Update mnistTest.py

Author: nulljcd

mnistTest.py

@@ -2,22 +2,16 @@
 from ml import Activation, Loss, WeightInitializer, BiasInitializer, Model, Optimizer
 
 print("loading data")
-trainingData = []
-with open("mnist/train.txt", "r") as file:
-  for line in file:
-    line = line.strip().split(",")
-    x = [int(pixel) / 255 for pixel in line[1:]]
-    y = [0] * 10
-    y[int(line[0])] = 1
-    trainingData.append((x, y))
-testingData = []
-with open("mnist/test.txt", "r") as file:
-  for line in file:
-    line = line.strip().split(",")
-    x = [int(pixel) / 255 for pixel in line[1:]]
-    y = [0] * 10
-    y[int(line[0])] = 1
-    testingData.append((x, y))
+trainData = np.loadtxt("mnist/train.txt", delimiter=',', dtype=int)
+xTrain = trainData[:, 1:] / 255.0
+yTrain = np.zeros((trainData.shape[0], 10), dtype=int)
+yTrain[np.arange(trainData.shape[0]), trainData[:, 0]] = 1
+trainData = np.array([(xTrain[i], yTrain[i]) for i in range(xTrain.shape[0])], dtype=object)
+testData = np.loadtxt("mnist/test.txt", delimiter=',', dtype=int)
+xTest = testData[:, 1:] / 255.0
+yTest = np.zeros((testData.shape[0], 10), dtype=int)
+yTest[np.arange(testData.shape[0]), testData[:, 0]] = 1
+testData = np.array([(xTest[i], yTest[i]) for i in range(xTest.shape[0])], dtype=object)
 
 model = Model(
   [784, 128, 10],
@@ -27,28 +21,30 @@
   BiasInitializer.Constant(0.1))
 loss = Loss.CrossEntropy()
 optimizer = Optimizer.Adam(model, 0.0003, 0.9, 0.999, 1e-8, 0.0001)
+model.initialize()
 
+print("training")
 numEpochs = 5
 batchSize = 32
-
-print("training")
-model.initialize()
 for epoch in range(numEpochs):
   print(f" epoch: {epoch + 1}/{numEpochs}")
-  np.random.shuffle(trainingData)
-  for i in range(batchSize, len(trainingData), batchSize):
-    batch = trainingData[i - batchSize : i]
-    for j in range(0, batchSize):
-      model.backPropagate(batch[j][0], batch[j][1], loss)
+  indices = np.random.permutation(len(trainData))
+  trainDataShuffled = trainData[indices]
+  for i in range(0, len(trainDataShuffled), batchSize):
+    batch = trainDataShuffled[i:i + batchSize]
+    for x, y in batch:
+        model.backPropagate(x, y, loss)
     optimizer.step()
     model.zeroGradients()
 
 print("evaluating")
-numCorrectTraining = 0
-numCorrectTesting = 0
-for i in range(len(trainingData)):
-  numCorrectTraining += int(np.argmax(model.feedForward(trainingData[i][0])) == np.argmax(trainingData[i][1]))
-for i in range(len(testingData)):
-  numCorrectTesting += int(np.argmax(model.feedForward(testingData[i][0])) == np.argmax(testingData[i][1]))
-print(f"training accuracy: {round(numCorrectTraining / len(trainingData) * 100, 2)}%")
-print(f"testing accuracy: {round(numCorrectTesting / len(testingData) * 100, 2)}%")
+numCorrectTrain = 0
+numCorrectTest = 0
+trainPredictions = np.argmax([model.feedForward(x) for x, _ in trainData], axis=1)
+trainLabels = np.argmax([y for _, y in trainData], axis=1)
+numCorrectTrain = np.sum(trainPredictions == trainLabels)
+testPredictions = np.argmax([model.feedForward(x) for x, _ in testData], axis=1)
+testLabels = np.argmax([y for _, y in testData], axis=1)
+numCorrectTest = np.sum(testPredictions == testLabels)
+print(f"training accuracy: {round(numCorrectTrain / len(trainData) * 100, 2)}%")
+print(f"testing accuracy: {round(numCorrectTest / len(testData) * 100, 2)}%")