--- a
+++ b/Visualization.py
@@ -0,0 +1,58 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Apr 06 11:35:51 2017
+
+@author: Shreyas_V
+"""
+
+'''
+Visualizes the weights of a keras model's layers, keeping the dimensions intact'''
+
+import theano
+import numpy as np
+import numpy.ma as ma
+import pylab
+
+def make_mosaic(imgs, nrows, ncols, border=1):
+    """
+    Given a set of images with all the same shape, makes a
+    mosaic with nrows and ncols
+    """
+    nimgs = imgs.shape[0]
+    imshape = imgs.shape[1:]
+    
+    mosaic = ma.masked_all((nrows * imshape[0] + (nrows - 1) * border,
+                            ncols * imshape[1] + (ncols - 1) * border),
+                            dtype=np.float32)
+    
+    paddedh = imshape[0] + border
+    paddedw = imshape[1] + border
+    for i in xrange(nimgs):
+        row = int(np.floor(i / ncols))
+        col = i % ncols
+        
+        mosaic[row * paddedh:row * paddedh + imshape[0],
+               col * paddedw:col * paddedw + imshape[1]] = imgs[i]
+    return mosaic
+
+
+def visualize_layer_output(model, layer_num, inp, nr=6, nc=6):
+    '''
+    Given a keras model, a layer number, a particular input for the model,
+    shows a mosaic image of all filters in that layer with the image dimension of nr x nc,
+    activated by the model's input given to the first layer
+    '''
+    out = theano.function([model.get_input(train=False)], model.layers[layer_num].get_output(train=False))
+    op = out(inp)
+    op = np.squeeze(op)
+    pylab.imshow(make_mosaic(op, nr, nc), 'gray')
+    
+def visualize_layer(model, layer_num, nr=6, nc=6):
+    '''
+    Given a model and its layer number,
+    shows a mosaic image of all the filter weights, 
+    with the image dimension being nr x nc
+    '''
+    W = model.layers[layer_num].W.get_value(borrow=True)
+    W = np.squeeze(W)
+    pylab.imshow(make_mosaic(W, nr, nc))
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