53 lines
1.3 KiB
Python
53 lines
1.3 KiB
Python
#Import libaries and datasets
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
import pandas as pd
|
|
import seaborn as sns
|
|
import pickle
|
|
import random
|
|
import os
|
|
import numpy as np
|
|
from keras.utils import np_utils
|
|
|
|
with open(os.path.join("test", "test.p"), mode='rb') as validation_data:
|
|
valid = pickle.load(validation_data)
|
|
|
|
X_valid, y_valid = valid['features'], valid['labels']
|
|
|
|
from sklearn.utils import shuffle
|
|
X_valid, y_valid = shuffle(X_valid, y_valid)
|
|
|
|
# Normalize image to [0, 1]
|
|
X_valid_norm = X_valid / 255
|
|
|
|
#Wähle Klassen 0-19
|
|
mask = np.isin(y_valid, range(20))
|
|
X_valid_subset = X_valid_norm[mask]
|
|
y_valid_subset = y_valid[mask]
|
|
|
|
num_classes = 20 # Anzahl der Straßenschilder-Klassen
|
|
y_valid_subset = np_utils.to_categorical(y_valid_subset, num_classes)
|
|
|
|
|
|
|
|
#ownModel
|
|
#convolutionalNeuralNetwork
|
|
#fullyConnectedNeuralNetwork
|
|
model = tf.keras.models.load_model('saved_model/fullyConnectedNeuralNetwork.h5')
|
|
|
|
|
|
|
|
score = model.evaluate(X_valid_subset, y_valid_subset)
|
|
print('Test Accuracy: {}'.format(score[1]))
|
|
|
|
from sklearn.metrics import confusion_matrix
|
|
predicted_classes = np.argmax(model.predict(X_valid_subset), axis=-1)
|
|
y_true = np.argmax(y_valid_subset, axis=-1)
|
|
|
|
cm = confusion_matrix(y_true, predicted_classes)
|
|
|
|
plt.figure(figsize = (25, 25))
|
|
sns.heatmap(cm, annot = True)
|
|
|
|
plt.show() |