problem while generating features at convfeatures.py

[Soumennn]

NotFoundError (see above for traceback): flickr30k_images/ff1000092795.jpg; No such file or directory
[[node ReadFile (defined at convfeatures.py:24) ]]
[[node DecodeJpeg (defined at convfeatures.py:25) ]]

please help me with this issue as i am trying to make my college project with this. Also a python3 version migration would have helped a lot.

[dljzx]

Same error

[aayux]

Is it possible for you to share the traceback for this error?

[sreenupadidapu]

when you run that command plz try this one
python2 convfeatures.py --data_path Dataset/flickr30k-images/ --inception_path ConvNets/inception_v4.pb
i hope this will work fine.
thank you

[dljzx]

when you run that command plz try this one
python2 convfeatures.py --data_path Dataset/flickr30k-images/ --inception_path ConvNets/inception_v4.pb
i hope this will work fine.
thank you

Thank you ! I found there was a '/' lost behind data_path , but still an IndexError in main.py --mode train
Traceback (most recent call last):
File "main.py", line 66, in
features, captions = training_data[:, 0], training_data[:, 1]
IndexError: too many indices for array
Could you help me solve that question? Thanks a lot.

[sreenupadidapu]

could you please send me the lines from 62 to 71 *Sreenu.Padidapu,* *Jr,Software Engineer,* *Aadhya Analytics,* *2-39,Old SBI Road,* *Sri nagar colony,* *Gannavaram-521101.*

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On Wed, May 22, 2019 at 10:05 AM dljzx ***@***.***> wrote: when you run that command plz try this one python2 convfeatures.py --data_path Dataset/flickr30k-images/ --inception_path ConvNets/inception_v4.pb i hope this will work fine. thank you Thank you ! I found there was a '/' lost behind data_path , but still an IndexError in main.py --mode train Traceback (most recent call last): File "main.py", line 66, in features, captions = training_data[:, 0], training_data[:, 1] IndexError: too many indices for array Could you help me solve that question? Thanks a lot. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#38?email_source=notifications&email_token=AKDKCK3XIQGJAXAU7AXZVGLPWTERXA5CNFSM4HEPBIZ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODV5323Y#issuecomment-494648687>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AKDKCK7WAW6H5II7KN547GTPWTERXANCNFSM4HEPBIZQ> .

[dljzx]

could you please send me the lines from 62 to 71 Sreenu.Padidapu, Jr,Software Engineer, Aadhya Analytics, 2-39,Old SBI Road, Sri nagar colony, Gannavaram-521101.
…
On Wed, May 22, 2019 at 10:05 AM dljzx @.***> wrote: when you run that command plz try this one python2 convfeatures.py --data_path Dataset/flickr30k-images/ --inception_path ConvNets/inception_v4.pb i hope this will work fine. thank you Thank you ! I found there was a '/' lost behind data_path , but still an IndexError in main.py --mode train Traceback (most recent call last): File "main.py", line 66, in features, captions = training_data[:, 0], training_data[:, 1] IndexError: too many indices for array Could you help me solve that question? Thanks a lot. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#38?email_source=notifications&email_token=AKDKCK3XIQGJAXAU7AXZVGLPWTERXA5CNFSM4HEPBIZ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODV5323Y#issuecomment-494648687>, or mute the thread https://github.com/notifications/unsubscribe-auth/AKDKCK7WAW6H5II7KN547GTPWTERXANCNFSM4HEPBIZQ .

if config.mode == "train":
vocab, wtoidx, training_data = generate_captions(
config.word_threshold, config.max_len, args.caption_path, args.feature_path,
config.data_is_coco)
features, captions = training_data[:, 0], training_data[:, 1]
features = np.array([feat.astype(float) for feat in features])
data = (vocab.tolist(), wtoidx.tolist(), features, captions)
model = Caption_Generator(config, data=data)
loss, inp_dict = model.build_train_graph()
model.train(loss, inp_dict)

These are the lines you need. It is so kind of you to respond me so quick.

