From 9ca08c7afbbf7f7b058a88b905310b36f3e5a7cf Mon Sep 17 00:00:00 2001
From: s452693 <wikbos@st.amu.edu.pl>
Date: Sun, 20 Jun 2021 17:59:01 +0200
Subject: [PATCH] neural_networks2

---
 src/neural_network.py | 60 ++++++++++++++++++-------------------------
 1 file changed, 25 insertions(+), 35 deletions(-)

diff --git a/src/neural_network.py b/src/neural_network.py
index a474cca..0a7f88d 100644
--- a/src/neural_network.py
+++ b/src/neural_network.py
@@ -15,20 +15,10 @@ dig_train_images, dig_train_labels = extract_training_samples('digits')
 dig_test_images, dig_test_labels = extract_test_samples('digits')
 let_train_images, let_train_labels = extract_training_samples('letters')
 let_test_images, let_test_labels = extract_test_samples('letters')
-#print(dig_train_images.shape)
 
-#def plotdigit(image):
-#    img = np.reshape(image, (-1, 28))
-#    imshow(img, cmap='Greys', vmin=0, vmax=255)
-print(dig_train_images.shape)
-"""
-dig_train_images = dig_train_images / 255
-dig_test_images = dig_test_images / 255
-let_train_images = let_train_images / 255
-let_test_images = let_test_images / 255
 
-dig_train_images = [torch.tensor(image, dtype=torch.float32) for image in dig_train_images]
-"""
+
+
 #print(dig_train_images[0])
 dig_train_images = dig_train_images.reshape(len(dig_train_images),28*28)
 d_train = dig_train_images[:1000]
@@ -45,13 +35,16 @@ print(d_labelstest)
 
 
 class NeuralNetwork:
-    def __init__(self, inputNodes, hiddenNodes, outputNodes, learningGrade):
+    def __init__(self, inputNodes, hiddenNodes, outputNodes, learningGrade, fileWeight, fileHidden):
         self.inodes = inputNodes
         self.hnodes = hiddenNodes
         self.onodes = outputNodes
 
-        self.weights = (np.random.rand(self.hnodes, self.inodes) - 0.5)
-        self.hidden = (np.random.rand(self.onodes, self.hnodes) - 0.5)
+        """te pierwsze dwa użyj przy nauce, potem zostaw cały czas te 2"""
+        #self.weights = (np.random.rand(self.hnodes, self.inodes) - 0.5)
+        #self.hidden = (np.random.rand(self.onodes, self.hnodes) - 0.5)
+        self.weights = np.load(fileWeight)
+        self.hidden = np.load(fileHidden)
 
         #print( 'Matrix1 \n', self.weights)
         #print( 'Matrix2 \n', self.hidden)
@@ -89,6 +82,10 @@ class NeuralNetwork:
 
         pass
 
+    def saveTraining(self, fileWeight, fileHidden):
+        np.save(fileWeight, self.weights)
+        np.save(fileHidden, self.hidden)
+
     def query(self, inputsList):
         
         inputs = np.array(inputsList, ndmin=2).T
@@ -102,29 +99,20 @@ class NeuralNetwork:
 
         return finalOutputs
     
-
-    
-"""
-    def getAccurancy(predictons,Y):
-        print(predictons,Y)
-        return np.sum(predictons=Y)/Y.size
-    
-    def getPredictions(A2):
-        return np.argmax(A2,0)
-"""
-    
    
-#n = NeuralNetwork(inputNodes=3, hiddenNodes=5, outputNodes=2, learningGrade=0.2)
-n = NeuralNetwork(inputNodes=784, hiddenNodes=200, outputNodes=10, learningGrade=0.1)
 
-def trainNetwork(n):
+""" dodaj tablicę literek"""   
+#n = NeuralNetwork(inputNodes=3, hiddenNodes=5, outputNodes=2, learningGrade=0.2)
+digitNetwork = NeuralNetwork(inputNodes=784, hiddenNodes=200, outputNodes=10, learningGrade=0.1, fileWeight="Dweights.npy", fileHidden="Dhidden.npy")
+
+def trainNetwork(n, fWeight, fHidden, trainingSamples):
     epochs = 10
     outputNodes = 10
     for e in range(epochs):
         m=0
         print('Epoch', e+1)
 
-        for record in d_train:
+        for record in trainingSamples:
             inputs = (np.asfarray(record[0:])/255 * 0.99) + 0.01
             #print(inputs.shape)
 
@@ -136,15 +124,17 @@ def trainNetwork(n):
             m+=1 
             pass
         pass
+    n.saveTraining(fileWeight=fWeight, fileHidden=fHidden)
 
 
-trainNetwork(n)
 
-record = d_test[0]
+#####################################      ODPALANIE TRAINING
+#trainNetwork(digitNetwork, "Dweights.npy", "Dhidden.npy", d_train)
+
+#record = d_test[0]
 #print('Label', d_labelstest[0])
-inputs = np.asfarray(record[0:])/ 255 * 0.99 + 0.01
+#inputs = np.asfarray(record[0:])/ 255 * 0.99 + 0.01
 #print(n.query(inputs))
 
 
-
-
+#testing
\ No newline at end of file