from mlp.layers import Conv2D, Dense, Softmax, Relu, Flatten, Dropout, MaxPool2D from mlp.callbacks import MetricTracker, BestModelSaver, LearningRateScheduler from mlp.utils import plot_confusion_matrix np.random.seed(1) if __name__ == "__main__": # Load data x_test, y_test, names = read_names_test() classes = read_names_countries() print(x_test.shape) # Load model model model = Sequential(loss=CrossEntropy()) model.load("models/names_test") # model.load("models/names_no_compensation") y_pred_prob_test = model.predict(x_test) y_pred_test = model.predict_classes(x_test) print(y_pred_prob_test) print(y_test) plot_confusion_matrix(y_pred_test, y_test, classes, "figures/conf_test") import matplotlib.pyplot as plt plt.title("Prediction Vectors") pos = plt.imshow(y_pred_prob_test.T) plt.xticks(range(len(classes)), classes, rotation=45, ha='right') plt.yticks(range(len(names)), names) # plt.xticks(rotation=45, ha='right')
"/Toy-DeepLearning-Framework/") from mlp.metrics import Accuracy from mlp.models import Sequential from mlp.losses import CrossEntropy from mlp.layers import Conv2D, Dense, Softmax, Relu, Flatten, Dropout, MaxPool2D from mlp.callbacks import MetricTracker, BestModelSaver, LearningRateScheduler from mlp.utils import plot_confusion_matrix np.random.seed(1) if __name__ == "__main__": # Load data x_train, y_train, x_val, y_val, _, _ = read_names(n_train=-1) print(x_train.shape) classes = read_names_countries() # Load model model model = Sequential(loss=CrossEntropy()) # model.load("models/names_test") model.load( "models/name_metaparam_search_2/n1-39_n2-33_k1-2_k2-10_batch_size-50") y_pred_train = model.predict_classes(x_train) y_pred_val = model.predict_classes(x_val) plot_confusion_matrix(y_pred_train, y_train, classes, "figures/conf_best_model") plot_confusion_matrix(y_pred_val, y_val, classes, "figures/conf_best_model_val")