Update README.md

README.md

@@ -1,15 +1,29 @@
-					                                        GENERATING FONTS
+##Generating Fonts
 We want to create a neural network that can generate characters or in other words we will create a font vector (latent vector) which defines a character and then we embed all fonts in a space where similar fonts have similar vectors.
-We are using Lasagne/ Theano to build and train networks. With lasagne, it is possible to write Python code and execute the training on Nvidia GPUs with automatically generated CUDA code. Theano is a python library that allows us to define, optimize and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
-We are taking training data set from erikbern’s page where he has provided 50k fonts for analysis and reference purposes. All fonts need to be vertically aligned and scaled to fit bitmap and each character to be scaled to 64*64. 
+We are taking training data set from erikbern’s page where he has provided 50k fonts for analysis and reference purposes.
+Here's a link to the data:[Fonts data](https://drive.google.com/file/d/0B0GtwTQ6IF9AU3NOdzFzUWZ0aDQ/view)
+
+All fonts need to be vertically aligned and scaled to fit bitmap and each character to be scaled to 64*64. 
 Some notes on model:
-•	4 hidden layers of fully connected layers of width 1024.
-•	The final layer is a 4096 layer (64 * 64) with sigmoid nonlinearity so that the output is between 0 (white) and 1 (black).
-•	L1 loss between predictions and target. This works much better than L2 which generates very “gray” images – you can see qualitatively in the pictures above.
-•	Pretty strong L2 regularization of all parameters.
-•	Leaky rectified units (alpha=0.01) of nonlinearity on each layer.
-•	The first layer is 102D – each font is a 40D vector joined with a 62D binary one-hot vector of what is the character.
-•	Learning rate is 1.0 which is shockingly high – seemed to work well. Decrease by 3x when no improvements on the 10% test set is achieved in any epoch.
-•	Minibatch size is 512 – seemed like larger minibatches gave faster convergence for some weird reason.
-•	No dropout, didn’t seem to help. I did add some moderate Gaussian noise (of sigma 0.03) to the font vector and qualitatively it seemed to help a bit.
-•	Very simple data augmentation by blurring the input randomly with sigma sampled from [0, 1]. My theory was that this would help fitting characters that have thin lines.
+- 4 hidden layers of fully connected layers of width 1024.
+- The final layer is a 4096 layer (64 * 64) with sigmoid nonlinearity so that the output is between 0 (white) and 1 (black).
+- L1 loss between predictions and target. This works much better than L2 which generates very “gray” images – you can see qualitatively in the pictures above.
+- Pretty strong L2 regularization of all parameters.
+- Leaky rectified units (alpha=0.01) of nonlinearity on each layer.
+- The first layer is 102D – each font is a 40D vector joined with a 62D binary one-hot vector of what is the character.
+- Learning rate is 1.0 which is shockingly high – seemed to work well. Decrease by 3x when no improvements on the 10% test set is achieved in any epoch.
+- Minibatch size is 512 – seemed like larger minibatches gave faster convergence for some weird reason.
+- No dropout, didn’t seem to help. I did add some moderate Gaussian noise (of sigma 0.03) to the font vector and qualitatively it seemed to help a bit.
+- Very simple data augmentation by blurring the input randomly with sigma sampled from [0, 1]. My theory was that this would help fitting characters that have thin lines.
+
+###Requirements
+- Python2.7
+- Lasagne
+- Theano
+- numpy
+- model
+- random
+- h5py
+- math
+
+###Training