Reparameterizing the weight initialization results

Author: mnielsen

fig/weight_initialization.py

@@ -11,6 +11,7 @@
 
 # Standard library
 import json
+import random
 import sys
 
 # My library
@@ -22,32 +23,31 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-# Make results more easily reproducible
-import random
-random.seed(12345678)
-
-def main(filename):
-    run_network(filename)
+def main(filename, n, eta):
+    run_network(filename, n, eta)
     make_plot(filename)
 
-def run_network(filename):
+def run_network(filename, n, eta):
     """Train the network using both the default and the large starting
     weights.  Store the results in the file with name ``filename``,
     where they can later be used by ``make_plots``.
 
     """
+    # Make results more easily reproducible
+    random.seed(12345678)
+    np.random.seed(12345678)
     training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
-    net = network2.Network([784, 30, 10], cost=network2.CrossEntropyCost())
+    net = network2.Network([784, n, 10], cost=network2.CrossEntropyCost)
     print "Train the network using the default starting weights."
     default_vc, default_va, default_tc, default_ta \
-        = net.SGD(training_data, 30, 10, 0.01,
-                  evaluation_data=validation_data, lmbda = 0.001,
+        = net.SGD(training_data, 30, 10, eta, lmbda=5.0,
+                  evaluation_data=validation_data, 
                   monitor_evaluation_accuracy=True)
     print "Train the network using the large starting weights."
     net.large_weight_initializer()
     large_vc, large_va, large_tc, large_ta \
-        = net.SGD(training_data, 30, 10, 0.01,
-                  evaluation_data=validation_data, lmbda = 0.001,
+        = net.SGD(training_data, 30, 10, eta, lmbda=5.0,
+                  evaluation_data=validation_data, 
                   monitor_evaluation_accuracy=True)
     f = open(filename, "w")
     json.dump({"default_weight_initialization":

fig/weight_initialization_100.json

@@ -1 +1 @@
-{"default_weight_initialization": [[], [9549, 9694, 9682, 9693, 9738, 9738, 9740, 9742, 9770, 9747, 9743, 9756, 9745, 9749, 9744, 9747, 9726, 9764, 9778, 9773, 9742, 9773, 9770, 9761, 9771, 9776, 9779, 9742, 9759, 9777], [], []], "large_weight_initialization": [[], [9373, 9501, 9583, 9648, 9694, 9705, 9628, 9710, 9717, 9734, 9749, 9710, 9710, 9759, 9727, 9740, 9731, 9773, 9742, 9755, 9766, 9761, 9763, 9778, 9736, 9736, 9735, 9751, 9750, 9740], [], []]}
+{"default_weight_initialization": [[], [9295, 9481, 9547, 9592, 9664, 9673, 9702, 9719, 9726, 9726, 9732, 9732, 9730, 9734, 9745, 9751, 9757, 9761, 9764, 9766, 9758, 9767, 9756, 9752, 9777, 9775, 9770, 9770, 9771, 9781], [], []], "large_weight_initialization": [[], [8994, 9181, 9260, 9364, 9427, 9449, 9497, 9512, 9560, 9578, 9603, 9616, 9626, 9629, 9644, 9671, 9674, 9679, 9700, 9708, 9707, 9717, 9729, 9720, 9719, 9745, 9751, 9754, 9755, 9742], [], []]}

fig/weight_initialization_100.png

@@ -1 +1 @@
-{"default_weight_initialization": [[], [9284, 9395, 9440, 9481, 9527, 9548, 9552, 9560, 9567, 9580, 9604, 9589, 9590, 9595, 9602, 9603, 9612, 9615, 9616, 9625, 9626, 9628, 9622, 9618, 9639, 9628, 9630, 9615, 9633, 9632], [], []], "large_weight_initialization": [[], [8672, 8982, 9147, 9254, 9306, 9362, 9381, 9409, 9463, 9463, 9493, 9503, 9509, 9535, 9512, 9540, 9571, 9567, 9575, 9594, 9607, 9609, 9600, 9609, 9615, 9613, 9645, 9616, 9635, 9605], [], []]}
+{"default_weight_initialization": [[], [9270, 9414, 9470, 9504, 9537, 9550, 9587, 9594, 9596, 9594, 9616, 9595, 9622, 9630, 9636, 9641, 9625, 9652, 9637, 9634, 9642, 9639, 9649, 9646, 9646, 9653, 9646, 9653, 9640, 9650], [], []], "large_weight_initialization": [[], [8643, 9044, 9141, 9231, 9299, 9327, 9385, 9416, 9433, 9449, 9476, 9489, 9500, 9535, 9521, 9548, 9564, 9573, 9585, 9592, 9596, 9615, 9607, 9605, 9606, 9622, 9637, 9648, 9635, 9637], [], []]}