exported and formatted the code into a simple python file.
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LaTeX/exported_code.py
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256
LaTeX/exported_code.py
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# Import libraries
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import matplotlib.pyplot as plt
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import tensorflow as tf
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from tensorflow.keras import layers, models, applications, optimizers
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import tensorflow_datasets as tfds
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import tkinter.messagebox # for notifications
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# Load Dataset
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# https://www.tensorflow.org/datasets/keras_example
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# split into training and test data
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ds_train, ds_test = tfds.load(
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'svhn_cropped',
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split=['train', 'test'],
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shuffle_files=True,
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as_supervised=True
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)
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# Confirm Data
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for image, label in ds_train.take(1):
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plt.imshow(image)
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plt.show()
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# Define normalization function
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def normalize_img(image, label):
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return tf.cast(image, tf.float32) / 255.0, label
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# Build a training pipeline
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ds_train = ds_train.map(normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
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ds_train = ds_train.cache()
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ds_train = ds_train.batch(128)
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ds_train = ds_train.prefetch(tf.data.AUTOTUNE)
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# Build an evaluation pipeline
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ds_test = ds_test.map(normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
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ds_test = ds_test.cache()
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ds_test = ds_test.batch(128)
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ds_test = ds_test.prefetch(tf.data.AUTOTUNE)
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# Configurations
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configs = {}
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configs['regular Neural Network (3 hidden layers)'] = {
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'model': models.Sequential([
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layers.Flatten(input_shape=(32, 32, 3)),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(10, activation='softmax')
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]),
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'optimizer': 'adam',
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'loss': 'sparse_categorical_crossentropy',
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'metrics': ['accuracy'],
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'epochs': 10,
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'batch_size': 64
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}
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configs['regular Neural Network (5 hidden layers)'] = {
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'model': models.Sequential([
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layers.Flatten(input_shape=(32, 32, 3)),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(10, activation='softmax')
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]),
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'optimizer': 'adam',
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'loss': 'sparse_categorical_crossentropy',
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'metrics': ['accuracy'],
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'epochs': 10,
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'batch_size': 64
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}
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configs['regular Neural Network (10 hidden layers)'] = {
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'model': models.Sequential([
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layers.Flatten(input_shape=(32, 32, 3)),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(10, activation='softmax')
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]),
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'optimizer': 'adam',
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'loss': 'sparse_categorical_crossentropy',
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'metrics': ['accuracy'],
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'epochs': 10,
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'batch_size': 64
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}
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configs['convolutional neural network (1 convolution)'] = {
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'model': models.Sequential([
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layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),
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layers.MaxPooling2D((2,2)),
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layers.Flatten(),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(10, activation='softmax')
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]),
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'optimizer': 'adam',
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'loss': 'sparse_categorical_crossentropy',
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'metrics': ['accuracy'],
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'epochs': 10,
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'batch_size': 64
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}
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configs['convolutional neural network (3 convolutions)'] = {
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'model': models.Sequential([
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layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),
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layers.MaxPooling2D((2,2)),
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layers.Conv2D(64, (3,3), activation='relu'),
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layers.MaxPooling2D((2,2)),
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layers.Conv2D(128, (3,3), activation='relu'),
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layers.MaxPooling2D((2,2)),
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layers.Flatten(),
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layers.Dense(64, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(10, activation='softmax')
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]),
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'optimizer': 'adam',
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'loss': 'sparse_categorical_crossentropy',
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'metrics': ['accuracy'],
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'epochs': 10,
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'batch_size': 128
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}
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vgg16_base_model = applications.VGG16(input_shape=(32,32,3), include_top= False)
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# freeze the VGG16 model
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vgg16_base_model.trainable = False
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configs['frozen VGG16 base model'] = {
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'model': models.Sequential([
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vgg16_base_model,
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layers.Flatten(),
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layers.Dense(64, activation='relu'),
