Example #1
0
        plt.show()

        # Plot Training, testing ll vs iteration number
        fig, ax = plt.subplots()

        # Plot 0/1 loss
        ax.plot(iter_train, train_01, lw=2, color="green", label=r"Train")
        ax.plot(iter_train, test_01, lw=2, color="blue", label=r"Test")

        # Plot BGD best fit on the testing set from before
        plt.axhline(y=0.106, xmin=-1, xmax=100, ls="--", linewidth=2, color = 'k',
                    label="BGD Testing Best Fit")

        # Format plot
        ax.legend(loc="upper right")
        ax.set_xlabel("Iteration")
        ax.set_ylabel("0/1 Loss")
        fig.tight_layout()
        if save_plots:
                fig.savefig("sgd_nn_mnist_multi_train_test_01.pdf")

        plt.show()

    #######################################################
    #
    # Compute, output final losses
    #
    #######################################################
    val.summarize_loss(X_train, y_train_true, X_test, y_test_true, w, w0,
                       y_train_label=y_train, y_test_label=y_test,
                       classfn=cu.multi_logistic_classifier, lossfn=val.logloss_multi)
Example #2
0
    fig, ax = plt.subplots()

    # Plot -(log likelikehood) to get logloss
    ax.plot(iter_train, train_01, lw=2, color="green", label=r"Train")
    ax.plot(iter_train, test_01, lw=2, color="blue", label=r"Test")

    # Format plot
    ax.legend(loc="upper right")
    ax.set_xlabel("Iteration")
    ax.set_ylabel("0/1 Loss")
    fig.tight_layout()
    if save_plots:
        fig.savefig("bgd_mnist_multi_train_test_01.pdf")

    plt.show()

#######################################################
#
# Compute, output final losses
#
#######################################################
val.summarize_loss(X_train,
                   y_train_true,
                   X_test,
                   y_test_true,
                   w,
                   w0,
                   y_train_label=y_train,
                   y_test_label=y_test,
                   classfn=cu.multi_logistic_classifier,
                   lossfn=val.logloss_multi)
Example #3
0
    # Format plot
    ax.legend(loc="upper right")
    ax.set_xlabel("Iteration")
    ax.set_ylabel("LogLoss")
    fig.tight_layout()
    if save_plots:
            fig.savefig("mnist_bin_train_test_ll.pdf")

    plt.show()

    # Plot Training, testing ll vs iteration number
    fig, ax = plt.subplots()

    # Plot -(log likelikehood) to get logloss
    ax.plot(iter_train, train_01, lw=2, color="green", label=r"Train")
    ax.plot(iter_train, test_01, lw=2, color="blue", label=r"Test")

    # Format plot
    ax.legend(loc="upper right")
    ax.set_xlabel("Iteration")
    ax.set_ylabel("0/1 Loss")
    fig.tight_layout()
    if save_plots:
            fig.savefig("mnist_bin_train_test_01.pdf")

    plt.show()

# Output loss metrics!
val.summarize_loss(X_train, y_train_true, X_test, y_test_true, w, w0,
                       classfn=cu.logistic_classifier, lossfn=val.logloss_bin)
Example #4
0
    if save_plots:
        fig.savefig("mnist_bin_train_test_ll.pdf")

    plt.show()

    # Plot Training, testing ll vs iteration number
    fig, ax = plt.subplots()

    # Plot -(log likelikehood) to get logloss
    ax.plot(iter_train, train_01, lw=2, color="green", label=r"Train")
    ax.plot(iter_train, test_01, lw=2, color="blue", label=r"Test")

    # Format plot
    ax.legend(loc="upper right")
    ax.set_xlabel("Iteration")
    ax.set_ylabel("0/1 Loss")
    fig.tight_layout()
    if save_plots:
        fig.savefig("mnist_bin_train_test_01.pdf")

    plt.show()

# Output loss metrics!
val.summarize_loss(X_train,
                   y_train_true,
                   X_test,
                   y_test_true,
                   w,
                   w0,
                   classfn=cu.logistic_classifier,
                   lossfn=val.logloss_bin)