RNN final hidden state), and representations for each of the question words (the RNN outputs for each word). The method uses bidirectional LSTM, by default. Optionally projects the outputs of the LSTM (with linear projection / optionally with some activation). Args: questions: question word embeddings [batchSize, questionLength, wordEmbDim] questionLengths: the question lengths. [batchSize] projWords: True to apply projection on RNN outputs. projQuestion: True to apply projection on final RNN state. projDim: projection dimension in case projection is applied. Returns: Contextual Words: RNN outputs for the words. [batchSize, questionLength, ctrlDim] Vectorized Question: Final hidden state representing the whole question. [batchSize, ctrlDim] ''' def encoder(self, questions, questionLengths, projWords = False, projQuestion = False, projDim = None): with tf.variable_scope("encoder"): # variational dropout option varDp = None if config.encVariationalDropout: varDp = {"stateDp": self.dropouts["stateInput"], "inputDp": self.dropouts["encInput"], "inputSize": config.wrdEmbDim} # rnns for i in range(config.encNumLayers): questionCntxWords, vecQuestions = ops.RNNLayer(questions, questionLengths, config.encDim, bi = config.encBi, cellType = config.encType, dropout = self.dropouts["encInput"], varDp = varDp, name = "rnn%d" % i) # dropout for the question vector vecQuestions = tf.nn.dropout(vecQuestions, self.dropouts["question"]) # projection of encoder outputs if projWords: questionCntxWords = ops.linear(questionCntxWords, config.encDim, projDim, name = "projCW") if projQuestion: vecQuestions = ops.linear(vecQuestions, config.encDim, projDim, act = config.encProjQAct, name = "projQ") return questionCntxWords, vecQuestions ''' Stacked Attention Layer for baseline. Computes interaction between images and the previous memory, and casts it back to compute attention over the image, which in turn is summed up with the previous memory to result in the new one. Args: images: input image. [batchSize, H * W, inDim] memory: previous memory value [batchSize, inDim] inDim: inputs dimension hDim: hidden dimension to compute interactions between image and memory Returns the new memory value. ''' def baselineAttLayer(self, images, memory, inDim, hDim, name = "", reuse = None): with tf.variable_scope("attLayer" + name, reuse = reuse): # projImages = ops.linear(images, inDim, hDim, name = "projImage") # projMemory = tf.expand_dims(ops.linear(memory, inDim, hDim, name = "projMemory"), axis = -2) # if config.saMultiplicative: # interactions = projImages * projMemory # else: # interactions = tf.tanh(projImages + projMemory) interactions, _ = ops.mul(images, memory, inDim, proj = {"dim": hDim, "shared": False}, interMod = config.baselineAttType) attention = ops.inter2att(interactions, hDim) summary = ops.att2Smry(attention, images) newMemory = memory + summary return newMemory ''' Baseline approach: If baselineAtt is True, applies several layers (baselineAttNumLayers) of stacked attention to image and memory, when memory is initialized to the vector questions. See baselineAttLayer for further details. Otherwise, computes result output features based on image representation (baselineCNN), or question (baselineLSTM) or both. Args: vecQuestions: question vector representation [batchSize, questionDim] questionDim: dimension of question vectors images: (flattened) image representation [batchSize, imageDim] imageDim: dimension of image representations. hDim: hidden dimension to compute interactions between image and memory (for attention-based baseline). Returns final features to use in later classifier. [batchSize, outDim] (out dimension depends on baseline method) ''' def baseline(self, vecQuestions, questionDim, images, imageDim, hDim): with tf.variable_scope("baseline"): if config.baselineAtt: memory = self.linear(vecQuestions, questionDim, hDim, name = "qProj") images = self.linear(images, imageDim, hDim, name = "iProj") for i in range(config.baselineAttNumLayers): memory = self.baselineAttLayer(images, memory, hDim, hDim, name = "baseline%d" % i) memDim = hDim else: images, imagesDim = ops.linearizeFeatures(images, self.H, self.W, imageDim, projDim = config.baselineProjDim) if config.baselineLSTM and config.baselineCNN: memory = tf.concat([vecQuestions, images], axis = -1) memDim = questionDim + imageDim elif config.baselineLSTM: memory = vecQuestions memDim = questionDim else: # config.baselineCNN memory = images memDim = imageDim return memory, memDim ''' Runs the MAC recurrent network to perform the reasoning process. Initializes a MAC cell and runs netLength iterations. Currently it passes the question and knowledge base to the cell during its creating, such that it doesn't need to interact with it through inputs / outputs while running. The recurrent computation happens by working iteratively over the hidden (control, memory) states. Args: images: flattened image features. Used as the "Knowledge Base". (Received by default model behavior from the Image Input Units). [batchSize, H * W, memDim] vecQuestions: vector questions representations. (Received by default model behavior from the Question Input Units as the final RNN state). [batchSize, ctrlDim] questionWords: question word embeddings. [batchSize, questionLength, ctrlDim] questionCntxWords: question contextual words. (Received by default model behavior from the Question Input Units as the series of RNN output states). [batchSize, questionLength, ctrlDim] questionLengths: question lengths. [batchSize] Returns the final control state and memory state resulted from the network. ([batchSize, ctrlDim], [bathSize, memDim]) ''' def MACnetwork(self, images, vecQuestions, questionWords, questionCntxWords, questionLengths, name = "", reuse = None): with tf.variable_scope("MACnetwork" + name, reuse = reuse): self.macCell = MACCell( vecQuestions = vecQuestions, questionWords = questionWords, questionCntxWords = questionCntxWords, questionLengths = questionLengths, knowledgeBase = images, memoryDropout = self.dropouts["memory"], readDropout = self.dropouts["read"], writeDropout = self.dropouts["write"], # qDropoutMAC = self.qDropoutMAC, batchSize = self.batchSize, train = self.train, reuse = reuse) state = self.macCell.zero_state(self.batchSize, tf.float32) # inSeq = tf.unstack(inSeq, axis = 1) none = tf.zeros((self.batchSize, 1), dtype = tf.float32) # for i, inp in enumerate(inSeq): for i in range(config.netLength): self.macCell.iteration = i # if config.unsharedCells: # with tf.variable_scope("iteration%d" % i): # macCell.myNameScope = "iteration%d" % i _, state = self.macCell(none, state) # else: # _, state = macCell(none, state) # macCell.reuse = True # self.autoEncMMLoss = macCell.autoEncMMLossI # inputSeqL = None # _, lastOutputs = tf.nn.dynamic_rnn(macCell, inputSeq, # / static # sequence_length = inputSeqL, # initial_state = initialState, # swap_memory = True) # self.postModules = None # if (config.controlPostRNN or config.selfAttentionMod == "POST"): # may not work well with dlogits # self.postModules, _ = self.RNNLayer(cLogits, None, config.encDim, bi = False, # name = "decPostRNN", cellType = config.controlPostRNNmod) # if config.controlPostRNN: # logits = self.postModules # self.postModules = tf.unstack(self.postModules, axis = 1) # self.autoEncCtrlLoss = tf.constant(0.0) # if config.autoEncCtrl: # autoEncCtrlCellType = ("GRU" if config.autoEncCtrlGRU else "RNN") # autoEncCtrlinp = logits # _, autoEncHid = self.RNNLayer(autoEncCtrlinp, None, config.encDim, # bi = True, name = "autoEncCtrl", cellType = autoEncCtrlCellType) # self.autoEncCtrlLoss = (tf.nn.l2_loss(vecQuestions - autoEncHid)) / tf.to_float(self.batchSize) finalControl = state.control finalMemory = state.memory return finalControl, finalMemory ''' Output Unit (step 1): chooses the inputs to the output classifier. By default the classifier input will be the the final memory state of the MAC network. If outQuestion is True, concatenate the question representation to that. If outImage is True, concatenate the image flattened representation. Args: memory: (final) memory state of the MAC network. [batchSize, memDim] vecQuestions: question vector representation. [batchSize, ctrlDim] images: image features. [batchSize, H, W, imageInDim] imageInDim: images dimension. Returns the resulted features and their dimension. ''' def outputOp(self, memory, vecQuestions, images, imageInDim): with tf.variable_scope("outputUnit"): features = memory dim = config.memDim if config.outQuestion: eVecQuestions = ops.linear(vecQuestions, config.ctrlDim, config.memDim, name = "outQuestion") features, dim = ops.concat(features, eVecQuestions, config.memDim, mul = config.outQuestionMul) if config.outImage: images, imagesDim = ops.linearizeFeatures(images, self.H, self.W, self.imageInDim, outputDim = config.outImageDim) images = ops.linear(images, config.memDim, config.outImageDim, name = "outImage") features = tf.concat([features, images], axis = -1) dim += config.outImageDim return features, dim ''' Output Unit (step 2): Computes the logits for the answers. Passes the features through fully-connected network to get the logits over the possible answers. Optionally uses answer word embeddings in computing the logits (by default, it doesn't). Args: features: features used to compute logits [batchSize, inDim] inDim: features dimension aEmbedding: supported word embeddings for answer words in case answerMod is not NON. Optionally computes logits by computing dot-product with answer embeddings. Returns: the computed logits. [batchSize, answerWordsNum] ''' def classifier(self, features, inDim, aEmbeddings = None): with tf.variable_scope("classifier"): outDim = config.answerWordsNum dims = [inDim] + config.outClassifierDims + [outDim] if config.answerMod != "NON": dims[-1] = config.wrdEmbDim logits = ops.FCLayer(features, dims, batchNorm = self.batchNorm if config.outputBN else None, dropout = self.dropouts["output"]) if config.answerMod != "NON": logits = tf.nn.dropout(logits, self.dropouts["output"]) interactions = ops.mul(aEmbeddings, logits, dims[-1], interMod = config.answerMod) logits = ops.inter2logits(interactions, dims[-1], sumMod = "SUM") logits += ops.getBias((outputDim, ), "ans") # answersWeights = tf.transpose(aEmbeddings) # if config.answerMod == "BL": # Wans = ops.getWeight((dims[-1], config.wrdEmbDim), "ans") # logits = tf.matmul(logits, Wans) # elif config.answerMod == "DIAG": # Wans = ops.getWeight((config.wrdEmbDim, ), "ans") # logits = logits * Wans # logits = tf.matmul(logits, answersWeights) return logits # def getTemp(): # with tf.variable_scope("temperature"): # if config.tempParametric: # self.temperatureVar = tf.get_variable("temperature", shape = (), # initializer = tf.constant_initializer(5), dtype = tf.float32) # temperature = tf.sigmoid(self.temperatureVar) # else: # temperature = config.temperature # if config.tempDynamic: # temperature *= self.tempAnnealRate # return temperature # Computes mean cross entropy loss between logits and answers. def addAnswerLossOp(self, logits, answers): with tf.variable_scope("answerLoss"): losses = tf.nn.sparse_softmax_cross_entropy_with_logits(labels = answers, logits = logits) loss = tf.reduce_mean(losses) self.answerLossList.append(loss) return loss, losses # Computes predictions (by finding maximal logit value, corresponding to highest probability) # and mean accuracy between predictions and answers. def addPredOp(self, logits, answers): with tf.variable_scope("pred"): preds = tf.to_int32(tf.argmax(logits, axis = -1)) # tf.nn.softmax( corrects = tf.equal(preds, answers) correctNum = tf.reduce_sum(tf.to_int32(corrects)) acc = tf.reduce_mean(tf.to_float(corrects)) self.correctNumList.append(correctNum) self.answerAccList.append(acc) return preds, corrects, correctNum # Creates optimizer (adam) def addOptimizerOp(self): with tf.variable_scope("trainAddOptimizer"): self.globalStep = tf.Variable(0, dtype = tf.int32, trainable = False, name = "globalStep") # init to 0 every run? optimizer = tf.train.AdamOptimizer(learning_rate = self.lr) return optimizer ''' Computes gradients for all variables or subset of them, based on provided loss, using optimizer. ''' def computeGradients(self, optimizer, loss, trainableVars = None): # tf.trainable_variables() with tf.variable_scope("computeGradients"): if config.trainSubset: trainableVars = [] allVars = tf.trainable_variables() for var in allVars: if any((s in var.name) for s in config.varSubset): trainableVars.append(var) gradients_vars = optimizer.compute_gradients(loss, trainableVars) return gradients_vars ''' Apply gradients. Optionally clip them, and update exponential moving averages for parameters. ''' def addTrainingOp(self, optimizer, gradients_vars): with tf.variable_scope("train"): gradients, variables = zip(*gradients_vars) norm = tf.global_norm(gradients) # gradient clipping if config.clipGradients: clippedGradients, _ = tf.clip_by_global_norm(gradients, config.gradMaxNorm, use_norm = norm) gradients_vars = zip(clippedGradients, variables) # updates ops (for batch norm) and train op updateOps = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(updateOps): train = optimizer.apply_gradients(gradients_vars, global_step = self.globalStep) # exponential moving average if config.useEMA: ema = tf.train.ExponentialMovingAverage(decay = config.emaDecayRate) maintainAveragesOp = ema.apply(tf.trainable_variables()) with tf.control_dependencies([train]): trainAndUpdateOp = tf.group(maintainAveragesOp) train = trainAndUpdateOp self.emaDict = ema.variables_to_restore() return train, norm # TODO (add back support for multi-gpu..) def averageAcrossTowers(self, gpusNum): self.lossAll = self.lossList[0] self.answerLossAll = self.answerLossList[0] self.correctNumAll = self.correctNumList[0] self.answerAccAll = self.answerAccList[0] self.predsAll = self.predsList[0] self.gradientVarsAll = self.gradientVarsList[0] def trim2DVectors(self, vectors, vectorsLengths): maxLength = np.max(vectorsLengths) return vectors[:,:maxLength] def trimData(self, data): data["question"] = self.trim2DVectors(data["question"], data["questionLength"]) return data ''' Builds predictions JSON, by adding the model's predictions and attention maps back to the original data JSON. ''' def buildPredsList(self, prediction): return self.answerDict.decodeId(prediction) ''' Processes a batch of data with the model. Args: sess: TF session data: Data batch. Dictionary that contains numpy array for: questions, questionLengths, answers. See preprocess.py for further information of the batch structure. images: batch of image features, as numpy array. images["images"] contains [batchSize, channels, h, w] train: True to run batch for training. getAtt: True to return attention maps for question and image (and optionally self-attention and gate values). Returns results: e.g. loss, accuracy, running time. ''' def runBatch(self, sess, data, images, train, getAtt = False): data = self.trimData(data) predsOp = self.predsAll time0 = time.time() feed = self.createFeedDict(data, images, train) time1 = time.time() predsInfo = sess.run( predsOp, feed_dict = feed) time2 = time.time() predsList = self.buildPredsList(predsInfo[0]) return predsList def build(self): self.addPlaceholders() self.optimizer = self.addOptimizerOp() self.gradientVarsList = [] self.lossList = [] self.answerLossList = [] self.correctNumList = [] self.answerAccList = [] self.predsList = [] with tf.variable_scope("macModel"): for i in range(config.gpusNum): with tf.device("/gpu:{}".format(i)): with tf.name_scope("tower{}".format(i)) as scope: self.initTowerBatch(i, config.gpusNum, self.batchSizeAll) self.loss = tf.constant(0.0) # embed questions words (and optionally answer words) questionWords, qEmbeddings, aEmbeddings = \ self.embeddingsOp(self.questionsIndices, self.embeddingsInit) projWords = projQuestion = ((config.encDim != config.ctrlDim) or config.encProj) questionCntxWords, vecQuestions = self.encoder(questionWords, self.questionLengths, projWords, projQuestion, config.ctrlDim) # Image Input Unit (stem) imageFeatures = self.stem(self.images, self.imageInDim, config.memDim) # baseline model if config.useBaseline: output, dim = self.baseline(vecQuestions, config.ctrlDim, self.images, self.imageInDim, config.attDim) # MAC model else: # self.temperature = self.getTemp() finalControl, finalMemory = self.MACnetwork(imageFeatures, vecQuestions, questionWords, questionCntxWords, self.questionLengths) # Output Unit - step 1 (preparing classifier inputs) output, dim = self.outputOp(finalMemory, vecQuestions, self.images, self.imageInDim) # Output Unit - step 2 (classifier) logits = self.classifier(output, dim, aEmbeddings) # compute loss, predictions, accuracy answerLoss, self.losses = self.addAnswerLossOp(logits, self.answersIndices) self.preds, self.corrects, self.correctNum = self.addPredOp(logits, self.answersIndices) self.loss += answerLoss self.predsList.append(self.preds) self.lossList.append(self.loss) # compute gradients gradient_vars = self.computeGradients(self.optimizer, self.loss, trainableVars = None) self.gradientVarsList.append(gradient_vars) # reuse variables in next towers tf.get_variable_scope().reuse_variables() self.averageAcrossTowers(config.gpusNum) self.trainOp, self.gradNorm = self.addTrainingOp(self.optimizer, self.gradientVarsAll) self.noOp = tf.no_op()