[sreenupadidapu]

i think all are correct here could you send me the caption_generator.py file it will more helpful for me. Thanks, *Sreenu.Padidapu,* *Jr,Software Engineer,* *Aadhya Analytics,* *2-39,Old SBI Road,* *Sri nagar colony,* *Gannavaram-521101.*

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On Wed, May 22, 2019 at 10:17 AM dljzx ***@***.***> wrote: could you please send me the lines from 62 to 71 *Sreenu.Padidapu,* *Jr,Software Engineer,* *Aadhya Analytics,* *2-39,Old SBI Road,* *Sri nagar colony,* *Gannavaram-521101.* … <#m_7082800599257854298_> On Wed, May 22, 2019 at 10:05 AM dljzx *@*.***> wrote: when you run that command plz try this one python2 convfeatures.py --data_path Dataset/flickr30k-images/ --inception_path ConvNets/inception_v4.pb i hope this will work fine. thank you Thank you ! I found there was a '/' lost behind data_path , but still an IndexError in main.py --mode train Traceback (most recent call last): File "main.py", line 66, in features, captions = training_data[:, 0], training_data[:, 1] IndexError: too many indices for array Could you help me solve that question? Thanks a lot. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#38 <#38>?email_source=notifications&email_token=AKDKCK3XIQGJAXAU7AXZVGLPWTERXA5CNFSM4HEPBIZ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODV5323Y#issuecomment-494648687>, or mute the thread https://github.com/notifications/unsubscribe-auth/AKDKCK7WAW6H5II7KN547GTPWTERXANCNFSM4HEPBIZQ . if config.mode == "train": vocab, wtoidx, training_data = generate_captions( config.word_threshold, config.max_len, args.caption_path, args.feature_path, config.data_is_coco) features, captions = training_data[:, 0], training_data[:, 1] features = np.array([feat.astype(float) for feat in features]) data = (vocab.tolist(), wtoidx.tolist(), features, captions) model = Caption_Generator(config, data=data) loss, inp_dict = model.build_train_graph() model.train(loss, inp_dict) These are the lines you need. It is so kind of you to respond me so quick. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#38?email_source=notifications&email_token=AKDKCK5EDPVKJIRICLRK4Q3PWTF5JA5CNFSM4HEPBIZ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODV54JKI#issuecomment-494650537>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AKDKCK2GBKH4PMLSW6XFE4DPWTF5JANCNFSM4HEPBIZQ> .

[dljzx]

i think all are correct here could you send me the caption_generator.py file it will more helpful for me. Thanks, Sreenu.Padidapu, Jr,Software Engineer, Aadhya Analytics, 2-39,Old SBI Road, Sri nagar colony, Gannavaram-521101.
…
On Wed, May 22, 2019 at 10:17 AM dljzx @.> wrote: could you please send me the lines from 62 to 71 Sreenu.Padidapu, Jr,Software Engineer, Aadhya Analytics, 2-39,Old SBI Road, Sri nagar colony, Gannavaram-521101. … <#m_7082800599257854298_> On Wed, May 22, 2019 at 10:05 AM dljzx @.> wrote: when you run that command plz try this one python2 convfeatures.py --data_path Dataset/flickr30k-images/ --inception_path ConvNets/inception_v4.pb i hope this will work fine. thank you Thank you ! I found there was a '/' lost behind data_path , but still an IndexError in main.py --mode train Traceback (most recent call last): File "main.py", line 66, in features, captions = training_data[:, 0], training_data[:, 1] IndexError: too many indices for array Could you help me solve that question? Thanks a lot. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#38 <#38>?email_source=notifications&email_token=AKDKCK3XIQGJAXAU7AXZVGLPWTERXA5CNFSM4HEPBIZ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODV5323Y#issuecomment-494648687>, or mute the thread https://github.com/notifications/unsubscribe-auth/AKDKCK7WAW6H5II7KN547GTPWTERXANCNFSM4HEPBIZQ . if config.mode == "train": vocab, wtoidx, training_data = generate_captions( config.word_threshold, config.max_len, args.caption_path, args.feature_path, config.data_is_coco) features, captions = training_data[:, 0], training_data[:, 1] features = np.array([feat.astype(float) for feat in features]) data = (vocab.tolist(), wtoidx.tolist(), features, captions) model = Caption_Generator(config, data=data) loss, inp_dict = model.build_train_graph() model.train(loss, inp_dict) These are the lines you need. It is so kind of you to respond me so quick. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#38?email_source=notifications&email_token=AKDKCK5EDPVKJIRICLRK4Q3PWTF5JA5CNFSM4HEPBIZ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODV54JKI#issuecomment-494650537>, or mute the thread https://github.com/notifications/unsubscribe-auth/AKDKCK2GBKH4PMLSW6XFE4DPWTF5JANCNFSM4HEPBIZQ .

