| from datasets import load_dataset | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import tensorflow as tf | |
| from PIL import Image | |
| import json | |
| 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=101) | |
| model.save("./foodTrain.pd") | |
| model.save_weights("./foodTrainWeights.h5") | |
| loaded_saved_model = tf.keras.models.load_model("./foodTrain.pd") | |
| results_loaded_saved_model = loaded_saved_model.evaluate(test_data) | |
| results_loaded_saved_model | |
| with open("config.json", "w") as outfile: | |
| json.dump(model.get_config(), outfile) |