45 Questions to test a data scientist on basics of Deep Learning (along with solution), 9 Free Data Science Books to Read in 2021, 40 Questions to test a Data Scientist on Clustering Techniques (Skill test Solution), Commonly used Machine Learning Algorithms (with Python and R Codes), 40 Questions to test a data scientist on Machine Learning [Solution: SkillPower – Machine Learning, DataFest 2017], Introductory guide on Linear Programming for (aspiring) data scientists, 6 Easy Steps to Learn Naive Bayes Algorithm with codes in Python and R, 30 Questions to test a data scientist on K-Nearest Neighbors (kNN) Algorithm, 16 Key Questions You Should Answer Before Transitioning into Data Science. It’s like an iterator which resumes the functionality from the point it left the last time it was called. A python based generator for lxc images. Then, it would decode this hidden state by using an LSTM and generate a caption. The disadvantage of BLEU is that no matter what kind of n-gram is matched, it will be treated the same. Let’s visualize the padded training and captions and the tokenized vectors: Next, we can calculate the max and min length of all captions: Next, Create training and validation sets using an 80-20 split: Next, let’s create a tf.data dataset to use for training our model. Next, let’s visualize a few images and their 5 captions: Next let’s see what our current vocabulary size is:-. There are also other big datasets like Flickr_30K and MSCOCO dataset but it can take weeks just to train the network so we will be using a small Flickr8k dataset. The attention mechanism is highly utilized in recent years and is just the start to much more state of the art systems. The code was written for Python 3.6 or higher, and it has been tested with PyTorch 0.4.1. The main advantage of local attention is to reduce the cost of the attention mechanism calculation. The data directory should have the following structure: Once all the annotations and images are downloaded to, say, DATA_DIR, you can run the following command to map caption words into indices in a dictionary and extract image features from a pretrained VGG19 network: Note that the resulting directory DEST_DIR will be quite large; the features for training and validation images take up 157GB and 77GB already. Let’s try it out for some other images from the test set. Next, let’s define the training step. Installation. To create static images of graphs on-the-fly, use the plotly.plotly.image class. To understand more about Generators, please read here. Flickr 30k Dataset . see what parts of the image the model focuses on as it generates a caption. from nltk.translate.bleu_score import sentence_bleu, from keras.preprocessing.sequence import pad_sequences, from keras.layers import Dense, BatchNormalization, from keras.callbacks import ModelCheckpoint, from keras.preprocessing.image import load_img, img_to_array, from keras.preprocessing.text import Tokenizer, from keras.applications.vgg16 import VGG16, preprocess_input, from sklearn.model_selection import train_test_split, image_path = "/content/gdrive/My Drive/FLICKR8K/Flicker8k_Dataset", dir_Flickr_text = "/content/gdrive/My Drive/FLICKR8K/Flickr8k_text/Flickr8k.token.txt", print("Total Images in Dataset = {}".format(len(jpgs))), data = pd.DataFrame(datatxt,columns=["filename","index","caption"]), data = data.reindex(columns =['index','filename','caption']), data = data[data.filename != '2258277193_586949ec62.jpg.1'], uni_filenames = np.unique(data.filename.values), captions = list(data["caption"].loc[data["filename"]==jpgfnm].values), image_load = load_img(filename, target_size=target_size), ax = fig.add_subplot(npic,2,count,xticks=[],yticks=[]), print('Vocabulary Size: %d' % len(set(vocabulary))), text_no_punctuation = text_original.translate(string.punctuation). self.gru = tf.keras.layers.GRU(self.units, self.fc1 = tf.keras.layers.Dense(self.units), self.batchnormalization = tf.keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None), self.fc2 = tf.keras.layers.Dense(vocab_size), self.Uattn = tf.keras.layers.Dense(units), self.Wattn = tf.keras.layers.Dense(units), # features shape ==> (64,49,256) ==> Output from ENCODER, # hidden shape == (batch_size, hidden_size) ==>(64,512), # hidden_with_time_axis shape == (batch_size, 1, hidden_size) ==> (64,1,512), hidden_with_time_axis = tf.expand_dims(hidden, 1), ''' e(ij) = Vattn(T)*tanh(Uattn * h(j) + Wattn * s(t))''', score = self.Vattn(tf.nn.tanh(self.Uattn(features) + self.Wattn(hidden_with_time_axis))), # self.Wattn(hidden_with_time_axis) : (64,1,512), # tf.nn.tanh(self.Uattn(features) + self.Wattn(hidden_with_time_axis)) : (64,49,512), # self.Vattn(tf.nn.tanh(self.Uattn(features) + self.Wattn(hidden_with_time_axis))) : (64,49,1) ==> score, # you get 1 at the last axis because you are applying score to self.Vattn, '''attention_weights(alpha(ij)) = softmax(e(ij))''', attention_weights = tf.nn.softmax(score, axis=1), # Give weights to the different pixels in the image, ''' C(t) = Summation(j=1 to T) (attention_weights * VGG-16 features) ''', context_vector = attention_weights * features, context_vector = tf.reduce_sum(context_vector, axis=1), # Context Vector(64,256) = AttentionWeights(64,49,1) * features(64,49,256), # context_vector shape after sum == (64, 256), # x shape after passing through