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from datasets import load_dataset
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from PIL import Image
from tensorflow import keras
import tensorflow_datasets as tfds
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
(train_data, test_data), dataset_info = tfds.load(name = 'food101', split = ['train', 'validation'], shuffle_files = True, as_supervised = True, with_info = True)
labels = dataset_info.features['label'].names
num_labels = len(labels)
def preprocess_img(image, label, img_size = 224):
image = tf.image.resize(image, [img_size, img_size])
image = tf.cast(image, tf.float32)
return image, label
train_data = train_data.map(preprocess_img, num_parallel_calls = tf.data.AUTOTUNE).batch(batch_size = num_labels)
test_data = test_data.map(preprocess_img, num_parallel_calls = tf.data.AUTOTUNE).batch(batch_size = num_labels)
model = keras.Sequential()
inputs = layers.Input(shape = (224, 224, 3))
layer1 = layers.GlobalAveragePooling2D()
layer2 = layers.Dense(101, activation = 'relu')
layer3 = layers.Activation('softmax', dtype = tf.float32)
model.add(inputs)
model.add(layer1)
model.add(layer2)
model.add(layer3)
model.compile(
loss = keras.losses.SparseCategoricalCrossentropy(from_logits = False),
optimizer = keras.optimizers.legacy.Adam(learning_rate = 0.001),
metrics = ['accuracy'],
)
model.summary()
model.fit(train_data, epochs=5, verbose = 2, batch_size=101)
model.evaluate(test_data, batch_size=32)
model.save("foodClassifierModel.h5", save_format= "h5", )
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