import tensorflow as tf
from tensorboard.plugins.hparams import api as hp
####### load the model and data here
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([32,64,128,256, 512]))
HP_DROPOUT = hp.HParam('dropout', hp.RealInterval(0.1, 0.9))
HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['Nadam','SGD','RMSprop','adam','Adagrad']))
HP_L2 = hp.HParam('l2 regularizer', hp.RealInterval(.00001,.01))
HP_LeakyReLU=hp.HParam('alpha', hp.RealInterval(0.1, 0.9))
METRIC_ACCURACY = 'accuracy'
with tf.summary.create_file_writer('raw-img/log/hparam_tuning/').as_default():
hp.hparams_config(
hparams=[HP_NUM_UNITS, HP_DROPOUT, HP_OPTIMIZER,HP_L2,HP_LeakyReLU],
metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],
)
def train_test_model(hparams):
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(hparams[HP_NUM_UNITS], kernel_regularizer=tf.keras.regularizers.l2(0.001)),
tf.keras.layers.LeakyReLU(hparams[HP_LeakyReLU]),
tf.keras.layers.Dropout(hparams[HP_DROPOUT]),
tf.keras.layers.Dense(10, activation='softmax'),
])
model.compile(
optimizer=hparams[HP_OPTIMIZER],
loss='sparse_categorical_crossentropy',
metrics=['accuracy'],
)
model.fit(x_train, y_train, epochs=2)
_, accuracy = model.evaluate(x_test, y_test)
return accuracy
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 = train_test_model(hparams)
tf.summary.scalar(METRIC_ACCURACY, accuracy, step=2)
session_num = 0
for num_units in HP_NUM_UNITS.domain.values:
for dropout_rate in (HP_DROPOUT.domain.min_value, HP_DROPOUT.domain.max_value):
for l2 in (HP_L2.domain.min_value, HP_L2.domain.max_value):
for alpha in (HP_LeakyReLU.domain.min_value, HP_LeakyReLU.domain.max_value):
for optimizer in HP_OPTIMIZER.domain.values:
hparams = {
HP_NUM_UNITS: num_units,
HP_DROPOUT: dropout_rate,
HP_L2: l2,
HP_LeakyReLU:alpha,
HP_OPTIMIZER: optimizer,
}
run_name = "run-%d" % session_num
print('--- Starting trial: %s' % run_name)
print({h.name: hparams[h] for h in hparams})
run('raw-img/log/hparam_tuning/' + run_name, hparams)
session_num += 1
我尝试在TF中使用hparams
。我设置了dropout
、l2
和OPTIMIZER
我需要为learning_rate
设置值并测试它。
我应该如何设置learning_rate
像dropout
和l2
并测试它
我尝试过这样做:
model.compile(
optimizer=hparams[HP_OPTIMIZER](lr=0.001),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'],
但它不起作用。我想选择learning_rate
的不同值learning_rate
like(dropout
,l2
)
您希望将使用过的优化器分离为一个单独的变量:
我找到了解决办法
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