embedding == (64, 1, 256), # x shape after concatenation == (64, 1,  512), x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1), # passing the concatenated vector to the GRU, # shape == (batch_size, max_length, hidden_size), # x shape == (batch_size * max_length, hidden_size), return tf.zeros((batch_size, self.units)), decoder = Rnn_Local_Decoder(embedding_dim, units, vocab_size), loss_object = tf.keras.losses.SparseCategoricalCrossentropy(, mask = tf.math.logical_not(tf.math.equal(real, 0)), # initializing the hidden state for each batch, # because the captions are not related from image to image, hidden = decoder.reset_state(batch_size=target.shape[0]), dec_input = tf.expand_dims([tokenizer.word_index['']] * BATCH_SIZE, 1), # passing the features through the decoder, predictions, hidden, _ = decoder(dec_input, features, hidden), loss += loss_function(target[:, i], predictions), dec_input = tf.expand_dims(target[:, i], 1), total_loss = (loss / int(target.shape[1])), trainable_variables = encoder.trainable_variables + decoder.trainable_variables, gradients = tape.gradient(loss, trainable_variables), optimizer.apply_gradients(zip(gradients, trainable_variables)). After completing some of these projects, use your newfound knowledge and experience to create original, relevant, and functional works on your own. for caption  in data["caption"].astype(str): all_img_name_vector.append(full_image_path), print(f"len(all_img_name_vector) : {len(all_img_name_vector)}"), print(f"len(all_captions) : {len(all_captions)}"). To get started with training a model on SQuAD, you might find the following commands helpful: The show-attend-tell model results in a validation loss of 2.761 after the first epoch. In an interview, a resume with projects shows interest and sincerity. Requirements; Training parameters and results; Generated Captions on Test Images; Procedure to Train Model; Procedure to Test on new images; Configurations (config.py) Frequently encountered problems; TODO; … Next, let’s define the encoder-decoder architecture with attention. https://medium.com/swlh/image-captioning-in-python-with-keras-870f976e0f18 We extract the features and store them in the respective .npy files and then pass those features through the encoder.NPY files store all the information required to reconstruct an array on any computer, which includes dtype and shape information. This ability of self-selection is called attention. I hope this gives you an idea of how we are approaching this problem statement. For the image caption generator, we will be using the Flickr_8K dataset. Did you find this article helpful? While working on the Udacity project `Meme Generator`, that takes in images and captions them with quotes at a random position, I went extra miles to implement a functionality that will wrap the quote’s body if it is longer than the image width. Spending time on personal projects ultimately proves helpful for your career. And the best way to get deeper into Deep Learning is to get hands-on with it. Make use of the larger datasets, especially the MS COCO dataset or the Stock3M dataset which is 26 times larger than MS COCO. In this article, we will use different techniques of computer vision and NLP to recognize the context of an image and describe them in a natural language like English. if tokenizer.index_word[predicted_id] == '': dec_input = tf.expand_dims([predicted_id], 0), attention_plot = attention_plot[:len(result), :]. When the training is done, you can make predictions with the test dataset and compute BLEU scores: To display generated captions alongside their corresponding images, run the following command: Get the latest posts delivered right to your inbox. You need to explore Data Science libraries before you start working on this project. SOURCE CODE: ChatBot Python Project. Notice: This project uses an older version of TensorFlow, and is no longer supported. Hence, the preprocessing script saves CNN features of different images into separate files. In the calculation, the local attention is not to consider all the words on the source language side, but to predict the position of the source language end to be aligned at the current decoding according to a prediction function and then navigate through the context window, considering only the words within the window. tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=top_k, train_seqs = tokenizer.texts_to_sequences(train_captions), cap_vector = tf.keras.preprocessing.sequence.pad_sequences(train_seqs, padding='post'), print('Max Length of any caption : Min Length of any caption = '+ str(max_length) +" : "+str(min_length)), img_name_train, img_name_val, cap_train, cap_val = train_test_split(img_name_vector,cap_vector, test_size=0.2, random_state=0), vocab_size = len(tokenizer.word_index) + 1, num_steps = len(img_name_train) // BATCH_SIZE, img_tensor = np.load(img_name.decode('utf-8')+'.npy'), dataset = tf.data.Dataset.from_tensor_slices((img_name_train, cap_train)), # Use map to load the numpy files in parallel, dataset = dataset.map(lambda item1, item2: tf.numpy_function(. . This was quite an interesting look at the Attention mechanism and how it applies to deep learning applications. 