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ML/TensorFlow/Basics/tutorial17-tensorboard/utils.py

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+import matplotlib.pyplot as plt
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+import io
+import sklearn.metrics
+from tensorboard.plugins import projector
+import cv2
+import os
+import shutil
+
+# Stolen from tensorflow official guide:
+# https://www.tensorflow.org/tensorboard/image_summaries
+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(data, labels, class_names):
+    # Data should be in (BATCH_SIZE, H, W, C)
+    assert data.ndim == 4
+
+    figure = plt.figure(figsize=(10, 10))
+    num_images = data.shape[0]
+    size = int(np.ceil(np.sqrt(num_images)))
+
+    for i in range(data.shape[0]):
+        plt.subplot(size, size, i + 1, title=class_names[labels[i]])
+        plt.xticks([])
+        plt.yticks([])
+        plt.grid(False)
+
+        # if grayscale
+        if data.shape[3] == 1:
+            plt.imshow(data[i], cmap=plt.cm.binary)
+
+        else:
+            plt.imshow(data[i])
+
+    return figure
+
+
+def get_confusion_matrix(y_labels, logits, class_names):
+    preds = np.argmax(logits, axis=1)
+    cm = sklearn.metrics.confusion_matrix(
+        y_labels, preds, labels=np.arange(len(class_names)),
+    )
+
+    return cm
+
+
+def plot_confusion_matrix(cm, class_names):
+    size = len(class_names)
+    figure = plt.figure(figsize=(size, size))
+    plt.imshow(cm, interpolation="nearest", cmap=plt.cm.Blues)
+    plt.title("Confusion Matrix")
+
+    indices = np.arange(len(class_names))
+    plt.xticks(indices, class_names, rotation=45)
+    plt.yticks(indices, class_names)
+
+    # Normalize Confusion Matrix
+    cm = np.around(cm.astype("float") / cm.sum(axis=1)[:, np.newaxis], decimals=3,)
+
+    threshold = cm.max() / 2.0
+    for i in range(size):
+        for j in range(size):
+            color = "white" if cm[i, j] > threshold else "black"
+            plt.text(
+                i, j, cm[i, j], horizontalalignment="center", color=color,
+            )
+
+    plt.tight_layout()
+    plt.xlabel("True Label")
+    plt.ylabel("Predicted label")
+
+    cm_image = plot_to_image(figure)
+    return cm_image
+
+
+# Stolen from:
+# https://gist.github.com/AndrewBMartin/ab06f4708124ccb4cacc4b158c3cef12
+def create_sprite(data):
+    """
+    Tile images into sprite image.
+    Add any necessary padding
+    """
+
+    # For B&W or greyscale images
+    if len(data.shape) == 3:
+        data = np.tile(data[..., np.newaxis], (1, 1, 1, 3))
+
+    n = int(np.ceil(np.sqrt(data.shape[0])))
+    padding = ((0, n ** 2 - data.shape[0]), (0, 0), (0, 0), (0, 0))
+    data = np.pad(data, padding, mode="constant", constant_values=0)
+
+    # Tile images into sprite
+    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3, 4))
+    # print(data.shape) => (n, image_height, n, image_width, 3)
+
+    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
+    # print(data.shape) => (n * image_height, n * image_width, 3)
+    return data
+
+
+def plot_to_projector(
+    x,
+    feature_vector,
+    y,
+    class_names,
+    log_dir="default_log_dir",
+    meta_file="metadata.tsv",
+):
+    assert x.ndim == 4  # (BATCH, H, W, C)
+
+    if os.path.isdir(log_dir):
+        shutil.rmtree(log_dir)
+
+    # Create a new clean fresh folder :)
+    os.mkdir(log_dir)
+
+    SPRITES_FILE = os.path.join(log_dir, "sprites.png")
+    sprite = create_sprite(x)
+    cv2.imwrite(SPRITES_FILE, sprite)
+
+    # Generate label names
+    labels = [class_names[y[i]] for i in range(int(y.shape[0]))]
+
+    with open(os.path.join(log_dir, meta_file), "w") as f:
+        for label in labels:
+            f.write("{}\n".format(label))
+
+    if feature_vector.ndim != 2:
+        print(
+            "NOTE: Feature vector is not of form (BATCH, FEATURES)"
+            " reshaping to try and get it to this form!"
+        )
+        feature_vector = tf.reshape(feature_vector, [feature_vector.shape[0], -1])
+
+        feature_vector = tf.Variable(feature_vector)
+        checkpoint = tf.train.Checkpoint(embedding=feature_vector)
+        checkpoint.save(os.path.join(log_dir, "embeddings.ckpt"))
+
+        # Set up config
+        config = projector.ProjectorConfig()
+        embedding = config.embeddings.add()
+        embedding.tensor_name = "embedding/.ATTRIBUTES/VARIABLE_VALUE"
+        embedding.metadata_path = meta_file
+        embedding.sprite.image_path = "sprites.png"
+        embedding.sprite.single_image_dim.extend((x.shape[1], x.shape[2]))
+        projector.visualize_embeddings(log_dir, config)