def PredictionMatrix():
print('Loading valid data...')
(X_valid, y_valid, the_images) = LARGEload.load_valid_data()
X_valid = X_valid.astype("float32")
X_valid /= 255
print('Generating prediction...')
print ("The actual values are:")
#print (y_valid)
y = np.bincount(y_valid)
ii = np.nonzero(y)[0]
print (zip(ii,y[ii]))
y_pred = model.predict_classes(X_valid, batch_size=20)
#print (y_pred)
y = np.bincount(y_pred)
ii = np.nonzero(y)[0]
print ("The predicted values are:")
print (zip(ii,y[ii]))
print ("Our confusion matrix is:")
cm = confusion_matrix(y_valid, y_pred)
print(cm)
plot_confusion_matrix(cm, weights=save_name[:-5])
# how many samples match the ground truth validation labels ?
correct = np.sum(y_pred == y_valid)
print ("Number of correct classifications is %d/5000" % (correct))
# accuracy = number correct / total number
accuracy = float(correct) / 5000
print ("Accuracy of %f" % accuracy)
return accuracy, correct
def test():
X_test, y_test = LARGEload.load_test_data()
Y_test = np_utils.to_categorical(y_test, 5)
print(X_test.shape[0], 'test samples')
X_test = X_test.astype("float32")
X_test /= 255
print('Testing...')
score = model.evaluate(X_test, Y_test, batch_size=20)
print('Test score:', score)
def BatchGenerator():
split = 0
while split < splits: # splits * split_size = training_size
(X_batch, y_batch, class_weight) = LARGEload.load_train_data(split)
y_batch = np_utils.to_categorical(y_batch, 5)
X_batch = X_batch.astype("float32")
X_batch /= 255
split += 1
yield (X_batch, y_batch, split, class_weight)