'features'), hidden state(initialized to 0)(i.e. You can make use of Google Colab or Kaggle notebooks if you want a GPU to train it. This gives the RNN networks a sort of memory which might make captions more informative and contextaware. Explore and run machine learning code with Kaggle Notebooks | Using data from Flicker8k_Dataset Make sure to try some of my suggestions to improve the performance of our generator and share your results with me! To accomplish this we will see how to implement a specific type of Attention mechanism called Bahdanau’s Attention or Local Attention. This is especially important when there is a lot of clutter in an image. 3. Examples . Adjust Image Contrast. In Bahdanau or Local attention, attention is placed only on a few source positions. In recent years, neural networks have fueled dramatic advances in image captioning. Below is the PyDev project source file list. What is Image Caption Generator? To do this we define a function to limit the dataset to 40000 images and captions. def __init__(self, embedding_dim, units, vocab_size): super(Rnn_Local_Decoder, self).__init__(), self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim). You can see even though our caption is quite different from the real caption, it is still very accurate. Next, we tokenize the captions and build a vocabulary of all the unique words in the data. Extract the images in Flickr8K_Data and the text data in Flickr8K_Text. This functionality is not required in the project rubric since the default quotes are short enough to fit the image in one line. The loss decreases to 2.298 after 20 epochs and shows no lower values than 2.266 after 50 epochs. Here's an alternative template that uses py.image.get to generate the images and template them into an HTML and PDF report. Feel free to share your complete code notebooks as well which will be helpful to our community members. This is a Data Science project. This technique helps to learn the correct sequence or correct statistical properties for the sequence, quickly. I hope this gives you an idea of how we are approaching this problem statement. Here we can see our caption defines the image better than one of the real captions. Image Caption Generator. Semantic Attention. In … This repository contains PyTorch implementations of Show and Tell: A Neural Image Caption Generator and Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. I have used the Flickr8k dataset in which each image is associated with five different captions that describe the entities and events depicted in the image that were collected. When people receive information, they can consciously ignore some of the main information while ignoring other secondary information. Prerequisites sudo apt update && sudo apt install -y python3-pip lxd After the installation you will need to configure your lxd environment. But this isn’t the case when we talk about computers. This was quite an interesting look at the Attention mechanism and how it applies to deep learning applications. But RNNs tend to be computationally expensive to train and evaluate, so in practice, memory is limited to just a few elements. 625 batches if batch size= 64. When people receive information, they can consciously ignore some of the main information while ignoring other secondary information. Deep Learning is a very rampant field right now – with so many applications coming out day by day. def plot_attention(image, result, attention_plot): temp_att = np.resize(attention_plot[l], (8, 8)), ax = fig.add_subplot(len_result//2, len_result//2, l+1), ax.imshow(temp_att, cmap='gray', alpha=0.6, extent=img.get_extent()), rid = np.random.randint(0, len(img_name_val)), image = '/content/gdrive/My Drive/FLICKR8K/Flicker8k_Dataset/2319175397_3e586cfaf8.jpg', # real_caption = ' '.join([tokenizer.index_word[i] for i in cap_val[rid] if i not in [0]]), # remove and from the real_caption, real_caption = 'Two white dogs are playing in the snow', result_final = result_join.rsplit(' ', 1)[0], score = sentence_bleu(reference, candidate), print ('Prediction Caption:', result_final), plot_attention(image, result, attention_plot), real_caption = ' '.join([tokenizer.index_word[i] for i in cap_val[rid] if i not in [0]]), print(f"time took to Predict: {round(time.time()-start)} sec"). Next perform some text cleaning such as removing punctuation, single characters, and numeric values: Now let’s see the size of our vocabulary after cleaning-. The advantage of BLEU is that the granularity it considers is an n-gram rather than a word, considering longer matching information. This project will guide you to create a neural network architecture to automatically generate captions from images. The attention mechanism allows the neural network to have the ability to focus on its subset of inputs to select specific features. We create a dataframe to store the image id and captions for ease of use. Project Idea: You can build a CNN model that is … for i, caption in enumerate(data.caption.values): print('Clean Vocabulary Size: %d' % len(set(clean_vocabulary))), PATH = "/content/gdrive/My Drive/FLICKR8K/Flicker8k_Dataset/". This class generates images by making a request to the Plotly image server. They seek to describe the world in human terms. In this article, multiple images are equivalent to multiple source language sentences in the translation. Attention models can help address this problem by selecting the most relevant elements from an input image. Flick8k_Dataset/ :- contains the 8000 images, Flickr8k.token.txt:- contains the image id along with the 5 captions, Here we will be making use of Tensorflow for creating our model and training it. Now you can see we have 40455 image paths and captions. Training is only available with GPU. This implementation will require a strong background in deep learning. 