add a plotter and modify network 1 to use it

florianwe · florianwe · commit efec6fffad2a · 2020-12-03T10:37:41.000+01:00
diff --git a/MyPlotter.py b/MyPlotter.py
@@ -0,0 +1,24 @@
+from numpy import asarray
+from matplotlib import pyplot
+import numpy as np
+from matplotlib.pyplot import plot, draw, show
+
+
+class MyPloter(object):
+    def __init__(self, framesize, max_rows, max_columns):
+        self.__data_array = np.zeros((framesize * max_rows, framesize * max_columns))
+        self.__framesize = framesize
+        self.__max_rows = max_rows
+        self.__max_columns = max_columns
+        pyplot.ion()
+        
+
+    def add(self, pic, row, column):
+        if row >= self.__max_rows or column >= self.__max_columns:
+            return
+        self.__data_array[row*self.__framesize:(row+1)*self.__framesize, column *self.__framesize:(column+1)*self.__framesize] = pic
+
+    def show(self):
+        pyplot.imshow(self.__data_array , cmap='gray', vmin=0, vmax=255)
+        pyplot.show()
+        pyplot.pause(0.001)
diff --git a/demo1.py b/demo1.py
@@ -0,0 +1,13 @@
+import mnist_loader
+
+training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
+training_data = list(training_data)
+
+# ---------------------
+# - network.py example:
+import network
+
+
+net = network.Network([784, 28, 10])
+net.SGD(training_data, 20, 10, 3.0, test_data=test_data)
+input("any key to continue")
diff --git a/network.py b/network.py
@@ -16,9 +16,13 @@
 # Standard library
 import random
 
+
+
 # Third-party libraries
 import numpy as np
 
+import MyPlotter
+
 class Network(object):
 
     def __init__(self, sizes):
@@ -35,8 +39,8 @@ def __init__(self, sizes):
         self.num_layers = len(sizes)
         self.sizes = sizes
         self.biases = [np.random.randn(y, 1) for y in sizes[1:]]
-        self.weights = [np.random.randn(y, x)
-                        for x, y in zip(sizes[:-1], sizes[1:])]
+        self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]
+        self.plotter = MyPlotter.MyPloter(28, 20, 20)
 
     def feedforward(self, a):
         """Return the output of the network if ``a`` is input."""
@@ -70,7 +74,7 @@ def SGD(self, training_data, epochs, mini_batch_size, eta,
             for mini_batch in mini_batches:
                 self.update_mini_batch(mini_batch, eta)
             if test_data:
-                print("Epoch {} : {} / {}".format(j,self.evaluate(test_data),n_test));
+                print("Epoch {} : {} / {}".format(j,self.evaluate(test_data, j),n_test))
             else:
                 print("Epoch {} complete".format(j))
 
@@ -125,13 +129,28 @@ def backprop(self, x, y):
             nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
         return (nabla_b, nabla_w)
 
-    def evaluate(self, test_data):
+    def evaluate(self, test_data, epoch):
         """Return the number of test inputs for which the neural
         network outputs the correct result. Note that the neural
         network's output is assumed to be the index of whichever
         neuron in the final layer has the highest activation."""
         test_results = [(np.argmax(self.feedforward(x)), y)
                         for (x, y) in test_data]
+        
+        count = 0
+        for result, data in zip(test_results, test_data):
+            if count == 30:
+                break
+            (x,y) = result
+            if x != y:
+                (a,_) = data
+                a = a.reshape(28,28)
+                self.plotter.add(a*255.0, epoch, count)
+                count += 1
+        self.plotter.show()
+
+
+
         return sum(int(x == y) for (x, y) in test_results)
 
     def cost_derivative(self, output_activations, y):
diff --git a/test.py b/test.py
@@ -17,19 +17,22 @@
 
 # ----------------------
 # - read the input data:
-'''
+
+
 import mnist_loader
+
 training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
 training_data = list(training_data)
-'''
+
 # ---------------------
 # - network.py example:
-#import network
+import network
 
-'''
-net = network.Network([784, 30, 10])
+
+net = network.Network([784, 28, 10])
 net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
-'''
+
+
 
 # ----------------------
 # - network2.py example:
@@ -148,18 +151,18 @@ def testTheano():
     else:
         print('Used the gpu')
 # Perform check:
-#testTheano()
+# testTheano()
 
 
 # ----------------------
 # - network3.py example:
-import network3
-from network3 import Network, ConvPoolLayer, FullyConnectedLayer, SoftmaxLayer # softmax plus log-likelihood cost is more common in modern image classification networks.
+#import network3
+#from network3 import Network, ConvPoolLayer, FullyConnectedLayer, SoftmaxLayer # softmax plus log-likelihood cost is more common in modern image classification networks.
 
 # read data:
-training_data, validation_data, test_data = network3.load_data_shared()
+#training_data, validation_data, test_data = network3.load_data_shared()
 # mini-batch size:
-mini_batch_size = 10
+#mini_batch_size = 10
 
 # chapter 6 - shallow architecture using just a single hidden layer, containing 100 hidden neurons.
 '''
@@ -195,6 +198,7 @@ def testTheano():
 '''
 
 # chapter 6 -  rectified linear units and some l2 regularization (lmbda=0.1) => even better accuracy
+'''
 from network3 import ReLU
 net = Network([
     ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
@@ -208,3 +212,4 @@ def testTheano():
     FullyConnectedLayer(n_in=40*4*4, n_out=100, activation_fn=ReLU),
     SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)
 net.SGD(training_data, 60, mini_batch_size, 0.03, validation_data, test_data, lmbda=0.1)
+'''
