Update accuracy test

1 files changed, 5 insertions, 4 deletions

diff --git a/lenet.py b/lenet.py
index c9ef78c..57ce218 100644
--- a/lenet.py
+++ b/lenet.py
@@ -1,7 +1,6 @@
 from __future__ import print_function
 import tensorflow.keras as keras
 import tensorflow as tf
-from tensorflow.keras.datasets import mnist
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, Dropout, Flatten
 from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
@@ -11,6 +10,7 @@ import matplotlib.pyplot as plt
 from tensorflow.keras.metrics import categorical_accuracy
 import numpy as np
 import random
+from sklearn.metrics import accuracy_score
 
 def import_mnist():
   from tensorflow.examples.tutorials.mnist import input_data
@@ -43,8 +43,6 @@ def plot_images(images, cls_true, cls_pred=None):
     plt.show()
 
 def plot_example_errors(y_pred, y_true, X_test):
-    y_pred = np.argmax(y_pred, axis=1)
-    y_true = np.argmax(y_true, axis=1)
     correct_prediction = np.equal(y_pred, y_true)
     incorrect = np.equal(correct_prediction, False)
     images = X_test[incorrect]
@@ -123,11 +121,14 @@ def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100,
 def test_classifier(model, x_test, y_true):
   x_test = np.pad(x_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')
   y_pred = model.predict(x_test)
-  print(categorical_accuracy(y_true, y_pred))
+  y_pred = np.argmax(y_pred, axis=1)
+  y_true = np.argmax(y_true, axis=1)
+  print("Test acc:", accuracy_score(y_true, y_pred))
   plot_example_errors(y_pred, y_true, x_test)
 
 # If file run directly, perform quick test
 if __name__ == '__main__':
   x_train, y_train, x_val, y_val, x_t, y_t = import_mnist()
+  print(y_t.shape)
   model = train_classifier(x_train[:100], y_train[:100], x_val, y_val, epochs=1)
   test_classifier(model, x_t, y_t)