Save figure

Author: cxyzs7

README.md

@@ -3,9 +3,7 @@ UFLDL_Tutorial
 
 UFLDL Tutorial
 
-A python implementation of the exercises on http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
-
-Including
+A python implementation of the exercises on http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial, including
 
 Sparse Autoencoder
 

sparse_autoencoder/display_network.py

@@ -5,7 +5,7 @@
 import matplotlib.cm as cm
 
 
-def display_network(data, cols=-1, opt_normalize=True, opt_graycolor=True):
+def display_network(data, cols=-1, opt_normalize=True, opt_graycolor=True, save_figure_path=None):
     # This function visualizes filters in matrix A. Each row of A is a
     # filter. We will reshape each row into a square image and visualizes
     # on each cell of the visualization panel.
@@ -68,4 +68,7 @@ def display_network(data, cols=-1, opt_normalize=True, opt_graycolor=True):
 
     plt.axis('off')
 
-    plt.show()
+    if save_figure_path:
+        plt.savefig(save_figure_path)
+
+    plt.show()

sparse_autoencoder/train.py

@@ -100,7 +100,7 @@ def train():
 
     # STEP 5: Visualization
     w1 = res.x[0: hidden_size*visible_size].reshape((visible_size, hidden_size))
-    display_network(np.transpose(w1), 5)
+    display_network(np.transpose(w1), 5, save_figure_path='../data/sparse_autoencoder.png')
 
 
 if __name__ == "__main__":