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.

On Wed, May 22, 2019 at 10:05 AM dljzx notifications@github.com 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 https://github.com/neural-nuts/image-caption-generator/issues/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.

On Wed, May 22, 2019 at 10:17 AM dljzx notifications@github.com 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. … <#m7082800599257854298> 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 https://github.com/neural-nuts/image-caption-generator/issues/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 https://github.com/neural-nuts/image-caption-generator/issues/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. … <#m7082800599257854298> 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.

On Wed, May 22, 2019 at 10:31 AM dljzx notifications@github.com wrote:

i think all are correct here could you send me the captiongenerator.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. … <#m7082800599257854298> 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 https://github.com/neural-nuts/image-caption-generator/issues/38 <#38 https://github.com/neural-nuts/image-caption-generator/issues/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 https://github.com/neural-nuts/image-caption-generator/issues/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

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[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.