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102 | # https://keras.io/examples/vision/image_classification_efficientnet_fine_tuning/
import numpy as np
import tensorflow_datasets as tfds
import tensorflow as tf
import matplotlib.pyplot as plt
import keras
from keras import layers
from keras.applications import EfficientNetB0
IMG_SIZE=224 # IMG_SIZE is determined by EfficientNet Model choice
BATCH_SIZE=64
# Loading data
dataset_name="stanford_dogs"
(ds_train, ds_test), ds_info=tfds.load(
dataset_name, split=["train", "test"], with_info=True, as_supervised=True
)
NUM_CLASSES=ds_info.features["label"].num_classes
# When the dataset include images with various size, we need to resize them into a shared size. The Stanford Dogs dataset includes only images at least 200x200 pixels in size. Here we resize the images to the input size needed for EfficientNet.
size=(IMG_SIZE, IMG_SIZE)
ds_train=ds_train.map(lambda image, label:(tf.image.resize(image, size), label))
ds_test=ds_test.map(lambda image, label:(tf.image.resize(image, size), label))
def format_label(label):
string_label=label_info.int2str(label)
return string_label.split("-")[1]
label_info=ds_info.features["label"]
for i, (image, label) in enumerate(ds_train.take(9)):
ax=plt.subplot(3, 3, i+1)
plt.imshow(image.numpy().astype("uint8"))
plt.title("{}".format(format_label(label)))
plt.axis('off')
% Data augmentation
img_augmentation_layers=[
layers.RandomRotation(factor=0.15),
layers.RandomTranslation(height_factor=0.1, width_factor=0.1), # A preprocessing layer which randomly translates images during training.
layers.RandomFlip(), # A preprocessing layer which randomly flips images during training.
layers.RandomContrast(factor=0.1), # A preprocessing layer which randomly adjusts contrast during training.
]
def img_augmentation(images):
for layer in img_augmentation_layers:
images=layer(images)
return images
# Here we plot 9 examples of augmentation result of a given figure.
for image, label in ds_train.take(1):
for i in range(9):
ax=plt.subplot(3, 3, i+1)
aug_img=img_augmentation(np.expand_dims(image.numpy(), axis=0))
aug_img=np.array(aug_img)
plt.imshow(aug_img[0].astype("uint8"))
plt.title("{}".format(format_label(label)))
plt.axis('off')
def input_preprocess_train(image, label): # One-hot/categorical encoding
image=img_augmentation(image)
label=tf.one_hot(label, NUM_CLASSES) # Returns a one-hot tensor.
return image, label
def input_preprocess_test(image, label):
label=tf.one_hot(label, NUM_CLASSES) # Returns a one-hot tensor.
return image, label
ds_train=ds_train.map(input_preprocess_train, num_parallel_calls=tf.data.AUTOTUNE)
ds_train=ds_train.batch(batch_size=BATCH_SIZE, drop_remainder=True)
ds_train=ds_train.prefetch(tf.data.AUTOTUNE)
ds_test=ds_test.map(input_preprocess_test, num_parallel_calls=tf.data.AUTOTUNE)
ds_test=ds_test.batch(batch_size=BATCH_SIZE, drop_remainder=True)
# Training a model from scratch
model=EfficientNetB0(
include_top=True,
weights=None,
classes=NUM_CLASSES,
input_shape=(IMG_SIZE, IMG_SIZE, 3),
)
model.compile(optimizer='adam', loss="categorical_crossentropy", metrics=["accuracy"])
model.summary()
# all model.summary() to print a useful summary of the model, which includes:
# Name and type of all layers in the model.
# Output shape for each layer.
# Number of weight parameters of each layer.
# If the model has general topology (discussed below), the inputs each layer receives
# The total number of trainable and non-trainable parameters of the model.
epochs=20
hist=model.fit(ds_train, epochs=epochs, validation_data=ds_test)
|
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