caption_generator.zip
Here is caption_generator.py

[sreenupadidapu]

instead of that file plz replace this one import matplotlib.pyplot as plt from random import shuffle from convfeatures import * import tensorflow as tf from PIL import Image import numpy as np import pickle import sys import os class Caption_Generator(): def __init__(self, config, data=None): self.dim_imgft = np.int(config.dim_imgft) self.embedding_size = np.int(config.embedding_size) self.num_hidden = np.int(config.num_hidden) self.batch_size = np.int(config.batch_size) self.num_timesteps = np.int(config.num_timesteps) self.max_len = config.max_len self.word_threshold = config.word_threshold self.bias_init = config.bias_init self.xavier_init = config.xavier_init self.dropout = config.dropout self.lstm_keep_prob = config.lstm_keep_prob self.beta = config.beta_l2 self.mode = config.mode self.batch_decode = config.batch_decode self.learning_rate = config.learning_rate self.resume = config.resume self.savedecoder = config.savedecoder self.saveencoder = config.saveencoder if self.mode == 'train': self.vocab, self.wtoidx, self.features, self.captions = data self.num_batch = int(self.features.shape[0]) / self.batch_size print("Converting Captions to IDs") self.captions = self.Words_to_IDs(self.wtoidx, self.captions) if self.resume == 1: self.vocab = np.load("Dataset/vocab.npy").tolist() self.wtoidx = np.load("Dataset/wordmap.npy").tolist() self.current_epoch = 0 self.current_step = 0 if self.resume is 1 or self.mode == 'test': if os.path.isfile('model/save.npy'): self.current_epoch, self.current_step = np.load( "model/save.npy") else: print("No Checkpoints, Restarting Training..") self.resume = 0 self.nb_epochs = config.nb_epochs if self.mode == 'test': self.vocab = np.load("Dataset/vocab.npy").tolist() self.wtoidx = np.load("Dataset/wordmap.npy").tolist() self.max_words = np.int(len(self.wtoidx)) self.idxtow = dict(zip(self.wtoidx.values(), self.wtoidx.keys())) self.model() self.image_features, self.IDs = self.build_decode_graph() self.load_image=config.load_image if not self.batch_decode: self.io = build_prepro_graph(config.inception_path) self.sess = self.init_decode() return self.max_words = np.int(len(self.wtoidx)) self.idxtow = dict(zip(self.wtoidx.values(), self.wtoidx.keys())) self.model() def Words_to_IDs(self, wtoidx, caption_batch): for i, caption in enumerate(caption_batch): cap = [] for word in caption.split(): try: cap.append(wtoidx[word]) except KeyError: cap.append(wtoidx["<UNK>"]) caption_batch[i] = np.array(cap) return np.vstack(caption_batch) def IDs_to_Words(self, idxtow, ID_batch): return [idxtow[word] for IDs in ID_batch for word in IDs] def generate_mask(self, ID_batch, wtoidx): nonpadded = map(lambda x: len( ID_batch[0]) - x.count(wtoidx["<PAD>"]), ID_batch.tolist()) ID_batch = np.zeros((ID_batch.shape[0], self.max_len + 2)) for ind, row in enumerate(ID_batch): row[:nonpadded[ind]] = 1 return ID_batch def get_next_batch(self): batch_size = self.batch_size for batch_idx in range(0, len(self.features), batch_size): images_batch = self.features[batch_idx:batch_idx + batch_size] caption_batch = self.captions[batch_idx:batch_idx + batch_size] # print caption_batch yield images_batch, caption_batch # From NeuralTalk by Andrej Karpathy def init_bias(self): bias_init_vector = np.array( [1.0 * self.vocab[self.idxtow[i]] for i in self.idxtow]) bias_init_vector /= np.sum(bias_init_vector) bias_init_vector = np.log(bias_init_vector) bias_init_vector -= np.max(bias_init_vector) return bias_init_vector def assign_weights(self, dim1, dim2=None, name=None, Xavier=False): if Xavier: weight_initializer = tf.contrib.layers.xavier_initializer() return tf.get_variable( name, [dim1, dim2], initializer=weight_initializer) return tf.Variable(tf.truncated_normal([dim1, dim2]), name=name) def assign_biases(self, dim, name, bias_init=False): if bias_init: return tf.Variable(self.init_bias().astype(np.float32), name=name) return