def evaluator(x_train, y_train, x_val, y_val, experiment_path="", **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(experiment_path + "/" + dict_to_string(kwargs)) # Minimizing value: validation accuracy val_acc = model.get_classification_metrics(x_val, y_val)[0] # Get accuracy result = {"value": val_acc, "model": model} # Save score and model return result
def evaluator(l2_reg): # Define model model = Sequential(loss=CrossEntropy(), metric=Accuracy()) model.add(Dense(nodes=800, input_dim=x_train.shape[0])) model.add(Relu()) model.add(Dense(nodes=10, input_dim=800)) model.add(Softmax()) ns = 800 # Define callbacks mt = MetricTracker() # Stores training evolution info lrs = LearningRateScheduler(evolution="cyclic", lr_min=1e-3, lr_max=1e-1, ns=ns) # Modifies lr while training callbacks = [mt, lrs] # Fit model iterations = 4 * ns model.fit(X=x_train, Y=y_train, X_val=x_val, Y_val=y_val, batch_size=100, iterations=iterations, l2_reg=l2_reg, shuffle_minibatch=True, callbacks=callbacks) model.save("models/yes_dropout_test") # Test model val_acc = model.get_metric_loss(x_val, y_val)[0] test_acc = model.get_metric_loss(x_test, y_test)[0] subtitle = "L2 param: " + str(l2_reg) + ", Test acc: " + str(test_acc) mt.plot_training_progress(show=True, save=True, name="figures/l2reg_optimization/" + str(l2_reg), subtitle=subtitle) print("Val accuracy:", val_acc) print("Test accuracy:", test_acc) return val_acc
k1 = 6 # First kernel y size n2 = 20 # Filters of second Conv2D k2 = 4 # Second kernel y size # Define model model = Sequential(loss=CrossEntropy(class_count=None), metric=Accuracy()) model.add( Conv2D(num_filters=n1, kernel_shape=(d, k1), input_shape=x_train.shape[:-1])) model.add(Relu()) model.add(Conv2D(num_filters=n2, kernel_shape=(1, k2))) model.add(Relu()) model.add(Flatten()) model.add(Dense(nodes=y_train.shape[0])) model.add(Softmax()) # Fit model model.fit(X=x_train, Y=y_train, X_val=x_val, Y_val=y_val, batch_size=100, epochs=500, lr=1e-3, momentum=0.8, l2_reg=0.001, compensate=True, callbacks=callbacks) model.save("models/names_best") mt.plot_training_progress(save=True, name="figures/names_best") # y_pred_prob = model.predict(x_train)
def evaluator(x_train, y_train, x_val, y_val, experiment_name="", **kwargs): print(kwargs) # Saving directories figure_file = "figures/" + experiment_name + "/" + dict_to_string(kwargs) model_file = "models/" + experiment_name + "/" + dict_to_string(kwargs) mt = MetricTracker() # Stores training evolution info (losses and metrics) # Define model d = x_train.shape[0] n1 = kwargs["n1"] # Filters of first Conv2D k1 = kwargs["k1"] # First kernel y size n2 = kwargs["n2"] # Filters of second Conv2D k2 = kwargs["k2"] # Second kernel y size batch_size = kwargs["batch_size"] try: # Define model model = Sequential(loss=CrossEntropy(class_count=None), metric=Accuracy()) model.add( Conv2D(num_filters=n1, kernel_shape=(d, k1), input_shape=x_train.shape[:-1])) model.add(Relu()) model.add(Conv2D(num_filters=n2, kernel_shape=(1, k2))) model.add(Relu()) model.add(Flatten()) model.add(Dense(nodes=y_train.shape[0])) model.add(Softmax()) # Fit model model.fit(X=x_train, Y=y_train, X_val=x_val, Y_val=y_val, batch_size=batch_size, epochs=1000, lr=1e-2, momentum=0.8, l2_reg=0.001, compensate=True, callbacks=[mt]) except Exception as e: print(e) return -1 # If configuration impossible model.save(model_file) # Write results n1 = str(n1) n2 = str(n2) k1 = str(k1) k2 = str(k2) batch_size = str(batch_size) subtitle = "n1:" + n1 + ", n2:" + n2 + ", k1:" + k1 + ", k2:" + k1 +\ ", batch_size:" + batch_size mt.plot_training_progress(show=False, save=True, name=figure_file, subtitle=subtitle) # Maximizing value: validation accuracy return model.val_metric
