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README.md

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+# Text To Image Synthesis Using Thought Vectors
+
+This is an experimental tensorflow implementation of synthesizing Images from captions using [Skip Thought Vectors][1]. The images are synthesized using the GAN-CLS Algorithm from the paper [Generative Adversarial Text-to-Image Synthesis][2]. This implementation is built on top of the excellent [DCGAN in Tensorflow][3]. The following is the model architecture. The blue bars represent the text encoding using Skip Thought Vectors.
+
+![Model architecture](http://i.imgur.com/dNl2HkZ.jpg)
+
+Image Source : [Generative Adversarial Text-to-Image Synthesis][2] Paper
+
+## Requirements
+- Python 2.7.6
+- [Tensorflow][4]
+- [h5py][5]
+- [Theano][6] : for skip thought vectors
+- [scikit-learn][7] : for skip thought vectors
+- [NLTK][8] : for skip thought vectors
+
+## Datasets
+- The model is currently trained on the [flowers dataset][9]. Download the images from [this link][9] and save them in ```Data/flowers/jpg```. Also download the captions from [this link][10]. Extract the archive and copy the ```text_c_10``` folder and paste it in ```Data/flowers```.
+- Download the pretrained models and vocabulary for skip thought vectors as per the instructions give here. Save the downloaded files in ```Data/skipthoughts```.
+- Make empty directories in Data, ```Data/samples``` and ```Data/val_samples```. They will be used for sampling the generated images, while training.
+
+## Usage
+- <b>Data Processing</b> : Extract the skip thought vectors for the flowers data set using :
+```
+python data_loader.py --data_set="flowers"
+```
+- <b>Training</b>
+  * Basic usage `python train.py --data_set="flowers"`
+  * Options
+      - `z_dim`: Noise Dimension. Default is 100.
+      - `t_dim`: Text feature dimension. Default is 256.
+      - `batch_size`: Batch Size. Default is 64.
+      - `image_size`: Image dimension. Default is 64.
+      - `gf_dim`: Number of conv in the first layer generator. Default is 64.
+      - `df_dim`: Number of conv in the first layer discriminator. Default is 64.
+      - `gfc_dim`: Dimension of gen untis for for fully connected layer. Default is 1024.
+      - `caption_vector_length`: Length of the caption vector. Default is 1024.
+      - `data_dir`: Data Directory. Default is `Data/`.
+      - `learning_rate`: Learning Rate. Default is 0.0002.
+      - `beta1`: Momentum for adam update. Default is 0.5.
+      - `epochs`: Max number of epochs. Default is 600.
+      - `resume_model`: Resume training from a pretrained model path.
+      - `data_set`: Data Set to train on. Default is flowers.
+
+- <b>Generating Images from Captions</b>
+  * Write the captions in text file, and save it as ```Data/sample_captions.txt```. Generate the skip thought vectors for these captions using:
+  ```
+  python generate_thought_vectors.py --caption_file="Data/sample_captions.txt"
+  ```
+  * Generate the Images for the thought vectors using:
+  ```
+  python generate_images.py
+  ```
+
+
+[1]:http://arxiv.org/abs/1506.06726
+[2]:http://arxiv.org/abs/1605.05396
+[3]:https://github.com/carpedm20/DCGAN-tensorflow
+[4]:https://github.com/tensorflow/tensorflow
+[5]:http://www.h5py.org/
+[6]:https://github.com/Theano/Theano
+[7]:http://scikit-learn.org/stable/index.html
+[8]:http://www.nltk.org/
+[9]:http://www.robots.ox.ac.uk/~vgg/data/flowers/102/
+[10]:https://drive.google.com/file/d/0B0ywwgffWnLLcms2WWJQRFNSWXM/view