使用TensorBoard进行超参数优化的实现

import tensorflow as tf
from tensorboard.plugins.hparams import api as hp
import datetime
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import numpy as np

# Load the TensorBoard notebook extension
%load_ext tensorboard

BASE_PATH = 'Data\\dogs-vs-cats\\train\\'
TRAIN_PATH='Data\\dogs-vs-cats\\train_data\\'
VAL_PATH='Data\\dogs-vs-cats\\validation_data\\'batch_size = 32 
epochs = 5
IMG_HEIGHT = 150
IMG_WIDTH = 150

train_image_generator = ImageDataGenerator(                        
rescale=1./255,
rotation_range=45, 
width_shift_range=.15,
height_shift_range=.15,
horizontal_flip=True,
zoom_range=0.3)

validation_image_generator = ImageDataGenerator(rescale=1./255)

train_data_gen = train_image_generator.flow_from_directory(
batch_size = batch_size,           
directory=TRAIN_PATH,
shuffle=True,
target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode='categorical')
val_data_gen = validation_image_generator.flow_from_directory(batch_size = batch_size,
directory=VAL_PATH,
target_size=(IMG_HEIGHT, IMG_WIDTH), 
class_mode='categorical')

## Create hyperparameters
HP_NUM_UNITS=hp.HParam('num_units', hp.Discrete([ 256, 512]))
HP_DROPOUT=hp.HParam('dropout', hp.RealInterval(0.1, 0.2))
HP_LEARNING_RATE= hp.HParam('learning_rate', hp.Discrete([0.001, 0.0005, 0.0001]))
HP_OPTIMIZER=hp.HParam('optimizer', hp.Discrete(['adam', 'sgd', 'rmsprop']))METRIC_ACCURACY='accuracy'

log_dir ='\\logs\\fit\\' + datetime.datetime.now().strftime('%Y%m%d-%H%M%S')
with tf.summary.create_file_writer(log_dir).as_default():
  hp.hparams_config(
  hparams=
  [HP_NUM_UNITS, HP_DROPOUT, HP_OPTIMIZER, HP_LEARNING_RATE],
  metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],
  )

def create_model(hparams):
  model = Sequential([
  Conv2D(64, 3, padding='same', activation='relu', 
      input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),
  MaxPooling2D(),
  #setting the Drop out value based on HParam
  Dropout(hparams[HP_DROPOUT]),
  Conv2D(128, 3, padding='same', activation='relu'),
  MaxPooling2D(),
  Dropout(hparams[HP_DROPOUT]),
  Flatten(),
  Dense(hparams[HP_NUM_UNITS], activation='relu'),
  Dense(2, activation='softmax')])
  
  #setting the optimizer and learning rate
  optimizer = hparams[HP_OPTIMIZER]
  learning_rate = hparams[HP_LEARNING_RATE]
  if optimizer == "adam":
    optimizer = tf.optimizers.Adam(learning_rate=learning_rate)
  elif optimizer == "sgd":
    optimizer = tf.optimizers.SGD(learning_rate=learning_rate)
  elif optimizer=='rmsprop':
    optimizer = tf.optimizers.RMSprop(learning_rate=learning_rate)
  else:
    raise ValueError("unexpected optimizer name: %r" % (optimizer_name,))
  
  # Comiple the mode with the optimizer and learninf rate specified in hparams
  model.compile(optimizer=optimizer,
       loss='categorical_crossentropy',
       metrics=['accuracy'])
  
  #Fit the model 
  history=model.fit_generator(
  train_data_gen,
  steps_per_epoch=1000,
  epochs=epochs,
  validation_data=val_data_gen,
  validation_steps=1000,
  callbacks=[
    tf.keras.callbacks.TensorBoard(log_dir), # log metrics
    hp.KerasCallback(log_dir, hparams),# log hparams
    
  ])
  return history.history['val_accuracy'][-1]

def run(run_dir, hparams):
 with tf.summary.create_file_writer(run_dir).as_default():
  hp.hparams(hparams) # record the values used in this trial
  accuracy = create_model(hparams)
  #converting to tf scalar
  accuracy= tf.reshape(tf.convert_to_tensor(accuracy), []).numpy()
  tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)

session_num = 0for num_units in HP_NUM_UNITS.domain.values:
 for dropout_rate in (HP_DROPOUT.domain.min_value, HP_DROPOUT.domain.max_value):
  for optimizer in HP_OPTIMIZER.domain.values:
    for learning_rate in HP_LEARNING_RATE.domain.values:
     hparams = {
       HP_NUM_UNITS: num_units,
       HP_DROPOUT: dropout_rate,
       HP_OPTIMIZER: optimizer,
       HP_LEARNING_RATE: learning_rate,
     }
     run_name = "run-%d" % session_num
     print('--- Starting trial: %s' % run_name)
     print({h.name: hparams[h] for h in hparams})
     run('logs/hparam_tuning/' + run_name, hparams)
     session_num += 1

python -m tensorboard.main --logdir="logs/hparam_tuning"

%tensorboard --logdir='\logs\hparam_tuning'