lrs = LearningRateScheduler(evolution="cyclic", lr_min=1e-3, lr_max=1e-1, ns=ns) # Modifies lr while training # callbacks = [mt, bms, lrs] callbacks = [mt, lrs] # Fit model iterations = 4 * ns model.fit(X=x_train, Y=y_train, X_val=x_val, Y_val=y_val, batch_size=100, iterations=iterations, momentum=0.89, l2_reg=1e-5, shuffle_minibatch=True, callbacks=callbacks) model.save("models/yes_dropout_test") # Test model # best_model = bms.get_best_model() # test_acc, test_loss = best_model.get_metric_loss(x_test, y_test) # subtitle = "No Dropout, Test acc: " + test_acc subtitle = "" mt.plot_training_progress(show=True, save=True, name="figures/test_dropout_test", subtitle=subtitle) # print("Test accuracy:", test_acc)
model.add(Flatten()) model.add(Dense(nodes=200)) model.add(Relu()) model.add(Dense(nodes=10)) model.add(Softmax()) # for filt in model.layers[0].filters: # print(filt) # y_pred_prob = model.predict(x_train) # print(y_pred_prob) # Fit model # model.load("models/cifar_test_2") # mt.load("models/tracker") model.fit(X=x_train, Y=y_train, X_val=x_val, Y_val=y_val, batch_size=100, epochs=20, momentum=0.9, l2_reg=0.003, callbacks=callbacks) model.save("models/cifar_test_3") # model.layers[0].show_filters() # for filt in model.layers[0].filters: # print(filt) # print(model.layers[0].biases) mt.plot_training_progress() # y_pred_prob = model.predict(x_train) # # # model.pred # print(y_train) # print(np.round(y_pred_prob, decimals=2))
# 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)
model.add(Softmax()) # for filt in model.layers[0].filters: # print(filt) # y_pred_prob = model.predict(x_train) # print(y_pred_prob) # Fit model model.fit(X=x_train, Y=y_train, X_val=x_val, Y_val=y_val, batch_size=100, epochs=200, lr=1e-2, momentum=0.5, callbacks=callbacks) model.save("models/mnist_test_conv_2") # model.layers[0].show_filters() # for filt in model.layers[0].filters: # print(filt) # print(model.layers[0].biases) mt.plot_training_progress() y_pred_prob = model.predict(x_train) # # # model.pred # print(y_train) # print(np.round(y_pred_prob, decimals=2))
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=50, input_dim=x_train.shape[0])) model.add(Relu()) model.add(Dense(nodes=10, input_dim=50)) model.add(Softmax()) # Fit model x_train = x_train[:, 0:100] y_train = y_train[:, 0:100] model.fit(X=x_train, Y=y_train, X_val=x_val, Y_val=y_val, batch_size=100, epochs=200, lr=0.001, momentum=0.0, l2_reg=0.0, shuffle_minibatch=False, save_path="models/mlp_overfit_test") model.plot_training_progress(save=True, name="figures/mlp_overfit_test") model.save("models/mlp_overfit_test") # Test model test_acc, test_loss = model.get_classification_metrics(x_test, y_test) print("Test accuracy:", test_acc)
mt = MetricTracker() # Stores training evolution info lrs = LearningRateScheduler(evolution="cyclic", lr_min=1e-5, lr_max=1e-1, ns=ns) # Modifies lr while training callbacks = [mt, lrs] # Fit model iterations = 6 * ns model.fit(X=x_train, Y=y_train, X_val=x_val, Y_val=y_val, batch_size=100, iterations=iterations, l2_reg=10**-1.85, shuffle_minibatch=True, callbacks=callbacks) model.save("models/l2reg_optimization_good") # Test model val_acc = model.get_metric_loss(x_val, y_val)[0] test_acc = model.get_metric_loss(x_test, y_test)[0] subtitle = "Test acc: " + str(test_acc) mt.plot_training_progress(show=True, save=True, name="figures/l2reg_optimization/good", subtitle=subtitle) print("Val accuracy:", val_acc) print("Test accuracy:", test_acc)