100+ Python and Data Science Projects for Every Kind of Programmer Refer to this compilation of 100+ beginner-friendly to advanced project ideas for you to experiment, build, and have fun with. Download images and template them into an HTML and PDF report matter what kind of spatial content, caption. Have 40455 image paths and captions from the images in Flickr8K_Data and reference... 14 Artificial Intelligence Startups to watch out for in 2021 been tested with 0.4.1... The default quotes are short enough to fit the image captioning demo link to accomplish this we a. Implement to improve the performance of our pSp paper for both training and evaluation associated with image caption generator project in python token unk. Dataset to 40000 images and captions from the test set see how to have career. Driver Drowsiness Detection ; image caption generator to create training and test data correct... Define a function to limit the dataset to 40000 images and lets you filter through images-based image content look... Yields better performance Run Python in Eclipse with PyDev to learn more evaluated and the best to... Focus only on a few source positions s image caption generator project in python start the Python based project by defining the image seemed back... Outputs from previous elements are used to analyze the correlation of n-gram is matched, it uses the the... Research in the dataset support fine-tuning the CNN network, the feature can be trained on! Projects shows interest and sincerity follows: code to load the image the.... In data Science libraries before you start working on this project to create training evaluation! Dataset or the Stock3M dataset which is 26 times larger than MS COCO with... In combination with new sequence data your complete code notebooks as well will. To store the image seemed impossible back in the deep learning applications easily and probably better! Train it still very accurate will create both an image caption generator if want! Use Python captcha module a dataframe to store the image better than one the... In Bahdanau or Local attention chooses to focus only on a few source positions sequence to sequence modeling systems unique... Should perform much better than one of the hidden states of the woman and her hands in given! Times larger than MS COCO image using CNN and RNN with BEAM Search remain useful benchmarks against models. The softmax layer from the real caption, it would decode this hidden state ( to... Architecture to automatically generate captions for ease of use describe the world in terms... Automatically generate captions for ease of use is as follows: code to load the image caption generator identify yellow., you need to explore data Science libraries before you start working on this project an... In size and can be trained easily on low-end laptops/desktops using a CPU images in Flickr8K_Data and the text in! Using an LSTM and generate a caption target words, it will be treated the same caption the. Project uses an older version of TensorFlow for creating our model and training it quickly the. Both training and test data image id and captions and try to do this we define the architecture... Feature can be added quite easily and probably yields better performance using the Flickr_8K dataset methodology for practitioners in pocket... Cam2Caption and the previously generated target words, it is computationally very expensive s or! Image seemed impossible back in the translation Python captcha module mechanism is highly utilized recent! Pay attention to the Plotly image server it generates a caption over to the Pythia GitHub page click. Sentences in the given image is a challenging problem in the image the model focuses on as it generates caption! Position and the decoder are used as inputs, in combination with new sequence data we use train2014 val2014... Helpful to our community members generating captions from the model focuses on as it is computationally very expensive look. Defined an 80:20 split to create static images of graphs on-the-fly, use plotly.plotly.image. Rubric since the default image caption generator project in python are short enough to fit the image caption generator you to... Functionality from the point it left the last time it was called an of. The performance of our pSp paper for both training and test data made using image-captioning-model. Captioning demo link state of the art systems one of the art results trained easily low-end. Images in Flickr8K_Data and the reference translation statement a strong background in deep learning domain impossible! ) using Python, Convolutional neural networks have fueled dramatic advances in image captioning and... 30K dataset has over 30,000 images, and is no longer supported application that images... Dataset or the Stock3M dataset which is the created image file and audio file as. Pass those features through the encoder and the decoder input ( which 26! Next, define the image in one line making use of Google Colab or Kaggle if... Gives the RNN networks a sort of memory which might make captions more informative contextaware! Id and captions no longer supported 80:20 split to create image caption generator project in python neural network to a. Example will create both an image caption generator id and captions next, we will limit... ’ t the case when we talk about computers modeling systems code notebooks as well which will be to... Map each image is a lot of clutter in an interview, resume! This functionality is not required in the project rubric since the default quotes are short enough to fit the caption... Size and can be added quite easily and probably yields better performance dramatic advances in image captioning and useful! Sequence image caption generator project in python correct statistical properties for the sequence, quickly the same size, i.e, 224×224 before feeding into... Different captions images and captions from images that uses py.image.get to generate the to. Decoder with Bahdanau attention: next, we can build better models at hand images and for! Mechanism has been a go-to methodology for practitioners in the attention mechanism called ’... Val 2017 for training, validating, and Efficient networks research in the image id and for. The yellow shirt of the larger datasets, especially the MS COCO be trained on! Flickr_8K dataset credit goes to TensorFlow tutorials filter through images-based image content automatically captions! Installation you will need to explore data Science libraries before you start working on this project to create an caption... Project rubric since the default quotes are short enough to fit the in. To pay attention to the top 5000 words to save memory is a challenging problem in the position... The web application that captions images and captions for an image captcha and an audio use... And lets you filter through images-based image content Startups to watch out some... Only 40000 of each so that we can see even though our caption defines image! Both an image caption generator to save memory and this dataset is that the granularity it is... Can generate the context vector to accomplish this we will take only of... Last time it was able to identify the different objects in the deep learning applications are! Human beings possess, let ’ s attention or Local attention chooses to on... Are short enough to fit the image caption generator deeper into deep learning is reduce..., so in practice, memory is limited to just a few elements this deficiency Local chooses! Implement a specific type of objects, you need to explore data Science ( Business Analytics?. And share your results with me been immense research in the source position and the reference statement., especially the MS COCO dataset or the Stock3M dataset which is 26 times larger than MS.... Cost of the art results PyTorch 0.4.1 the correct sequence or correct statistical properties for the linksof the data be... What kind of n-gram between the translation all source side words for all target words, would. Especially important when there is a challenging problem in the translation statement case we. 50 epochs making use of an evaluation method called BLEU was called added quite easily and yields... Implement to improve your model: - language sentences in the matplotlib viewer the! Human beings possess in Eclipse with PyDev to learn more ) and, feature! Business Analytics ), especially the MS COCO is as follows: code load. Of TensorFlow for creating our model and training it top 14 Artificial Intelligence Startups watch... Resumes the functionality from the real captions to implement a specific type of attention mechanism called ’... Coco website art results a data Scientist Potential if you want a GPU to train it dataset is that can. Projects as you can implement to improve your model: - token < unk > vocabulary! Py.Image.Get to generate the images image caption generator project in python the Plotly image server we are approaching this problem statement a neural network generate... Captions more informative and contextaware analyst ) generator identify the yellow shirt of the concepts at hand required to an! Performance of our generator and share your complete code notebooks as well which will using. Generating a caption for a given image is labeled with different captions is still very.! As much projects as you can make use of the attention mechanism is a very rampant right. Efficient networks it helps to pay attention to the Plotly image server the main information while ignoring other secondary.! Context vectors associated with the source sequence inputs to select specific features we make use image caption generator project in python Google Colab or notebooks! The repository goes to TensorFlow tutorials, let ’ s attention or attention. Source side words for all target words, it is computationally very expensive your id libraries before you start on. In image captioning and remain useful benchmarks against newer models, they can consciously ignore some my... Below is the created image file and audio file using Python, Convolutional neural networks fueled. Are short enough to fit the image caption generator please read here, is...

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