def evaluator(x_train, y_train, x_val, y_val, **kwargs):
# Define model
model = Sequential(loss="cross_entropy")
model.add(
Dense(nodes=10, input_dim=x_train.shape[0], weight_initialization="fixed"))
model.add(Activation("softmax"))
# Fit model
model.fit(X=x_train, Y=y_train, X_val=x_val, Y_val=y_val, **kwargs)
model.plot_training_progress(show=False, save=True, name="figures/" + dict_to_string(kwargs))
# model.save("models/" + dict_to_string(kwargs))
# Minimizing value:
value = model.get_classification_metrics(x_val, y_val)[0] # Get accuracy
result = {"value": value, "model": model} # Save score and model
return result
x_train, y_train = getXY(LoadBatch("data_batch_1"))
x_val, y_val = getXY(LoadBatch("data_batch_2"))
x_test, y_test = getXY(LoadBatch("test_batch"))
# Preprocessing
mean_x = np.mean(x_train)
std_x = np.std(x_train)
x_train = (x_train - mean_x) / std_x
x_val = (x_val - mean_x) / std_x
x_test = (x_test - mean_x) / std_x
# Define SVM multi-class model
model = Sequential(loss="categorical_hinge")
model.add(Dense(nodes=10, input_dim=x_train.shape[0]))
model.add(Activation("softmax"))
# Fit model
model.fit(X=x_train,
Y=y_train,
X_val=x_val,
Y_val=y_val,
batch_size=100,
epochs=100,
lr=0.0001,
momentum=0.1,
l2_reg=0.1)
model.plot_training_progress()
# Test model
test_acc, test_loss = model.get_classification_metrics(x_test, y_test)
print("Test accuracy:", test_acc)
# Preprocessing
mean_x = np.mean(x_train)
std_x = np.std(x_train)
x_train = (x_train - mean_x) / std_x
x_val = (x_val - mean_x) / std_x
x_test = (x_test - mean_x) / std_x
# Define model
model = Sequential(loss=CrossEntropy())
model.add(Dense(nodes=10, input_dim=x_train.shape[0]))
model.add(Softmax())
# Fit model
# model.load("models/mlp_test")
model.fit(X=x_train,
Y=y_train,
X_val=x_val,
Y_val=y_val,
batch_size=100,
epochs=40,
lr=0.001,
momentum=0.0,
l2_reg=0.0,
shuffle_minibatch=False)
model.plot_training_progress(save=True, name="figures/mlp_test")
model.save("models/mlp_test")
# Test model
test_acc, test_loss = model.get_classification_metrics(x_test, y_test)
print("Test accuracy:", test_acc)
