I am trying to plot flower images with both the label and prediction that have a bounding box for each. I am using some lower layers of a pre-trained Xception model.
I have set the output layers to be 4 as there will be four coordinates for the bounding box:
loc_output = keras.layers.Dense(4)(avg)
For simplicity, I just set the four coordinates for the label as random numbers using tf.random.uniform.
How do I write a function using matplotlib that generates something like this:
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
dataset, info = tfds.load("tf_flowers", as_supervised=True, with_info=True)
test_set_raw, valid_set_raw, train_set_raw = tfds.load(
"tf_flowers",
split=["train[:10%]", "train[10%:25%]", "train[25%:]"],
as_supervised=True)
class_names = info.features["label"].names
n_classes = info.features["label"].num_classes
## Shuffle & Preprocess
def preprocess(image, label):
resized_image = tf.image.resize(image, [224, 224])
final_image = keras.applications.xception.preprocess_input(resized_image)
return final_image, label
batch_size = 32
train_set = train_set_raw.shuffle(1000).repeat()
train_set = train_set.map(preprocess).batch(batch_size).prefetch(1)
valid_set = valid_set_raw.map(preprocess).batch(batch_size).prefetch(1)
test_set = test_set_raw.map(preprocess).batch(batch_size).prefetch(1)
base_model = keras.applications.xception.Xception(weights="imagenet",
include_top=False) # Reuse lower layers of pretrained Xception model
avg = keras.layers.GlobalAveragePooling2D()(base_model.output)
class_output = keras.layers.Dense(n_classes, activation="softmax")(avg)
loc_output = keras.layers.Dense(4)(avg) # 4 coordinates for our bounding box
model = keras.models.Model(inputs=base_model.input, outputs=[class_output, loc_output])
# for layer in base_model.layers:
# layer.trainable = False
optimizer = keras.optimizers.SGD(lr=0.2, momentum=0.9, decay=0.01)
model.compile(loss=["sparse_categorical_crossentropy", "mse"],
loss_weights=[0.8, 0.2],
optimizer=optimizer, metrics=["accuracy"])
def add_random_bounding_boxes(images, labels):
fake_bboxes = tf.random.uniform([tf.shape(images)[0], 4])
return images, (labels, fake_bboxes)
fake_train_set = train_set.take(5).repeat(2).map(add_random_bounding_boxes)
model.fit(fake_train_set, steps_per_epoch=5, epochs=2)
