diff options
| -rw-r--r-- | lenet.py | 9 |
1 files changed, 5 insertions, 4 deletions
@@ -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) |