tf.Variable(tf.zeros([dim]), name=name) def model(self): self.word_embedding = { "weights": self.assign_weights( self.max_words, self.embedding_size, 'Weight_emb'), "biases": self.assign_biases( self.embedding_size, "Bias_emb")} self.image_embedding = { "weights": self.assign_weights( self.dim_imgft, self.embedding_size, 'Weight_img_emb'), "biases": self.assign_biases( self.embedding_size, 'Bias_img_emb')} self.target_word = { "weights": self.assign_weights( self.embedding_size, self.max_words, 'Weight_target'), "biases": self.assign_biases( self.max_words, 'Bias_target', bias_init=self.bias_init)} self.lstm_cell = tf.contrib.rnn.BasicLSTMCell(self.num_hidden) if self.dropout: self.lstm_cell = tf.contrib.rnn.DropoutWrapper( self.lstm_cell, self.lstm_keep_prob, self.lstm_keep_prob) self.inp_dict = { "features": tf.placeholder( tf.float32, [self.batch_size, self.dim_imgft], name="Train_Features"), "captions": tf.placeholder( tf.int32, [self.batch_size, self.num_timesteps], name="Train_Captions"), "mask": tf.placeholder( tf.float32, [self.batch_size, self.num_timesteps], name="Train_Mask") } def create_feed_dict(self, Ids, features, mask, mode="train"): feed_dict = {} feed_dict[self.inp_dict['captions']] = Ids feed_dict[self.inp_dict['features']] = features feed_dict[self.inp_dict['mask']] = mask return feed_dict def build_train_graph(self): init_c = tf.zeros([self.batch_size, self.lstm_cell.state_size[0]]) init_h = tf.zeros([self.batch_size, self.lstm_cell.state_size[1]]) initial_state = (init_c, init_h) image_emb = tf.matmul(self.inp_dict["features"], self.image_embedding[ 'weights']) + self.image_embedding['biases'] with tf.variable_scope("LSTM"): output, state = self.lstm_cell(image_emb, initial_state) loss = 0.0 for i in range(1, self.num_timesteps): batch_embed = tf.nn.embedding_lookup( self.word_embedding['weights'], self.inp_dict['captions'][ :, i - 1]) + self.word_embedding['biases'] tf.get_variable_scope().reuse_variables() output, state = self.lstm_cell(batch_embed, state) words = tf.reshape(self.inp_dict['captions'][ :, i], shape=[self.batch_size, 1]) onehot_encoded = tf.one_hot(indices=words, depth=len( self.wtoidx), on_value=1, off_value=0, axis=-1) onehot_encoded = tf.reshape(onehot_encoded, shape=[ self.batch_size, self.max_words]) target_logit = tf.matmul( output, self.target_word['weights']) + self.target_word['biases'] cross_entropy = tf.nn.softmax_cross_entropy_with_logits( logits=target_logit, labels=onehot_encoded) cross_entropy = cross_entropy * self.inp_dict["mask"][:, i] current_loss = tf.reduce_sum(cross_entropy) loss = loss + current_loss loss = loss / tf.reduce_sum(self.inp_dict["mask"][:, 1:]) # introducing L2 regularization in Loss/Cost Function # self.beta=0 #l2_loss = self.beta * sum([tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables() if not "Bias" in tf_var.name]) #loss = tf.reduce_mean(loss+l2_loss) return loss, self.inp_dict def build_decode_graph(self): image_features = tf.placeholder( tf.float32, [1, self.dim_imgft], name='Input_Features') image_emb = tf.matmul(image_features, self.image_embedding[ 'weights']) + self.image_embedding['biases'] init_c = tf.zeros([1, self.lstm_cell.state_size[0]]) init_h = tf.zeros([1, self.lstm_cell.state_size[1]]) initial_state = (init_c, init_h) IDs = [] with tf.variable_scope("LSTM"): output, state = self.lstm_cell(image_emb, initial_state) pred_ID = tf.nn.embedding_lookup( self.word_embedding['weights'], [ self.wtoidx["<S>"]]) + self.word_embedding['biases'] for i in range(self.num_timesteps): tf.get_variable_scope().reuse_variables() output, state = self.lstm_cell(pred_ID, state) logits = tf.matmul(output, self.target_word[ "weights"]) + self.target_word["biases"] predicted_next_idx = tf.argmax(logits, axis=1) pred_ID = tf.nn.embedding_lookup( self.word_embedding['weights'], predicted_next_idx) pred_ID = pred_ID + self.word_embedding['biases'] predicted_next_idx = tf.cast(predicted_next_idx, tf.int32, name="word_"+str(i)) IDs.append(predicted_next_idx) with open("model/Decoder/DecoderOutputs.txt", 'w') as f: for name in IDs: f.write(name.name.split(":0")[0] + "\n") return image_features, IDs def train(self, loss, inp_dict): self.loss = loss self.inp_dict = inp_dict saver = tf.train.Saver(max_to_keep=10) global_step = tf.Variable( self.current_step, name='global_step') starter_learning_rate = self.learning_rate learning_rate = tf.train.exponential_decay( starter_learning_rate, global_step, 100000, 0.95, staircase=True) optimizer = tf.train.AdamOptimizer(learning_rate).minimize( self.loss, global_step=global_step) tf.summary.scalar("loss", self.loss) tf.summary.scalar("learning_rate", learning_rate) summary_op = tf.summary.merge_all() with tf.Session() as sess: print("Initializing Training") init = tf.global_variables_initializer() sess.run(init) if self.resume is 1: print("Loading Previously Trained Model") print(self.current_epoch, "Out of", self.nb_epochs, "Completed in previous run.") try: ckpt_file = "./model/model.ckpt-" + str(self.current_step) saver.restore(sess, ckpt_file) print("Resuming Training") except Exception as e: print(str(e).split('\n')[0]) print("Checkpoints not found") sys.exit(0) writer = tf.summary.FileWriter( "model/log_dir/", graph=tf.get_default_graph()) for epoch in range(self.current_epoch, self.nb_epochs): loss=[] idx = np.random.permutation(self.features.shape[0]) self.captions = self.captions[idx] self.features = self.features[idx] batch_iter = self.get_next_batch() for batch_idx in xrange(self.num_batch): batch_features, batch_Ids = batch_iter.next() batch_mask = self.generate_mask(batch_Ids, self.wtoidx) run = [global_step, optimizer, self.loss, summary_op] feed_dict = self.create_feed_dict( batch_Ids, batch_features, batch_mask) step, _, current_loss, summary = sess.run( run, feed_dict=feed_dict) writer.add_summary(summary, step) if step % 100 == 0: print(epoch, ": Global Step:", step, "\tLoss: ", current_loss) loss.append(current_loss) print() print("Epoch: ", epoch, "\tAverage Loss: ", np.mean(loss)) print("\nSaving Model..\n") saver.save(sess, "./model/model.ckpt", global_step=global_step) np.save("model/save", (epoch, step)) def init_decode(self): saver = tf.train.Saver() ckpt_file = "./model/model.ckpt-" + str(self.current_step) #str(89994) sess = tf.Session() init = tf.global_variables_initializer() sess.run(init) saver.restore(sess, ckpt_file) return sess def decode(self, path): features = get_features(self.sess, self.io, path, self.saveencoder) caption_IDs = self.sess.run( self.IDs, feed_dict={ self.image_features: features}) sentence = " ".join(self.IDs_to_Words(self.idxtow, caption_IDs)) sentence = sentence.split("</S>")[0] if self.load_image: plt.imshow(Image.open(path)) plt.axis("off") plt.title(sentence, fontsize='10', loc='left') name=path.split("/")[-1] plt.savefig("./results/"+"gen_"+name) plt.show() else: print(sentence) if self.savedecoder: saver = tf.train.Saver() saver.save(self.sess, "model/Decoder/model.ckpt") #return path, sentence def batch_decoder(self, filenames, features): saver = tf.train.Saver() ckpt_file = "./model/model.ckpt-" + str(self.current_step) sentences = [] filenames = np.unique(filenames) with open("model/Decoder/Generated_Captions.txt", 'w') as f: with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) saver.restore(sess, ckpt_file) for i, feat in enumerate(features): feat = np.reshape(feat, newshape=(1, 1536)) caption_IDs = sess.run( self.IDs, feed_dict={ self.image_features: feat}) sentence = " ".join( self.IDs_to_Words( self.idxtow, caption_IDs)) sentence = sentence.split("</S>")[0] if i % 1000 == 0: print("Progress", i, "out of", features.shape[0]) f.write(filenames[i] + "\t" + sentence + "\n") *Sreenu.Padidapu,* *Jr,Software Engineer,* *Aadhya Analytics,* *2-39,Old SBI Road,* *Sri nagar colony,* *Gannavaram-521101.*

