model checkpoint keras
my_callbacks = [
tf.keras.callbacks.EarlyStopping(patience=2),
tf.keras.callbacks.ModelCheckpoint(filepath='model.{epoch:02d}-{val_loss:.2f}.h5'),
tf.keras.callbacks.TensorBoard(log_dir='./logs'),
]
model.fit(dataset, epochs=10, callbacks=my_callbacks)use model from checkpoint tensorflow
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph('my_test_model-1000.meta')
new_saver.restore(sess, tf.train.latest_checkpoint('./'))use model from checkpoint tensorflow
with tf.Session() as sess:
saver = tf.train.import_meta_graph('my-model-1000.meta')
saver.restore(sess,tf.train.latest_checkpoint('./'))
print(sess.run('w1:0'))
##Model has been restored. Above statement will print the saved value of w1.Copyright © 2021 Codeinu
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