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')
plt.colorbar(pos)
plt.savefig("figures/prob_vector_test")
plt.show()
"/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")