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On Wed, May 22, 2019 at 10:31 AM dljzx ***@***.***> wrote: i think all are correct here could you send me the caption_generator.py file it will more helpful for me. Thanks, *Sreenu.Padidapu,* *Jr,Software Engineer,* *Aadhya Analytics,* *2-39,Old SBI Road,* *Sri nagar colony,* *Gannavaram-521101.* … <#m_-5093071406617138178_> On Wed, May 22, 2019 at 10:17 AM dljzx *@*.*> wrote: could you please send me the lines from 62 to 71 Sreenu.Padidapu, Jr,Software Engineer, Aadhya Analytics, 2-39,Old SBI Road, Sri nagar colony, Gannavaram-521101. … <#m_7082800599257854298_> On Wed, May 22, 2019 at 10:05 AM dljzx @.*> wrote: when you run that command plz try this one python2 convfeatures.py --data_path Dataset/flickr30k-images/ --inception_path ConvNets/inception_v4.pb i hope this will work fine. thank you Thank you ! I found there was a '/' lost behind data_path , but still an IndexError in main.py --mode train Traceback (most recent call last): File "main.py", line 66, in features, captions = training_data[:, 0], training_data[:, 1] IndexError: too many indices for array Could you help me solve that question? Thanks a lot. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#38 <#38> <#38 <#38>>?email_source=notifications&email_token=AKDKCK3XIQGJAXAU7AXZVGLPWTERXA5CNFSM4HEPBIZ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODV5323Y#issuecomment-494648687>, or mute the thread https://github.com/notifications/unsubscribe-auth/AKDKCK7WAW6H5II7KN547GTPWTERXANCNFSM4HEPBIZQ . if config.mode == "train": vocab, wtoidx, training_data = generate_captions( config.word_threshold, config.max_len, args.caption_path, args.feature_path, config.data_is_coco) features, captions = training_data[:, 0], training_data[:, 1] features = np.array([feat.astype(float) for feat in features]) data = (vocab.tolist(), wtoidx.tolist(), features, captions) model = Caption_Generator(config, data=data) loss, inp_dict = model.build_train_graph() model.train(loss, inp_dict) These are the lines you need. It is so kind of you to respond me so quick. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#38 <#38>?email_source=notifications&email_token=AKDKCK5EDPVKJIRICLRK4Q3PWTF5JA5CNFSM4HEPBIZ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODV54JKI#issuecomment-494650537>, or mute the thread https://github.com/notifications/unsubscribe-auth/AKDKCK2GBKH4PMLSW6XFE4DPWTF5JANCNFSM4HEPBIZQ . caption_generator.zip <https://github.com/neural-nuts/image-caption-generator/files/3205684/caption_generator.zip> Here is caption_generator.py — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#38?email_source=notifications&email_token=AKDKCK3YI64O5XJXNHCNUYLPWTHRVA5CNFSM4HEPBIZ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODV543QI#issuecomment-494652865>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AKDKCK36K6PMUAQFDX3QJOTPWTHRVANCNFSM4HEPBIZQ> .

[dljzx]

instead of that file plz replace this one
...

Thank you for your effort. I have tried your code but still the same error...

[aayux]

when you run that command plz try this one
python2 convfeatures.py --data_path Dataset/flickr30k-images/ --inception_path ConvNets/inception_v4.pb
i hope this will work fine.
thank you

Thank you ! I found there was a '/' lost behind data_path , but still an IndexError in main.py --mode train
Traceback (most recent call last):
File "main.py", line 66, in
features, captions = training_data[:, 0], training_data[:, 1]
IndexError: too many indices for array
Could you help me solve that question? Thanks a lot.

It appears that training_data is only one-dimensional. Although, I am not sure why that should be the case.

You should try verifying the shape of training_data, and whether features and captions are being assigned correct values.