When I touched TensorFlow (Keras) for the first time in a long time, I often forgot it, so I will leave it as a memo.
In TensorFlow (TF)
① Data set preparation
② Model preparation
③ Specify optimizer, loss, metrics with model.compile
④ Specify callbacks
⑤ Start learning with model.fit
⑥ Evaluate the test with model.evaluate
⑦ To get only the expected probability of the image, model.predict
It is a flow like. This is a relatively simple way to write. If you want to describe in a little more detail, please refer to the article I wrote before.
For those who want to start machine learning with TensorFlow2
Use flow_from_directory () to save an image in a directory and load it from there.
Before that, use ImageDataGenerator to describe the contents of the augmentation.
train_datagen = ImageDataGenerator(
rescale=1./255,
zoom_range=0.2,
horizontal_flip=True,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
)
val_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
TRAIN_DIR,
target_size=(img_size, img_size),
batch_size=batch_size,
classes=classes,
class_mode='categorical')
val_generator = val_datagen.flow_from_directory(
VAL_DIR,
target_size=(img_size,img_size),
batch_size=batch_size,
classes=classes,
class_mode='categorical')
train_generator.class_indices. You can output the class correspondence table. Basically in alphabetical order.There are many augmentations that can be used as shown below, so decide according to your own task. If you want to know more, please check Official Document.
tf.keras.preprocessing.image.ImageDataGenerator(
featurewise_center=False, samplewise_center=False,
featurewise_std_normalization=False, samplewise_std_normalization=False,
zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0,
height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0,
channel_shift_range=0.0, fill_mode='nearest', cval=0.0,
horizontal_flip=False, vertical_flip=False, rescale=None,
preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None
)
With flow_from_directory, an augmented image is generated for each batch size, specifying the directory path.
flow_from_directory(
directory, target_size=(256, 256), color_mode='rgb', classes=None,
class_mode='categorical', batch_size=32, shuffle=True, seed=None,
save_to_dir=None, save_prefix='', save_format='png',
follow_links=False, subset=None, interpolation='nearest'
)
class_mode: categorical, binary, sparse, input, None can be selected. The default is categorical.
shuffle: Flase alphabetically
save_to_dir: None or str (default: None). It saves augmented data by specifying a directory. Useful for visualization.
save_prefix: str. Used for the file name of the saved image. (Valid if save_to_dir is set)
save_format : png or jpeg (Default:'png')
interpolation: nearest (default), bilinear, bicubic
This time, we will use MobileNet v2 as an example. There are other trained models in tf.keras.applications.
IMG_SHAPE = (img_size, img_size, channels)
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
prediction_layer = tf.keras.layers.Dense(n_classes, activation='softmax')
model = tf.keras.Sequential([
base_model,
global_average_layer,
prediction_layer
])
Set the learning details for the model.
model.compile(optimizer=optimizers.SGD(lr=0.0001, momentum=0.99, decay=0, nesterov=True),
loss='categorical_crossentropy',
metrics=['accuracy'])
compile(
optimizer='rmsprop', loss=None, metrics=None, loss_weights=None,
weighted_metrics=None, run_eagerly=None, steps_per_execution=None, **kwargs
)
optimizer : string or optimizer instance (default: rmsprop)
Personally, if it's an image-related task, is it SGD?
ex) SGD, RMSprop, Adam, Adadelta,...
sgd = tf.keras.optimizers.SGD(
learning_rate=0.01, momentum=0.0, nesterov=False, name='SGD', **kwargs
)
sgd to the optimizer in model.compileloss : string, object function or tf.keras.losses.Loss instance
metrics: List of metrics used in training and test
loss_weights: Weight the loss
You can set CSVLogger, History, ProgbarLogger, TensorBoard, EarlyStopping, ReduceLROnPlateau, etc.
You can pass it when learning with model.fit. For details, please refer to Official Document.
Here are some examples.
tf.keras.callbacks.EarlyStopping(
monitor='val_loss', min_delta=0, patience=0, verbose=0,
mode='auto', baseline=None, restore_best_weights=False
)
tf.keras.callbacks.ModelCheckpoint(
filepath, monitor='val_loss', verbose=0, save_best_only=False,
save_weights_only=False, mode='auto', save_freq='epoch',
options=None, **kwargs
)
filepath: string or PathLike, the path to save the model. The variables that can be specified are epoch, loss, acc, val_loss, val_acc.
ex) filepath = '{val_loss:.2f}-{val_acc:.2f}.hdf5'
monitor: What is the basis for saving the model? accuracy, val_accuracy, loss, val_loss
history.history withhistory = model.fit ().
mode : {auto, min, max}
save_best_only: If filepath does not contain a format like {epoch}, filepath will be overridden
save_weights_only: Save only model weights
save_freq : epoch, or integer.tf.keras.callbacks.LearningRateScheduler(
schedule, verbose=0
)
tf.keras.callbacks.ReduceLROnPlateau(
monitor='val_loss', factor=0.1, patience=10, verbose=0,
mode='auto', min_delta=0.0001, cooldown=0, min_lr=0, **kwargs
)
tf.keras.callbacks.RemoteMonitor(
root='http://localhost:9000', path='/publish/epoch/end/',
field='data', headers=None, send_as_json=False
)
tf.keras.callbacks.CSVLogger(
filename, separator=',', append=False
)
Example
history = model.fit(
train_generator,
steps_per_epoch=steps_per_epoch,
validation_data=val_generator,
validation_steps=validation_steps,
epochs=CONFIG.epochs,
shuffle=True,
callbacks=[cp_callback])
fit(
x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None,
validation_split=0.0, validation_data=None, shuffle=True, class_weight=None,
sample_weight=None, initial_epoch=0, steps_per_epoch=None,
validation_steps=None, validation_batch_size=None, validation_freq=1,
max_queue_size=10, workers=1, use_multiprocessing=False
)
validation_split: [0 ~ 1]. Let a validation data be part of the training data. The data after x, y is used (before shuffling).
validation_data: validation data. Overridden if validation_split exists
class_weight: How to focus on loss for classes with less data. Pass in dictionary type. Example) {0: 0.66, 1: 1.33}
steps_per_epoch: Integer or None. Training data // Obtained by batch_size.
validation_steps: Integer or None. Validation data // Obtained by batch_size.
test_loss, test_acc = model.evaluate(test_generator, steps=test_steps)
evaluate(
x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None,
callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False,
return_dict=False
)
x: Input data. Numpy array, tensor, tf.data.dataset, (inputs, targets) or (inputs, targets, sample_weights)
y: Target data.
batch_size : Integer or None.
verbose : 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
sample_weight :
steps : Integer or None
callbacks : list of keras.callbacks.Callback instances
max_queue_size : Integer.
workers : Integer.
use_multiprocessing : boolean
return_dict: If True, return metric results with dict
predict(
x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10,
workers=1, use_multiprocessing=False
)
+α
** checkpoint **: Create only one file. You can check which file is the latest. You don't have to do it during the test because it is necessary for learning. Necessary when starting learning again from the saved data.
** XXXXX.data-0000-of-00001 **: A unique format that maps variable names as tensor values.
** XXXXX.index **: This file is a binary file. When data is saved in multiple steps, the ".data-0000-of-00001" file with the same name is uniquely determined as which data in which step.
It was a good opportunity to read through the detailed settings this time.
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