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layers.Dense(32, activation='relu'),
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layers.Dense(10, activation='softmax'),
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]),
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'optimizer': 'adam',
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'loss': 'sparse_categorical_crossentropy',
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'metrics': ['accuracy'],
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'epochs': 5,
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'batch_size': 64
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}
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vgg16_base_model = applications.VGG16(input_shape=(32,32,3), include_top= False)
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configs['fully trainable VGG16 base model'] = {
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'model': models.Sequential([
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vgg16_base_model,
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layers.Flatten(),
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layers.Dense(64, activation='relu'),
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layers.Dense(32, activation='relu'),
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layers.Dense(10, activation='softmax'),
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]),
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'optimizer': optimizers.Adam(learning_rate=0.001),
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'loss': 'sparse_categorical_crossentropy',
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'metrics': ['accuracy'],
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'epochs': 5,
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'batch_size': 64
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}
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vgg16_base_model = applications.VGG16(input_shape=(32,32,3), include_top= False)
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# freeze the VGG16 model
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vgg16_base_model.trainable = False
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for layer in vgg16_base_model.layers[-6:]:
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layer.trainable=True
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configs['partly trainable VGG16 base model'] = {
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'model': models.Sequential([
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vgg16_base_model,
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layers.Flatten(),
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layers.Dense(64, activation='relu'),
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layers.Dense(32, activation='relu'),
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layers.Dense(10, activation='softmax'),
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]),
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'optimizer': optimizers.Adam(learning_rate=0.001),
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'loss': 'sparse_categorical_crossentropy',
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'metrics': ['accuracy'],
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'epochs': 5,
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'batch_size': 64
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}
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# Compile the Models
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for config in configs.values():
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if 'history' not in config.keys():
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config['model'].compile(
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optimizer=config['optimizer'],
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loss=config['loss'],
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metrics=config['metrics']
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)
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# Train and Evaluate the Models
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try:
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for config_name, config in configs.items():
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if 'history' in config.keys():
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print(f'Already trained model "{config_name}"')
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else:
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print(f'Now training model "{config_name}"')
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config['history'] = config['model'].fit(ds_train, epochs=config['epochs'], validation_data=ds_test, batch_size=config['batch_size'])#, verbose=0)
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except Exception as e:
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print(e)
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tkinter.messagebox.showerror("ERROR", f"ERROR: {e}")
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# Plot the accuracy
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plt.figure(figsize=(10,5))
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for config_name, config in configs.items():
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plt.plot(config['history'].history['accuracy'], label=config_name)
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plt.xlabel('Epoch')
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plt.ylabel('Accuracy')
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# plt.ylim([0.75, 1])
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plt.legend(loc='lower right')
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plt.title("Accuracy")
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plt.show()
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# Plot the validation accuracy
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plt.figure(figsize=(10,5))
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for config_name, config in configs.items():
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plt.plot(config['history'].history['val_accuracy'], label=config_name)
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plt.xlabel('Epoch')
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plt.ylabel('Accuracy')
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# plt.ylim([0.8, 1])
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plt.legend(loc='lower right')
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plt.title("Validation Accuracy")
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plt.show()
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# Notify when done
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tkinter.messagebox.showinfo("DONE", "DONE")
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# Save data for future use
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# store histories with pickle
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import pickle
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histories = {}
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for config_name, config in configs.items():
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histories[config_name] = config['history']
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with open('savepoint.pkl', 'wb') as out:
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pickle.dump(histories, out)
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# store models with tensorflow
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for config_name, config in configs.items():
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config['model'].save(f'saved_models/{config_name}')
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# store models in HDF5 format
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for config_name, config in configs.items():
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config['model'].save(f'hdf5_models/{config_name}.h5')
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# Test Save File
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new_model = tf.keras.models.load_model('hdf5_models/fully trainable VGG16 base model.h5')
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print(new_model.summary())
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# Evaluate the restored model
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loss, acc = new_model.evaluate(ds_test)
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print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))
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