List of helpers to generate images for tensorboard

helpers_tb.py

def plot_confusion_matrix(cm, class_names=class_names):
    """
    Returns a matplotlib figure containing the plotted confusion matrix.

    Args:
    cm (array, shape = [n, n]): a confusion matrix of integer classes
    class_names (array, shape = [n]): String names of the integer classes
    """
    figure = plt.figure(figsize=(8, 8))
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
    plt.title("Confusion matrix")
    plt.colorbar()
    tick_marks = np.arange(len(class_names))
    plt.xticks(tick_marks, class_names, rotation=45)
    plt.yticks(tick_marks, class_names)

    # Compute the labels from the normalized confusion matrix.
    labels = np.around(cm.astype('float') / cm.sum(axis=1)[:, np.newaxis], decimals=2)

    # Use white text if squares are dark; otherwise black.
    threshold = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        color = "white" if cm[i, j] > threshold else "black"
        plt.text(j, i, labels[i, j], horizontalalignment="center", color=color)

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    return figure
  
  
def plot_to_image(figure):
    """Converts the matplotlib plot specified by 'figure' to a PNG image and
    returns it. The supplied figure is closed and inaccessible after this call."""
    # Save the plot to a PNG in memory.
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    # Closing the figure prevents it from being displayed directly inside
    # the notebook.
    plt.close(figure)
    buf.seek(0)
    # Convert PNG buffer to TF image
    image = tf.image.decode_png(buf.getvalue(), channels=4)
    # Add the batch dimension
    image = tf.expand_dims(image, 0)
    return image
  
def image_grid(labels, preds, miss_class, class_names, images):
    # Create a figure to contain the plot.
    figure = plt.figure(figsize=(10,3*len(miss_class)))
    i = 0
    for idx in miss_class:
        # Start next subplot.
        label=f"Predicted: {class_names[preds[idx]]}, Acc label: {class_names[labels[idx]]}"
        plt.subplot(len(miss_class), 2, i + 1, title=label)
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)
        img = tf.cast(tf.reshape(images[idx], [IMAGE_WIDTH, IMAGE_WIDTH, 3])*255, tf.uint8)
        plt.imshow(img, cmap=plt.cm.binary)
        i += 1

    return figure